Advancing Climate Change Adaptation
by
Thea L Dickinson
A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy in Environmental Science Department of Physical and Environmental Sciences University of Toronto
© Copyright by Thea L Dickinson 2019
Advancing Climate Change Adaptation
Thea L Dickinson
Doctor of Philosophy in Environmental Science
Department of Physical and Environmental Sciences University of Toronto
2019 Abstract
The impacts of climate change are vast and consequential. The years 2012 to 2016,
using conservative estimates, revealed more than 1500 climate-related disaster events
displacing over 3.2 million people and causing USD$450 billion in financial losses.
Recent studies confirm that extreme weather events and the failure of climate change
mitigation and adaptation are the leading risks to global stability. Climate change
adaptation is advancing differently in nations across the world. Using pragmatism as a
theoretical framework and applying a mixed methods approach, this dissertation seeks
to identify determinants of adaptation. Grounded theory analysis of 403 national documents from 192 parties to the United Nations Framework Convention on Climate
Change identified 35 themes of climate change adaptation. A mixed methods data
transformation model was applied to study the influence of several relatively stable
parameters on the level of climate change adaptation. Nineteen variables were suitable
for Principal Component Analysis, which further categorized 15 as determinants. The
top 5 determinants were identified using between groups multivariate analysis. The
influence of geopolitical variables on climate change adaptation were analyzed, these
included: GDP per capita, economic classification, system of government, level of
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democracy, land type, region, level of conflict, and refugees. The research resulted in
several significant and unexpected findings. Countries with the highest levels of
adaptation appear to have many key commonalities. This dissertation seeks to identify ways in which we can collectively advance climate change adaptation.
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Acknowledgments
It is with deep gratitude I thank the many individuals who made this journey possible.
The completion of a PhD is not done in isolation. Thank you to William Gough, doctoral supervisor, for his guidance navigating this challenging terrain. Ian Burton for his continued mentorship and unwavering support. All the members of my doctoral committee, past and present, George Arhonditsis, Leonard Tsuji, Tanzina Mohsin, your advice and reviews refined and strengthened my thinking. I am eternally grateful for the patience each member of my supervisory committee granted me over the years.
The Climate Change Adaptation community who unknowingly inspired me to pursue a
PhD. Johanna Wandel for the stimulating and thought-provoking external review. The generous financial support from Social Sciences and Humanities Research Council of
Canada (SSHRC). Joanne Sulman for endless draft reviews, ceaseless motivation and encouragement; this wouldn’t have been possible without your support. To all my family and friends providing me with understanding while this continually stole my attention away from all of you. To those who left us along the way: Sara McColl who remembered where I last left off better than I did; and my father, Dr. Gordon Dickinson, you can call me Dr. Dickinson now.
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Table of Contents
Contents
Acknowledgments ...... iv
Table of Contents ...... v
List of Tables ...... xii
List of Figures ...... xv
List of Appendices ...... xvii
Chapter 1 Introduction and Methodological Summary ...... 1
Failure to Adapt ...... 1
1.1 Climate Change Adaptation ...... 2
1.2 Observations and Research Question ...... 4
1.2.1 Challenges ...... 5
1.3 Summary of Preliminary Methodology & Analysis ...... 5
1.3.1 Level of Vulnerability (Climate Risk) (Index-2) ...... 6
1.3.2 Level of Climate Change Adaptation (Index-1) ...... 13
1.4 Summary Table of all Variables ...... 14
1.5 Redefining Adaptation ...... 18
1.6 References ...... 19
Chapter 2 The Need for Adaptation ...... 20
Are we there yet? ...... 20
2.1 Dangerous Anthropogenic Interference ...... 21
2.2 A Note on the Special Report on Global Warming of 1.5oC...... 21
2.3 The Ultimate Objective ...... 22
2.4 Two Degrees: Science versus Political Narrative ...... 28
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2.5 Climate Change Detection and Attribution: Impacts from below 1oC ...... 30
2.6 Response to Dangerous Anthropogenic Interference: Mitigation and Adaptation ...... 35
2.6.1 Mitigation: Misunderstanding and limitations ...... 35
2.7 References ...... 36
Chapter 3 Contemporary Climate Change Adaptation: A Brief Review ...... 41
Elements of Contemporary Climate Change Adaptation ...... 41
3.1 Current Definition of Climate Change Adaptation ...... 42
3.2 Selection of previous definitions and typologies ...... 42
3.3 Climate Change Adaptation in the 1992 UNFCCC ...... 45
3.3.1 Climate Change Adaptation in Peer Reviewed Literature ...... 51
3.4 Climate Finance for Climate Change Adaptation ...... 54
3.5 Damages from Climate-Related Events ...... 54
3.6 Costing Climate Change Adaptation ...... 55
3.7 Funding Climate Change Adaptation ...... 58
3.8 References ...... 59
Chapter 4 Theoretical Framework ...... 68
Introduction ...... 68
4.1 The Philosophical Complexity of Climate Change ...... 70
4.2 Pragmatism: The Selection of a Paradigm ...... 72
4.2.1 Statement of Research and Philosophical Positionality ...... 74
4.3 Mixed Methods from a Climate Change Adaptation Perspective...... 76
4.3.1 Approaches to Mixed Methods Research ...... 78
4.3.2 Mixed Methods Model Design ...... 80
4.4 Mixed Methods Approaches used in this Dissertation ...... 82
4.5 Grounded Theory ...... 82
4.5.1 Separating Content Analysis from Grounded Theory ...... 82
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4.5.2 Content Analysis ...... 82
4.6 Grounded Theory ...... 84
4.6.1 Theoretical Sampling and Saturation ...... 86
4.7 Debates in Grounded Theory: Three Philosophical Schools of Thought ...... 87
4.8 Types of Coding & Schools of Thought (Philosophy) ...... 89
4.9 The Confusion between Frequency Counts, Coding in Grounded Theory and Autocoding ...... 89
4.10 Theoretical Memoing ...... 91
4.11 Quantitizing Data ...... 93
4.11.1 Quantifying Responses to Climate Change ...... 93
4.11.2 Philosophical Underpinnings of Quantitizing ...... 95
4.11.3 Methodological Concerns with Quantitizing ...... 97
4.11.4 Methodological Arguments for Quantitizing ...... 99
4.11.5 Considerations before Quantitizing ...... 99
4.11.6 Overview of Quantitizing with Examples ...... 100
4.12 References ...... 104
Chapter 5 Methodology: Applying Mixed Methods Research to Climate Change Adaptation .. 119
Mixed Methods as a Research Paradigm ...... 119
5.1 Methodologies in Climate Change Adaptation Research ...... 120
5.2 Limitations of Current Mixed Methods in Climate Change Adaptation Research ...... 122
5.3 Using Mixed Methods Research in Climate Change Adaptation ...... 123
5.4 Methodological Design Model ...... 126
5.5 Applying Mixed Methods Design to Climate Change Adaptation Research ...... 126
5.6 Method ...... 127
5.6.1 Note to the Reader ...... 127
5.7 Phase I: Data Collection ...... 128
5.7.1 Qualitative Data Collection: National Level Documents ...... 128
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5.7.2 "Relatively Stable Parameters" ...... 131
5.7.3 Quantitative Data Collection: Demographic and Climatic Data ...... 132
5.8 Phase II: Data Analysis and Index Construction ...... 135
5.8.1 Developing the Adaptation Index (Index-1) from Qualitative Data ...... 135
5.8.2 Quantitizing Qualitative Data (QUAL -> QUAN) ...... 137
5.9 Data Reduction: Principal Component Analysis Index-1 (QUAN)...... 139
5.9.1 Principal Component Analysis...... 139
5.9.2 Developing the Level of Vulnerability (Climate Risk) (Index-2) from Quantitative Data ...... 146
5.10 Phase III: Data Matrix ...... 148
5.11 Contributions of this Study to Climate Change Adaptation and Mixed Methodology Research ...... 150
5.12 References ...... 150
Chapter 6 Identifying Determinants of Climate Change Adaptation from National Documents 161
Introduction ...... 161
6.1 Data Collection: Coding and Variable Identification ...... 162
6.2 Correlation and Principal Component Analysis ...... 169
6.3 Results ...... 171
6.3.1 Correlation: All data ...... 171
6.3.2 Principal Component Analysis: All Data ...... 171
6.3.3 Between-Groups Principal Component Analysis ...... 173
6.4 Discussion ...... 175
6.4.1 Multi-level government involvement ...... 176
6.4.2 Tool development and usage ...... 177
6.4.3 Climate shock leading to policy change ...... 178
6.4.4 Reviews and evaluations of program plans ...... 180
6.4.5 Funding for Adaptation ...... 180
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6.4.6 Other notable trends ...... 182
6.4.7 Policy Implications and Conclusions ...... 182
6.5 References ...... 184
Chapter 7 Identifying Geopolitical Determinants of Climate Change Adaptation...... 191
Threat Multipliers ...... 191
7.1 Interconnected and Cascading Impacts of Climate Change ...... 195
7.2 On Displacement ...... 196
7.3 On Conflict ...... 197
7.4 Hypothesis ...... 201
7.5 Methodology ...... 203
7.5.1 Data Transformation Model: Phase I to Phase III ...... 203
7.6 Phase I: Qualitative and Quantitative Data Collection ...... 204
7.6.1 Economics ...... 204
7.6.2 Political ...... 205
7.6.3 Geographic ...... 207
7.6.4 Security ...... 207
7.7 Phase II: Data Transformation ...... 208
7.8 Phase III: Statistical Analysis and Preliminary Results ...... 208
7.8.1 Spearman’s Rho ...... 209
7.8.2 PCA Biplot Interpretation ...... 210
7.8.3 Mean Level of Adaptation ...... 211
7.9 Trend Analysis and Discussion ...... 213
7.10 Economic influence on Climate Change Adaptation ...... 214
7.10.1 Political System influence on Climate Change Adaptation ...... 216
7.10.2 Geographic influence on Climate Change Adaptation ...... 218
7.10.3 Indirect Influences on Climate Change Adaptation ...... 220
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7.11 Summary of Findings ...... 223
7.12 References ...... 225
Chapter 8 The Progress of Multilateral Funding in Overcoming Barriers to Climate Change Adaptation ...... 230
Introduction to this Chapter ...... 230
8.1 Setting the Foundation for Multilateral Aid ...... 230
8.1.1 Multilateral Funds for Climate Change Adaptation ...... 231
8.1.2 Is Adaptation Funding Working? ...... 231
8.2 Data and Methodology ...... 233
8.2.1 Data Analysis: Barriers to Climate Change Adaptation ...... 233
8.3 Primary Analysis ...... 235
8.3.1 Initial Finding ...... 235
8.4 Secondary Analysis ...... 236
8.4.1 Analysis of Progress on Climate Change Adaptation ...... 237
8.4.2 Relatively Stable Parameters ...... 238
8.4.3 World Bank Lending Category ...... 242
8.5 Interpretation ...... 242
8.6 Current Status of Climate Change Adaptation Funds ...... 245
8.7 Conclusions and Recommendations ...... 247
8.8 References ...... 249
Chapter 9 Overview of Theoretical Findings, Limitations and Future Research ...... 252
Advancing Climate Change Adaptation: Review of Research ...... 252
9.1 Key Elements of Adaptation Based on Research Contained in this Dissertation ...... 253
9.2 Theory: Optimal Environment for Climate Change Adaptation ...... 254
9.3 Summary of Theoretical Findings ...... 254
9.4 Key Epistemological and Methodological Findings ...... 257
9.5 Limitations of Methods ...... 259
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9.5.1 Limitations of Mixed Methods Research ...... 259
9.6 Limitations in Grounded Theory ...... 260
9.6.1 Note on Statistical Significance Discussions ...... 262
9.7 Limitations in Principal Component Analysis ...... 263
9.8 Limitations Using Atypical Datasets ...... 264
9.9 Knowledge Mobilization and Future Research ...... 265
9.10 Potential Papers or Projects ...... 266
9.10.1 Adaptation: The Idea of Progress (Textbook Chapter) ...... 266
9.10.2 Revisiting Climate Change Adaptation and Vulnerability Dynamic (Paper, Workshop) ...... 267
9.10.3 Historical Adaptation Intervention through Bilateral and Multilateral Projects (Project, Thesis topic) ...... 267
9.10.4 Partnerships & Climate Change Adaptation (Project, Thesis topic) ...... 267
9.10.5 Bilateral and Multilateral Bank Involvement (Project, Paper) ...... 268
9.10.6 Current Institutional Support (Project, Workshop) ...... 269
9.10.7 The Slow Down: Warming Pause and the Adaptation Lag (Project, Paper)...... 269
9.10.8 Gender (Project/Paper) ...... 269
9.10.9 Culture, Climate Change and Climate Dispossession (Thesis topic, Papers, Projects) ...... 270
9.10.10 Repurposing Adaptation (Paper) ...... 270
9.11 In Closing – Personal Reflection ...... 272
Appendices ...... 274
Additional Information ...... 274
10.1 NVivo Case Nodes ...... 274
10.2 Methodological Summary Diagrams ...... 276
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List of Tables
Table 1 Summary of Variables used to Develop Index-2...... 7
Table 2 Qualitative and Quantitative Geopolitical variables ...... 10
Table 3 Summary of Variables and Corresponding Influence on Climate Change Adaptation ... 14
Table 4 Summary of Correlation Analysis using Variables Gathered during Data Analysis ...... 17
Table 5 General Examples of Attribution of Recent Climate Change, with Confidence Levels ... 32
Table 6 Specific Examples of Extreme Events Attributed to Climate Change...... 33
Table 7 Examples of Binary Typologies 1945 Compared to 1999 ...... 43
Table 8 Evolution of the range of adaptation options ...... 43
Table 9 Key Developments under the UNFCCC ...... 48
Table 10. Inclusion of Adaptation in the IPCC Reports ...... 53
Table 11. Estimated Costs of Climate Change Adaptation ...... 56
Table 12 Terms used in this chapter and their origin ...... 69
Table 13 Features of Mixed Methods Research as applied to Climate Change Adaptation (in this dissertation) ...... 77
Table 14 Examples of qualitative analysis and data analysis software ...... 80
Table 15 Comparative Summary of Content Analysis and Grounded Theory...... 83
Table 16 Classic, Straussian and Constructivist Comparison Chart ...... 88
Table 17. Types and Examples of Quantitizing ...... 103
Table 18. Research Methodologies used in Climate Change Adaptation Literature ...... 121
Table 19. Preliminary Stages in the Methodological Design ...... 126
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Table 20. Case Node: Canada ...... 134
Table 21. Loadings for Principal Components with Varimax Rotation: Variable Differentiation for the Development of Index-1 and Determinant Identification ...... 141
Table 22. Stages of the Climate Change Adaptation Process and corresponding weights ...... 144
Table 23. A Section of the Stages of the Adaptation Database used in the Calculation of Index-1 ...... 145
Table 24. A Selection of the Vulnerability (Climate Risk) Database for the Calculation of Index-2 ...... 147
Table 25. Summary of MMR Approaches Used in this Analysis ...... 162
Table 26. Themed Nodes: Identified Determinants of Adaptation ...... 163
Table 27. Sample Coded Text ...... 164
Table 28. Pearson Correlation Coefficients and p-values for Determinants of Climate Change Adaptation Identification ...... 169
Table 29. Determinants of Climate Change Adaptation Identification: Principal Component Loadings for All Countries (Bolded values are considered significant) ...... 172
Table 30. Loadings for Between-Groups PCA: Countries Separated by High, Intermediate and Low Levels of Adaptation for Determinant Identification ...... 174
Table 31. Funded Climate Change Adaptation Projects and Programmes ...... 181
Table 32. Qualitative and Quantitative variables by category ...... 204
Table 33. Definitions of Different forms of Government ...... 205
Table 34. Spearman’s Rank Correlation with Adaptation ...... 209
Table 35. Economic Mean Levels of Adaptation ...... 212
Table 36. Political Mean Levels of Adaptation ...... 212
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Table 37. Geographic Mean Levels of Adaptation ...... 213
Table 38. Multilateral Funds for Climate Change Adaptation ($ USD millions) ...... 232
Table 39. Examples of Coded References for Barriers to Climate Change Adaptation ...... 233
Table 40. Relatively Stable Parameters for Countries with higher levels of barriers and higher levels of climate change adaptation ...... 240
Table 41. Original Climate Change Adaptation Funding of 22 identified countries ...... 244
Table 42. Multilateral Funds: from Pledged to Disbursed ($ USD millions) ...... 245
Table 43. Summary of Dissertation Findings ...... 255
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List of Figures
Figure 1 Canada Case Node: Qualitative and Quantitative Data ...... 6
Figure 2 Chapter Outline ...... 12
Figure 3 Observed Global and Projected Emissions Scenarios from 1980 – 2100 ...... 24
Figure 4 Global Emissions from Fossil Fuel and Industry ...... 26
Figure 5 Monthly Mean CO2 at Mauna Loa 2013 - 2017...... 27
Figure 6 Global Land-Ocean Temperature Anomaly relative to baseline 1951-1980 ...... 30
Figure 7 Publications per year on Climate Change Adaptation ...... 52
Figure 8 Total Damages from Climate Related Events 1967-2016 (in USD ‘000) ...... 55
Figure 9 Four Major Mixed Method Model Designs ...... 81
Figure 10 Types of Data ...... 97
Figure 11. Data Transformation Design Model Embedded in a Policy Process Model ...... 125
Figure 12. Scree Plot for the Development of Index-1 and Determinant Identification ...... 141
Figure 13. Logarithmic Plot of Index-1 and Index-2 for Illustration ...... 149
Figure 14. Correlation Biplot ...... 211
Figure 15. GDP per Capita against the Level of Climate Change Adaptation ...... 214
Figure 16. Institutional Economic Classification against the Level of Climate Change Adaptation ...... 215
Figure 17. Political and Economic Influences on Climate Change Adaptation (Combined Graph) ...... 217
Figure 18. Geopolitical Influences on Climate Change Adaptation (Combined Graph) ...... 219
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Figure 19. Climate Change Adaptation and the Global Peace Index ...... 221
Figure 20. Climate Change Adaptation and Refugees...... 222
Figure 21. Log plot of National level of barriers and climate change adaptation ...... 236
Figure 22. Countries with Higher Levels of Barriers and Higher Levels of Adaptation ...... 237
Figure 23. Frequency count of actions and measures of climate change adaptation for the 22 countries ...... 238
Figure 24. Multilateral Adaptation Funds: Pledged, Received, Approved, Disbursed ...... 246
Figure 25 Methodological Development of Index-1 ...... 277
Figure 26. Methodological Development of Index-1 (continued) ...... 278
Figure 27 Methodological Development of Index-2 ...... 279
Figure 28 Methodological Identification of Determinants of Climate Change Adaptation ...... 280
Figure 29 Methodological Identification of Direct and Indirect Geopolitical Determinants ...... 281
Figure 30 Methodological Identification of Multilateral Funding in Overcoming Barriers ...... 282
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List of Appendices
10 Additional Information………………………………..………………………………………291
10.1 NVivo Case Nodes ………………………………..………………………………291
10.2 Methodological Summary Diagrams ………………..…………………………..293
xvii 1
Chapter 1 Introduction and Methodological Summary Failure to Adapt
In 982 Erik the Red, a Norwegian Viking, travelled from Hornstrandir, Iceland to settle in
Brattahlíð, Greenland. While his Norse ships were settling into the fjords of Greenland,
the Thule Inuit were swiftly migrating from the Bering Strait across the Canadian Arctic to
the Danish Realm. A climate epoch, termed the Little Ice Age (1350-1850), bore down on
the two settlements, goading them to adapt. Only one was successful. Why, within the
same geographic region did Thule Inuit adapt to the Little Ice Age while the Norse Vikings
did not? The answer continues to puzzle researchers, historians and archeologists alike.
Several observations provide evidence that the two cultures were differently prepared for
this climatic shift.
Which of these factors allowed the Thule to adapt more successfully than their neighbours? Was it possible that human-choice determined the survivorship of an entire
community and the failure of another? Did the semi-subterranean whale bone houses
from the Thule Inuit provide greater protection from the harsh conditions than the sod and
timber-walled houses of the Norse Vikings? Was the Viking quest for growth over
environmental stewardship a premonition for their demise? Or was their reliance on a
sheep and goat livestock-based livelihood simply unsustainable in cold climate? Were the
Thule’s whaler capabilities more advantageous than the caribou-hunting Vikings? Or was
2
it the Norse’s limited vision: the past would be the future; assuming their historical ways
of life were transportable to their new-found land1.
Explanations notwithstanding, the conclusions are the same: the Norse Vikings failed to
adapt to a changing climate. They were a literate, advanced community of the time. Why
were they unable to adapt and thrive? Why did they fail to modify their way of life and
adjust to the changing conditions? The Thule demonstrated that entire cultures can adapt
to unforeseen climate realities - even without the advanced technological capabilities of
our century.
A global failure to adapt is not an option.
1.1 Climate Change Adaptation
Anthropogenic climate change of the current era necessitates the survival instincts of
the Thule. However, the lens has now expanded to all communities across the globe.
As espoused by the Thule-Viking saga, climate change is not merely an environmental
issue, but one of human survival. If society is to continue to flourish, there is an
imperative to adapt to our changing climate. Nevertheless, responding to the impacts of
climate change in our globalized system is complex and multifaceted. The challenge is
compounded by generational poverty, institutional restraints, gender inequality,
1 For additional discussion see: Dugmore, A. J., McGovern, T. H., Vésteinsson, O., Arneborg, J., Streeter, R., & Keller, C. (2012). Cultural adaptation, compounding vulnerabilities and conjunctures in Norse Greenland. Proceedings of the National Academy of Sciences, 109(10), 3658-3663. McGhee, R. (1984). Contact between native North Americans and the medieval Norse: a review of the evidence. American Antiquity, 49(1), 4-26. McGovern, T. H. (1980). Cows, harp seals, and churchbells: Adaptation and extinction in Norse Greenland. Human Ecology, 8(3), 245-275. Morrison, D. (1999). The earliest Thule migration. Canadian Journal of Archaeology, 139-156. Park, R. W. (2008). Contact between the Norse Vikings and the Dorset culture in Arctic Canada. Antiquity, 82(315), 189- 198.
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diminished adaptive capacity, overconsumed natural and financial resources, and
technological hurdles.
The resulting losses of life and ‘silent genocides’ turn climate change into a human rights issue. Climate change necessitates a swift and fervent response that parallels the injustice, inequity and inequality that blameless nations experience. Climate change adaptation is a key factor in responding to these injustices. Through well-informed decision-making that is grounded in both culturally and conflict-sensitive responses, the impacts from climate change can be lessened.
According to the Intergovernmental Panel on Climate Change (IPCC), adaptation is defined as “adjustment in natural or human systems in response to actual or expected climatic stimuli or their effects, which moderates harm or exploits beneficial opportunities” (IPCC, 2007). Human societies are inherently adaptable. Can policy-
driven climate change adaptation lead to increases in quality of life and our global
standard of living? If billions (trillions) of dollars are invested in climate change
adaptation over the coming decades, is there potential for innovative economic
opportunity, shifts in governance, strengthened institutional responses, climate-informed
development, conflict resolution, and technological advancement? Adaptation offers
novel opportunities for collaboration and renewed thinking to address the global
challenges humanity now faces as consumption and energy demands continue to
increase. However, there is a key component missing - understanding what helps to
advance adaptation in our current epoch. This dissertation seeks to identify ways in
which we can collectively begin to understand ways to advance adaptation to climate
change.
4 1.2 Observations and Research Question
Globally, climate change adaptation has witnessed considerable advancement2 over
the past two and a half decades. This initial observation has many supports: an
increase in scholarly interest, in multilateral funding, and in peer-reviewed literature.
Specific to this research, in 2008, an inventory of National Adaptation Strategies was
completed for the Federal Government of Cana da. At the time, only 23 countries had
comprehensive national adaptation plans (Dickinson, 2008). Comparatively, by 2015,
almost every country globally had a national plan or strategy (this research). An
additional observation was noted: Through knowledge disseminated from formal
meetings of the United Nations Framework Convention on Climate Change (UNFCCC),
climate change literature, communities of practice, national, regional and local
governmental and non-governmental reports, all contemporary information points to the
perception that the advancement of adaptation is not uniform across nations. A central observation appeared: Climate change adaptation is advancing differently in nations
across the world.
Research Question and Goals related to central observation
Central Question: Why is climate change adaptation advancing differently in nations around the
world?
Central Goal 1: To study 192 countries (to the UNFCCC) and to assess whether it is possible to
identify what is influencing different levels of climate change adaptation.
2 The term advancement may be interchanged for others such as: progression, rise, growth, uptake, academic interest, financial investment, etc.
5
Central Goal 2: To identify macro determinants of climate change adaptation.
1.2.1 Challenges
There are many challenges in researching adaptation on a macro scale. First,
adaptation does not have a single metric like mitigation3. Second, adaptation takes
place in (i) every sector, (ii) for every impact, and (iii) in every geographic region of the
world. Third “adaptation is messy and complex because society is messy and complex”
(Debora Davies, Western Norway Research Institute). Given this complexity, how do
we identify macro determinants of climate change adaptation? In other words, how do
we identify determinants that are enabling climate change adaptation to occur? If it were
possible to identify why some countries were advancing faster than others, to determine
what circumstances were allowing some nations to increase their level of adaptation
and holding back the pace of others, the potential then exists to use this information to
advance adaptation. Adaptation experts and researchers could speculate that the rise
is linked to a nation’s wealth, government system, geographic location, funding
availability or other credible possibilities. One recurring explanation is vulnerability.
This latter proposition set the initial platform for the development of this research.
1.3 Summary of Preliminary Methodology & Analysis
To evaluate the correlation between adaptation and vulnerability, this research began
with the development of two composite indexes: Index-1 Level of Adaptation and Index-
2 Level of Vulnerability (Climate Risk). Qualitative and quantitative data were gathered
3 This is a simplistic view of mitigation. There is no single metric for mitigation, rather it is easier to quantify than adaptation, and is often conceptualized as a single metric.
6
for each country in case nodes (Figure 1) and two distinct methodologies were used to generate the indexes.
Figure 1 Canada Case Node: Qualitative and Quantitative Data
1.3.1 Level of Vulnerability (Climate Risk) (Index-2)
For Index-2, the level of vulnerability (climate risk), an extensive literature review initially
discovered over 100 potential variables of vulnerability (review not included in
dissertation). The variables were then categorized into themes. Five overarching
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themes emerged capturing climate risk exposure and sensitivity4: climate change, sea
level, population, sustainability, and economics. For each of these categories, 9
variables were selected to create Index-2 (see Table 1). The construction of Index-2
was influenced in part by Vyas & Kumaranayake (2006). Methodology included variable
selection and data collection, data transformation, aggregation, normalization and index
construction.
Table 1 Summary of Variables used to Develop Index-2
Variable Category Variables selected for Index-2 Notes Development
Climate change Precipitation (mm/day) RCP 6, simulated using the GCM CMIP5 multimodel
ensemble (IPCC AR5 Atlas Temperature subset) for the relative period 1986-2005 with a future time period of 2081–2100
Sea level Mean sea level trend (mm/yr)
Coastline area ratio (m/km2)
Population Population density (/km2)
Population growth (%)
Sustainability Environmental Performance Index
Economics GDP per Capita Variables controlled
Disaster Losses
4 There are multiple definitions of vulnerability. The IPCC definition includes three components: exposure, sensitivity and adaptive capacity. Index-2 attempts to directly capture exposure and sensitivity.
8
Following the development of the two indexes, correlation analysis was performed and a weak correlation (r=0.211, p<0.005) was identified between adaptation and vulnerability. Thus, from this preliminary mixed methods analysis, vulnerability has a low-medium influence on the level of climate change adaptation. This analysis was completed in 2015. Recently, a resistance to the reliance on vulnerability has been developing. Ford and colleagues (2018), concluded after the analysis of 587 peer reviewed papers that the use of vulnerability analysis has served to promote a static understanding of human-environment interactions. Thus, if vulnerability is a weak determinant of climate change adaptation, what is enabling countries to move adaptation forward? The remainder of this thesis explores this question.
***
The dissertation begins with a brief examination of the impacts necessitating climate change adaptation (Chapter 2). A review of contemporary literature (Chapter 3) precedes a discussion of the theoretical framework and philosophical paradigm
(Chapter 4).
The central methodology of this dissertation is presented in Chapter 5, and seeks to:
1. Identify and order the Stages in the Adaptation Process using a mixed
methodology based in grounded theory,
2. Identify climate-vulnerable and climate-risk related variables, ensuring that these
variables capture the risks and vulnerability posed to Small Island Developing
States (SIDS),
3. Develop both Index-1 and Index-2
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Using the knowledge gained from the first stage of research, Chapter 6 identifies
determinants of climate change adaptation based on national level documentation.
Based on this analysis, 35 themed nodes were discovered through grounded theory
(see Appendix 10.1). Of these, 19 variables were suitable for Principal Component
Analysis (PCA), which identified 15 as determinants of climate change adaptation:
Determinants of Climate Change Adaptation based on National Level Documentation:
1. Adaptation Policy Creation 9. Legislation, Laws & Acts,
2. Climate Change Adaptation Standards & Codes
Research 10. Multi-Level Government
3. Climate Shock (Leading to Policy Involvement
Change) 11. National Strategy or Plan
4. Costing Climate Change 12. Public Transparency
Adaptation 13. Reviews & Evaluations of
5. Establishing Partnerships Programs & Plans
6. Evaluation of the Adaptation 14. Tool Development & Usage
Outcome 15. Workshops, Public Education &
7. Funding for Adaptation Training
8. Insurance & Financial
Instruments
Between-Groups PCA subsequently identified the top 5 determinants of climate change
adaptation as: (1) Multi-Level Government Involvement, (2) Tool Development & Usage,
(3) Climate Shock (Leading to Policy Change), (4) Reviews & Evaluations of Programs
10 Plan, and (5) Funding for Adaptation. The chapter subsequently suggests policy implications for these findings.
Chapter 7, explores an intersection of geopolitical factors, categorized into geography, economy, political system, and security, and compares the factors to national levels of climate change adaptation using a mixed methods research approach. The chapter broadly asks the question: Do geopolitical determinants of climate change adaptation exist? And further, do correlations exists between the national level of climate change adaptation and geographic location, level of GDP, political system, democracy strength, or number of refugees?
Eight variable sets of comprised of both qualitative (QUAL) and quantitative (QUAN) data were gathered to represent the four categories (Table 2)
Table 2 Qualitative and Quantitative Geopolitical variables
Component Qualitative Quantitative
Economic Institutional Economic Classification GDP per capita
Political System of Government Level of Democracy Geographic Land Type Region Security Global Peace Index Refugees
The research resulted in several findings, including evidence that autocracies and emerging economies are falling behind in adapting to climate change, whereas the greatest levels of advancement are seen in South Asia and in countries with strong
11 democratic systems. Additionally, the analysis showed that a nation’s wealth is a poor indicator of the level of adaptation; emerging economies are falling behind on adaptation; and countries with high levels of conflict and high levels of refugees have low levels of adaptation. Further, geopolitical determinants of climate change adaptation are an underdeveloped area of research.
During this research, several other incidental observations were discovered. Chapter 8 explores the influence of historical climate change adaptation funding in advancing adaptation. The Chapter discusses an unexpected finding from Chapter 6, Identifying
Determinants of Climate Change Adaptation from National Documents. Following the application of grounded theory and quantitization, a graph of the number of barriers that countries faced when responding to climate change was plotted against the national level of climate change adaptation. It was within this analysis that an anomaly stood out. By evaluating historical funding from the original climate change adaptation funds,
22 countries were able to make advancements on climate change adaptation while facing high levels of barriers. Furthermore, the World Bank acted as a fund trustee or implementing agency for 19 out of 22 of the countries. The chapter aims to demonstrate that multilateral funding can be successful, and it provides recommendations to increase the advancement of adaptation in vulnerable nations, including the importance of adaptation funding and the call for fast disbursement of funding following project approval.
Chapter 9 provides a summary of all findings. This thesis represents an initial exploration of macro-scale key commonalities and determinants of climate change adaptation. Subsequent refinement will follow a critical assessment of the output.
12
Figure 2 Chapter Outline
13 1.3.2 Level of Climate Change Adaptation (Index-1)
Index-1, the level of adaptation, began with the analysis of 403 national level documents with the qualitative analysis software program, NVivo 11, QSR International. Coding, memoing, data transformation using quantitization, and multivariate statistical analysis identified several determinants of national level climate change adaptation. Quantitative analysis and construction of the index was influenced by the Human Development Index methodology by Sen (1994). Fifteen variables were used to create Index-1:
1. Assessment Phase 9. Examples of Climate Change
2. National Strategy and Plan Adaptation
Development 10. Adaptation Policy Creation
3. Costing Climate Change 11. Legislation, Laws & Acts,
Adaptation Standards & Codes
4. Recommendations, Priorities & 12. Funding for Adaptation
Options 13. Implementation
5. Climate Change Adaptation 14. Reviews and Evaluations of
Research Programmes & Plans (prior to
6. Workshops, Public Education & climate-related impact)
Training 15. Evaluation of the Adaptation
7. Project Example, or Programme Outcome (post climate-related
Development impact)
8. Insurance & Financial
Instruments
14
1.4 Summary Table of all Variables
Table 3 presents all the determinants analyzed during this mixed methods research.
Each variable in the table includes the quantitative loadings from PCA (if available), the
Pearson r correlation coefficient or the Spearman’s Rank correlation coefficient and
corresponding statistical significance in the form of a p-value, where significance is an α
value less than 0.05. The table also includes a qualitative valuation of the influence the
variable has on advancing climate change adaptation.
Table 3 Summary of Variables and Corresponding Influence on Climate Change
Adaptation
Variable Influence on Loading Pearson r Statistical Climate from Correlation Significance Change PCA Coefficient P-value Adaptation Analysis with
advancement Adaptation
Vulnerability to climate Low-medium - 0.211 <0.005
change5
Determinants Influence PCA Pearson r P-value
Multi-level Government Highly 0.913 0.484 <0.0001
influential Involvement
5 Using RCP6 pathway; GCM CMIP5 multimodel ensemble (IPCC AR5 Atlas subset) for the relative period 1986-2005 with a future time period of 2081–2100
15
Determinants Influence PCA Pearson r P-value
Tool Development & Usage Highly 0.906 0.505 <0.0001
influential
Climate Shock (Leading to Highly 0.799 0.242 0.0017
Policy Change) influential
Reviews & Evaluations of Highly 0.608 0.614 <0.0001
Programs Plans influential
Funding for Adaptation Highly 0.558 0.696 <0.0001
influential
Establishing Partnerships Medium-High 0.573 0.698 <0.0001
National Strategy or Plan Medium-High 0.454 0.767 <0.0001
Public Transparency Medium 0.446 0.258 0.0008
Climate Change Adaptation Medium 0.388 0.590 <0.0001
Research
Adaptation Policy Creation Medium 0.372 0.547 <0.0001
Workshops, Public Low 0.226 0.300 <0.0001
Education & Training
Legislation, Laws & Acts, Low 0.157 0.446 <0.0001
Standards & Codes
Evaluation of the Low 0.100 0.235 0.0024
Adaptation Outcome
16
Insurance & Financial Low 0.066 0.309 <0.0001
Instruments
Costing Climate Change N/A - *6 0.132 0.0894
Adaptation
Barriers Influence PCA Pearson r P-value
Barriers to Climate Change Medium - 0.360 <0.0001
Adaptation
Geopolitics Influence PCA Spearman’s P-value
biplot Rank
System of Government Highly -0.999 - 0.708 <0.005
influential
GDP per Capita Medium 0.913 0.517 <0.05
Institutional Economic Medium 0.829 0.291
Classification
Global Peace Index Indirect -0.325 - 0.429
Refugees Indirect - - 0.329
Preliminary Correlational Influence PCA Pearson r P-value
Analysis of other variables
Emissions (metric tons of - - 0.175 <0.05
CO2)
6 Costing Climate Change Adaptation: Not statistically significant
17
Disaster Losses (climate, - - 0.169 <0.05 hydrological, meteorological)
While analyzing the variables in Table 3, a correlation data matrix was generated allowing for the analysis of other combinations of variables in the data set. Notable correlations were found and are presented in Table 4. These correlations may prove valuable for future research directions.
Table 4 Summary of Correlation Analysis using Variables Gathered during Data
Analysis
Variable A Variable B Pearson r Correlation Coefficient
Emissions (metric tons of Disaster Losses (climate, 0.944
CO2) hydrological, meteorological)
Emissions (metric tons of System of Government 0.682
CO2)
Disaster Losses (climate, System of Government 0.636 hydrological, meteorological)
Vulnerability Disaster Losses (climate, 0.517 hydrological, meteorological)
Vulnerability Institutional Economic 0.512 Classification
Vulnerability Emissions 0.503
(metric tons of CO2)
18 1.5 Redefining Adaptation
The complexity and inherent ‘messiness’ of responding to climate change affords the liberty of a redefinition that seeks to provide guidance; an evolution of the defining parameters beyond the condensed institutional definition. During this research several defining characteristics of adaptation appeared. Adaptation to climate change should:
1. Be complementary to mitigation (both adaptation and mitigation; not either/or)
2. Decrease vulnerability
3. Seek to avoid maladaptation (maladaptation may cause increase in conflict,
emissions, vulnerability)
4. Promote innovation and take advantage of beneficial opportunities
5. Be inherently interdisciplinary
6. Take climate change into account
7. Be proactive
8. Agree with the 17 goals of sustainable development for 2030
9. Maintain or increase quality of life (not be palliative adaptation)
10. Maintain or increase standard of living
11. Apply cultural and conflict sensitive approaches
12. Consider not only primary and secondary impacts, but also interconnected and
cascading impacts
19 1.6 References
Dickinson, T. 2008. Inventory of National Adaptation Strategies. Natural Resources
Canada. 76 pp.
IPCC, (2007). Parry, M., Parry, M. L., Canziani, O., Palutikof, J., Van der Linden, P., &
Hanson, C. (Eds.). (2007). Climate change 2007-impacts, adaptation and
vulnerability: Working group II contribution to the fourth assessment report of the
IPCC (Vol. 4). Cambridge University Press.
Sen, A. (1994). Human Development Index: Methodology and Measurement.
Vyas, S., & Kumaranayake, L. (2006). Constructing socio-economic status indices: how
to use principal components analysis. Health Policy and Planning, 21(6), 459-
468.
20 Chapter 2 The Need for Adaptation Are we there yet?
There was a time in recent history when climate change was discussed as a future threat. Perhaps it was October 28th,1956 when The New York Times ran an article in
Science in Review titled, Warmer Climate on Earth May Be Due to More Carbon
Dioxide in the Air by Waldemar Kaempffert. Or possibly the “recent” UNFCCC Kyoto
Protocol in February 2005. Even then, the 31st of December 2012 seemed a distant future. As too did the 2020 dates in the UNFCCC Paris Agreement. On the eve of
2019, those years have merged in a blink.
The task list set before negotiators and diplomats seemed to hold a wealth of time.
Now, as though an accordion has retracted, the distance between these time periods has unified into a singularity. Impacts have been irreversibly set in motion and news agencies barely attain the space to report disasters in “faraway lands” as current ones threaten their very soil.
There is no wonder left. The veil of “what will a planet under 400ppm or +1oC above pre-industrial levels look like” has been lifted. Climate signals are identifiable in almost all recent disasters (Blunden et al., 2018). The impacts on livelihoods in developing countries are being upstaged by headlines on the Wildfires ravaging thousands of acres of drought-prone California, Hurricanes devastating Puerto Rico, biblical Floods in
Hiroshima, Japan. With the shocks of climate change bearing down, our way forward rests in action with one goal: limit the impacts. Limit the loss of life, the loss of habitat, of biodiversity, of culture, of hope.
21 2.1 Dangerous Anthropogenic Interference
The impacts of Hurricane Harvey in Texas and Hurricane Maria in Puerto Rico
convinced the author that there are limits to in-situ adaptation. Loss and damage from
climate change is certain. Wealthy communities, historically exposed to violent
hurricane seasons, again lost homes and lives. The message was clear: Building on
historically vulnerable land sends communities into certain peril. The “build back better”
mantra is an idealistic cry of those who do not want to lose their land, their history, their
cherished communities. The triumph of victory over environmental hazards is
subservient to the extremes of our changed climate system. Even our overindulgence
in interventionism with multi-billion-dollar recovery efforts surrounded by senior minds were unable to protect all those who long to hold on to their idealizations.
Pangea knows that not all that was will be.
2.2 A Note on the Special Report on Global Warming of 1.5oC
In 2015, the negotiators at the 21st UNFCCC Conference of the Parties in Paris, France
tasked the broader climate community with the development of a special report on the
comparative impacts of examining the impacts of warming of 1.5oC and a 2.0oC climate.
This is a controversial task. The target of 1.5oC is below the 2oC target set by policy
makers in the recent years (see discussion below). The special report confirmed, “global
mean surface temperatures for 2006-2015 was 0.87oC higher than preindustrial levels
of 1850-1900”, where “Human activities [have been] estimated to cause 1.0°C of
warming globally above pre-industrial levels” (IPCCa, 2018). With very few points of
optimism, the report suggested that historical anthropogenic emissions (pre-industrial to
present) will persist for centuries to millennia, but, “emissions alone are unlikely to
22
cause global warming of 1.5°C”. Thus, if current emissions are reduced to 0, the
historical emissions are unlikely to add more than 0.5oC to the current increase in global
temperature.
Adaptation was also highlighted in the report, underscoring the importance of
transformational measures, including the acknowledgment that adaptation is indeed
occurring (high confidence). “Future climate-related risks would be reduced by the
upscaling and acceleration of far-reaching, multilevel and cross-sectoral climate
mitigation and by both incremental and transformational adaptation (high confidence).”
(IPCCb, 2018). Despite the effort and sentiment held in the 1.5oC report, the continued
increase in emissions places undue pressure on the adaptation community to put vast
and sweeping policy measures into action. The devastation at 1.5oC creates an uncertain future for all of humanity, regardless of privilege.
2.3 The Ultimate Objective
In 1992, the Earth Summit, formally known as the United Nations Conference on
Environment and Development (UNCED), was held in Rio de Janeiro, Brazil. Three
legally binding agreements were opened for signature, including the United Nations
Framework Convention on Climate Change (UNFCCC, the Convention). This
international environmental treaty, running approximately 33 pages, stated with
concern:
human activities have been substantially increasing the atmospheric
concentrations of greenhouse gases…and that this will result on average
in an additional warming of the Earth’s surface…and may adversely
affect natural ecosystems and humankind (UNFCCC, 1992).
23
Article 2 outlines the “ultimate objective” of the Convention and the criteria for dangerous anthropogenic interference. The full text of Article 2 reads:
The ultimate objective of this Convention and any related legal
instruments that the Conference of the Parties may adopt is to achieve,
in accordance with the relevant provisions of the Convention,
stabilization of greenhouse gas concentrations in the atmosphere at a
level that would prevent dangerous anthropogenic interference with the
climate system. Such a level should be achieved within a time frame
sufficient to allow ecosystems to adapt naturally to climate change, to
ensure that food production is not threatened and to enable economic
development to proceed in a sustainable manner (UNFCCC, 1992).
From the ultimate objective, stabilization of greenhouse gases (GHGs) in the atmosphere is to occur at “a level that would prevent dangerous anthropogenic interference”. The Convention text, however, was written 25 years ago at a time when disasters were not as frequent, impacts were not as visible, and there seemed to be ample time to prevent this ominous dangerous anthropogenic interference.
Established just prior to the Convention, in 1988 the Intergovernmental Panel on
Climate Change (IPCC) began its journey detailing the scientific understandings of climate change. The 2014 Fifth Assessment Report (AR5) of the IPCC developed new radiative forcing7 trajectories, termed Representative Concentration Pathways (RCPs).
7 Radiative forcing is a measure of the influence a factor has in altering the balance of incoming and outgoing energy in the Earth- atmosphere system and is an index of the importance of the factor as a potential climate change mechanism (IPCC, 2014)
24
Four pathways were selected (RCP 2.6, 4.5, 6 and 8.5), each pathway representing a
different level of radiative forcing in watts per meter squared (W/m2).
Figure 3 Observed Global and Projected Emissions Scenarios from 1980 – 21008
Figure 3 provides four types of data to help understand the implications of RCP pathway selection:
1. Historical emissions data (“stabilized” in 2016 at approximately 36 GtCO2/yr),
2. Future projected emissions from 1200 scenarios based on the radiative forcings
of the four RCPs,
8 Le Quéré, C., Andrew, R. M., Canadell, J. G., Sitch, S., Ivar Korsbakken, J., Peters, G. P., ... Zaehle, S. (2016). Global Carbon Budget 2016. Earth System Science Data, 8(2), 605-649. http://www.earth-syst-sci-data.net/8/605/2016/essd-8-605-2016.pdf
25
3. The potential increase in global temperature relative to the pre-industrial level of
1850-1900, and
4. Atmospheric concentrations of carbon dioxide (CO2) in parts per million (ppm).
The pathway with the largest decrease in radiative forcing, RCP2.6, also termed “peak
and decline” has a temperature increase range of 0.9oC to 2.3oC above pre-industrial
levels9. This scenario (RCP2.6) was the only scenario thought (by the scientific
community) to be compatible with preventing dangerous anthropogenic interference by
limiting warming to below 2oC above pre-industrial levels (IPCC, 2014).
From Figure 3 we can observe historical emissions (in black, 2016 estimate) already surpassing the highest emissions peak outlined by the RCP2.6 scenario (in blue). This scientific knowledge coupled with slow-to-develop and unaggressive mitigation targets set at the turn of the 21st century have triggered a furious debate: whether society can even achieve a 2oC limit above preindustrial levels, or whether a 2oC limit is sufficient to
prevent (further) damage to human life and the ecosystem.
In 2014, a global stabilization of emissions was becoming visible (Figure 4). In 2011, a
value of 34.6 GtCO2/yr was recorded. By 2014 this value had increased by 1.3 GtCO2
to 35.9 GtCO2/yr. The following year, 2015, global emissions had increased by only 0.4
GtCO2, and the increase for 2016 is projected to be 0.1 GtCO2 to an estimated value of
36.4 GtCO2/yr (Le Quéré, 2016).
9 IPCC RCP6 temperature range: 1.3oC (0.8 to 1.8) by 2046–2065 and 2.2oC (1.4 to 3.1) by 2081–2100
26
Figure 4 Global Emissions from Fossil Fuel and Industry10
While stabilization of emissions represents successful progress on the mitigation agenda, three key components underlie the objective of preventing dangerous anthropogenic interference through climate change mitigation:
i. Achieving a stabilization of global emissions (it may be argued that this is
currently being achieved via Figure 4),
ii. A rapid and substantial reduction in global emissions, and
iii. A detectable and significant decrease in the concentration of CO2 in the
atmosphere (not achieved, Figure 5).
10 Le Quéré, C., Andrew, R. M., Canadell, J. G., Sitch, S., Ivar Korsbakken, J., Peters, G. P., ... Zaehle, S. (2016). Global Carbon Budget 2016. Earth System Science Data, 8(2), 605-649. DOI: 10.5194/essd-8-605-2016
27
11 Figure 5 Monthly Mean CO2 at Mauna Loa 2013 - 2017
Figure 5 provides the concentration of CO2 (in ppm) in the atmosphere at the Mauna
Loa Observatory in Hawaii over the same time period as global emissions were stabilizing. A value of 395 ppm in 2013 shifted to a seasonal peak of 410ppm on April
th 18 , 2017. To comprehend the vast historical significance of these levels of CO2 in the
th atmosphere, on May 9 , 2013 a CO2 level of 400ppm was reached for the first time in
800,000 to 15 million years12.
11 Scripps Institution of Oceanography, NOAA Earth System Research Laboratory https://www.esrl.noaa.gov/gmd/ccgg/trends/ 12 800,000 based on Lüthi et al., 2008, and 15 million in Tripati et al., 2009.
28 2.4 Two Degrees: Science versus Political Narrative
Perhaps it was during the conference convened by the then British Prime Minister Tony
Blair on “Avoiding Dangerous Climate Change”13 where the number, two degrees
Celsius (2oC), became synonymous with the “dangerous” threshold (Schellnhuber &
Cramer, 2006). Shaw (2015) notes it was following this conference that 2oC began gaining traction in the media and public policy narratives. In an interview with the late professor, Stephen Schneider, of Stanford University, Shaw quotes Schneider as saying:
I’ve been dealing with the issue of meaningless numbers for so long…
‘We cannot exceed two degrees above preindustrial otherwise the world
turns into a climatic pumpkin’… I was at a meeting where [two degrees]
was first proposed and I said ‘you can’t just pluck a number [2oC] out of
your head’, and they said, ‘no, we have to have a number because
without a number we can’t get their attention’. So I understand there is a
political strategy in approaching this in terms of number (Stephen
Schneider interviewed in Shaw, 2015).
However, there is a lack of scientific consensus for the numerical value of 2oC. James
Hansen, an atmospheric physicist and former head of NASA, has argued that the 2oC
threshold has inadequate scientific basis, and that 1oC should be the global target,
13 Conference Report: Schellnhuber, H. J., & Cramer, W. P. (Eds.). (2006). Avoiding dangerous climate change. Cambridge University Press.
29
beyond which we are committing to dangerous anthropogenic interference (Hansen, et
al., 2013).
At the 21st UNFCCC Conference of the Parties (COP) in Paris, France in 2015, 195 countries drafted the Paris Agreement to enhance the implementation of the
Convention. In Article 2 of the Paris Agreement, Parties to the Convention aimed to hold
“the increase in the global average to well below 2oC” and pursue efforts to “limit the
temperature increase to 1.5oC”. Rogelj et al., (2015) examined the ability of society to
limit warming to 1.5oC by 2100, concluding that, “achieving 1.5 °C by 2100 will require
immediate attention to push mitigation in every individual sector of the economy”.
Existing climate policies, as analyzed by Climate Action Tracker (CAT), would result in a
range of climate warming from +2.6oC to +4.9oC above pre-industrial levels. Pledges
submitted by countries under the Nationally Determined Contributions (NDCs) decrease
this range to +2.3oC to +3.5oC by 2100 (CAT, 2016). These values far exceed Hansen’s
1oC.
While the debate over 2oC continues, global temperatures have been rising steadily
since the mid-1960s (Figure 6). NASA14 reported the annual global surface temperature
of 2016 to be +0.99oC above the 1951-1980 average. NOAA15 calculated the global
temperature across land and ocean surface in 2016 to be +0.94oC using the 20th
century as the baseline. And Hansen’s team16 at Columbia University, using what they
refer to as the pre-industrial time period of 1880-1920, calculated a global surface
14 NASA https://climate.nasa.gov/vital-signs/global-temperature/ 15 NOAA https://www.ncdc.noaa.gov/sotc/global/201613 16 Hansen, Columbia University: http://csas.ei.columbia.edu/2017/01/18/global-temperature-in-2016/
30 temperature of +1.26oC. All three of these calculations border or exceed the +1oC warning in Hansen’s 2013 paper, making 1.5oC by 2100 seem extremely unrealistic.
1.2
1 1980) - 0.8
0.6
C (Base: (Base: C 1951 0.4 o
0.2
0
-0.2
-0.4 Temperature Anomaly
-0.6 1880 1887 1894 1901 1908 1915 1922 1929 1936 1943 1950 1957 1964 1971 1978 1985 1992 1999 2006 2013 Year
Annual_Mean 5-year_Mean
Figure 6 Global Land-Ocean Temperature Anomaly relative to baseline 1951-
198017
2.5 Climate Change Detection and Attribution: Impacts from below 1oC
Attributing a single weather-related event to climate change has historically been cautioned against:
17 Figure 6 created by Author using NASA data from: data.giss.nasa.gov/gistemp/graphs_v3/
31
• …quantitative separation of the observed warming into anthropogenic
and naturally forced components requires considerable caution
(Mitchell et al, 2001); and
• Although the tails of climate distributions have been analyzed for
many years, quantifying the contribution of historical warming to
unprecedented events presents an imposing scientific challenge at
the nexus of climate dynamics and statistical analysis (Diffenbaugh
et al., 2017).
Nevertheless, observations of the impacts of climate change have been increasing over
the past decade. The IPCC’s AR5 reported observed incremental changes with varying
levels of confidence (Table 5). Very high confidence, for example, was attributed to glacial shrinkage and coral mortality, while coastal erosion and declining tree density were given a medium level of confidence (see further examples in Table 5).
Consequently, recent advancements in detection and attribution have allowed the
climate science community to shift from broad statements crediting climate change with
intensifying events (IPCC, 2014) to attribution of climate change to specific weather
events (Diffenbaugh et al., 2017). In 2011, the American Meteorological Society started
synthesizing papers on the detection and attribution of extreme events to climate
32 change. The resulting annual reports are being published in the Bulletin of the
American Metrological Society (BAMS)18.
Table 5 General Examples of Attribution of Recent Climate Change, with
Confidence Levels
Very High Confidence High Confidence Medium Confidence
Glacial shrinkage Decreasing spring snowpack Increased coastal erosion
Sea ice recession, earlier Early spring peak flow Changes in species richness breakup and abundance
Arctic sea ice retreat Permafrost degradation Decreased tree density
Increased coral mortality Effects on non-migratory Increased tree mortality and bleaching marine Animals
Increased runoff in glacial-fed rivers
Range shifts of fish and macroalgae
(Information summarized by Author from IPCC 2014, Figure 1.12)
Progress in modeling, statistical tools and data analysis have increased the probability to which these publications can assess the attribution level to climate change (Thomas,
2016). Rather than relying on varying levels of certainty, Partain and colleagues
18 See Herring, S. C., A. Hoell, M. P. Hoerling, J. P. Kossin, C. J. Schreck III, and P. A. Stott, Eds., (2016): Explaining Extreme Events of 2015 from a Climate Perspective. Bulletin of the American Meteorological Society (BAMS), 97 (12), S1–S145.
33
(2016) were able to determine that anthropogenic climate change increased the wild fire potential in Alaska in 2015 by 34-60%. Additional examples provided in Table 6.
Table 6 Specific Examples of Extreme Events Attributed to Climate Change19
Study Location & Climate Detection of climate Attribution of climate Reference Change change change Impact
Alaska, United Wild Fire, Max temp >30oC + Anthropogenic climate States (Partain et 5.1 million other factors (relative change increased the wild al., 2016) acres burned humidity, sunlight fire potential by 34-60% hours, lightning)
Miami, Fla.,United Tidal flood, Rising mean sea levels Anthropogenic climate States (Sweet et 0.57m + other factors change increased the al., 2016) (astronomical probability of tidal flood by underpinnings) >500%
Egypt, Northeast Heat Wave, High temperatures + Anthropogenic climate Africa, Middle East 90 lives lost other factors (relative change increased the (Mitchell, 2016) humidity, population) probability of the event by 69%
Ethiopia and Drought, food Increased El Niño Sea Anthropogenic climate Southern Africa insecurity Surface Temperatures, change substantially (Funk et al., 2016) reduced regional contributed to the drought rainfall by 16% and event 24%, decreased runoff by 35% and 48%
19 Table created by Author using data from BAMS special issue (Herring et al., 2016) and corresponding peer-reviewed publications cited within the table
34
Southeast China Extreme Shorter more intense Anthropogenic climate (Burke et al., 2016) Precipitation storms change increased total levels, precipitation by 50% above flooding 1971-2000 average.
Northwest China Extreme Heat Record-breaking Anthropogenic climate (Miao et al., 2016) maximum temperature change increased the
of 47.7oC likelihood of this event by 3- fold
The global surface temperature in 2016, whether bordering on +1.0oC over the 1951-
1980 baseline or exceeding it by +1.26oC above Hansen’s 1880-1920 pre-industrial
levels, is already creating detectible changes, that are – using the Conventions’
terminology – adversely affecting natural ecosystems and humankind20. Given this
knowledge, have we not surpassed a threshold and moved into dangerous
anthropogenic interference as outlined in Article 2 of the Convention?
Moreover, given advancements in climate science attribution and detection, it can be
argued that we had already passed the dangerous threshold of anthropogenic interference at the time of the drafting of the Convention. Article 2, therefore, was out of date as soon as it was written. From this perspective, the semantics of dangerous anthropogenic interference was incorrect. It surmised that dangerous anthropogenic interference was a future event. It had already arrived.
20 UNFCCC, 1992 Paragraph 2, Preamble
35 2.6 Response to Dangerous Anthropogenic Interference: Mitigation and Adaptation 2.6.1 Mitigation: Misunderstanding and limitations
The term, 'emissions', is mentioned in the Convention 23 times, in contrast to
adaptation’s 6 mentions. This may relate to the fact that the negotiations for the
Convention followed the success of the Montreal Protocol on Substances that Deplete
the Ozone Layer and the Resolution of the Regional Acid Rain problem in Europe and
North America. The Parties may therefore have assumed that a reduction in GHGs
would be enough to control this new ‘pollution problem’ (i.e., climate change and global
warming).
There are many explanations as to why a mitigation agenda alone is insufficient:
i. Climate change is not a future event; it is happening now; impacts are already
occurring.
ii. Despite great advances in alternative energy, we continue to live in a fossil-fuel
dominated society. iii. The removal of “all the human-emitted CO2 from the atmosphere by natural
processes will take a few hundred thousand years (high confidence)” (Archer &
Brovkin, 2008).
iv. Even with a full cessation of greenhouse gas emissions, upwards of 20% of CO2
already present will continue to remain in the atmosphere for thousands of years
(Inman, 2008).
v. Temperatures will therefore continue to rise, and thermal expansion of the
oceans will continue to occur long after the last greenhouse gases are emitted
36
(IPCC, 2014).
vi. Current national level emission reductions are not on track to meet a +2oC target,
with +1.5oC having less likelihood of being achieved (UNFCCC, 2015).
In 2010, the 16th UNFCCC Conference of the Parties met in Cancun, Mexico. The
Cancun Agreement included the statement, “Adaptation must be addressed with the
same priority as mitigation” (UNFCCC, 2011). The remainder of this dissertation will
centre on this mandatory priority: Climate Change Adaptation.
2.7 References
Archer, D., & Brovkin, V. (2008). Millennial atmospheric lifetime of anthropogenic CO2.
Climatic Change, (90), 283–297.
Blunden, J., Hartfield, G., & Arndt, D. S. (2018). State of the Climate in 2017. Bulletin of
the American Meteorological Society, 99(8), Si–S332.
Burke, C., Stott, P., Sun, Y., & Ciavarella, A. (2016). Attribution of extreme rainfall in
southeast china during May 2015. Bulletin of the American Meteorological
Society, 97(12), S92-S96.
Diffenbaugh, N. S., Singh, D., Mankin, J. S., Horton, D. E., Swain, D. L., Touma, D., ...
& Rajaratnam, B. (2017). Quantifying the influence of global warming on
unprecedented extreme climate events. Proceedings of the National Academy of
Sciences, 114(19), 4881-4886.
Funk, C., Harrison, L., Shukla, S., Hoell, A., Korecha, D., Magadzire, T., . . . Galu, G.
(2016). 15. Assessing the contributions of local and east pacific warming to the
2015 droughts in Ethiopia and southern Africa. Bulletin of the American
Meteorological Society, 97(12), S75-S80.
37
Hansen J, Kharecha P, Sato M, Masson-Delmotte V, & Ackerman F. (2013). Assessing
“Dangerous Climate Change”. Required Reduction of Carbon Emissions to
Protect Young People, Future Generations and Nature. PLoS ONE 8(12):
e81648.
Hansen, J., Satoa, M., Ruedy, R. et al. (2017). Climate Science, Awareness and
Solutions, Earth Institute, Columbia University:
Herring, S. C., A. Hoell, M. P. Hoerling, J. P. Kossin, C. J. Schreck III, and P. A. Stott,
Eds., (2016): Explaining Extreme Events of 2015 from a Climate Perspective.
Bulletin of the American Meteorological Society (BAMS), 97 (12), S1–S145.
Inman, M. (2008). Carbon is forever. Nature reports climate change, 156-158.
IPCC (Intergovernmental Panel on climate Change). (2014). Climate Change 2014:
Synthesis Report. Contribution of Working Groups I, II and III to the Fifth
Assessment Report of the Intergovernmental Panel on Climate Change [Core
Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland,
151 pp.
IPCC. (2018a) Special Report: Global Warming of 1.5 °C: https://www.ipcc.ch/sr15/
IPCC (2018b). Special Report: Global Warming of 1.5 °C - Summary for Policymakers
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Kaempffert, W. (1956). Warmer climate on the earth may be due to more carbon dioxide
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41 Chapter 3 Contemporary Climate Change Adaptation: A Brief Review Elements of Contemporary Climate Change Adaptation
The United Nations Framework Convention on Climate Change (UNFCCC) of 1992
called on nations to enact measures to “facilitate adequate adaptation”. In the years that
followed, the contemporary climate change adaptation literature started to take form
(Burton, 1996; Smith & Lenhart, 1996; Tol, Fankhauser, & Smith, 1998). Tol and
colleagues (1998) showed that adaptation research was driven by vulnerability and
impacts research. Soon after, researchers shifted their focus to identifying ways that
vulnerable populations could adapt to future changes in climate (Adger, 2006; Brooks et
al., 2005; Burton et al., 2002; Smit et al., 2000). Most recently, discussions in the
literature have concentrated on whether societies are, in fact, adapting rapidly enough
to climate change (Averyt, 2010; Berrang-Ford et al., 2011) and how effective their actions are likely to be (Adger et al., 2005; Doria et al, 2009; Eakin & Patt, 2011; Osbahr et al., 2010).
There are several contemporary definitions of adaptation. Chapter 1 explored the historical development of the term adaptation. This section will briefly review contemporary definitions and typologies. The following publications contributed to the early development of contemporary thought on adaptation: Burton et al., 1993; Carter et al., 1994; Easterling, 1996; Fankhauser et al.,1999; Klein 2003; Klein & Tol, 1997; Parry
& Carter, 1998; Smit, 1999; Smit, et al., 1996; Smit & Skinner 2002; Smithers & Smit,
1997; and Stakhiv, 1993.
42 3.1 Current Definition of Climate Change Adaptation
In 2001, the Third Intergovernmental Panel on Climate Change report (IPCC, 2001)
defined adaptation as the “adjustment in natural or human systems in response to
actual or expected climatic stimuli or their effects, which moderates harm or exploits
beneficial opportunities”. This is the current working definition of climate change adaptation used by the UNFCCC.
In 2014, the IPCC updated the UNFCCC’S definition to read, “The process of adjustment to actual or expected climate and its effects. In human systems, adaptation seeks to moderate or avoid harm or exploit beneficial opportunities. In some natural systems, human intervention may facilitate adjustment to expected climate and its effects.”
3.2 Selection of previous definitions and typologies
An earlier definition of “adaptation to climate” (note, not climate change) was “the
process through which people reduce the adverse effects of climate on their health and
well-being and take advantage of the opportunities that their climatic environment
provides” (Burton, 1992). Smit (1993) incorporated socioeconomic elements and used
the full term “climate change”, to update the definition to “adjustments to enhance the
viability of social and economic activities and to reduce their vulnerability to climate,
including its current variability and extreme events as well as longer-term climate
change”.
In his 1945 dissertation, Gilbert White, identified three binary typologies for the ordering
of occupance to the adjustments to floods: systematic or unsystematic, rational or
irrational, conscious or unconscious. This type of dichotomous system has continued to
43 flourish in climate change adaptation, present in several publications including Smit et al., 1999 based on seven differing concepts or attributes: purposeful, timing, temporal scope, spatial scope, function/effect, form, and performance. See Table 7 for a comparative selection of binary typologies.
Table 7 Examples of Binary Typologies 1945 Compared to 1999
White 1945 Smit, Burton, Klein & Street 1999
Binary typologies Systematic or Unsystematic Spontaneous or purposeful
Rational or Irrational Autonomous or planned
Conscious or Unconscious Automatic or Intentional
Passive or Active
Anticipatory or Responsive
Proactive or Reactive
Similarly, in White’s 1961 publication, The Choice of Use in Resource Management, he outlines his theoretical range of adjustments. This range begins with the “bear the loss” option that is continued throughout the literature until the present day (Table 8)
Table 8 Evolution of the range of adaptation options White (1961) Burton, Kates, & White, Burton et al. Burton 1996/IPCC 2001 (1968) 1993 Bear the Bear the losses Bear losses Bear the loss losses Adjust to losses Change Change location Structural location change Affect the cause Change use Change use Modify loss potential Modify the events Modify threats
44
Flood Modify the hazard Prevent Prevent the effects abatement effects Plan for losses Educational behavior Flood Share losses protection Spread the losses Research
Land evaluation Share the loss
Emergency • Structural, technical action • Legislative, regulatory financial Insurance • Institutional administrative Public relief • Market based • On site operations
Recently, a new typology of adaptation has surfaced: incremental, transitional and
transformational. Much of the current literature, measures and plans focus on the first
term “incremental measure”, which can be defined as, “adaptation processes [that]
operate to maintain a system within its current state” (Hadarits et al., 2017). In 2014, the
IPCC report defined incremental adaptation as, “actions where the central aim is to
maintain the essence and integrity of a system or process at a given scale.” Smith,
Horrocks and colleagues (2011) described incremental adaptation as changes to a
system with the objective of allowing “decision-maker[s] to continue to meet [their] current objectives under changed [climatic] conditions”. They illustrate this with the example of changing crops to maintain current farming practices. Transitional adaptation can then be thought of as an intermediary stage where adaptation measures attempt to cause a shift in these ‘incremental processes’, transitioning adaptation into a new state.
45
The most drastic adaptation measures are transformational, where adaptation now becomes a radically new endeavour, no longer seeking to maintain the status quo. It could be argued that all the current RCPs would require some level of transformational adaptation, with increasing levels of transformational adaptation required as the level of radiative forcing increases. Kates and colleagues (2012) discussed three categories of transformational adaptation:
(1) Enlarged scale or intensity,
(2) New or novel adaptations, and
(3) Different places and locations
They provided the examples of the Thames Estuary 2100 Plan in the United Kingdom as one of transformational scale; the use of genetically modified crops representing a novel transformational measure; and the forced migration of residents on Carteret
Islands in Papua New Guinea to Bougainville as transformational relocation.
Given the increasing pace of climate change detection and attribution (see section 1.4), a greater effort to shift the current literature, measures and plans from incremental adaptation to transformational adaptation is becoming necessary. The current IPCC definition of transformational adaptation is “changes [to] the fundamental attributes of a system in response to climate and its effects” (IPCC, 2014).
3.3 Climate Change Adaptation in the 1992 UNFCCC Adaptation is mentioned in the Convention five times following its mention in Article 2.
i. Article 3.3 states, “The Parties should take precautionary measures to
anticipate, prevent or minimize the causes of climate change” and “lack of full
46
scientific certainty should not be used as a reason for postponing such
measures… as … adaptation…”.
ii. Article 4.1(b) states that all parties to the Convention are to “formulate,
implement, publish and regularly update national and, where appropriate,
regional programmes containing measures to … facilitate adequate
adaptation to climate change”. This signals that the 195 parties to the
Convention should develop national and regional programmes. It should be
noted that it does not specify only “developing nations”, but all parties.
Further, it refers to “adequate adaptation”, that again is undefined, but could
be interpreted to mean “adequately enough to prevent dangerous
anthropogenic interference”.
iii. Article 4.1(e) states that All Parties shall, “Cooperate in preparing for
adaptation to the impacts of climate change; develop and elaborate
appropriate and integrated plans…particularly in Africa”. The term cooperate
could be interpreted in many ways – it could be interpreted as being civil, but
more importantly, it could be interpreted as an obligation to help other
countries (the text specifically points out the entire continent of Africa). Since
the text does not differentiate between developing and developed countries,
again, it can be understood that all countries must prepare.
iv. Article 4.1(f) continues by stating that All Parties shall, “Take climate change
considerations into account, to the extent feasible, in their relevant social,
47
economic and environmental policies … to minimiz[e] adverse effects on the
economy, on public health and on the quality of the environment, of projects
or measures undertaken by them to mitigate or adapt to climate change” …
This provides a qualification to the previous other two sections of Article 4;
viz., that parties are to take climate change into account where it is “feasible”
and “relevant”, while maintaining that they are to minimize economic, health,
and environmental impacts.
v. Article 4.4 of the Convention states that, “The developed country Parties …
shall also assist the developing country Parties that are particularly vulnerable
to the adverse effects of climate change in meeting costs of adaptation to
those adverse effects”.
Since the signing of the Convention, adaptation’s importance has increased in the
international climate arena (Table 9). In 1994, when the UNFCCC came into effect, the conversation surrounding adaptation was “do we need to adapt?” There was pushback by the mitigation community that adaptation was an alternative to mitigation rather than a complementary imperative as it is now viewed. A decade after the signing of the
Convention, the literature started to expand on climate change adaptation. By the third assessment report released in 2001, the conversation shifted to “how do we adapt?”, with researchers concentrating their studies on ways that vulnerable populations could adapt to future changes in climate and variability (Adger, 2006; Brooks et al., 2005;
Burton et al., 2002; Smit et al., 2000).
48
Once the fourth assessment report was released in 2007, discussions in the literature shifted to whether societies are, in fact, adapting rapidly enough to climate change
(Averyt, 2010; Berrang-Ford et al., 2011) and how effective their actions are likely to be
(Doria et al., 2009; Eakin & Patt, 2011; Osbahr et al., 2010). During the same timeframe, governments (national, state level and local), the private sector and community-based organizations globally started to develop strategies to respond to the threat of a changing climate (Dickinson & Burton, 2011; Schipper et al., 2014). Table 9 summarizes key advancements of the prominence of adaptation under the UNFCCC over the past 38 years.
Table 9 Key Developments under the UNFCCC
Year Key Development
1979 First World Climate Conference (WCC)
1988 IPCC established
1990 IPCC First Assessment Report released. Global treaty on climate change requested.
1991 Intergovernmental Negotiating Committee (INC) holds initial meeting
1992 INC adopts UNFCCC text. At the Earth Summit in Rio, UNFCCC is opened for signature
1994 UNFCCC enters into force
Conversation on Adaptation is “Do we need to adapt?”
1995 First Conference of the Parties (COP1) takes place in Berlin.
1996 UNFCCC Secretariat set up, COP2 National Communications are established
1997 Kyoto Protocol formally adopted in December at COP3
49
2001 IPCC Third Assessment Report. Marrakesh Accords adopted at COP7
COP7 Least Developed Country (LDC) Support established;
Adaptation Fund established
Conversation shifts to “How do we adapt?”
2002 COP (decision 8/CP.8) requested the Global Environment Facility (GEF) to help prepare National Adaptation Programmes of Action (NAPAs)
Least Developed Countries Fund and Special Climate Change Fund become operational
2003 COP (decision 6/CP.9), requested the GEF to support the implementation of NAPAs
2004 In November 2004, the first NAPA (Mauritania) was submitted to the UNFCCC secretariat.
2005 Kyoto Protocol comes into force; first Meeting of the Parties to the Kyoto Protocol
2006 Five-year Nairobi Work Programme (NWP) mandated to address impacts, vulnerability and adaptation
2007 IPCC Fourth Assessment Report released; COP13 Parties agreed to Bali Road Map
Adaptation Fund launched
[Canada] Regional Adaptation Collaborative Climate Change Program (2007-2011) launched
Climate science mainstreamed into popular culture
2008 [Canada] From Impacts to Adaptation: Canada in a Changing Climate 2008 released
2009 Copenhagen Accord drafted at COP15 in Copenhagen.
50
Countries submitted non-binding emissions reductions pledges/mitigation action pledges
First mention of the Green Climate Fund
Adaptation Fund Operational
2010 COP16 Cancun Adaptation Framework established: Adaptation Committee created
Formal establishment of the Green Climate Fund
2011 The Durban Platform for Enhanced Action drafted and accepted at COP17
Adoption of the Green Climate Fund
[Canada] Federal Adaptation Policy Framework created
[Canada] Regional Adaptation Collaborative II: Enhancing Competitiveness in a Changing Climate (2011-2016) launched
2012 First report of the Adaptation Committee released
Commitment to USD$100 billion for mitigation and adaptation by 2020
[Canada] Canada’s climate change Adaptation Platform launched
2013 Guidelines for the formulation of NAPs established; 50 countries received funding, 49 of them submitted NAPAs to UNFCCC secretariat
USD$100 million target met for Adaptation Fund
2014 Sixth UNFCCC National Communications submitted to the secretariat
Green Climate Fund to be operational
COP20 Lima, Peru, December 2014
[Canada] Canada in a Changing Climate: Sector Perspectives on Impacts and Adaptation was released in 2014
2015 The Paris Agreement
Established a Global Goal for Adaptation (Article 7)
51
All parties to report on adaptation; review adaptation progress
Global Stocktake (Article 14) every five years (beginning in 2023)
2016 Countries begin submitting their Intended Nationally Determined Contributions (INDCs) & Nationally Determined Contributions (NDCs) to the UNFCCC
[Canada] Pan-Canadian Framework on Clean Growth and Climate Change released
2017 Discussions continue on Adaptation Communications, Global Stocktake, and the Global Goal for Adaptation (GGA)
Technical Expert Meeting on Adaptation (TEM-A) seeks to integrate Climate Change Adaptation, the Sustainable Development Goals 2030 and the Sendai Framework on Disaster Risk Reduction".
2018 Special Report on Global Warming of 1.5 °C published
(Sources: UNFCCC.int; Developed by Author in 2012, Updated in 2014, 2017, 2018)
3.3.1 Climate Change Adaptation in Peer Reviewed Literature
The past two decades have not only witnessed a rise in adaptation’s prominence in the
Convention, there has also been a sizable and accelerating increase in the number of
climate change adaptation publications in the peer reviewed literature. A literature
search for adaptation and climate change in keywords, abstracts or titles in Scopus
Database yields 20,499 documents for the years 1987 to 2017. The growth in publications also reflects the increasing awareness of the concept of adaptation in the
IPCC Assessment Reports over this time span.
52
3000
AR5 2013, 2396 publications 2500
2000
1500
1000
AR4 2007, 470 publications 500 TAR 2001, Number of Publications on Climate Change + Adaptation + Change Climate on Publications of Number FAR 1990, SAR 1995, 127 publications 17 publications 33 publications 0 1985 1990 1995 2000 2005 2010 2015 Year
Figure 7 Publications per year on Climate Change Adaptation
Figure 7 shows an almost exponential growth in adaptation literature since 1987.
Specifically, the increasing trend not only coincides with the creation of the UNFCCC in
1992, but also the introduction of discussion about adaptation in the Intergovernmental
Panel on Climate Change (IPCC) Reports. In 1990, only 17 papers on adaptation were
published – the year of the First Assessment Report (FAR). At that time Working Group
II was titled Impacts Assessment of Climate Change and FAR had no chapters
dedicated to adaptation. By the Third Report (TAR) in 2001, WGII’s title was updated to
Impacts, Adaptation and Vulnerability, and the number of publications on adaptation
had increased to 127.
53
The most recent report AR5 (2013) coincided with a remarkable increase in
publications, 2396. AR5 was also notable for containing the highest level of text
dedicated to adaptation, namely, four chapters on adaptation and one chapter shared
with mitigation and sustainable development (Table 10).
Table 10. Inclusion of Adaptation in the IPCC Reports
Year Chapter and Working Groups
1990 WGII: Impacts Assessment of Climate Change
No chapter dedicated to Climate Change Adaptation
1995 WGII: Impacts, Adaptations and Mitigation of Climate Change: Scientific- Technical Analyses
Separate chapters on mitigation, but not on adaptation
2001 WGII: Impacts, Adaptation and Vulnerability
Mitigation has an independent working group (WGIII)
(18. Adaptation to Climate Change in the Context of Sustainable Development and Equity)15
2007 WGII: Impacts, Adaptation and Vulnerability
17. Assessment of adaptation practices, options, constraints and capacity
(18. Inter-relationships between adaptation and mitigation)15
2014 WGII: Impacts, Adaptation and Vulnerability
14. Adaptation needs and options
15. Adaptation planning and implementation
16. Adaptation opportunities, constraints, and limits
17. Economics of adaptation
54
(20. Climate-resilient pathways: adaptation, mitigation, and sustainable development)21
2021 Adaptation to be included in all chapters in Working Group II
Section 1: Risks, adaptation and sustainability for systems impacted by climate change
Section 2: Regions
Section 3: Sustainable development pathways: integrating adaptation and mitigation
3.4 Climate Finance for Climate Change Adaptation
The pivotal feature missing from this conversation is finance. There are three key components in the climate finance discussion: 1) estimating the damages from climate change; 2) the cost of adapting to climate change; and 3) how adaptation will be financed.
3.5 Damages from Climate-Related Events
Between the years 2012 to 2016, the most conservative values (from EM-DAT, The
International Disaster Database) show more than 1500 climate-related disaster events.
These events caused close to 56,000 deaths, displaced more than 3.2 million people and produced almost USD$470 billion in financial losses22. Furthermore, over the past
50 years, losses from climate-related disaster events have been escalating. Figure 8 shows climate-related disasters categorized as hydrological (e.g., flood and mass
21 Bracketed items are chapters with shared themes that include adaptation 22 The figures are: 1506 climate-related disaster events; 55,991 deaths; 3,211,146 left homeless. And USD$469,746,018,000 in total financial damages
55
movement) or meteorological (e.g., storm and extreme temperature) on a decadal
timescale.
$900,000,000
$804,945,836 $800,000,000
$700,000,000 $646,030,224 $600,000,000
$500,000,000
$400,000,000
Damages Damages USD ($ '000) $300,000,000
$215,744,271 $200,000,000
$100,000,000 $72,070,167 $22,868,114 $- 1967-1976 1977-1986 1987-1996 1997-2006 2007-2016 Year
Figure 8 Total Damages from Climate Related Events 1967-2016 (in USD ‘000)
3.6 Costing Climate Change Adaptation
Following the 2007 release of the Stern Review which cited the cost of climate change
to be 5-20% of GDP per year indefinitely, several consortia set out to determine the cost
of adapting to climate change. In 2007, the UNFCCC reported that $49-171 billion per
year by 2030 would be needed to adapt to climate change, and of this, $27-66 billion
would be for developing countries. The World Bank (2010) stated that a 2oC warmer world would cost $70-100 billion per year by 2050. The highest estimate came from the
56
2009 paper by Parry and colleagues that took both the adaptation deficit and natural ecosystems into account, resulting in a $134-230 billion per year estimate. In 2016, the
UNEP cautioned that the “costs of adaptation are likely to be two-to-three times higher than current global estimates by 2030, and potentially four-to-five times higher by 2050”.
Table 11 summarizes the estimated costs of climate change adaptation from these reports and other publications.
Table 11. Estimated Costs of Climate Change Adaptation
GLOBAL Estimated Cost Notes (USD)
World Bank, $9-$41 billion per Cost of climate proofing foreign direct 2006 year investments (FDI), gross domestic investments (GDI) and Official Development Assistance (ODA)
Stern, 2007 $4-$37 billion starting Update of World Bank (2006) in 2015
Oxfam, 2007 >$50 billion per year WB (2006) plus extrapolation of cost estimates from national adaptation plans (NAPAs) and NGO projects.
UNDP, 2007 $86-$109 billion per WB (2006) plus costing of targets for year adapting poverty reduction programs and strengthening disaster response systems
UNFCCC, $49-171 billion per $27-66 billion needed for developing 2007 year by 2030 countries. Includes: agriculture, forestry and
57
fisheries; water supply; human health; coastal zones; infrastructure
Parry et al., $134-230 billion per Both adaptation deficit and natural 2009 year ecosystems taken into account
World Bank, $70-100 billion per Adapting to 2oC warmer world by 2050. 2010 & year for 2010-2050 Improvement on UNFCCC, 2007: more Narain, 2011 precise unit cost, inclusion of cost of maintenance and port upgrading, risks from sea-level rise and storm surges.
UNEP, 2016 $500 billion per year If current emissions are not rapidly by 2050 decreased
AFRICA Estimated Cost Notes (USD)
Watkiss et $25 billion per year in Estimate only for Africa; 1.5 - 3% of GDP al., 2010 the next 2 years, each year by 2030 in Africa. increasing to $60 billion per year by 2030
UNEP, 2013 $350 billion per year Adapting to a 3.5oC - 4oC world by 2100. by 2070 Cost for Africa only for infrastructure and
farming.
LATIN Estimated Cost Notes AMERICA (USD)
Vergara et $17-27 billion for Based on 2oC stabilization target and al., 2013 adaptation and $100 projected impacts to the region billion for mitigation
annually
58
PACIFIC Estimated Cost Notes REGION (USD)
Asian $447 million – $775 Adaptation cost would be significantly lower
Development million per year till if CO2 remained below 450ppm ($158 million Bank, 2013 2050 (1.5%-2.5% of or 0.5% of GDP) GDP) for Business as
Usual (BAU) Scenario
Table Developed by Author in 2011 for Smith et al., 2011, updated in 2014 and 2017
3.7 Funding Climate Change Adaptation
Article 4.4 of the Convention, described previously, is rarely cited in the literature or the negotiations; however, its content has been of recent debate, namely, developed countries assisting developing countries in “meeting costs” of climate change adaptation. Developing countries are arguing for climate justice – decreasing the uneven burden shared by developing nations at the hands of the high emitting developed nations. The developing countries’ narrative is that they have done less to cause the impacts that they are enduring from a changed climate – thus, the obligation is on developed countries to provide funding for adaptation. Several multilateral institutions set up financial mechanisms to provide funding for adaptation (see Chapter
9). However, current funding structures are not distributing funding to mitigation and adaptation equally.
Article 9.1 of the Agreement, calls for, “Developed country Parties [to] provide financial resources to assist developing country Parties with respect to both mitigation and adaptation”. Further, Article 9.4, clarifies that these financial resources, “should aim to
59
achieve a balance between adaptation and mitigation”. While funding efforts are
underway to mobilize increased climate finance to secure USD $100 billion per year for
mitigation and adaptation in developing countries by 2020, current funding levels fall
short of both the high climate finance goal and the balance between mitigation and
adaptation (UNEP, 2016). The need for both secure and sustainable climate finance is
intensified by a rapidly changing climate system that has ushered in a significant
increase in financial losses by climate related events.
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68 Chapter 4 Theoretical Framework Introduction
The selection of a theoretical framework precedes the selection of a methodological
process. Moreover, the theoretical framework influences the methods selected, thus
determining the research process itself. In critical social theories, the selection of a
theoretical framework such as gender theory, feminist theory or postcolonial theory
provides the researcher with a breadth of literature, conceptual frameworks and
methodologies to select from, or to identify gaps to explore. Climate change adaptation,
an emerging discipline, does not currently have a wealth of literature on theoretical
frameworks to structure the research process. Often climate change adaptation
research is devoid of an identified theoretical framework.
Climate change adaptation has tremendous diversity, and for this reason, adaptation
researchers may be able to borrow theoretical frameworks from other disciplines.
Alternatively, as the discipline of climate change adaptation continues to develop, so too
may specific theoretical frameworks for adaptation research. This chapter develops an
overarching theoretical framework for climate change adaptation research in this thesis,
and potentially may provide a foundational structure for the broader scope of climate
change research. The chapter centers on the understanding of several philosophical
terms. Table 12 provides a description of seven terms and their place in philosophical history.
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Table 12 Terms used in this chapter and their origin
Term Definition Philosopher Paradigm Theory or set of beliefs Thomas Kuhn in The Structure of Scientific Revolutions Ontology Branch of metaphysics, the nature Traced back to Aristotle of reality Epistemology Branch of philosophy, the theory of Traced back to Descartes knowledge Pragmatism Where meaning and truth are William James, Traced back derived by the practical relevance to the Greek historian of the outcome Polybius Realism Mind-independent; “climate change Traced back to ideas in still exists whether you believe it or Plato’s Theory of Forms not” Relativism No absolute truth; denial of Protagoras of Abdera absolutism, objectivism, monism and realism Positivism Objective; Values scientific method Auguste Comte in Cours de and scientific knowledge. Similar to Philosophie Positive empiricism (empirical knowledge); Quantitative Interpretivism Subjective; nature of reality is Response to positivism socially constructed; values non- (anti-positivism); scientific methods; Qualitative Montesquieu Constructivism Knowledge is a mental construct Jean Piaget influenced by prior knowledge Post-positivism Acknowledgment that previous Critique of positivism knowledge can bias observation; qualitative & quantitative
70 4.1 The Philosophical Complexity of Climate Change
Several methodologies are available to a researcher embarking on theoretical or empirical research. The choices are fundamentally based in the ontological (nature of reality) and epistemological (theory of knowledge) beliefs held by the researcher.
Climate change adaptation challenges traditional ontological, epistemological and methodological norms because the ability to understand its scientific, political and social discourses potentially necessitates not only a realist or positivist perspective, but also an interpretivist and relativistic point of view. The idea, however, of merging multiple philosophical research paradigms is not without criticism. A positivist, surrounded with quantitative data, may argue that such a diverse array of epistemological and ontological options borders on ‘inconsistency’ (Mason, 2006), where the allowance of mixing paradigms should be avoided to preserve philosophical purity.
With the complexity of climate change adaptation in mind, the following chapter will work to understand pragmatism as the possible paradigmatic position for a climate change adaptation researcher. The chapter will then explain why the choice of pragmatism-of-the-right, (also termed realistic pragmatism or objective pragmatism) was selected as the theoretical paradigm for the research. Further, this chapter may lend understanding as to why climate change adaptation challenges these often binary and polarizing philosophical positions. The chapter may also explain why adaptation research may require a more nuanced form of pluralism where researchers are allowed a greater diversity of methodological options when conducting research (Harrits, 2011).
From an ontological perspective, the question, “what is the reality of climate change adaptation” centers on the researchers’ interpretation of the nature of reality. If a
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researcher views reality from a realist perspective, it allows for the understanding of
climate change adaptation from an objective space where a reality of adaptation exists,
independent of the researcher. Thus, from an epistemological perspective, that reality
can then be measured and quantitized. An alternative view, however, is possible, where the researcher observes the nature of reality as relative, and meaning is subjectively created by the researcher, where, in a natural setting, multiple researchers can view the “identical environment differently” (Lee, 2012). In this relativistic reality, the object of research (climate change adaptation) does not exist independently from the (adaptation) researcher.
This thinking has been applied to the knowledge of climate change. Leyshon, in her
2014 paper titled Critical issues in social science climate change research, provided
thought provoking arguments from Brace & Geoghegan (2011) asserting, “climate
change is not an object, it is a category of knowing”, and Naustdalslid (2011) arguing,
“climate change is ‘man-made’… in the sense that it is only visible to man [sic] and
society through science”. And it is through this science that we determine the
temperature anomaly, hence, the amount of warming (change) to the climate system
that has occurred; however, depending on the baseline selected23 (1880-1920, 1951-
1980, or the 20th century) the amount of “warming” is higher or lower. Does this limit the
reality of knowing, or shift climate scientists from a realist paradigm to a relativistic one?
No. But the complexity of climate change does create ontological and epistemological
23 NASA determines the temperature anomaly against the average temperature from the time period of 1951-1980.; NOAA uses the 20th century; and James Hansen uses the timeframe 1880-1920 to equate with pre-industrial levels.
72 quandaries and makes paradigm selection to a climate change adaptation researcher challenging.
4.2 Pragmatism: The Selection of a Paradigm
“The pragmatic method is primarily a method of settling metaphysical
disputes that otherwise might be interminable” (James, 1907).
Pragmatism is open to a broad range of worldviews, methodologies, data and analysis types, philosophies, and assumptions (Johnson et al., 2007). This breadth of diversity makes pragmatism an ideal companion for a “wicked problem” such as climate change.
The philosophical paradigm of pragmatism centres on the view that, “the best method is the one that solves the problem”. This interpretation removes the dualistic nature of positivist versus interpretivist: pragmatism permits the use of a spectrum of methodologies to answer a research question.
The central question in this dissertation hinges on the ability of the researcher to determine whether there is evidence that climate change adaptation is advancing at different rates in countries across the world. Given that climate change adaptation does not function as a single observable ‘object’ (whereas a lake, for example, allows a limnologist to directly measure temperature, pH and dissolved oxygen content), climate change adaptation lends poorly to a single methodological analysis type. Thus, the study of climate change adaptation necessitates a research paradigm that allows for the understanding of both quantifiable actions (changes in crop yield) and qualitative aims
(preserving traditional knowledge in indigenous populations). Creswell (2013) defined many elements of pragmatism. The following points in that 2013 paper have been
73 rewritten from the perspective of a climate change adaptation researcher. They help to understand why pragmatism readily lends itself to adaptation research:
i. Pragmatism is not committed to any one system of philosophy and reality;
climate change adaptation researchers can apply both quantitative and
qualitative assumptions when engaging in their research
ii. Individual researchers have a freedom of choice; climate change adaptation
researchers are free to choose methodologies, techniques and procedures to
best meet the needs and purpose of their research
iii. Pragmatists do not see the world as an absolute unity; climate change adaptation
researchers may look to many sources and approaches for collecting and
analyzing data
iv. Pragmatism is not based in a duality; climate change adaptation can apply mixed
methods research to provide the best understanding of a research problem.
Thus, the selection of a pragmatist paradigm as a critical lens through which to view climate change adaptation allows for the inclusion of both qualitative social and political characteristics and quantitative scientific data. This allowance extends to the data being able to be combined and analyzed without causing a philosophical inconsistency.
The philosophy of pragmatism-of-the-right24 was selected as the research paradigm for this dissertation research. Also termed objective pragmatism or realistic pragmatism, Rescher (1999), states that this research philosophy allows for the following worldviews:
24 Original philosophers include Charles Sanders Peirce, Clarence Irving Lewis and Nicolas Rescher
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i. “our world evolved out of natural reality;
ii. between the social and the natural worlds there is no ontological line of
separation;
iii. there is no need to draw relativistic conclusions…because the presence of an
objective reality underlies the data”
Pragmatism-of-the-right contains stronger elements of realism and objectivism than a central pragmatist paradigm (Onwuegbuzie et al., 2009). Resher stated that, “scientific knowledge … represent[s] the best currently available estimate of the actual truth of things”. From a climate scientist perspective, this subset of pragmatism is welcome, as it allows for an increased quantitative component. From this perspective, permitting the use of both qualitative and quantitative assumptions, including qualitative and quantitative data types and analysis methods, all elements of climate change adaptation can be studied. Hence, the philosophical research paradigm, pragmatism-of-the- right, allows for the use of mixed methods research, where an objective reality of adaptation exists independently of the researcher, and this “reality of adaptation” can be measured and mathematized.
4.2.1 Statement of Research and Philosophical Positionality
In 1872, the Metaphysical Club, made up of 12 Harvard-educated men, was established to hold philosophical discussions. The members, from different disciplines, came together and proposed a new philosophical paradigm: Pragmatism. Four of the members Chauncy Wright, a pro-positivist, Oliver Wendell Holmes, a judge, Charles
Sanders Peirce, a logician and scientist, and William James, a psychologist, embodied a collective of differing worldviews underpinning the development of pragmatism. This
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pragmatic worldview reflects the author’s philosophical positionality, and like the
members of the metaphysical club, the author’s preexisting worldview provides insight
into the research positionality of this dissertation.
According to William James, Pragmatism:
• “unstiffens our theories” • Is completely welcoming
• Has no prejudices whatsoever • Entertains any hypothesis
• No obstructive dogmas • Considers any evidence
• No rigid canons of what counts as
proof
The author was drawn to pragmatism due to its ability to maximize the inclusion of different methodological approaches to address the complexities of the global commons. Further, the author’s own positionality is rooted in pragmatism and practicality, supported by a scientific, realist and positivist perspective from the authors’ previous formal education. Charles Sanders Peirce embodies the pragmatism-of-the- right, with its objectivist slant and ‘real world’ applicability. However, the author does not align with the rejection of other variations (subjectivist, relativistic, social constructivist).
Rather, the author acknowledges a bias towards Peirce’s ontological realism, while maintaining an openness for the inclusion of all aspects of pragmatism as outlined above from James.
The output of this research is based in grounded theory. However, the author’s worldview necessitates the repetition of a study for theory generation. This is in
76 opposition to a left-leaning pragmatic worldview, and potentially grounded theory itself.
The author places value in empiricism and the emergence of theory with study replication. And while theoretical saturation was attained in this study, as new data become available the intention is to repeat the analysis. Thus, the outputs of this research are foundational with practical application. The author acknowledges a bias towards a worldview where objective reality exists independently of the researcher, and where adaptation can be measured and quantified.
4.3 Mixed Methods from a Climate Change Adaptation Perspective
Pragmatism is regarded as the “philosophical partner of mixed methods research”
(MMR) (Denscombe, 2008). Using a pragmatic philosophy allows for the application of both inductive and deductive reasoning. At the most fundamental level of understanding, MMR allows for the combination of qualitative and quantitative research methodologies. Several elements can be mixed during MMR as outlined by Johnson
(2014) including paradigms, theoretical frameworks, analysis methods, data collection, concepts, languages, and disciplines. As such, MMR permits the use of atypical data sets, such as constructed or secondary (existing) data. The research in this dissertation relies upon this allowance.
Johnson & Christensen (2010) expand on this understanding in a table summarizing the
“emphases of quantitative, mixed and qualitative research”. This summary influenced the development of Table 13, where elements of MMR are explained within the scope of climate change adaptation research contained the in the Chapters of this dissertation.
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This table provides examples of how MMR was applied to CCA in this research, rather than an exhaustive list.
Table 13 Features of Mixed Methods Research as applied to Climate Change
Adaptation (in this dissertation)
Feature Mixed Research MMR as applied to CCA in this dissertation
Scientific Top-down and Starting hypothesis ‘Climate change adaptation Method Bottom-up; increases linearly with the wealth of a nation,
Hypothesis testing unless there is successful financial and knowledge intervention’ and theory Application of grounded theory, theoretical generation memoing and quantitization to additional atypical data types (documents, qualitative data, categorical variables) leads to knowledge generation, “emerging economies are falling behind least developed countries and developing nations”
And the formation of preliminary theories, “intervention (bilateral, multilateral and institutional) between rich and poor countries is suggested as the cause for the observed statistical non-linearity, where limited amounts of institutional support may cause decreased national level progress on adaptation”.
Research Identification of To obtain a numerical level of climate change Objectives both numerical adaptation for each country
and descriptive To identify descriptive determinants for climate objectives change adaptation to further the understanding of the levels of adaptation
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Form and Numerical data To develop a level of vulnerability to climate nature of data and variables, change and level of climate change adaptation collected documents, text for comparative analysis using both numerical data, categorical data (including, precipitation, temperature, data: words, population statistics) and word-based themes; multiple documents (including, national adaptation languages strategies and plans in different languages) documents
Data Analysis Identify statistical Application of both Grounded Theory Analysis relationships and to generate patterns and themes and correlations; Multivariate Statistical Analysis to find identify patterns quantitative correlations and themes
Data Transforming Converted qualitative text on barriers from 192 Transformation qualitative data countries into a quantitative level of barriers for into quantitative statistical analysis data through the process of quantitizing
Results and Statistics and Linear regression data and graphs, charts from Form of final narrative principal component analysis, coded text and report quotes from countries, tables with determinants identified
4.3.1 Approaches to Mixed Methods Research
Several mixed methods approaches are available for climate change adaptation research including: ethnography, case study, phenomenological research, narrative research, participatory action research and grounded theory (see Table 14; includes examples of qualitative data analysis software) Hirsch (2015) used an ethnographic
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approach to study the effect of climate change on pacific islanders in the Maldives,
capturing the poignant quote, “It won't be any good to have democracy if we don't have
a country” in response to the impacts of sea level rise on the Maldivian peoples and the
election of a democratic president. In Timor-Leste, Chandra et al., (2016) studied community-based adaptation (CBA) in smallholder farms using two comparative case studies, finding mitigation co-benefits of CBA in the communities. While no phenomenological study was found in the literature specifically on climate change adaptation25, Mearidy-Bell (2013) used the approach to understand behavioural change
of adolescents who were victims of natural disasters, most commonly, hurricanes.
Similarly, no climate change adaptation specific study was found for narrative research,
although colleagues Paschen and Ison, in their 2014 paper, explored using narrative as
a research paradigm for climate change adaptation, providing promise for its use in
future research. Narrative research was also used by Prince and Davies (2007)
studying survivors in two communities following flooding disasters; this study and the
phenomenological study completed by Mearidy-Bell (2013) both from disaster literature, offer a second discipline for climate change adaptation researchers to find mixed methodologies not currently found in the climate change adaptation literature.
Participatory action researchers (Bizikova et al., 2009) conducted workshops in
Hungary and Ghana, realizing the challenge of linking quantitative and qualitative information and translating that information into policy. The last approach, grounded theory, is the methodology applied in this dissertation.
25 Several search permutations in Scopus were used including, climate change AND adaptation AND phenomenological AND (lemmatization of the term) mixed methods; expanded to ALL FIELDS
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Table 14 Examples of qualitative analysis and data analysis software
Types of Qualitative Analysis Qualitative Data Analysis Software 1. Content analysis 1. Atlas.ti 2. Narrative analysis 2. HyperRESEARCH 3. Discourse analysis 3. MAXQDA 4. Framework analysis 4. Dedoose 5. Grounded theory 5. The Ethnograph 6. Ethnography 6. QSR N6 7. Phenomenology 7. QSR NVivo 8. Participatory action research 8. QDA Miner 9. Case study
4.3.2 Mixed Methods Model Design
There are upwards of 40 different mixed methods research designs (Tashakkori and
Teddlie, 2010). In the book Designing and Conducting Mixed Methods Research,
Creswell & Clark (2007) outline four major types of mixed methods designs (models)
(Figure 9). Each of the models uses qualitative (QUAL) and quantitative (QUAN) methods in different space and time: sequentially (i.e., QUAN first, QUAL second) or concurrently (QUAL and QUAN at the same time). The Embedded model (1), not currently found in climate change adaptation literature, applies a concurrent model with a dominant and non-dominant data type. To illustrate, Ozgelen (2012) applied an embedded design with a dominant QUAL seven-item open ended questionnaire with a non-dominant QUAN 18-item questionnaire on a 5-point Likert scale, where the QUAN data provided further information for the QUAL data.
The sequential Explanatory Model (2) was used by Campbell (2014) to assess 282 smallholder farmers in Jamaica, using a QUAN survey followed by QUAL interviews and
81 focus groups. The Exploratory Model (3) applies the reverse sequence to the explanatory model, where QUAN data builds upon QUAL data initially gathered. The last major model type, the Data Transformation Model (4), was chosen for this thesis.
This mixed methods model collects both QUAL and QUAN data and then transforms the
QUAL data into QUAN (via quantitizing) followed by comparative analysis and interpretation of the transformed data sets. The ability to study a complex and interdisciplinary topic such as climate change adaptation is made increasingly possible by mixing methodologies, data types, and disciplines.
Figure 9 Four Major Mixed Method Model Designs
82 4.4 Mixed Methods Approaches used in this Dissertation
Four main methods were used in this dissertation during mixed methods research: (1)
grounded theory, (2) memoing, (3) quantitizing, and (4) principal component analysis.
Since there is persistent confusion and misunderstanding surrounding both grounded
theory and quantitizing, the following sections expand on both methodologies.
4.5 Grounded Theory 4.5.1 Separating Content Analysis from Grounded Theory There is misinterpretation in the published literature about the difference between
content analysis and grounded theory. People believe that they are doing grounded
theory when they are in fact really doing content analysis.
What is grounded theory and how is it different from content analysis? The central
methodology in this thesis rests upon an understanding of grounded theory
methodology. Developed by Glaser and Strauss (1967), grounded theory “is perhaps
one of the most abused phrases in the qualitative health literature. Increasingly
researchers are making claims to have used grounded theory approach in what
emerges as rather superficial thematic content analysis” (Green & Thorogood, 2018).
4.5.2 Content Analysis
A central feature that separates content analysis from grounded theory is the goal or aim. Content analysis is used by a researcher to gain a greater understanding of an existing phenomenon. The aim of grounded theory, on the other hand, is to generate theory. Content analysis is defined as a “research method for the subjective interpretation of content of text data through the systematic classification process of coding and identifying themes or patterns” (Hsieh & Shannon, 2005). There are varying
83 degrees to which content analysis may be used. A simplistic methodology is to apply a word count to documents or interviews. Mayring & Flick (2004) define content analysis with greater complexity, describing the process as a “strict and systematic set of procedures for the rigorous analysis, examination and verification of the contents of written data”. Krippendorf (1980) states that content analysis is a “research technique for making replicable and valid inference from texts (or other meaningful matter) to the context of their uses”.
Content analysis emerged from linguistics and communication. Approaches to it can be both inductive and deductive. However, content analysis is not recommended for exploratory research as the output from the analysis is primarily used as a data reduction technique rather than to develop substantive theory. Table 15 provides a comparative summary of content analysis and grounded theory.
Table 15 Comparative Summary of Content Analysis and Grounded Theory
Content Analysis Grounded Theory
Deductive (commonly) Inductive methodology
Emerged from linguistics and Emerged from sociology communication
Reaction to quantitative content analysis; Reaction to positivistic view of science go beyond the numbers, to understand the content
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Generates a list of codes, themes or Generates theory (substantive) categories; data reduction, abstraction
Not appropriate for open and exploratory Used for open and exploratory research research where: 1. No relevant theory exists (i.e., this has not been done before) 2. Examine phenomena with “new eyes” 3. New perspective without being restricted to hypothesis
4.6 Grounded Theory
The original definition of grounded theory is “the discovery of theory from data
systematically obtained from social research” (Glaser & Strauss, 1967). The aim of
grounded theory is theory development. The research (and theory) are said to be
“grounded” in the data. Glaser notes that grounded theory, “provides a series of
systematic, exact methods that start with collecting data and take the researcher to a
theoretical piece that is publishable” (Glaser,1999). If the theory is not “grounded” in the
data, the conclusions are only conjecture (Strauss, 1987). Further, grounded theory is
said to be a discovery because the theory already exists in the data, the research using the methodology to reveal theory [waiting to be found]. Thus, “grounded theory is what is, not what should, could or ought to be” (Glaser 1999).
The objective is not to verify an existing theory. Rather, grounded theory is an inductive
methodology, starting with data (case nodes) and generating conclusions from the data
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– as opposed to testing or confirming a hypothesis. At the outset, data is systematically collected and analyzed. Any pre-existing conceptualization is not to be used. This central crux distinguishes it further from content analysis. In content analysis, an entire theory may already exist. Grounded theory, conversely, rests on the foundational basis that any “pre-existing conceptualization” is not to be used. Moreover, the codes, categories and themes generated in grounded theory come from the data itself rather than a) subjectively from the researcher’s mind, b) from a previous study or body of literature c) from an already identified theory, or d) from a panel of experts.
Strauss in his 1987 book creates a profound connection between grounded theory, pragmatism (the central ideology of this dissertation), John Dewey (pragmatist and contributor to the advancement of thought on climate change adaptation) and the
University of Chicago (alumni including, Gilbert White, Ian Burton):
Contributing to its development were two streams of work and thought:
first, the general thrust of American Pragmatism (especially the writings
of John Dewey…) and including its emphases on action and the
problematic situation, and the necessity for conceiving of method in the
context of problem solving; second, the tradition in Chicago Sociology at
the University of Chicago from the 1920s through the mid-
1950s…(Strauss, 1987)
86 4.6.1 Theoretical Sampling and Saturation
In grounded theory, “theoretical sampling happens as the data collection progresses”
(Glaser, 1978). Originally, the analysis focused on four countries. As information was gathered, four countries proved insufficient for theory generation, therefore, the data collection was expanded to 16 countries. As new codes and themes emerged, a greater number of subjects (countries) were included until the 192 countries under the
UNFCCC were all captured in the analysis. This was repeated with document inclusion.
Initially, only National Communications were included in the analysis. This was then expanded to include the National Adaptation Plans of Action for developing nations.
When the Nationally Determined Contribution documents were released, those too were included in the analysis. This method of theoretical sampling continues until theoretical saturation occurs. Theoretical saturation arises when new data “fail[s] to uncover any new ideas about the developing theory” (Bowen, 2006). All schools of thought on grounded theory stand by this original 1967 sentiment, including:
A key feature of grounded theory is not that hypotheses remain
unverified, but that hypotheses (whether involving qualitative or
quantitative data) are constantly revised during the research until they
hold true for all of the evidence concerning the phenomena under study,
as gathered in repeated interviews, observations or documents. (Corbin
& Strauss, 1990)
We gather data, compare them, remain open to all possible theoretical
understandings of the data, and develop tentative interpretations about
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these data through our codes and nascent categories. Then we go back
to the field and gather more data to check and refine our categories.
(Charmaz & Henwood 2008)
Thus, the analysis will continue to uncover the need for greater data collection until theoretical saturation has occurred (Glaser & Strauss, 1967; Glaser 1978)
4.7 Debates in Grounded Theory: Three Philosophical Schools of Thought
As time evolved so did the vision Strauss had of grounded theory. In his 1987 book
Qualitative Analysis (Strauss, 1987) and a subsequent paper coauthored by Juliette
Corbin (1990), Strauss began to diverge from the previous grounded theory philosophies he developed with Glaser. The departure from the original tradition unnerved Glaser who responded with the 1992 rebuttal, Emergence vs Forcing: Basics of Grounded Theory Analysis (Glaser, 1992). Glaser argued that Strauss (and Corbin) had developed a new methodology, one that was no longer valid grounded theory. A divide emerged, not only between the two original authors, but among researchers and users of grounded theory. Glaser’s classic methodology is still favored by many (Stern,
1994; Bowen, 2005). However, the divergence allowed for an evolution of grounded theory, where new philosophies emerged including the Constructivist grounded theory philosophy described by Charmaz (1995, 2006) and Feminist grounded theory (Wuest
1995). Table 16 provides a comparative summary of the three most popular methods of grounded theory.
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Table 16 Classic, Straussian and Constructivist Comparison Chart
Similarities Differences General Coding Literature Comments Classic Flexible 1. Abstain from literature Grounded Original coding Substantive until the very end Theory framework Coding Glaser (1967 designed to a) open & 1978) discover a coding grounded b) selective theory coding 2. Theoretical Coding Memo
writing Straussian Rigorous 1. Open Use literature Constant (Strauss 1987 coding Coding appropriately at every comparison & 1990) framework 2. Axial stage Theoretical Coding sampling 3. Selective Coding
Constructivist Interpretive, 1. Initial or Use literature at every (Charmaz intuitive; social open stage and compile a 1995 & 2006) interaction coding literature review approach; 2. narrow in Refocused scope coding
89 4.8 Types of Coding & Schools of Thought (Philosophy)
According to Charmaz (2006), “Coding means that we attach labels to segments of data
that depict what each segment is about. Coding distils data, sorts them, and gives us a
handle for making comparisons with other segments of data.” The coding paradigm
used in this theoretical framework is based on Strauss’s (1987, 1990) typology: open,
axial and selective. Open coding begins the process of fragmentation of the data into broad preliminary themes. Open coding is unrestrictive, line-by-line or word-by-word
coding. The objective is to ‘open up’ the inquiry and expand all areas of the data. The
second stage is axial coding. This extends the process by conceptualizing subthemes
within the fragmented data. Axial coding is “intense analysis done around one category
at a time” (Strauss, 1987). This process continues until the analyst is “committed” to the
categories and themes identified in the data. The third stage, selective coding, allows
for the integration of data into relational statements. This process allows for the,
“advancement of theory development at each stage of data collection and analysis”
(Strauss, 1987).
4.9 The Confusion between Frequency Counts, Coding in Grounded Theory and Autocoding
Advancements in qualitative software have enabled users and researchers to “auto
search” for terms in collected data (John & Johnson 2000). As the previous sections
noted, coding in the context of grounded theory is a complete, rigorous, and often
complex methodology, markedly distinct from automatic coding enabled by qualitative
software. During the initial stages of open coding, automatic interpretation of text
generated by qualitative software may be applied to account for the “occurrence” of a
90 word within collected textual data. In 1991, Muhr cautioned, “automatic interpretation of text cannot succeed in grasping the complexity, lack of explicitness, and contextuality of everyday knowledge. Any attempt to formalize this knowledge runs the risk of eliminating precisely the contextuality that is essential for human understanding, whether every day or scientific.” Since 1991, substantial advances have been made in qualitative software packages, including: Atlas ti; QSR NVivo, MAXQDA, QDA Miner,
Dedoose, Open Code, and HyperRESEARCH. Auto-coding done with these advanced software packages can capture the content and context of a complete answer to a question, usually in a semi-structured interview format. It is then up to the researcher to interpret the response. However, autocoding in either context (simplistic word search or advanced content capture) is not grounded theory nor a discrete component of coding within grounded theory (i.e., autocoding is not synonymous with open, axial or selective coding).
To illustrate, the theme or category funding was identified during the initial stages of coding in grounded theory. Within the data there were several instances where the concept of funding was contained in the text where the term funding (or a synonym thereof such as finance, aid, endowment, grant, subsidy, support) does not appear.
Simple automatic content analysis or automatic coding of text based on word selection
(even with expansion to include synonyms) would be unable to capture this occurrence within the data. Additionally, erroneous frequency counts may result with automatic coding for terms. A document may include several instances of the term adaptation with or without relevance to the objective of study. Frequency counts alone can only indicate
91 the presence of the term within the text data but cannot indicate the context or help support the generation of theory.
To reiterate, within this dissertation, several country documents contained greater frequency counts of terms while demonstrating less advancement than other countries with fewer counts. Counts alone are insufficient. The counts contained in this analysis are counts of the frequency of occurrence of the categories as opposed to counts of the frequency of a word. Similarly, if country x used the term project six times in a response in a National Communication, this does not translate into six different projects being conducted in the country. The overemphasis of this point is necessary as these methods spark confusion within both the reader and researchers. Several studies of peer-reviewed research are based on frequency counts of terms in literature.
Reiterating the sentiment contained in Green & Thorogood (2018), this method is not grounded theory but rather a superficial thematic content analysis” (Green &
Thorogood, 2018).
4.10 Theoretical Memoing
Theoretical memoing is an ever-present ‘core stage’ in grounded theory. Memoing allows for the derivation of meaning from qualitative data captured during coding
(Glaser, 1998). Memoing might be dismissed as simply note-taking, but as Glaser argued, “If the analyst skips this stage by going directly from coding to sorting or to writing, [they are] not doing grounded theory” (Glaser, 1978).
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Spanning a few words or paragraphs,26 memos reflect the connection of ideas and are central to credible qualitative research (Birks et al., 2008). Hutchison et al., 2010 summarizes different memo structures: (1) Research memos for conceptual development and general events (2) Reflective memos (3) Conceptual memos (4)
Emergent questions (5) Explanatory memos that are either literature related, technical or model descriptions.
During grounded theory, memoing is stored separately from data analysis. Recent technological advancements have allowed qualitative analysis software, such as NVivo
11, to create linked memos during the coding process. This technological advancement furthers the ability of the researcher to create connections and advance conceptual understanding as a permanent (digital) link is created between the memo and the source of the researcher’s ideation (data contained in a node). During this dissertation, memoing was used to identify:
• themes • determinants
• sub-themes • to decrease limitations of quantitative
• nodes methodologies
• research questions • and to differentiate between the
• hypotheses Stages of the Climate Change
• gaps in knowledge Adaptation Process and the
• future research
26 Memoing during grounded theory for this dissertation totalled 68 pages
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determinants of climate change
adaptation
4.11 Quantitizing Data Quantitizing, the process of converting qualitative data into numbers, made its beginnings as an extension of content analysis. In a 1952 publication Bernard Berelson described content analysis as a quantitative research method, signaling the transformational nature of the conversion of qualitative data. What potentially started out as counting word frequencies (Morse, 1990) has evolved in recent decades. Citing the works of Carley (1990), Miles and Huberman (1994) strengthened the causal link between content analysis and quantitizing, stating, “There is, of course, a long and well- developed tradition of dealing quantitatively with qualitative data: content analysis”.
This paper aims to contribute to the evolution of this technique by demonstrating that quantitizing can deepen the understanding of qualitative data in mixed methods research.
4.11.1 Quantifying Responses to Climate Change
The foray into quantitizing for this author began as quest for a methodology to quantify climate change adaptation. Defined as an “adjustment in natural or human systems in response to actual or expected climatic stimuli or their effects, which moderates harm or exploits beneficial opportunities” (IPCC, 2001), climate change adaptation lacks a definitive quantitative measure. Further, the binary qualitative-quantitative relationship is reflected in the two responses to climate change: adaptation and mitigation. The latter falls neatly into a quantitative category, with measurable greenhouse gas emissions
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targets. The quantitative nature of mitigation is credited for its faster rise in popularity
than adaptation. Conversely, most adaptation actions, policies and measures are
reported in qualitative documents, devoid of any quantitative features. This research
initially set out to identify a methodology to quantify adaptation in order to answer
several research questions and to identify key determinants that encourage the
advancement of climate change adaptation.
Quantifying27 such a multidimensional intervention as climate change adaptation is
complex. This study therefore looked for methods that would allow the creation of a
composite index, where multiple variables are combined into one measure (Nardo,
2005). Since methodologies currently available for the development of composite
indicators are quantitative in nature and lack the ability to transform qualitative data
(such as climate change adaptation policies, barriers or priorities) into quantitative
measures, another step was necessary. The Data Transformation Design Model allows
the researcher, “to gather qualitative data [and] analyze it for codes and themes”
(Creswell et al., 2004); transform or combine multi-variable themes or codes before the
qualitative data is quantitized by, “taking qualitatively derived codes and converting
them into numbers for counting or statistical description” (Weaver–Hightower, 2014).
There are many methodological aims in this chapter centered on quantitizing. These
include: (i) advancing the understanding and use of quantitizing, (ii) developing a
27 Quantifying is the general term used in all methodological domains. Quantitizing is the term used in the mixed methods and grounded theory literature.
95 methodology to create a quantitative composite indicator from qualitative data, (iii) demonstrating how grounded theory can be used as a precursor to quantitizing, and (iv) contributing to the evolution of the literature on quantitizing.
Additionally, this chapter spends considerable time exploring quantitizing as a methodology. Although the definition of quantitizing implies a sense of simplicity, the complexity and importance of transforming qualitative data should not be underestimated. Analysis following the integration and mixing of quantitized datasets relies on the ability of the researcher to understand and identify several key elements in the quantitizing process. The following section will discuss: (i) the philosophical reasoning underpinning quantitization, (ii) understanding the arguments for and against quantitizing, (iii) selecting a method of quantitizing suitable for the research, (iv) identifying the stage(s) in the mixed methods design model where quantitizing will occur, (v) determining possible methods of validation or triangulation to test the soundness and statistical integrity of the data transformation, and (vi) considering that quantitizing is not always appropriate.
4.11.2 Philosophical Underpinnings of Quantitizing
Climate change adaptation research and mixed methods research share a striking similarity: they both challenge traditional ontological, epistemological and methodological norms. Climate change adaptation challenges these norms because the ability to understand its scientific, political and social discourses necessitates not only a realist or positivist perspective, but also an interpretivist or relativistic one. Mixed methods challenge both positivist and relativist philosophies and paradigms, and it has
96
been argued that a diverse array of epistemological options in mixed methods borders
on ‘inconsistency’ (Mason, 2006). Thus, in those views, the mixing of paradigms,
methodologies, or data should be avoided to preserve philosophical purity. Quantitizing
presents a similar set of challenges.
The basis of quantitizing rests on a philosophical understanding that more than one type of data exists. This well accepted norm is not without opposition. Fred Kerlinger pronounced, “All data is quantitative” (Miles & Huberman, 1994). This is countered by
Berg (1989) affirming that, “All data is qualitative”. Thus, the foundational necessity for quantitizing in mixed methods is first the acceptance that both qualitative and quantitative data exist (Sandelowski et al., 2009). Following this understanding, the argument shifts to conflicting philosophies. Creamer (2011) calls this a “paradigmatic objection”: Researchers can agree that both qualitative and quantitative data exist, but they contend that they must not be mixed. Broad arguments against mixing include that qualitative and quantitative data are ‘incommensurable’, or that quantitative methods
(positivist perspective) are nobler than qualitative methods (relativist perspective) or vice versa.
In 2004, Johnson and Onwuegbuze opposed this limited dichotomy of research paradigms. They called mixed methods a third ‘research paradigm whose time has come’. Philosophical support for a mixed methods paradigm comes from pragmatism, acting as the ‘philosophical partner of mixed methods research’ (Denscombe, 2008).
Pragmatism centers on the view that, ‘the best method is the one that solves the problem’. Creswell (2013) outlined several features of pragmatism that lend itself to
97 mixed methods research, including allowing for the use of both qualitative and quantitative philosophies in the same research study. A pragmatic paradigm can also extend to quantitizing. Applying the ‘best method is the one that solves the problem’ assertion, qualitative can be transformed into quantitative (quantitizing). The quantitized data set can then be integrated with other quantitative data and analyzed statistically.
Figure 10 provides a simplified schematic of this discussion.
Types of Data
Both All data is All data is QUAN & QUAL Quantitative Qualitative data exist
QUAL & QUAN QUAL & QUAN must be kept separate can be Mixed (Mixed (Positivist vs. Relativist) Methods, Pragmatist)
QUAL can be converted into QUAN (Quantitizing)
Figure 10 Types of Data
4.11.3 Methodological Concerns with Quantitizing
The previous section presented reasoning for the philosophical acceptance of quantitizing in mixed methods research. Nonetheless, several researchers, while
98
agreeing that both mixed methods and quantitizing are legitimate approaches to
research, have outlined valid concerns about quantitizing. Heyvaert and colleagues
(2016) write that in quantitizing, “you discard the qualitative nature of the data and the
specific characteristics associated with it…[when] you convert the data into something
quantitative”. They argue that quantitizing discards the methodological identity and
intrinsic nature of the data. Isaac et al., (2016) cites the criticism that quantitizing simply
“relegates the qualitative component to a secondary status”. Both Heyvaert and Isaac
echo Maxwell (2010) who states that, “quantitizing can strip qualitative data of their
meaning degenerating [the] rich information gathered through qualitative research”.
Several of the critiques of content analysis can be extended to quantitizing where quantitizing can be viewed as reductionist: decreasing the complexity of qualitative information. Further, it can be argued that relegating “rich” text to 1s and 0s removes the interpretive value of the original research in that quantitizing may oversimplify complex information into basic numerical or seemingly ‘ambiguous’ counts. Other researchers accept that quantitizing is a legitimate method but maintain that to preserve the interpretive quality of the data, quantitizing should only be used for categorical or descriptive qualitative findings. Thus, for the “highly interpreted findings in the form of grounded theories, phenomenologies, and the like” quantitizing should be avoided
(Sandelowski et al., 2007). Having applied quantitizing to qualitative data obtained through grounded theory, the author debates the last point that grounded theory and quantitizing are incompatible.
99 4.11.4 Methodological Arguments for Quantitizing
While there is legitimacy to several of these concerns and criticisms of quantitizing, there are many arguments for its use, including quantitizing following grounded theory.
The main support for quantitizing is that a superior level of understanding can be obtained when we are able to perform quantitative analysis during qualitative research
(Sandelowski et al. 2009). From this perspective, transforming qualitative data into a quantitized data set allows not only for the analysis of the new data, but the ability to integrate and analyze quantitized data alongside a quantitative data set (Jang et al.,
2008). This expands the depth of understanding rather than reducing it. Cremer (2011) supports this, noting, “Under certain conditions, transformation of qualitative data to a numerical measure can make it possible to demonstrate a relationship, point to causality, and provide predictions.” Quantitizing therefore “advances the inquiry”.
A valid observation is raised in Chang et al., (2009): that it is “less informative to count numbers of persons expressing a theme”, than to apply a quantitizing method that allows for the interpretation of entire “thematic lines”. Thus, quantitizing becomes expansive in the knowledge that may be gained from qualitative data. Seltzer-Kelly et al., (2012) limit quantitizing to “recogniz[ing] patterns that could otherwise be overlooked”. However, this is a diminishing representation of quantitizing. It is this author's contention that the ability to transform qualitative data into quantitative data opens doors to completely new avenues of research and understanding.
4.11.5 Considerations before Quantitizing
Prior to quantitizing, several decisions should be made by the researcher, including:
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1. Identifying the qualitative data to be quantitized (is there more than one set of
qualitative data to be quantitized in the mixed methods research?)
2. Selecting the method(s) of quantitizing (see Table 17 for examples)
3. Identifying the stage(s) in the mixed methods design model where quantitizing
will occur; e.g.,
a. During data collection (e.g., in a questionnaire with a rating or Likert scale)
b. Following data collection (e.g., converting categorical characteristics into
numeric codes; frequency counts)
c. After qualitative data analysis (e.g.., vote counting)
4. Deciding whether the conversion(s) will be performed by the researcher or by
analytical software
5. Assessing whether the method(s) of quantitization decrease the interpretive
nature of the qualitative data, and
6. Identifying triangulation methods to validate the quantitized data set.
4.11.6 Overview of Quantitizing with Examples
Quantification of qualitative data has had proponents dating back decades. Acclaimed propaganda analyst Harold Lasswell, in a 1949 chapter, aptly titled “Why Be
Quantitative?”, argued that not only could the limits of qualitative analysis be surmounted, but the discourse of political science could be advanced with quantitative analysis of qualitative data. Berelson (1952), another proponent of quantification, defined the method of content analysis as a “research technique for the objective, systematic, and quantitative description of the manifest content of communication.”
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Many consider that quantitizing had its origin in content analysis (Padgett, 2016).
Krippendorff (2013) argues that “quantification is not a defining criterion for content analysis”, yet this does not disqualify the use of content analysis in quantitizing.
Several initial studies using quantitizing were based in content analysis techniques. An early publication on quantitizing by Morse (1990) studied the frequency of specific words used by teenage mothers’ in open-ended interviews. Sandelowski and colleagues (1991) examined the prevalence of amniocentesis using comparative frequency counts from interview data between patients having the procedure and physicians’ level of encouragement. The popularity of quantitizing in content analysis led several researchers to falsely assume that "content analysis is the first step in the process of quantitizing qualitative data" (Kerrigan, 2014). However, content analysis is not the only qualitative foundation for quantitizing. Such a view limits quantitizing to a perimeter of its transformational abilities.
Miles & Huberman (1994) discuss ‘quantizing’ [sic] as the first of three quali-quant linkages levels in two main forms: i) counting “number of times a doctor interrupts a patient” and ii) rankings or scales. This latter form starts to extend the use of quantitizing beyond counts in content analysis. The definition of quantitizing itself as the
“numerical translation, transformation or conversion of qualitative data” from
Sandelowski et al., (2009) allows for a much broader application of the term. Thus, grounded theory, used in this research along with other qualitative methods, can also be the first step in quantitizing data. As such, the past decade has witnessed a rapid
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evolution and application of quantitizing in mixed methods research. This next section
will briefly review other examples.
Qualitative analysis software, NVivo (QSR International), was used by Driscoll and
colleagues (2007) “to transform individual responses…into a series of coded response
categories that were…quantified as binary codes…either 0 or 1, corresponding to
absence or presence of prospective coded responses to each question.”. Voils and
colleagues (2008) applied quantitizing in a unique vote counting technique in a meta-
analysis of 42 reports on antiretroviral adherence. In the study, 119 hypotheses were
examined to identify the “relationship between the independent and the dependent
variable”. Relationships were placed in three categories (significantly positive,
significantly negative, or no relationship) and counted. The group with the highest count
is, “assumed to give the best estimate of the direction of the true relationship between
the independent and dependent” variables (Bushman & Wang, 1994).
Two different quantitizing methods, using rating scales and percent conversion, were
applied in Larson (2010). The study interviewed 39 caregivers of children with
disabilities and each completed both a subjective and psychological well-being rating scale with categorical details. Interview transcripts were coded and the data were
“quantified by calculating the percentage of caregivers identifying each category and subcategory” with the quantitized data sets being statistically analyzed. In Wao et al.,
(2011) transcripts from focus groups of students and faculty were analyzed, coded, and scored. Theme frequency was determined by calculating the “number of participants who mentioned a particular theme” by “total number of participants in the group”. The
103 results were then ranked. Quantitizing was also applied in a 2014 study, following the implementation of the United States policy No Child Left Behind. In that study, the researcher, Kerrigan, developed qualitative scales of capacity that were quantitized using a rating scales (1 = low, 2 = moderate, 3 = high) to explore data-driven decision- making in four community colleges (Kerrigan, 2014). Lastly, Weaver-Hightower (2014) coded transcripts to measure the amount of influence on educational policy. An
‘agreement score’ was calculated by totaling the number of arguments and recommendations in common and dividing by the total possible points of agreement.
Table 17 provides a summarized overview of these different quantitizing methods.
Table 17. Types and Examples of Quantitizing
Quantitizing Method Description Example References
Dichotomizing values Present = 1, Absent = 0 Driscoll et al., 2007
(Binary)
Frequency counts In content analysis, count Morse, 1990; number of words Krippendorff, 2013 In coding, count number of This study codes
Percentage conversion Percentage of agreement Weaver-Hightower, 2014
Percentage in each Larsen, 2010 category
104
Frequency distributions In survey (frequency of Harris et al., 1991 response)
Theme frequency or Wao et al., 2011 100
Intensity 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑝𝑝𝑝𝑝𝑝𝑝 𝑡𝑡ℎ𝑒𝑒𝑒𝑒𝑒𝑒 � � ∗ 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐
Rating scales (Likert 1 to 5; Miles & Huberman, 1994
Scale) 7-point scale with Kerrigan, 2014 categorical features Larsen, 2010
Numeric coding In a categorical (nominal) This study data set (1, 2, 3, 4…)
Vote Counting (in Meta- Three categories Voils et al., 2008 analysis) positive (confirming), (procedure defined in Bushman & Wang, 1994) negative (disconfirming), or
no relationship
Table created by author in 2017
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119 Chapter 5 Methodology: Applying Mixed Methods Research to Climate Change Adaptation Mixed Methods as a Research Paradigm
In 2013, Johnson and Onwuegbuze called mixed methods, “a research paradigm whose
time has come”, highlighting the need for a third research paradigm in educational
research. This call should be heeded by those in the field of climate change adaptation
research. Climate change is one of the most complex problems in the global commons
where local, regional and global impacts necessitate both community-based and international cooperation. Furthermore, a changing climate will impact questions of ethics, equity and environmental justice. Studies on responses to climate change
(including mitigation and adaptation) require pragmatic research using mixed methodological approaches. However, as noted in Nielsen and D’haen (2014), mixed methods approaches are almost absent from the climate change literature. Specifically, they observed that qualitative methods applied in the field of climate research are “poor or lacking information…”.
The field of climate change is rooted in scientific inquiry, and the impacts of climate change require complex policy responses ideal for mixed methodological research.
Being limited to a binary research paradigm system will only serve to decrease the quality and strength of climate change research and in turn limit the ability of climate change researchers and policy makers to prevent losses that will not only affect the economy, but traditional cultures, ecosystem services and the natural environment. This chapter will briefly review the literature and research methodologies in climate change adaptation, including the limited use of mixed methods, followed by the development of
120 a detailed mixed methodology using the data transformation model to create two
composite indices to analyze several climate change adaptation research questions.
5.1 Methodologies in Climate Change Adaptation Research
A non-exhaustive review of climate change adaptation literature yields a considerable number of both qualitative and quantitative methodologies ranging from case studies and participatory action research to computer-aided modelling and economic and statistical analysis (see Table 18). It is unsurprising that such a sizable number of methodologies were identified given that climate change is expected to have impact on all sectors and geographic regions. The integration of qualitative and quantitative methods in climate change research would be expected; however, much of climate research (science, policy, and the human dimension) is done in qualitative or quantitative silos. For a study to achieve interdisciplinary or mixed-method status, it often requires the pursuit of lengthy multi-department or multi-institutional collaborations. A review on the interdisciplinary nature of the Third Intergovernmental
Panel on Climate Change report by Bjurstrom and Polk (2011) found that interdisciplinarity, “is not a prominent feature of climate research”, arguing that researchers from different disciplines coming together to share their findings are insufficient; the research itself must be interdisciplinary.
121
Table 18. Research Methodologies used in Climate Change Adaptation Literature
Qualitative Method Citation Quantitative Method Citation Research Research
Case Study (Deshingkar, 1998) Computer–aided (Sposito et al., modelling (GIS 2009) numerical model)
Ethnography (Palframan, 2015) Cost Benefit (Tol, 2005) Analysis
Focus group (Lasco et al., 2016) Economic Analysis (Kabubo-Mariara, 2008)
Conceptual (Pahl-Wostl, 2009) Integrated (Wing & Fisher- Framework Assessment Model Vanden, 2013)
Expert Opinion (Sussams et al., Modelling (Dickinson, 2007) 2015)
Interviews (Reid et al., 2007) Questionnaire (Keogh et al., 2011)
Systematic (Biesbroek et al., Regional Climate (Easterling et al., Literature 2013) Model 2001) review
Participatory (Bele et al., 2013) Scenario Analysis (Bhave et al., 2013) Action Research (SRES & RCP)28
Simulation (Parson and Ward, (Computer based) 1998)
Statistical Analysis (Szendro et al., 2012)
28 Special Report on Emissions Scenarios (SRES) and Representative Concentration Pathways (RCPs)
122 5.2 Limitations of Current Mixed Methods in Climate Change Adaptation Research
Various instances of climate change adaptation research using mixed methods
approaches exist; e.g. Elrick-Barr et al., (2016) examined “actions taken to address
climate change in two Australian coastal communities” using both survey questionnaires
and semi-structured interviews during data collection. However, limitations are present.
An initial limitation is the omission of classifying climate change adaptation research as
mixed method. Biesbroek et al., (2011) and Keogh et al., (2011) used structured
questionnaires, surveys and interviews combined with both qualitative (ATLAS-ti) and quantitative (SPSS) data analysis, yet neither study identified itself as mixed method.
Harrison and colleagues (2013) provide a mixed methods title, “Combining qualitative and quantitative understanding for exploring cross-sectoral climate change impacts, adaptation and vulnerability in Europe”, while neglecting to use the term (or terms similar to) mixed methods in the paper. Other studies such as Haque, Louis, Phalkey, and Sauerborn (2014), and James and Friel (2014), highlight the use of a mixed methods approach, while also neglecting to provide a detailed methodology to support the mixed methods designation. Abegaz and Wims (2015) reference the use of a
“mixed methods approach” for data collection, but do not elaborate beyond stating that semi-structured key-informant interviews and survey questionnaires were conducted.
Furthermore, mixed methods in other studies are limited to gathering both qualitative and quantitative data in a survey (e.g., Mwalukasa, 2013). In addition, no climate change adaptation studies were found identifying a mixed methods process model. The limitations of mixed methods research (denoted MMR) in climate change adaptation are summarized below.
123 Climate change adaptation literature:
1. neglects to identify research as MMR
2. identifies as MMR but neglects to provide a detailed methodology
3. uses MMR, but provides very few points of mixing
4. uses MMR, but does not identify an MMR model type
5. relies on a single MMR source (e.g. Johnson & Onwuegbuzie, 2004)
6. underutilises MMR
This methodological chapter will attempt to ensure that the recognized limitations are resolved by identifying this research as mixed methods, providing a detailed methodology, outlining multiple points of mixing, specifying a mixed methods process model, and referencing multiple sources of mixed methods literature. In doing so, the aim is that those in the field of climate change adaptation research will follow suit.
5.3 Using Mixed Methods Research in Climate Change Adaptation
The foremost responses to climate change are adaptation and mitigation. The latter
falls neatly into a quantitative category, with measurable greenhouse gas emissions
targets, and this is sometimes credited for its faster rise in popularity than adaptation.
Climate change adaptation, defined as an ‘adjustment in natural or human systems in
response to actual or expected climatic stimuli or their effects, which moderates harm or
exploits beneficial opportunities’ (IPCC, 2001) lacks a definitive quantitative measure.
Moreover, it is tremendously complex to quantify such a multidimensional intervention.
124 Since a single variable for adaptation is insufficient, this research sets out to create a methodology for the development of a composite index (i.e., where multiple variables are combined into one measure). The benefit of a composite index (also termed composite indicator) is in its ability to capture, “multidimensional concepts which cannot be captured by a single [variable]” (Nardo, 2005). However, the methodologies currently available for the development of composite indicators and indexes are quantitative in nature only and lack the ability to transform qualitative data (such as climate change adaptation) into quantitative measures. To overcome this shortcoming in composite indicator methodology, this study will apply the Data Transformation Design Model identified in Creswell et al. (2004). The model, “allows the researcher to gather qualitative data, analyze it for codes and themes…and numerically count the codes and themes” (Creswell et al., 2004), and then quantitize the qualitative data by, “taking qualitatively derived codes and converting them into numbers for counting or statistical description” (Weaver–Hightower, 2014).
This study focuses on the question, why is climate change adaptation advancing at different rates in nations across the world? While some argue that there are limits to adaptation accounting for this apparent deficiency in transformational change (Adger et al., 2009; Biesbroek, et al., 2013; Dow et al., 2013), others debate that greater funding is needed (Ebi et al., 2009; Bowen, 2011), or claim that in policy subsystems, change
(viz., adaptation policy development and implementation) takes place slowly, over long periods of time (Irwin & Kroszner, 1999; Hysing & Olsson, 2008; Ollila, 2011). However, as a measure for adaptation has yet to be developed, this author maintains that these arguments present a limited version of the narrative.
125
Figure 11. Data Transformation Design Model Embedded in a Policy Process Policy Issue Model Identification
Literature Review
Hypothesis & Research Questions
Objectives & Purpose of Mixed Research
Select Research Methodology
Mixed Methods
QUAL Data QUAN Data PHASE I Collection Collection
QUAL Data Analysis
QUAN Transformation Data PHASE II QUAL to QUAN Analysis
Data Reduction
Compare + Interrelate Two QUAN Composite Indices PHASE III
Interpretation QUAL + QUAN
Recommendations for Policy + Practice
Implementation & Evaluation in Policy + Practice
126 5.4 Methodological Design Model
A summary of the mixed method design model is found in Figure 11. The model is an expanded version of the data transformation model (Creswell et al., 2004) embedded in the general policy process models of Lasswell (1971) and Anderson (1984): problem identification, policy formulation, policy recommendation, policy implementation and evaluation. In this combined model, five stages of identification and discovery occur prior to the use of mixed methodology: identification of the policy issues, literature review, research question development, identification of the need for a mixed methodology, and selection of a mixed model design.
5.5 Applying Mixed Methods Design to Climate Change Adaptation Research
The stages outlined in Figure 11 could be applied to numerous policy research questions in multiple disciplines. Table 19 shows how this general model structure was applied to climate change adaptation research.
Table 19. Preliminary Stages in the Methodological Design
General Stages Specific Stages as Applied to Climate
Change Adaptation
(i) Identification of the policy issues The need to respond to climate change
(ii) Extensive literature reviews Background literature review on climate
change adaptation
(iii) Identified research question Why is climate change adaptation
advancing at different rates?
127
(iv) Purpose of selecting a mixed To answer questions of the global methodology commons that cannot be answered by a
single methodology
(v) Model design selection Embedded policy process data
transformation model
The mixed methodology process begins in Phase I by collecting qualitative and quantitative data for 192 countries for the development of two composite indexes: an
Adaptation Index (Index-1) and a Level of Vulnerability (climate risk) (Index-2). Phase II transforms the qualitative data into quantitative data (quantitizing) and constructs both indexes. Phase III combines both indexes into one data matrix for analysis and interpretation along with additional qualitative and quantitative data, leading to policy recommendations. There are several points of mixing in this study that will be highlighted in the next sections. This chapter will focus on detailing the methodology for
Phase I and Phase II outlined below.
5.6 Method 5.6.1 Note to the Reader
During this dissertation, the trajectory of the theoretical exploration shifted. The two indexes developed (below) were expected to play a greater role in the research output.
Specifically, Index-2. The original intended purpose of index development was to aid in the understanding of the relationship between adaptation and vulnerability. It was suggested to the author that a direct correlation existed between the two variables and therefore the remainder of the research (as to what determines the advancement of adaptation) was moot. Thus, the author chose to test this hypothesis by the
128 development of one index that captured adaptation and one that captured vulnerability
(note discussion below as to why currently available vulnerability indexes were
insufficient for this purpose). Index-1 (for adaptation) was created from qualitative data
via grounded theory, and Index-2 (for vulnerability) from standardized quantitative data
the author gathered from trusted third-party sources (i.e., the World bank, IPCC, and others). Following the development of both indexes, the data were plotted. A statistically significant low correlation was found (r= 0.211, α<0.005). Many potential uses for Index-1 were identified and explored in the latter chapters of this research.
However, there are several possible areas of research not explored, especially with the data gathered from Index-2. The author acknowledges this omission.
5.7 Phase I: Data Collection 5.7.1 Qualitative Data Collection: National Level Documents 5.7.1.1 National Communications
Information on national level climate change adaptation is primarily gathered for groups
of countries (e.g., Small Island Developing States, Least Developed Countries, and
those receiving funding from multilateral institutions and programs). The UNFCCC
National Communications are an exception. The Convention Text (1992) defines the
requirements for each Party. Article 4.1 and 12.1 outline Party commitments and
communication expectations, respectively, to be submitted via National Communication
to the Secretariat. At COP8 in New Delhi (2002) these requirements were updated and
revised (decision 17/CP.8) to a “uniform reporting format”. Further, amended guidelines
for the technical review were adopted in Lima Peru at COP20 (decision 13/CP.20). The
UNFCCC describes the expert reviews:
129 The in-depth reviews of national communications (NCs) are conducted by an
international team of experts, coordinated by the UNFCCC secretariat. The
review of each NC typically involves a desk-based study and an in-country visit,
and aims to provide a comprehensive, technical assessment of a Party's
implementation of its commitments. The in-depth review results in an in-depth
review report, which typically expands on and updates the NC29.
The review aims to, “promote the provision of consistent, transparent, comparable,
accurate” information provided by each Party (FCCC/CP/2014/10/Add.3)30.
National communications are a substantial undertaking, requiring advanced technical
knowledge and are financially burdensome. The Global Environment Facility (GEF)
provides both financial and expert technical support for Non-Annex I countries to
complete and submit National Communications. To assist Niger in the preparation of its
Fourth National Communication the total cost was quoted as $900,000.00USD
($500,000.00 USD provided by the GEF and $400,000.00 USD in co-financing). The
implementing agency providing support is (commonly) the United Nations Development
Programme (UNDP). The “UNDP Egypt will act as GEF Implementing Agency for the
project and will assist the country for the entire project length to implement the activities
set forth and will monitor and supervise the project on behalf of the GEF.”
29 https://unfccc.int/process/transparency-and-reporting/reporting-and-review-under-the-convention/national- communications-and-biennial-reports--annex-i-parties/international-assessment-and-review/review-reports
30 https://unfccc.int/resource/docs/2014/cop20/eng/10a03.pdf
130 The UNFCCC requires all parties to submit National Communications approximately
every four years. This standardized format provides a reasonably uniform source of
climate change information for qualitative analysis. NCs are presently the only uniform
reporting method for climate change adaptation at the national level31. Furthermore,
NCs capture both national level and subnational level data. While NCs are not
exhaustive, they cover national (federal), regional (provincial), local (municipal) and
community level adaptation. Additionally, each of the gathered National Adaptation
Programmes of Action (NAPAs) identifies priority areas for action at the sub-national
level. For example, the Nepal Priority Project, “Promoting community-based
adaptation”32 highlights the following measures:
• Integrating watershed management in Churia (home to 6000 local peoples)
• Promoting water management in river basin areas at the municipal level
• Reducing vulnerability of communities & increasing adaptive capacity though
flood management
• Promoting and upscaling multi-use system (MUS) for the benefit of poor and
vulnerability communities in the mid-hills and Churia range
• Implementing non-conventional irrigation system in water stressed communities
• Estimated cost USD 50 million
31 The new Nationally Determined Contribution reports (NDCs) do not provide a standard form of reporting and are limited in their inclusion of adaptation
32 Nepal (2010). National Adaptation Programme of Action NAPA to Climate Change, pp 29.
131
For qualitative analysis, National Communications (NCs) (n=191) were gathered33 along with the following additional documents: Intended Nationally Determined
Contributions (INDCs) (n=162) and National Adaptation Programmes of Action (NAPAs)
(n=50). Documents submitted in other official languages (Arabic, French, Spanish and
Russian) were translated into English by the author using multiple translation software platforms. All documents for each country were imported into NVivo 11 Qualitative Data
Analysis Software (QSR International, 2016) and into individual case nodes. This methodology provides a living database that can be continually updated as new and additional information becomes available, including the UNFCCC National
Communications expected in 2018 and beyond.
5.7.2 "Relatively Stable Parameters" "Relatively stable parameters" are defined by Sabatier and Jenkins-Smith (1993) as
either i) basic attributes of the problem area, ii) distribution of natural resources, iii)
fundamental cultural values and social structures or iv) constitutional structure, all of
which remain unchanged over decades or centuries. For each country a comprehensive
set of relatively stable parameters was gathered. Weible and Sabatier (2006) noted five
significant reasons why relatively stable parameters are central to understanding the
research problem:
a) they structure the nature of the problem
b) they constrain the resources available to policy participants
33 All documents were obtained from unfccc.int
132 c) they establish rules and procedures for changing policy and reaching collective
decisions
d) they broadly frame the values that inform policymaking
e) they are (routinely) not strategically targeted by policy participants
For each of the 192 countries, the following relatively stable parameters were assembled into an external database and imported into NVivo as country attributes.
These included: Land characteristics (e.g., landlocked, coastal, island), Region (e.g.,
South Asia, Europe, North America), Government Structure (e.g., Unitary Republic,
Federal Monarchy, Islamic Republic), UNFCCC and Annex Designation, Income Group and Economic Classification (e.g., LCD, OECD), and Group Affiliations (e.g., AOSIS,
OPEC, G20) (see Table 20 for the Canada case node example).
5.7.3 Quantitative Data Collection: Demographic and Climatic Data
Although national level indices for climate vulnerability and climate risk are available
(Kreft, et al., 2014; World Risk Report, 2014) they do not accurately capture Small
Island Developing States (SIDS) or consistently represent developed nations.
Additionally, indices that include SIDS plot them with both high and low levels of climate risk and vulnerability. Note that SIDS are considered to be highly vulnerable and at-risk nations (Ghina, 2003; Hay, 2013; Pelling & Uitto, 2001; Wong, 2011). Using normalized data sets from the Climate Risk Index, the World Risk Index and the Environmental
Vulnerability Index, Vanuatu, a highly vulnerable and at-risk SIDS scored a 13, 100 and
42, respectively (i.e., low risk, high risk, and medium vulnerability). This trend is not restricted to SIDS, as the Netherlands also scored with a varied level of risk and vulnerability in these indices with values of 17, 74 and 82, respectively. The
133 comparative discrepancy of 6 to 8 times the other indices, the lack of SIDS inclusion,
and the inability to compare or combine existing indices into a composite index calls for
the development of a new index that captures climate risk, vulnerability and exposure.
The methodology to create a composite index from quantitative data was influenced by
Vyas & Kumaranayake's (2006) use of principal component analysis to create
socioeconomic status indices; Kotzee & Reyers' (2016) paper on social-ecological index to measure flood resilience; and the methodology from Murdie et al., (2013) and Sens
(1994). Variables were selected based on an extensive literature review (Cutter, 2006;
Dow, 1992; IPCC 2014; Kaly et al., 1999; World Bank, 2015) with preference given to
Liverman (1990). Initially, over 100 potential measures of vulnerability were identified.
The index was differentiated into five core components: population, sustainability, economics, climate change, and coastline. The literature also provided a baseline for the inclusion or exclusion of variables with the aim of using primary data collected from country level; the avoidance of multi-variable indices; controlling for Gross Domestic
Product (GDP), Gross National Income (GNI), disaster loss and mortality, if included (as these variables skew data); and the necessity for the inclusion of sea level (a key variable missing from current climate risk and vulnerability indices). In total, 9 variables were identified to represent the five components used in the creation of Index-2.
The following will briefly summarize the variables selected and the rationale for inclusion. To signify the current and future path of a nation's population, both population density and population growth were selected. Mean sea level trend and coast-to-area- ratio were chosen to capture coastline vulnerability. To attain future change in climate, the peak and decline Representative Concentration Pathway ‘RCP6’ was selected and
134 used to determine precipitation and temperature change at the national level for the
future period 2081-2100. While the objective was to avoid the inclusion of multiple variables, the Environmental Performance Index provided a good indicator of sustainability and avoided the limitations of the indexes previously outlined34. Both GDP
and disaster losses were selected to represent the economics component; however,
these two variables skew the data and are only included in a separate (controlled)
analysis. While data were gathered for CO2 emissions, it was not included as a variable in the index but used in the study during trend analysis. Table 20 provides a summary of
the qualitative characteristics (including relatively stable parameters) and quantitative
variables for a sample country, Canada.
Table 20. Case Node: Canada
Qualitative Characteristics: Relatively Quantitative Characteristics Stable Parameters Land Coastal Population density (/km2) 3.866 Region North America Population growth (%) 1.155 Government Federal Disaster Losses 23,230,100 Structure Constitutional (from 1980-2014 in USD Monarchy ‘000) UNFCCC Umbrella Group GDP per capita (USD) 50230.80 Designation
34 Data obtained: Temperature & Precipitation http://climexp.knmi.nl/plot_atlas_form.py Mean sea level rise trend (mm/yr) http://tidesandcurrents.noaa.gov/sltrends/mslGlobalTrendsTable.htm & http://uhslc.soest.hawaii.edu/data/download/rq (raw) Environmental Performance Index http://epi.yale.edu/downloads & Population Density & Population Growth Rate & GDP http://data.worldbank.org/indicator/EN.POP.DNST & http://data.worldbank.org/indicator/SP.POP.GROW & http://data.worldbank.org/indicator/NY.GDP.MKTP.CD Coast/Area Ratio (m/km2) (CIA World Fact Book via) http://en.wikipedia.org/wiki/List_of_countries_by_length_of_coastline
135
Annex Annex I Coast/Area Ratio 20.24 Designation (m/km2) Income Group High Income: OECD Environmental 73.14 Performance Index Economic OECD Mean Sea Level Trend 3.12 Classification (mm/yr) Group G7 & G20 Mean Temperature 5 Affiliations RCP6 (oC)35 Mean Precipitation RCP6 0.35 (mm/day)
CO2 Emissions 536.32
(Mt of CO2) 5.8 Phase II: Data Analysis and Index Construction 5.8.1 Developing the Adaptation Index (Index-1) from Qualitative Data 5.8.1.1 Grounded Theory: Coding for Climate Change Adaptation (QUAL)
Introduced in 1967 by Glaser and Strauss, Grounded Theory offers a systematic and
inductive approach to theory development. The qualitative methodology allows the
researcher to develop theory from data, with pragmatism being its ‘theoretical
underpinning’ (Corbin & Strauss, 1990; Straus & Corbin, 1994). Open coding was the
first level of data analysis used to explore the data in the national communications and
included sections of line-by-line coding to identify preliminary concepts. Axial coding
followed and identified preliminary themes in the data. To illustrate, the following three
references were coded to the theme Barriers to Climate Change Adaptation:
35 Change in temperature and precipitation for the RCP6 pathway was simulated using the GCM CMIP5 multimodel ensemble (IPCC AR5 Atlas subset) for the relative period 1986-2005 with a future time period of 2081–2100
136 “There is still not enough information about possible climate change in
Angola and its impacts, so it is not appropriate to make specific
recommendations about adaptation strategies.” - Angola, 2012
“The recommendations made in previous assessments have met with
limited success for a number of reasons, including lack of funding, lack
of coordination and unavailability of personnel.” - Belize, 2011
“Lack of awareness of farmers/fishermen due to their low education level;
Poor dissemination of information by the media and relevant institutions
on Climate Change issues; Lack of information/training in school
curricula” - Cape Verde, 2011
New themes appeared as coding continued, including: Climate Shock (defined by the author as a climate-related disaster event that leads to policy change), Climate Change
Adaptation Research, Public Transparency, Migration, Climate Change Adaptation
Tools, Culture, Public Education & Training; and Role of Government. During extensive memoing (see Glaser, 1998), the theme Repurposing for Climate Change Adaptation emerged, as exemplified in this reference from Finland:
“Various measures promoting the provision for climate change, such as
flood protection, have already been taken on at the regional or municipal
level for quite a long time, though they have not been seen as adaptation
measures as such.” - Finland, 2013
A memo on the lack of gender inclusion prompted the creation of a new node entitled
Gender to capture the rare instance when gender was included:
137 “The main objective of the CC:GAP is to ensure that gender equality is
mainstreamed into Liberia’s climate change policies, programs, and
interventions so that both men and women have equal opportunities to
implement and benefit from mitigation and adaptation initiatives in
combating climate change and positively impact the outcome of Vision
2030.” – Liberia, 2013
In total, 35 variables (themed nodes) were created with 6744 coded references for the
192 case nodes. Subsequently, selective coding (along with Principal Component
Analysis) was used to identify and divide the themed nodes into two central themes: (1) nodes that represented Stages in the Adaptation Process (e.g., developing a national strategy; enacting new laws, standards and codes; providing or receiving funding;
implementing adaptation actions; reviewing and evaluating programs) and (2) nodes
that represented Determinants of Adaptation (climate change adaptation research;
climate shock (leading to policy change); establishing partnerships; public transparency; public education and training). The use of Principal Component Analysis (a multivariate statistical technique) is discussed in Section 6.9.
5.8.2 Quantitizing Qualitative Data (QUAL -> QUAN)
Following NVivo analysis, qualitative data was transformed into numerical codes; the
data is then said to be quantitized (Miles & Huberman, 1994; Tashakkori & Teddlie,
1998). An excellent primer entitled, “On Quantitizing” is found in Sandelowski, et al.,
2009, who highlights that, “the purpose of quantitizing…is to answer research
questions...addressing relationships between independent variables and dependent
variables constructed from both qualitative and quantitative data sets”.
138 In this research quantitizing is performed so that the qualitative data can be used in statistical analysis. Several methods are available to quantitize data outlined here. In grounded theory, codes can be converted into a frequency count (Morse, 1990).
Krippendorff (2013) noted that, “In qualitative studies, researchers sometimes code the qualitative data and then count the frequency of codes or domains identified, a process known also as content analysis”. To illustrate, text coded to the themed node National
Adaptation Plan contained 478 references. This value can be transformed into a numeric value (quantitized) or converted into a percentage (by dividing the node frequency count by the total frequency count for all nodes). Other methods of quantitizing data include the use of rating scales (1 to 5) on questionnaires (Miles &
Huberman, 1994), frequency distributions (Harris et al., 1991), binary values (1 or 2 to represent present or absent) (Driscoll et al., 2007), and sequential numbering (e.g., 1-
10).
Two methods were used to quantitize data in this study: frequency counts (Morse,
1990) and sequential numbering. In this first point of mixing, frequency counts of the qualitative data captured in NVivo were exported using a Matrix Coding Query that cross-tabulated coding intersections between the 35 variables (nodes), 192 case nodes
(countries) and 6744 references. This method allows for the creation of a database where frequency counts were provided not only for each of the 35 case nodes, but for all 192 countries (case nodes) across all 35 themes. This new quantitative data is then available for use in statistical analysis. A second point of mixing occurred when the qualitative relatively stable parameters (attributes) were quantitized by “quantitative coding” where a value was assigned to each attribute sequentially (i.e., landlocked = 1,
139 coastal = 2, island = 3). This method is similar to dichotomizing (Collingridge, 2013), but
moves beyond binary values.
5.9 Data Reduction: Principal Component Analysis Index-1 (QUAN) 5.9.1 Principal Component Analysis
Principal component analysis (PCA) is a multivariate statistical technique used to identify and retain significant variables in a data set while reducing and discarding insignificant ‘noise’ (Ogwang, 1994; Jolliffe, 2002; Fujii, 2008).
Wold and colleagues (1987) identified several objectives for the use of PCA:
• data reduction • variable classification
• data simplification • variable prediction
• modeling • variable selection
• outlier detection
In this research PCA was used to:
• find patterns and trends in the data
• discover important variables influencing the data
• compare countries with high levels of adaptation to those of intermediate and
lower levels
• with the help of memoing, differentiate between the stages in the adaptation
process and the determinants that advance climate change adaptation
• identify similarities and differences in determinants
• discover the top 5 determinants of national level climate change adaptation
140 Following quantitization, Principal Component Analysis (PCA), which is a multivariate
statistical technique (see Jolliffe, 2002), was applied to the variables that were either (i)
potential stages of the adaptation process or (ii) potential determinants of climate
change adaptation. Based on the theoretical memos, nodes identified as sub-themes,
such as gender, barriers, and community-based adaptation, were excluded from this
stage as it was determined during theoretical coding that these would neither be stages
nor determinants. PCA was therefore used as (1) a data reduction technique to reduce
the number of variables (themed nodes) identified during coding, and (2) combined with
selective coding to create a variable selection method to differentiate between variables
that are stages of the climate change adaptation process versus those that are determinants of climate change adaptation (a third point of mixing). The data for 19 variables were analyzed using PCA with Varimax orthogonal rotation and Kaiser normalization (Software: Analyse-it ® Statistical analysis).
Based on the scree plot (Figure 12), five principal components (PC) with eigenvalues above 1.00 were retained for analysis. Of the five components, PC1, PC2 and PC3
(shown in Table 21) yielded significant results that corresponded with the selective coding and memoing from grounded theory. The first component (PC1) was identified as the stages of adaptation, while the second component (PC2) was considered to be the determinants of adaptation. A third component (PC3) retained variables relevant to both the stages and determinants of adaptation. Table 21 shows the three principal
components and the corresponding loadings for each variable. Note loading scores
above a 0.400 significance level are in bold.
141
Figure 12. Scree Plot for the Development of Index-1 and Determinant
Identification
7
6
5
4
Eigenvalue 3
2
1
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Component Number
Table 21. Loadings for Principal Components with Varimax Rotation: Variable
Differentiation for the Development of Index-1 and Determinant Identification
PC1 PC2 PC3
Variable36 Stages of Determinants Stages & Adaptation of Determinants Adaptation Assessment Phase 0.755 0.145 0.208
Examples of Climate Change Adaptation 0.685 0.061 0.169
Recommendations, Priorities and Options 0.659 0.161 0.224
Evaluation of the Adaptation Outcome 0.572 -0.271 0.290
36 Bolded values are significant
142
Implementation 0.488 -0.326 -0.373
Insurance & Financial Instruments 0.487 -0.105 0.237
Project Example, or Programme Development 0.468 -0.130 -0.361
Establishing Partnerships -0.135 0.771 -0.014
Tool Development & Usage 0.153 0.769 0.328
Multi-Level Government Involvement 0.097 0.750 0.361
Public Transparency 0.329 0.500 0.333
Climate Shock (Leading to Policy Change) -0.198 0.403 0.367
National Strategy or Plan -0.135 -0.014 0.861
Funding for Adaptation -0.065 0.366 0.692
Reviews & Evaluations of Programs & Plans 0.148 -0.347 0.661
Workshops, Public Education & Training 0.215 0.332 0.657
Climate Change Adaptation Research -0.241 0.027 0.638
Adaptation Policy Creation 0.309 -0.282 0.618
Legislation, Laws & Acts, Standards & Codes 0.347 -0.329 0.487
Eigenvalue (λ) 6.660 2.468 1.365
Theoretical memoing, an iterative process in grounded theory, allows for the derivation
of meaning from qualitative data captured while the researcher is coding (Glaser, 1998).
Memoing is central to credible qualitative research (Birks et al., 2008). During the
grounded theory process, 68 pages of memo notes were recorded. In this fourth point of
mixing, the memos were used alongside selective coding and PCA to establish the
order for each of the variables to create the Stages of the Climate Change Adaptation
Process; i.e., the process by which climate change adaptation actions and policy are developed, implemented and assessed. This process was discovered by grounded theory. Weights for each stage of the adaptation process were determined by memos
143 and theoretical coding. Stages with the same value were observed to occur at relatively
the same time. Memoing allowed for the identification of the stages in sequence (see
Table 22). The higher the weight for a given stage, the further along is the country in
the climate change adaptation process. The scale of 0 to 100 was selected for its
simplicity. The lowest value represents the least progress on adaptation (i.e.,
Assessment Phase with a value of 5); this was the first level in the process identified by
grounded theory. The highest level is represented by the Evaluation of the Adaptation
Outcome with a value of 100; this is the highest level of progress achievable on a linear
scale where the adaptation measure has not only been implemented, but a climate
related event has occurred, and the country has been able to evaluate the ability of the
adaptation measure to decrease vulnerability.
After the weight for each of the variables in the hierarchy was established, the database created from the matrix coding query was used to calculate the Index-1 rankings.
Frequency counts for each country were multiplied by the weight for the corresponding
stage. A country with a frequency count of 10 under the Climate Change Adaptation
Research node (with a weight of 40) would receive a score of 400 for that stage.
The Index1 formula was then applied, where m is the value of the stage weight, and a
scaling constant, n, is 103. Table 23 provides a section of the database for the
calculation of Index-1.
144
Table 22. Stages of the Climate Change Adaptation Process and corresponding weights
Variable Weight
Assessment Phase 5
National Strategy and Plan Development 10
Costing Climate Change Adaptation 20
Recommendations, Priorities & Options37 30
Climate Change Adaptation Research 40
Workshops, Public Education & Training 40
Project Example, or Programme Development 50
Insurance & Financial Instruments 50
Examples of Climate Change Adaptation 60
Adaptation Policy Creation 60
Legislation, Laws & Acts, Standards & Codes 60
Funding for Adaptation 70
Implementation 80
Reviews and Evaluations of Programmes & Plans (prior to climate- 90 related impact)
Evaluation of the Adaptation Outcome (post climate-related impact) 100
37 Globally, we are currently at this stage in the adaptation process.
145
Table 23. A Section of the Stages of the Adaptation Database used in the Calculation of Index-1 Recommendations, Project Adaptation Adaptation Evaluation Sum of Country Weighted Index1 Priorities and Example or Policy Funding of the Weighted Stage Coding Options & Programme Creation (70) Adaptation Coding Based Stage Regulations Development (60) Outcome on Countries (30) (50) (100) Coding Afghanistan 60 350 180 350 0 1250 70 87500 87.5 Albania 240 500 60 700 0 1790 70 125300 125.3 Algeria 60 0 60 0 0 320 60 19200 19.2 Angola 270 100 0 0 0 415 30 12450 12.4 Antigua & Barbuda 630 50 60 0 100 1630 30 48900 48.9 Argentina 60 150 0 70 0 470 50 23500 23.5 Armenia 120 250 0 210 0 1650 80 132000 132 Australia 330 350 360 2450 100 6570 70 459900 459.9 Austria 120 50 60 140 0 1120 40 44800 44.8 Azerbaijan 300 0 0 0 0 325 30 9750 9.7 Bahamas 120 150 0 0 0 370 50 18500 18.5 Bahrain 570 0 60 0 0 690 30 20700 20.7 Bangladesh 870 200 300 490 0 2545 30 76350 76.3 Barbados 240 100 60 140 0 865 30 25950 25.9 Belarus 150 150 0 0 0 405 50 20250 20.2 Belgium 150 200 300 70 0 3600 60 216000 216 Belize 570 100 180 0 0 990 30 29700 29.7 Benin 270 0 0 0 0 320 30 9600 9.6 Bhutan 780 0 0 0 0 860 30 25800 25.8 Bolivia 30 400 120 140 0 840 50 42000 42 Botswana 390 0 120 0 0 660 30 19800 19.8 146
To review, the development of Index-1 began with grounded theory in NVivo that
identified 35 variables (QUAL). The counts in the nodes were exported using a matrix
coding query (QUAL QUAN). Selective coding, memoing and PCA were applied to
select variables for a hierarchy of the stages of climate change adaptation (QUAN).
Memoing was used to develop rankings for the quantitative index (QUAL QUAN).
The resulting values were aggregated and weighted. Index-1 was then plotted alongside
Index-2, described below in a data matrix (see Figure 13). The next section outlines the development of the quantitative Index-2.
5.9.2 Developing the Level of Vulnerability (Climate Risk) (Index-2) from Quantitative Data
Developing an index from quantitative data requires fewer steps than creating an index
from qualitative data, since data collection and variable selection occurred
simultaneously in Phase I. Thus, three steps remain to develop Index-2: transformation,
aggregation, and normalization of the data. A scale transformation was performed prior
to normalization for three of the seven variables (Population Density, Environmental
Performance Index, and Coastline Area Ratio). This ensured that the values were
comparable as the coast-to-area ratio was a factor of 103 higher than mean sea level
trend. Additionally, the absolute value of precipitation was taken from the rating scale
given that a positive (increased precipitation i.e., flood) or negative (decreased
precipitation, i.e., drought) both increase a nations’ vulnerability; thus when aggregating
the data it was important to ensure that a negative precipitation value for drought would
not inadvertently decrease a country’s risk. Table 24 provides a section of the
standardized vulnerability database for the calculation of Index-2. 147
Table 24. A Selection of the Vulnerability (Climate Risk) Database for the Calculation of Index-2
Population Population EPI Coastline Mean Temperature Precipitation Total Standardized Index-2 o Density Growth (%) Land Sea ( C) (mm/day) Score Score /km2 Ratio Level Trend m/km2 Countries (mm/yr) Afghanistan 0.47 2.41 0.78 0.00 0.00 3.50 3.50 10.66 0.20 19.87 Albania 1.01 -1.01 0.45 0.13 -3.68 2.50 5.00 4.41 0.06 6.46 Algeria 0.16 1.87 0.50 0.00 0.53 3.50 1.50 8.07 0.14 14.32 Angola 0.17 3.08 0.71 0.01 0.60 3.50 1.50 9.58 0.18 17.55 Antigua and 2.05 1.02 0.51 3.46 2.08 1.75 1.50 12.37 0.24 23.54 Barbuda Argentina 0.15 0.87 0.50 0.02 1.57 3.50 3.50 10.11 0.19 18.70 Armenia 1.05 0.25 0.38 0.00 0.00 3.50 1.50 6.68 0.11 11.33 Australia 0.03 1.78 0.18 0.03 3.48 3.50 3.50 12.49 0.24 23.81 Austria 1.03 0.52 0.22 0.00 0.00 3.50 1.50 6.76 0.11 11.51 Azerbaijan 1.14 1.29 0.45 0.00 0.00 3.50 1.50 7.88 0.14 13.90 Bahamas 0.38 1.45 0.53 3.54 3.34 1.75 5.00 15.98 0.31 31.30 Bahrain 17.53 1.08 0.48 2.10 3.10 1.25 5.00 30.54 0.63 62.54 Bangladesh 12.03 1.22 0.74 0.05 9.60 2.50 7.50 33.64 0.69 69.18 Barbados 6.62 0.50 0.55 2.30 2.65 1.75 1.50 15.87 0.31 31.04 Belarus 0.47 0.02 0.32 0.00 0.00 3.50 1.50 5.81 0.09 9.47 Belgium 3.70 0.60 0.33 0.02 2.53 2.50 5.00 14.68 0.28 28.50 Belize 0.15 2.39 0.50 0.17 2.50 2.50 1.50 9.70 0.18 17.81 Benin 0.92 2.68 0.68 0.01 2.16 2.50 1.50 10.44 0.19 19.40 Bhutan 0.20 1.62 0.53 0.00 0.00 3.50 7.50 13.35 0.26 25.64 Bolivia 0.10 1.65 0.50 0.00 0.00 2.50 1.50 6.25 0.10 10.40 Bosnia and 0.75 -0.12 0.54 0.00 0.45 2.50 0.50 4.63 0.07 6.92 Herzegovina Botswana 0.04 0.86 0.52 0.00 0.00 3.50 1.50 6.42 0.11 10.76 148
5.10 Phase III: Data Matrix
The development of this methodology centers on the question, why is climate change adaptation advancing at different rates in nations across the world? This question leads to several sub-questions: do countries with similar political, geographic and economic attributes respond to climate change with similar strategies? Are all small island developing states at the same level on the adaptation agenda? Are there identifiable differences? What geographic, economic, political characteristics or policy influences advance climate change adaptation? If a country is expected to experience a greater number of climate change impacts, is that expectation associated with the nation being further along on the adaptation agenda?
This chapter provides a methodology to create a quantitative database from qualitative data and two composite indexes. While the initial impetus for this research was to answer the previously noted questions, two new benefits emerged: first, for the climate change research community to recognize the transformational power of mixed methodological approaches for qualitative and quantitative research and, secondly, for mixed methodologists to observe the relevance of mixed methods research to the physical sciences. For illustration, Figure 13 is a Log10 plot of both Index-1 and Index-2; outliers have been removed to increase the spread of the data.
The plot is based on 22 different indicators between the two indexes (plus two controlled variables GDP and Disaster Losses), with the climate variables based on future climate risk in an IPCC RCP6 scenario (2081-2100).
149
Figure 13. Logarithmic Plot of Index-1 and Index-2 for Illustration
150
5.11 Contributions of this Study to Climate Change Adaptation and Mixed Methodology Research
Creswell and colleagues (2003) argue that, “mixed methods research helps answer questions that cannot be answered by quantitative or qualitative approaches alone”.
Problems of the global commons are filled with these questions. One such question, why climate change adaptation is advancing at different rates in nations across the world, led the author to search for a single methodology. The lack of a suitable single qualitative or quantitative approach resulted in the need to develop a mixed methodology. This research highlights the untapped potential that mixed methodological research offers to climate change adaptation and doubtless many other fields. This chapter also proposes that researchers in climate change adaptation who are engaging in mixed methods research identify ‘mixed methods’ in their studies. Detailed methodologies should be required by all journals, not only so that other researchers can replicate the studies, but also to demonstrate to the reader the breadth of research that is possible with mixed methods research. Future studies should identify points of mixing and reflect that climate change researchers have spent time exploring mixed methods literature, beyond the most popular MMR publications. These initial steps would not only help to advance MMR and climate change research, but also would enhance research output – an outcome that may lead to changes in policy and decrease global risk from our changing climate.
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161 Chapter 6 Identifying Determinants of Climate Change Adaptation from National Documents Introduction
To examine ‘why’ adaptation is proceeding at different rates, in other words, what steps
countries with higher levels of adaptation are undertaking that countries with lower
levels are not, this research uses a mixed methods approach. There are several
benefits of mixed methods research (MMR). Green et al., (1989) identified a conceptual
framework that categorized the purposes of MMR as expansion, development, initiation,
complementarity and triangulation. For this study, a mixed methods approach was
selected for its completeness (as described in Hackney et al., 2007) and the ability for
both qualitative and quantitative weaknesses to be compensated by the combined
methodology (Dennis and Garfield 2003). Johnson and Onwuegbuzie (2004) state, “a
key feature of MMR is its methodological pluralism…which frequently results in superior
research (compared with monomethod research)”. Venkatesh et al. (2013) highlights
that, “proponents of mixed methods research appreciate the value of both quantitative and qualitative worldviews to develop a deep understanding of a phenomenon of interest”. Additionally, MMR can answer complex research questions, such as the one presented in this paper that may otherwise be unfeasible. A summary of the qualitative and quantitative methodologies, software, analytical processes and methodological advantages used in this research are presented in Table 25.
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Table 25. Summary of MMR Approaches Used in this Analysis
Description Qualitative Quantitative Purpose Exploration, data collection and Statistical analysis analysis Methodological Grounded theory Multivariate analysis technique Software NVivo 11 Qualitative Data Analysis Analyse-it ® Standard Software (QSR International, 2016) Edition: Statistical Analysis and Data Visualization (2016) Analytical Coding Correlation analysis process Matrix queries Principal Component Memoing Analysis, Mean, Scatterplots Advantages Data-rich phenomena, theory Precise, time conserving, generation, sequential pattern objective identification
6.1 Data Collection: Coding and Variable Identification
A variant of the MMR Triangulation Design called the Data Transformation Model was used (Creswell et al., 2004). In this method both qualitative and quantitative data are collected at the outset, with qualitative data being quantitized (Sandelowski, et al., 2009) into quantitative data for analysis. Only this latter analysis (QUAL QUAN) is being discussed in this chapter. The full methodology is outlined in Chapter 3.
Initially, 192 parties under the UNFCCC were identified and qualitative documents available for each of these countries were gathered: National Communications (NCs)
(n=191), Intended Nationally Determined Contributions (INDCs) (n=162), and National
Adaptation Programmes of Action (NAPAs) (n=50). Each of the 403 documents were
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analyzed using Grounded Theory (see Glaser and Strauss, 1967) with NVivo 11 (QSR
International, 2016). Grounded Theory is an inductive approach aimed at theory discovery. The method removes the researcher’s subjective assumptions or previously developed theories (that are commonly created a priori) from the research and analysis
process, allowing the data to generate core phenomena. Thus, the findings from this
research are “grounded in data [that has been] systematically gathered and analyzed”
(Straus & Corbin, 1994). Grounded Theory is iterative and shaped by three types of
coding used in qualitative analysis: open, axial and selective (described further in
Strauss and Corbin, 1990). Open coding was used to reveal initial categories.
Exhaustive axial coding distinguished 35 themed nodes. A Matrix Coding Query, which
cross-tabulates coding intersections between the 35 identified nodes and the 192
countries, was run. This step quantitized the qualitative data. Of the 35 themed nodes,
19 were suitable for PCA.
Principal component analysis was performed on the quantitized data and 15
determinants of adaptation were identified. Table 26 outlines the 15 variables (themed
nodes) identified during qualitative analysis, the number of countries (sources) that were
coded under each node and the number of times those sources were coded
(references). For the 15 variables categorized, 2890 sections of text were coded.
Table 26. Themed Nodes: Identified Determinants of Adaptation
Variable Sources References
Adaptation Policy Creation 101 273
Climate Change Adaptation Research 83 222
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Climate Shock (Leading to Policy Change) 12 20
Costing Climate Change Adaptation 39 105
Establishing Partnerships 99 331
Evaluation of the Adaptation Outcome 16 22
Funding for Adaptation 71 254
Insurance & Financial Instruments 40 74
Legislation, Laws & Acts, Standards & Codes 77 162
Multi-Level Government Involvement 30 104
National Strategy or Plan 121 455
Public Transparency 29 56
Reviews & Evaluations of Programs & Plans 17 33
Tool Development & Usage 27 74
Workshops, Public Education & Training 109 262
Total 988 2890
Table 27 provides the reader further information as to the types of text that contributed to each node.
Table 27. Sample Coded Text
Variable Country Coded Text Source38
38 Sources available from http://unfccc.int/national_reports/items/1408.php
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Adaptation St. Lucia National Climate Change Policy St. Lucia (2011), Policy Creation and Adaptation Plan (NCCPAP) p164 formulated in 2001. Draft Revised Climate Change Adaptation Policy (2011).
Climate Sudan Assessment to explore Sudan (2013), p. Change sustainable livelihood and 30 Adaptation environmental management Research measures considered as climate change adaptation options in planning of future adaptation strategies
Climate Shock Estonia The regions that are most Estonia (2013), (Leading to frequently a ected by weather p. 187
Policy Change) conditions haveff been the most active in implementing adaptation measures
Costing Guinea- Coastal Erosion Zone Northern Guinea-Bissau Climate Bissau Province (Cacheu) Region (2011) p. 73 Change Southern Province Adaptation (Bolama/Bijajos Region) $400,000
Establishing Canada Natural Resources Canada has Canada (2014), Partnerships established a national Adaptation p124 Platform, whose participants have collaborated to advance adaptation in several areas such
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as coastal management and Cook Island economics. Cook Island With the support of the World (2012), p97 Health Organisation (WHO), the Ministry had developed a draft Climate Change and Health Adaptation Plan for the Cook Islands.
Evaluation of Tuvalu The Government assisted the Tuvalu (2007), p. the Adaptation construction of the Seawall on 35 Outcome Nanumaga, with community participation through the provision of labour, to minimized intrusion of saline water from the sea into the pulaka pit. However, the seawall has deteriorated and the problem has returned.
Funding for Australia $4.5 million investment on 13 Commonwealth Adaptation Coastal Adaptation Decision of Australia, Pathways projects (2013), p. 162
Insurance & Seychelles A study was carried out to set up Seychelles Financial the Seychelles National Disaster (2011), p. 51 Instruments Insurance Scheme as a Public- Private Partnership as Seychelles Agriculture & Fisheries Insurance Fund (SAFIF)
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Legislation, United California is implementing building United States Laws & Acts, States standards mandating energy and (2014), p. 169 Standards & water efficiency savings, Codes advancing both adaptation and mitigation. The State Adaptation Plan calls for a 20 percent reduction in per-capita water use.
Multi-level Norway National level: Contribute to the Norway (2014), Government development of national climate p. 141 Involvement change adaptation strategies, develop/manage websites, undertake and coordinate cross- cutting and/or sector specific CCA initiatives. Local/regional level: Develop and support pilot programme. Promote networks for local planners, regional policymakers and research communities
National Finland Finland’s National Strategy for Finland (2013), Strategy or Adaptation to Climate Change p. 20 Plan was published in 2005.
Afghanistan Afghanistan’s NAPA Identified 51 Afghanistan different adaptation actions in (2013), p. 48 seven thematic areas and prioritizes programs to address the climatic risks in Afghanistan
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Public Indonesia A national website where decision Indonesia (2010) Transparency makers and their advisers can pVII-5 access information about climate projections, likely climate change impacts, tools, guides and approaches to adaptation planning.
Reviews & United National Adaptation Programme HM Government, Evaluations of Kingdom (NAAP) must be put in place and (2013) p. 165 Programs reviewed every five years to Plans address the most pressing climate change risks
Tool Australia The Commonwealth Scientific and Commonwealth Development & Industrial Research Organisation of Australia Usage (CSIRO) and Tourism (2013) p. 178 Queensland have trialled an adaptation planning tool (‘Climate Futures Tool’) to assist tourism operators in two Queensland regions to plan for locally relevant impacts of climate change.
Workshops, Tajikistan The Youth Ecological Centres Tajikistan (2014), Public disseminate knowledge on coping pp. 135 - 136 Education & strategies, application of simple Training plant protection methods, use of seed reserves, use of drought- resistant crops, and more effective use of irrigation water.
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More than 300 farmers have been trained on adaptation measures and more than 3 thousand households have benefited from adaptation activities.
6.2 Correlation and Principal Component Analysis
Pearson correlation coefficients and statistical significance (p <0.05) were calculated
(Table 28) for each determinant of adaptation (independent variable) (n=15) against a derived level of adaptation (Index-1) for each of the 192 countries (see Chapter 3 for derivation of Index-1). Only one of the variables originally selected, Costing Climate
Change Adaptation, had a non-statistically significant p-value of 0.0894 and was removed from the subsequent analysis.
Table 28. Pearson Correlation Coefficients and p-values for Determinants of
Climate Change Adaptation Identification
Variable Pearson's r P value
Adaptation Policy Creation 0.547 <0.0001
Climate Change Adaptation Research 0.590 <0.0001
Climate Shock (Leading to Policy Change) 0.242 0.0017
Costing Climate Change Adaptation 0.132 0.0894
Establishing Partnerships 0.698 <0.0001
Evaluation of the Adaptation Outcome 0.235 0.0024
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Funding for Adaptation 0.696 <0.0001
Insurance & Financial Instruments 0.309 <0.0001
Legislation, Laws & Acts, Standards & Codes 0.446 <0.0001
Multi-level Government Involvement 0.484 <0.0001
National Strategy or Plan 0.767 <0.0001
Public Transparency 0.258 0.0008
Reviews & Evaluations of Programs Plans 0.614 <0.0001
Tool Development & Usage 0.505 <0.0001
Workshops, Public Education & Training 0.300 <0.0001
A multivariate statistical technique, Principal Component Analysis (PCA) (see Jolliffe,
2002) was applied to the data set to discover the most important variables driving the data. PCA can be used for multiple objectives including: data simplification, data reduction, modeling, outlier detection, variable selection, classification, and prediction
(Wold et al., 1987). In this chapter, PCA was used initially to identify similarities and differences in the determinants of adaptation for all 192 countries. It was then applied to find patterns and trends in the data when comparing countries between groups of high levels of adaptation to those of intermediate and lower levels. Finally, PCA allowed for the discovery of the top 5 determinants of national level adaptation through the use of data (variable) reduction.
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Data for all 192 countries were analyzed for each of the 14 variables using PCA with
Varimax rotation and Kaiser normalization39. Components with eigenvalues (a value that characterizes the explained variation in the data) above 1.00 were retained for analysis. The highest eigenvalue represents the principal component in the dataset, where each component is ranked in order of the total variance it explains. The data were then separated into three sets, using percentiles, and categorized into higher, intermediate and lower national levels of adaptation.
6.3 Results 6.3.1 Correlation: All data
The variables with the highest correlation with national level of adaptation were:
National Strategy or Plan (r=0.767), Establishing Partnerships (r=0.698) and Funding for
Adaptation (r=0.696). Variables showing the least correlation included Evaluation of the
Adaptation Outcome (r=0.235) and Climate Shock (Leading to Policy Change).
However, correlation provides a limited view, as it only compares two variables.
Therefore, in this chapter, the interpretation of results will rely on PCA, as it is able to
analyze multiple variables providing a more complete understanding of the methods
used to move adaptation forward.
6.3.2 Principal Component Analysis: All Data
PCA generates values called loadings (presented in Table 29). The first three principal
components had eigenvalues greater than 1.00 (λ = 5.35, 2.09, 1.28, respectively) and
39 This orthogonal rotation increases ability to interpret the output by maximizing the variance of the squared factor loadings (Kaiser, 1958)
172 were retained (an eigenvalue of 5.35 represents a principal component that can explain the variance in five variables). Loadings lower than 0.400 were removed. PCA generates Principal Components (PCs) in order of importance. The first component,
PC1, had high loadings for National Strategy or Plan, Tool Development & Usage,
Establishing Partnerships, Multi-level Government Involvement, and Reviews &
Evaluation of Program Plans. PC1 accounts for the most variation in the data, and therefore includes the most significant variables. The second component PC2 was characterized by Workshops, Public Education & Training, Insurance & Financial
Instruments and Climate Shock (Leading to Policy Change). Funding for Adaptation was crossloaded, i.e., found in two components: PC1 and PC3. This is termed a complex variable where the value of the variable (as a determinant of climate change adaptation) should be interpreted cautiously.
Table 29. Determinants of Climate Change Adaptation Identification: Principal
Component Loadings for All Countries (Bolded values are considered significant)
Variable (Determinant) PC1 PC2 PC3
National Strategy or Plan 0.849 -0.125 -0.067
Tool Development & Usage 0.781 -0.372 0.059
Establishing Partnerships 0.760 0.113 -0.225
Multi-level Government Involvement 0.752 -0.321 0.044
Reviews & Evaluations of Programs Plans 0.704 -0.190 -0.173
Funding for Adaptation 0.668 -0.240 -0.524
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Adaptation Policy Creation 0.641 0.339 0.052
Climate Change Adaptation Research 0.641 0.304 -0.237
Workshops, Public Education & Training 0.327 0.643 0.167
Insurance & Financial Instruments 0.246 0.560 -0.237
Climate Shock (Leading to Policy Change) 0.330 -0.397 0.209
Public Transparency 0.345 -0.201 0.560
Legislation, Laws & Acts, Standards & 0.312 0.374 0.414 Codes
Eigenvalue (λ) 5.35 2.09 1.28
6.3.3 Between-Groups Principal Component Analysis
The limitations observed using correlation analysis to explain complex socioeconomic and geopolitical questions are also present when looking at all countries as one group in
PCA. Neither correlation nor the standard PCA methodology are able to provide a complete understanding of what differentiates countries with higher levels of adaptation from those with lower levels. To achieve this distinction, between-groups PCA was used
(Krzanowski, 1979). Between-groups PCA analysis (a) allows for relationships between the variables to be established (b) detects the variables with the greatest significance
(c) determines the variables that are missing in countries exhibiting lower levels of adaptation and (d) ranks a variable's relative importance in moving a country from a lower level of adaptation to a higher level. Table 30 shows PCA with Varimax rotation and Kaiser Normalization for the top principal component for each of the three datasets.
Loadings higher than 0.4 were considered significant.
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Table 30. Loadings for Between-Groups PCA: Countries Separated by High,
Intermediate and Low Levels of Adaptation for Determinant Identification
Variable (Determinant) Level of Adaptation
Higher Intermediate Lower
Multi-Level Government Involvement 0.913 0.617 -
Tool Development & Usage 0.906 0.545 -
National Strategy or Plan 0.807 0.165 0.454
Climate Shock (Leading to Policy Change) 0.799 0.189 -
Public Transparency 0.674 0.452 0.446
Reviews & Evaluations of Programs Plans 0.608 0.014 -
Establishing Partnerships 0.573 0.375 0.816
Funding for Adaptation 0.558 0.044 -
Climate Change Adaptation Research 0.388 0.254 0.079
Adaptation Policy Creation 0.372 0.167 0.574
Workshops, Public Education & Training 0.226 0.382 0.469
Legislation, Laws & Acts, Standards & Codes 0.157 0.314 0.083
Evaluation of the Adaptation Outcome 0.100 0.133 -
Insurance & Financial Instruments 0.066 0.174 0.119
The results of between-groups PCA is as follows. The principal component for countries with higher levels of adaptation was characterized by high loadings in (1) Multi-Level
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Government Involvement (2) Tool Development & Usage (3) National Strategy or Plan
(4) Climate Shock (Leading to Policy Change) (5) Public Transparency and (6) Reviews
& Evaluations of Programs Plans. The variables that loaded highly on the principal component for intermediate countries contained three of the top five variables in the higher-level countries: (1) Multi-Level Government Involvement (2) Tool Development &
Usage and (3) Public Transparency.
6.4 Discussion
By using a grounded theory approach 15 variables were identified. These variables are methods used by countries to move adaptation forward. They may also be termed determinants, motivators, enablers or influencers. In identifying and examining the variables and how they are used by the 192 parties to the Convention, through a deductive methodology, this research is able to determine the variables with the most importance in advancing climate change adaptation. For each of the determinants in
Table 30, the variables with the greatest level of influence in advancing climate change must have the smallest loadings in the group of countries with the least advancement on adaptation. As the level of advancement increases, so too should the value of the loading; an increasing loading should be observable in the intermediate group of countries. The highest loading would therefore be in the group of countries with the greatest progress. Thus, an increasing trend should be visible in the loadings from PCA for the variable to be a significant determinant of climate change adaptation (e.g., Multi-
Level Government Involvement: countries with the lowest level of adaptation 0.00 < countries with an intermediate level of adaptation 0.617 < countries with the highest level of adaptation 0.913).
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When contrasting nations with higher, intermediate and lower levels of adaptation using
a between-groups PCA methodology, five variables showed an increasing trend.
Countries with the lowest levels of adaptation had no loading value for the adaptation
determinant (i.e., this variable was not present in this country). In the intermediate group
there was an increasing trend that displayed a higher loading; and the greatest loading
was found in the highest level of adaptation group. Thus, the most significant
determinants (variables) that are advancing climate change adaptation, ranked in level
of importance, were identified by PCA as: (1) Multi-Level Government Involvement, (2)
Tool Development & Usage, (3) Climate Shock (Leading to Policy Change), (4) Reviews
& Evaluations of Programs Plan, and (5) Funding for Adaptation. Each determinant will be discussed with examples from the data in the subsequent section.
6.4.1 Multi-level government involvement
Multi-level government40 involvement is the primary determinant in both higher level and
intermediate national level adaptation. The component loading increased from 0.617 in
the intermediate group to 0.913 in countries with higher levels of adaptation, suggesting
increased importance. This quantitative value can also be cross-validated in the
qualitative coding where 75 examples were in higher adaptation countries and 24 in
intermediate. Only two sources (countries) had instances of multi-level government
involvement in the lower level nations. Depending on government structure, multi-level
40 Note: governance is a broader term, encompassing both national and international levels, public and private sectors. In this paper multi-level government is looking at the public sector component within one country with the involvement of national (e.g, federal), subnational, if applicable, (e.g., provincial) and/or local (e.g., municipal) level.
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government takes several forms. In Australia, state, territory and local governments
display ‘different yet, complementary roles’ in proactively informing the development of
adaptation policies (Commonwealth of Australia, 2013, p. 164). Ireland’s National
Climate Change Adaptation Framework delineates a strategy requiring both multi-level government and multi-sector involvement in the implementation of adaptation measures
(Ireland, 2014, p.11). Kazakhstan highlighted the importance of strengthening local institutions to provide support for adaptation in local communities (Kazakhstan, 2013, p.
174). Land-use, transportation planning and waste management services are designated at the municipal level in Finland, with municipalities having a substantial part in the implementation of adaptation measures and climate policy (Finland, 2013, p. 98).
Municipal and regional adaptation measures are also integrated with National Plans as in the Republic of Korea (Republic of Korea, 2012, p. 99).
6.4.2 Tool development and usage
The second variable that appears to determine national level adaptation is the
development and use of climate change adaptation tools. Like Multi-level Government
Involvement, Tool Development & Usage is not present in the component for lower level
of adaptation, nor were any sources coded at the themed node. Tools encompass a
diverse range of types and uses, including: simulation, cost-benefit analysis, screening,
decision support, risk based, optimization, planning, economic analysis and evaluation
(Dickinson, 2007; UNFCCC Secretariat, 2008). They are also used in all sectors and for
all climate change impacts. Climate change adaptation tools may be developed by
governments or non-government organizations, and often are publically available (e.g.,
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CRiSTAL41). Denmark has been using a sea-level rise risk assessment tool to assist in climate-proofing vulnerable infrastructure (Denmark, 2013, p. 263). The SaskAdapt Self-
Assessment Tool was developed in Canada by the Government of Saskatchewan in partnership with the Prairie Adaptation Research Collaborative allowing ‘individuals, farmers, ranchers, small businessmen and communities to evaluate decisions about adapting to climate change’ (Canada, 2014, p. 133). Tools are often employed in the preliminary stages of adaptation planning: in Samoa a Climate Risk Profile (CRP) was the fundamental tool for adaptation assessment (Samoa, 1999, p. 42). In 2012, New
Zealand announced the Impacts of Climate Change on Urban Infrastructure and the
Built Environment Toolbox that includes the costs and benefits of different adaptation options (New Zealand, 2013 p. 130). Modelling has also been used as a tool to track mosquito-borne malaria in The Gambia in conjunction with adaptation to create proactive measures to decrease its impact (Gambia, 2014, p. 82)
6.4.3 Climate shock leading to policy change
Although the National Strategy or Plan variable has a higher loading (0.807) than
Climate Shock (Leading to Policy Change) (0.799), National Strategy or Plan had
loadings above 0.400 both in countries with high levels of adaptation and in countries
with lower levels (0.807; 0.454, respectively). Thus, there is an increased development
of climate change adaptation strategies and plans both in countries advancing
adaptation and in those showing less advancement. This lack of variation among the
countries for this variable signifies that National Strategy or Plan is not a current
41 http://www.iisd.org/cristaltool/
179
determinant of climate change adaptation; rather, it demonstrates that national
strategies and plans have been a successful intervention in the climate change
community. This is also supported by the qualitative analysis. Therefore, the variable
with the third highest between-group significance is “climate shocks” (author defined as
a climate-related event that leads to policy change).
Examples of climate shocks in five different countries illustrate these findings. In 2009,
with high drought conditions, devastating bushfires in Victoria, Australia prompted
government officials to increase funding to intensify fire prevention planning, emergency
management, and early warning systems (Commonwealth of Australia, 2013, p. 176).
Severe and recurring flooding in the Commune of Gnojnik, Estonia led to the
development of the ‘neighbour help scheme’. This framework partners community
members in lower risk areas with those in high risk flood zones. The partners provide shelter and support during extreme events (Estonia, 2013, p. 163). In Argentina, autonomous adaptation actions such as planting new crop species (soybeans) have been occurring in the agricultural sector in response to ongoing changes in climate
(Argentina, 2015). In Sweden, the Planning and Building Act included several
provisions, including the requirement that municipal development plans take into
account increased flood and erosion risk. These provisions were motivated by existing
climate impacts (Sweden, 2014, p. 95). Similarly, in Canada, municipal level actions
(infrastructure upgrades, heat alerts, the creation of guidebooks and adaptation tools)
have often been triggered by observed damages from past extreme weather events’
(Canada, 2014, p. 126).
180 6.4.4 Reviews and evaluations of program plans
Although only 17 countries had examples of implementing reviews and evaluations of adaptation measures, programs and plans, the findings were statistically significant
(r=0.614, p<0.0001). They showed a significant increase in component loading from no loading in the lower group, to 0.014 in the intermediate group, reaching 0.608 in the highest level of adaptation group. It should be highlighted that the reviews and evaluations were proactive, including in Moldova where $100,000USD was committed to monitor the climate change adaptation strategy and provide progress reports
(Republic of Moldova, 2013, p. 397). The United Kingdom requires that its National
Adaptation Programme (NAP) be reviewed every five years (HM Government, 2013, p.
166); the Austrian government plan includes a report on the state of adaptation implementation (Austria, 2014, p. 101) and, similarly, the Republic of Korea requires
‘Implementation Assessments’ for their National Climate Change Adaptation Master
Plan to be completed annually, with a full assessment every 3 and 5 years to review the results of implementing.
6.4.5 Funding for Adaptation
The last variable of significance with a component loading higher than 0.4 was Funding for Adaptation. Sources of funding are vast, including all levels of government, bilateral and multilateral institutions, neighbouring countries, private sectors, non-governmental organizations and development banks. Funding may also be provided to several nations, e.g., small island developing states, or through an international fund. Evidently, countries with increased access to funding display a higher level of adaptation (r=0.696, p<0.001); however, funding may not always procure decreasing vulnerability as
181 maladaptation and misappropriation of funds are possible and may account for why the correlation value is not higher. Table 31 provides examples of funded projects.
Table 31. Funded Climate Change Adaptation Projects and Programmes
Country 42 Project / Programme Source Amount
(USD)
Yemen Integrated Coastal Zone Pilot Program for $20,000,000
Management Climate Resilience
Tuvalu Adaptation to Near-Shore Coastal NAPA $388,000
Shellfish Fisheries Resources and
Coral Reef Ecosystem
Productivity
Australia Adaptation Decision Pathways Australian $4,500,000
projects Government
Uganda Global Climate Change Alliance: European Union $13,425,628
Adaptation to climate change in
Uganda
Bangladesh Bangladesh Climate Resilient UK, Sweden, $120,000,000
Fund Denmark and the
EU
Canada Forest Change Government of $4,000,000
Canada
42 Yemen, 2015 p13; Tuvalu, 2007 p38; Commonwealth of Australia, 2013 p162; Uganda, 2014 p141; Bangladesh, 2012 p xiii; Canada, 2014 p129.
182
Multiple Pacific Adaptation to Climate Special Climate $800,000
Nations Change Change Fund of
the Global
Environment
Facility
6.4.6 Other notable trends
A final component that showed an increasing trend with increasing level of adaptation
was Climate Change Adaptation Research (0.079, 0.254, and 0.388). However, this
value was below the 0.4 threshold and presented very minor influence on the principal
component. Additionally, this research indicated that the current international climate
community has been successful at promoting national strategies and plans, public
transparency, and supporting and developing partnerships at all levels.
6.4.7 Policy Implications and Conclusions
Methods to move climate change adaptation forward are complex. A multifaceted
approach is required to create significant progress on a national scale. Of the 15 original
determinants of adaptation identified, between-groups PCA revealed five determinants with the greatest influence on adaptation: Multi-Level Government Involvement, Tool
Development & Usage, Climate Shock (Leading to Policy Change), Reviews &
Evaluations of Programs Plan, and Funding for Adaptation.
Several policy implications can be garnered from these findings. International and national level conferences plans and strategies should be inclusive and require the involvement of delegates from all levels of government. Nations should be encouraged to share tools for climate change adaptation (akin to technology transfer), whereby the
183 international community could provide support (through an accessible online portal) for an ‘adaptation tool programme’ that would prevent redundancies and would benefit countries still beginning the process of developing adaptation options and implementation measures. Although the ideal response to climate change is a proactive one, this research supports the observation that countries who respond reactively to climate change impacts with policy change create more overall progress than those who do not. However, it is difficult to translate this into a positive policy measure.
The recent Paris Agreement recognized the need for planned review periods. This study supports the need for that process to be expanded to climate change adaptation, specifically to all National Adaptation Plans (NAPs) in both Annex I and Non-Annex I countries, and to include automatic review cycles and recommitment periods. Lastly, the need for funding in adaptation is strongly supported by this research. Mobilizing funding from multiple sources and for all nations is crucial to the advancement of climate change adaptation. The importance of the source of funding has yet to be evaluated.
The grounded theory approach used in this study provided an objective methodology to analyze adaptation literature through an uncommon lens. The findings were cross- validated using an iterative mixed methodological approach, and PCA results were compared with output from qualitative research analysis. In addition to identifying determinants of adaptation, this approach provides a novel methodology to assess climate change adaptation.
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191 Chapter 7 Identifying Geopolitical Determinants of Climate Change Adaptation Threat Multipliers
« In an era of unprecedented scientific and societal knowledge, failure to respond
adequately to climate change will be a human rights tragedy. The challenges in
responding, however, are ostensibly endless. Climate change allows for seemingly
innocuous changes in temperature and precipitation to trigger potentially vast
geopolitical shocks. Literature on the impacts of climate change frequently fixate on the
primary (e.g., increased temperature) and secondary (e.g., increasing drought) impacts of climate change (Alexander et al., 2006; Stringer et al., 2009), leaving tertiary impacts
(e.g., increasing conflict) in the depths of a foray into political fiction. Nevertheless, climate change as a ‘threat multiplier’ (Hagel, 2014) is a very real and present hazard.
The interconnected and cascading impacts of small and incremental changes in
temperature and precipitation may further destabilize already fragile states, increasing
internal displacement and shifting existing conflicts or providing the platform for the
creation of new ones. As the level of forced migration and refugees increases, the
potential for ‘silent genocides’, loss of language and loss of culture, increases in
likelihood.
Mitigation of greenhouse gas emissions, the leading response to climate change, is of
primary importance. However, there are two key reasons why mitigation alone is not
enough to prevent a humanitarian crisis: i) the impacts from anthropogenic climate
change are already occurring (IPCC, 2014; Partain et al., 2016), and ii) despite the
potential of an efficient global carbon cycle following a cessation of emissions, at least
192
20% of CO2 will remain in the atmosphere for thousands of years. This outcome will lock-in lifetimes of some degree of climate change (Inman, 2008). These two points prompted the international community, during the United Nations Framework
Convention on Climate Change (UNFCCC) annual Conference of the Parties in 2010, to include the statement that, “Adaptation must be addressed with the same priority as mitigation” (UNFCCC, 2011).
To develop an informed climate response requires extensive understanding of the direct and indirect economic, political, geographic, and security factors that influence action on climate change. The impacts of climate change should not only include terms such as
‘flood’ and ‘sea level rise’, but also 'internal displacement', 'forced migration' and 'silent genocide'. Current sub-disciplines of climate change adaptation do specialize in these topics (migration, conflict and culture), and millions of dollars of climate change adaptation funding are being invested in fragile states. However, these terms and their understanding have yet to penetrate the mainstream climate lexicon.
A devastating compounding effect can happen when events in countries with economic systems reliant on climate-dependent livelihoods coincide with decreasing levels of peace, high levels of poverty or political instability. Five climate-change adaptation projects in fragile or failed states, totaling over $225 million, are being carried out in countries not simply being impacted by a changing climate. Conflict, famine, internal displacement, silent genocides and political instability are the pre-existing or potential conditions.
193
In Afghanistan, for example, over 60% of its workforce is supported by drought-prone
agriculture, representing 24% of the GDP43. The Gambia has very similar statistics.
These two countries received funding from the Global Environment Facility (GEF) through the United Nations Environment Programme (UNEP) for ecosystem-based adaptation projects, with Afghanistan receiving $14.6 million, and The Gambia $25.5 million in total financing for 6 years. The programs seek to increase climate resilient crops, restore degraded forests, and develop water reservoirs in local communities44.
Similarly, in Papua New Guinea 85% of the workforce is in climate-dependent industries
(fishing, forestry and agriculture) where a $24.25 million initiative led by the Asian
Development Bank (ADB) Strategic Climate Fund is supporting 9 atoll communities in
the fishing industry to confront food insecurity and preserve income generating
livelihoods, and to support upgrading coastal infrastructure45. Papua New Guinea is
also home to the greatest number of native languages and is at an increased risk of
‘silent genocide’ if responses to climate change fail.
Yemen, a country in conflict at current risk of famine, is receiving $127.4 million in
funding and co-financing from multiple sources including the International Fund for
Agricultural Development (IFAD), the GEF, the Islamic Development Bank, and the
European Union for a rural growth programme. The funds aim to assist 1.18 million
43 Agricultural data accessed: World Bank data.worldbank.org/indicator/NV.AGR.TOTL.ZS; Percent of Workforce accessed: World Factbook: https://www.cia.gov/library/publications/the- world-factbook/fields/2048.html 44 Projects database for GEF http://www.thegef.org/projects 45 http://unfccc.int/adaptation/knowledge_resources/ldc_portal/items/5632.php
194
smallholder farmers in increasing food security, climate proofing 244km of rural roads,
and rehabilitating over 1200ha of agricultural land.
A similar project is being conducted in Chad with $36.2 million in funding from IFAD and
the GEF’s Least Developed Countries Fund (LDCF) targeting agricultural production for
35,000 households to decrease risk from climate change. In Chad 26% of the 12.8- million-person population is facing food insecurity, and conflict in surrounding countries is causing an increased level of internally displaced persons and refugees46.
As these five projects demonstrate, climate change adaptation can no longer be thought
of in a climate-centric silo. Failure to implement, or failure to properly implement
(maladaptation), can take already fragile conditions and accelerate their deterioration.
This chapter explores an intersection of geopolitical factors, categorized into geography,
economy, politics and security, and compares the factors to national levels of climate
change adaptation. In doing so, the chapter asks: do geopolitical determinants of
climate change adaptation exist? And further, do correlations exists between the
national levels of climate change adaptation and geographic location, levels of GDP,
political systems, democracy strength, or numbers of refugees? In asking these
questions the chapter seeks to develop a preliminary understanding of how greater
knowledge of these interconnections can contribute to the advancement of climate
change adaptation.
46United Nations Office for the Coordination of Humanitarian Affairs (OCHA) http://reliefweb.int/sites/reliefweb.int/files/resources/tcd_viz_humanitariansituationovervieweng_ 20170418_v2.pdf
195 7.1 Interconnected and Cascading Impacts of Climate Change
In the 1907 publication, ‘The Pulse of Asia’, Ellsworth Huntington illustrated his
environmental determinist position with the conclusion that droughts from a changing
climate led to migration and the collapse of nations in Asia. The influence of climate on
humanity is a historical narrative dating back to the Greek physicians, Galen and
Hippocrates, who noted the cause and effect of climate on health and disease
(Hippocrates, 2004). Philosophers Aristotle and Montesquieu proposed their own
climate theories. In Montesquieu’s voluminous collection, The Spirit of the Laws (1748),
Book XIV endeavours to create a superiority of climates for various temperaments of
‘man’47; and Book XVIII evolves the theory from the nature of man, to political society,
as he notes, “…monarchy is more frequently found in fruitful countries, and a republican
government in those which are not so”48 (de Montesquieu, 1989). This shifting
influence of climate on human beings to climate on politics echoes Aristotle’s theories in
his work, Politics, where he writes, “Those who live in a cold climate…are full of
spirit….but have no political organization, and are incapable of ruling over others” (Book
VII, Part 7). While these theories49, laced with prejudice, prove overly simplistic, they shine a light on the initial thinking about a connection and the possible importance between the geography and climate of a nation and its impact on human beings.
47 Book XIV Of Laws in Relation to the Nature of the Climate 48 Book XVIII Of Laws in the Relation They Bear to the Nature of the Soil 49 Possibilism opposes determinism, “there are no necessities, but everywhere possibilities” - Lucien Febvre
196 7.2 On Displacement
The present-day relationship between climate, human nature and politics cannot simply
be abridged to mere temperature zones. Even in a past age when climate was relatively
stable, there were a myriad of relationships between geographic, economic, political
and demographic factors of a country that are not captured in these propositions. In our
contemporary age of climate uncertainty, these interconnections only become
increasingly complex. Changes in climate are responsible for vast transformations of
human geography. In 1999, Rand published a study examining the hypothesis “Extreme
climatic events act to trigger ‘politically’ forced migration in countries and regions
already under resource, economic, political, racial, religious, and/or ethnic tensions”.
Using population data from 1964 - 1995 and climate indicators (Multivariate ENSO
Index), the study found a correlation of 0.925 between levels of migration and changes
in climate (Auclair, 1999).
In addition, the current and future impacts of climate change have shifted this narrative;
internal displacement and temporary forced migration are being replaced by debates of
permanent relocation of entire populations. In the wake of the 2004 tsunami, the
Republic of the Maldives began developing reclaimed islands, including the lavishly
envisioned ‘designer islands’ of Hulhumalé on the Malé Atoll (Government of
Maldives50). Over 40,000 Maldivians have moved to these ‘safe islands’, with Phase 2 of reclamation intending to draw thousands more. It is unknown whether these anthropogenic islands are a true maladaptation as suggested by Magnan and
50 Government of Maldives https://hdc.com.mv/hulhumale/
197
colleagues (2016) or whether they are a palliative adaptation (defined in Dickinson &
Burton, 2014) that gives a false sense of safety and security for populations that will
eventually require migration to new countries. In a proactive bid against rising sea
levels, Kiribati purchased 6000ha of land in Fiji (Government of Kiribati, 2014), a
measure the Maldivians are hoping to avoid.
The interconnected and cascading effects of climate change do not end at changes in temperature or rising sea levels. They do not even end at reclaiming islands and migrating entire populations, since these impacts have yet to explore the potential loss of centuries’ old cultures and indigenous languages that may suddenly vanish on new lands. Nor do these climatic terms (temperature, precipitation and sea level rise) consider the potential for the repudiation of new populations, or the rejection of unwanted assimilation. The current reluctance in the European Union to accept more refugees from conflict zones paints a concerning picture for these impending environmentally-displaced populations. The impacts of a changing climate cannot be thought of in isolation.
7.3 On Conflict
The climate-conflict debate has arguments on both sides (Feitelson & Tubi, 2017;
Kelley, et al., 2015; Reuveny, 2007), whether from the viewpoint that there is limited
‘proof’ of a connection between climate and conflict51 ( & Meng, 2014; Theisen et al.,
51 See following publications for additional information on the climate-conflict debate: Hsiang, S. M., Burke, M., & Miguel, E. (2013). Quantifying the influence of climate on human conflict. Science, 341(6151), 1235367. Buhaug, H. (2014). Concealing agreements over climate–conflict results. Proceedings of the National Academy of Sciences, 111(6), E636-E636. Hsiang, S. M., & Meng, K. C. (2014). Reconciling disagreement over climate–conflict results in
198
2013), or the position that drought was the main driver forcing Somalians into conflict-
ridden Mogadishu (Maystadt & Ecker, 2014), or that climate change was a significant
motivator in the current Syrian civil war (Gleick, 2014). It is difficult to refute the potential
of these secondary climate change impacts (increasing water scarcity, rising crop
failure, and loss of food security) to increase loss of livelihood, escalate poverty and
decrease economic stability. Regardless of the side of the climate-conflict debate one
resides on, rising levels of instability and scarcity can lead to social unrest and
population displacement, laying fertile ground for conflict. The contemporary debates
are concerned with present and past conflicts taking place in current and historical
climates, not in the potentially drastically different climate of the future.
The statement, ‘oil wars may turn into water wars’52 looks increasingly possible when
examining a future climate change scenario for the countries Sudan and Egypt, who are
transboundary water partners on the Nile River. On November 8th, 1959, in Cairo,
leaders from Egypt (viz United Arab Republic) and Sudan signed the Nile Waters
Agreement.
The Agreement outlines the sharing of the Nile waters to be:
“…the total share from the net yield of the Nile after the full operation of
the Sudd el Aali Reservoir shall be 18½ Milliards for the Republic of the
Africa. Proceedings of the National Academy of Sciences, 111(6), 2100-2103. Hsiang, S. M., Burke, M., & Miguel, E. (2014). Reconciling climate-conflict meta-analyses: reply to Buhaug et al. Climatic change, 127(3-4), 399-405. 52 World Bank Vice-President Dr. Ismail Serageldin in a 1995, full quote, "if the wars of this century were fought over oil, the wars of the next century will be fought over water”
199
Sudan and 55½ Milliards [billion cubic meters] for the United Arab
Republic [Egypt]”53- Second Article, Nile Waters Agreement
To explore the impact of future climate change the following scenario was examined:
Using GCM CMIP5 from the 5th IPCC assessment report54 and applying the RCP 6 scenario (a reasonable expectation for future changes in the climate system), Sudan is expected to see a 3oC to 4oC increase in temperature by 2080; in the same time period
Egypt is expected to witness a reduction in precipitation on average of 0.1mm/day to
0.2mm/day. While this value seems unsubstantial, the average annual precipitation in
Egypt ranges between 20mm to 200mm and the RCP 6 scenario predicts a maximal decrease of 73mm per year. Given this scenario, if the region does not [witness] dramatic changes in water security in the area over the next two decades, it is not unforeseeable that there will be increased tensions over water withdrawals; or at the very least a revised version of the Nile Waters Agreement, the dissolution of the
Agreement, or an entirely new policy.
There are multiple potential levels of interaction between climate and conflict. The following list provides five simplified examples of direct and indirect interaction in the climate-conflict dynamic:
Direct: where climate change and conflict have a direct relationship
53 Full text Nile Waters Agreement: http://www.internationalwaterlaw.org/documents/regionaldocs/uar_sudan.html 54 Changes in temperature and precipitation for the RCP6 pathway were simulated using the GCM CMIP5 multimodel ensemble (IPCC AR5 Atlas subset) for the relative period 1986-2005 with a future time period of 2081-2100
200
1. Climate change causes conflict (e.g., Syria)
a. conflict arises over decreases in natural resources due to climate change
(e.g., conflict over transboundary waterways)
b. conflict occurs over increases in natural resources (e.g., increased
withdrawals from a shared resource)
c. climate change causes state destabilization by removing sources of income
and livelihoods, allowing for non-state actors (armed groups or terrorist
organizations) to rise up; opportunistic conflict
2. Climate change drives populations into conflict zones (e.g., Somalia)
3. Conflict drives populations into highly vulnerable climate-impacted zones
Indirect: Where responses to climate change shift the climate-conflict dynamic
1. Climate change maladaptation55 causes conflict (Bob & Bronkhorst, 2014), e.g.,
Adaptation projects allow for selected communities to access new resources while
neighbouring communities do not receive support, and this can be a cause of conflict
“In Kasese, Uganda, tensions arose due to competing demands for
available water supplies. Efforts to provide communities with additional
water taps also stirred tensions, as an initial effort only placed a tap in
the Rukoki area, causing anger among the Mahango people” (Dabelko,
et al., 2013)
55 Many definitions of maladaptation exist (Magnan et al., 2016); the IPCC defines maladaptation as ‘an adaptation that does not succeed in reducing vulnerability but increases it instead’
201
2. Climate change adaptation decreases conflict: Adaptation programmes improve
livelihood, increase income stability, and decrease fragility
Briefly examining the climate-migration and climate-conflict dynamics highlights the multitude of interconnected and cascading impacts of a changing climate. While the environmental determinist would ask, ‘How does climate influence geopolitics?’, in an era of rapidly changing climate, this chapter seeks to reverse the point of observation by asking, 'How does geopolitics influence a nation's response to climate change?'
7.4 Hypothesis
A dominant theme of the response to climate change captured by the Convention, and by more recent texts including the Paris Agreement, is economic disparity. The terminology used to frame these documents reflects binary categorization based on domestic wealth: Annex I/Non-Annex I, Developed/Developing. The texts imply that countries with low economic means are incapable of responding to climate change without substantial financial support from developed countries: Article 4.3 states, “The developed country Parties…shall provide new and additional financial resources to meet the agreed full costs incurred by developing country Parties”, and Article 4.4 further supports this statement by adding, “The developed country Parties…shall also assist the developing country Parties…in meeting costs of adaptation”. Without this co- operation, the text implies that countries with lower levels of GDP will be unable to respond to climate change. This leads to the following propositions:
1.1 Countries with low levels of GDP may not respond to climate
change without financial support from developed countries.
202
1.2 If co-operation between rich and poor countries is not occurring, or
is not working effectively, one expects to see a linear relationship
between level of climate change adaptation and GDP.
1.3 Without support from the financial mechanisms outlined in the
Convention text, countries with low levels of GDP should have low
levels of climate change adaptation,
Article 9.1 of the Paris Agreement, written almost 25 years later, reiterates the
Convention text, affirming, “Developed country Parties shall provide financial resources
to assist developing country Parties with respect to both mitigation and adaptation in
continuation of their existing obligations under the Convention.” Thus, this economic
norm has become entrenched in the tenets of climate policy. The central economic
hypothesis then becomes: Climate change adaptation increases linearly with the wealth
of a nation, unless there is successful financial co-operation.
A second hypothesis is possible with regards to land type. Countries with a greater level
of shoreline exposure will be at a heightened risk of exposure to climate change via
rising sea levels. These countries may therefore have increased level of motivation to respond to the changing climate. This leads to the following proposition:
1.4 Countries with an increased level of shoreline exposure will have a
greater response to climate change than less exposed countries
203
Consequently, the central land type hypothesis becomes: Less exposed landlocked countries will have a lower rate of response to climate change than the more exposed island nations.
While testing these hypotheses, several other geopolitical variables were also tested to see whether they correlate with the level of climate change adaptation. One hesitates to create earnest hypotheses for these variables as it is unknown whether they have any direct or indirect correlation with the level of adaptation.
7.5 Methodology 7.5.1 Data Transformation Model: Phase I to Phase III
To analyze the dependent variable (climate change adaptation) against several independent geopolitical variables, a mixed methodology data transformation model
(see Creswell et al., 2004) was applied in three distinct phases: (I) qualitative and quantitative data collection, (II) data transformation and (III) statistical analysis. Phase I began with the collection of qualitative and quantitative data for 192 parties to the
UNFCCC for the development of a composite index on the national level of climate change adaptation (for the full methodology see Chapters 5 and 6). Simultaneously, qualitative and quantitative data for 10 variables were gathered to represent four components of geopolitics: economic, political, geographic, and security (See Table 32 for variables selected separated into qualitative and quantitative groupings).
204
Table 32. Qualitative and Quantitative variables by category
Component Qualitative Quantitative
Economic Institutional Economic GDP per capita Classification Political System of Government Level of Democracy Geographic Land Type Region Security Global Peace Index Refugees
7.6 Phase I: Qualitative and Quantitative Data Collection 7.6.1 Economics
To test the central hypothesis that climate change adaptation does not advance without financial co-operation, data for two indicators, one qualitative and one quantitative, were gathered. The qualitative indicator, Institutional Economic Classification, distinguishes between countries based on various economic groupings including (a) OECD
(democracies with highly advanced market economies), (b) Emerging Economy (low to middle income countries with fast growing economies that are shifting from closed systems to open market economies), (c) Developing Country, economies with a Gross
National Income per capita56 between $1,026 and $4,035 and (d) Least Developed
56 Institutional Economic Classification based on GNI per Capita: LDC $1,025 or less; Developing $1,026 and $4,035; Emerging $4,036 and $12,475; Developed $12,476 or more. (https://datahelpdesk.worldbank.org/knowledgebase/articles/906519)
205
Countries (LDCs) with GNI per capita of $1,025 or less. The quantitative economic
indicator was GDP per Capita57
7.6.2 Political
To evaluate the effect of different political systems’ ability to influence climate change
adaptation, each country was first categorized into one of ten systems of government
(defined in Table 33) using multiple institutional databases. The ten systems of
government were: (a) Absolute Diarchy, (b) Absolute Monarchy, (c) Commonwealth
Realm, (d) Communist State, (e) Constitutional Monarchy, (f) Federal Republic, (g)
Military Junta, (h) Parliamentary Republic, (i) Presidential Republic, or (j) Semi-
Presidential System. Using the Democracy Index and data from the Freedom in the
World report (Freedom House, 2017) each country was reclassified into the following
four categories: Strong Democracy, Weak Democracy, Hybrid Regime or Authoritarian.
Table 33. Definitions of Different forms of Government
Government Definition Country System Example
1 Commonwealth A parliamentary democracy under a Bahamas, Realm constitutional monarchy; member of the Canada,
Commonwealth of Nations (Elizabeth II is United Head of State). There are currently 16 Kingdom realms. Continued membership is voluntary.
57 World Bank (2015). GDP per Capita. Data retrieved from World Bank national accounts data, and OECD National Accounts data files. http://data.worldbank.org/indicator/NY.GDP.PCAP.CD
206
(Includes: Monarchy, Federal or Unitary, Parliamentary, Monarch, Governor General, Prime Minister)
2 Communist State State controlled single party system; all Cuba, property owned by the state, all goods Vietnam, ‘equally’ shared among population, Laos ‘a classless society’
(May include: President, Prime Minister, Vice President)
3 Absolute A Monarch is the supreme power/absolute Kuwait, Saudi Monarchy ruler, controls (reigns over) state (for Arabia duration of life)
(May include: King, Queen, Crown Prince)
4 Constitutional Prime minister is the head of government. Belgium, Monarchy Monarch has constitutionally limited Luxembourg, authority.
(Includes: Monarchy, Federal or Unitary, Monarch and Prime Minister)
5 Federal Republic Central power is limited; States or colonies United maintain a level of self-government; voters States, elect government representatives Micronesia,
(Includes: Republic, Federal, President and Vice President)
6 Parliamentary A republic operating under a parliamentary Botswana, Republic system. The government (executive branch) Fiji, is accountable the parliament (legislature).
(Includes: President & Prime Minister)
207
7 Presidential A republic operating under a president who Guyana Republic actively governs the country. Malawi (Includes: Republic, Presidential, Unitary, President & Vice President)
8 Semi-Presidential A system where both a fixed-term president Azerbaijan, System and a prime minister actively are Lithuania responsible to the legislature (may be a
transitional government)
(Includes: Republic, Unitary, Presidential, President & Prime Minister)
9 Absolute Diarchy Where the ruling government is shared (co- Swaziland,
rule), may be lawful or by force. San Marino (May include: King, Prime Minister, and others, Captains Reagent)
10 Military Junta Military ruled government (dictatorship), Thailand often established during a Coup d'état (Myanmar)
Table developed by Author in 2017
7.6.3 Geographic
To explore a possible geographic influence on climate change adaptation, each country’s region (e.g., Africa, South America, Asia) and land type (e.g., landlocked, island, coastal) were identified and added to the database.
7.6.4 Security
While several papers have discussed the lack of an ideal indicator for conflict, concurrently, there are multiple papers listing potential indicators (Pavlovic, et al., 2008).
208
For this chapter, two indicators were retained out of several tested: the Global Peace
Index58 and the number of Refugees (including refugee-like situations)59 per country.
7.7 Phase II: Data Transformation
Following data collection, Phase II transformed the qualitative data into quantitative data through a mixed method technique called quantitizing (Tashakkori & Teddlie, 1998;
Sandelowski, et al., 2009). Several different methods exist for transforming qualitative data including binary transformation, percentage conversion, frequency distributions, rating scales, and sequential numbering (Harris et al., 1991; Miles & Huberman, 1994;
Driscoll et al., 2007). The categorical variables for Region, Land Type, Economic
Classification, System of Government, and Level of Democracy were all quantitized using sequential numbering. Following quantitizing, all data was imported into a database for statistical analysis.
7.8 Phase III: Statistical Analysis and Preliminary Results
Phase III of the data transformation model identified preliminary trends for further analysis using multiple statistical methods. For each variable Spearman’s rank correlation coefficient and biplot principal component analysis were completed; for nominal variables (e.g., land type, region, level of democracy) analysis of the mean was determined. Results and preliminary analysis are presented below.
58 Global Peace Index http://visionofhumanity.org/indexes/global-peace-index/ 59 Internally displaced persons & Refugees data http://popstats.unhcr.org/en/time_series
209 7.8.1 Spearman’s Rho
While Pearson’s correlation coefficient identifies linear relationships, Spearman’s rank
correlation coefficient moves past linearity and seeks monotonic relationships in the
data. Additionally, Spearman’s’ Rank does not require removal of outliers prior to
analysis. Table 34 presents the rho (ρ) values of the Spearman’s Rank correlation
coefficients for 5 variables. Adaptation had moderately high and very significant
negative level of correlation with the political category, System of Government (ρ=0.708, p<0.005), and moderately weak and significant correlation with economic category,
GDP per Capita (ρ=0.517*, p<0.05). This first stage of analysis using Spearman’s rho
suggests that key geopolitical determinants in climate change adaptation are GDP per
Capita and System of Government, although their method of influence cannot be gained
from the rank correlation. The variables, Institutional Economic Classification, Refugees
and Global Peace Index are either not captured by the selected correlation
methodology, or they do not have direct correlation with adaptation.
Table 34. Spearman’s Rank Correlation with Adaptation
Variable Correlation with Adaptation Spearman’s Rho (ρ)
GDP per Capita 0.517*
Institutional Economic 0.291 Classification
System of Government - 0.708**
Refugees - 0.329
Global Peace Index - 0.429 * p<0.05; **p<0.005
210 7.8.2 PCA Biplot Interpretation A principal component analysis (PCA) biplot was produced using Analyse-it® Statistical
Software to further analyze the relationship between adaptation and the 8 selected
geological variables. Figure 14 shows a graphical representation of the first two
principal components PC1 and PC2 as a vector biplot. Vector interpretation is
summarized as follows: longer vectors represent increased levels of importance in the
component; the angles between vectors signify level of correlation; the closer the
vectors are in space the higher the level of correlation between the variables, where the
angles between the vectors are equal to the cosine of the angle. Therefore, variables
with 90o angle have zero correlation, i.e., cosine (90o) is equal to zero. Similarly, if the vectors express 45o the correlation would be 0.7071, i.e., cosine (45o). Variables with
180o angles have strong negative correlations (-1). Additionally, it should be noted that very short vector lengths are poorly represented variables and should not be interpreted from the biplot, but rather by using other statistical techniques.
From the PCA biplot, several preliminary observations can be drawn. Adaptation has an angle of 178o with System of Government representing a strong negative correlation,
cos(178) = -0.999. The two economic components, GDP per Capita and Institutional
Economic Classification displayed angles of 24o where cos(24) = 0.913 and 34o where
cos(34) = 0.829, respectively. From the security component, the Global Peace Index
had an angle of 109o where cos(109) = -0.325. The remaining variables (land, refugees
and region) were poorly represented in the biplot and their vectors will not be
interpreted. This second stage of statistical analysis using a correlation biplot captured a
third variable of potential influence: Institutional Economic Classification.
211
Figure 14. Correlation Biplot
1
0.8
0.6 Adaptation
0.4 Global Peace Index
Refugees Land 0.2
Region GDP per Capita 0
Economic Classification -0.2
-0.4
-0.6 Government Structure
-0.8
-1 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1
7.8.3 Mean Level of Adaptation
Tables 35 to 37 summarize the mean level of adaptation for the five qualitative datasets:
Institutional Economic Classification, System of Government, Level of Democracy, Land
Type, and Region. Each table is ranked according to the highest level of adaptation in that category.
212
Table 35. Economic Mean Levels of Adaptation
Institutional Economic Classification Mean Level of Adaptation
OECD 67.58
Developing Country 56.27
LDC 37.82
Emerging Economy 32.93
Table 36. Political Mean Levels of Adaptation
System of Mean Level of Level of Democracy Mean Level of Government Adaptation Adaptation
Commonwealth Realm 94.46 Strong Democracy 83.46
Communist State 61.50 Weak Democracy 51.50
Parliamentary 57.97 Hybrid Regime 44.38 Republic
Federal Republic 54.61 Autocracy 28.77
Constitutional 50.65 Monarchy
Presidential Republic 47.94
Semi-Presidential 41.05 System
213
Absolute Diarchy 13.01
Absolute Monarchy 11.98
Military Junta 6.23
Table 37. Geographic Mean Levels of Adaptation
Region Mean Level of Land Type Mean Level of Adaptation Adaptation
South Asia 84.55 Island 60.94
East Asia & Pacific 63.89 Coastal 50.09
Europe & Central Asia 47.76 Landlocked 34.05
Sub-Saharan Africa 42.92
Latin America & 33.15 Caribbean
Middle East & North 32.38 Africa
7.9 Trend Analysis and Discussion
The following section describes each of the four geopolitical components (economic, political, geographic, and security) to identify trends or relationships in the data. The section uses the previous statistical analysis for direction.
214 7.10 Economic influence on Climate Change Adaptation
To test the central hypothesis that climate change adaptation increases linearly with the
wealth of a nation unless there is successful financial co-operation, several methods of analysis were used. There is a statistically significant correlation between adaptation and GDP; however, this correlation is not linear. Both analyses using Spearman’s Rho and the PCA Biplot provided evidence that a moderate to high correlation exists between climate change adaptation and GDP per Capita. However, it is visually evident in Figure 15, this relationship is not linear.
Figure 15. GDP per Capita against the Level of Climate Change Adaptation
300
250
200
150
100
50 National Level of Climate Change Adaptation Change Climate of Level National
0 0 20000 40000 60000 80000 100000 120000 140000 160000 180000 Level of GDP per Capita
Analysis of the outliers from Figure 15 and analysis of the mean level of adaptation for the four Institutional Economic Classifications provide further understanding. At extremely high levels of GDP (>USD $90,000) there is a collection of 5 outliers all with
215 very low to extremely low levels of adaptation, skewing the data. Using the qualitative categorical labels from the Institutional Economic Classification dataset, emerging economies (classified as countries with GNI per Capita between $4,036 and $12,475, with an average GDP of approximately USD$9000) have a lower mean level of adaptation (32.93) than LDCs (with an average GDP of $1200). Thus, adaptation does not increase linearly with level of GDP (Figure 16). There are countries with extremely high levels of GDP per Capita that are failing to respond to climate change; and emerging economies, that are not provided with the same level of financial or institutional support, that are falling behind LDCs and developing nations, both with lower levels of GDP per Capita.
Figure 16. Institutional Economic Classification against the Level of Climate Change Adaptation
80 50000
45000 70
40000 60 35000
50 30000
40 25000
20000 30
15000 GDP per Capita USD) ($ 20 10000
National Level of Climate Change Adaptation Change Climate of Level National 10 5000
0 0 OECD Emerging Economy Developing Country LDC
Mean Level of Adaptation GDP per Capita (Average)
216 7.10.1 Political System influence on Climate Change Adaptation
Both the PCA biplot and Spearman’s rank correlation coefficient pointed to a high level of correlation between Systems of Government and the level of climate change adaptation, where the biplot vectors displayed a strong negative correlation of almost -
1.00 and Spearman’s Rank provided a correlation coefficient of ρ = -0.708. Given the nominal nature of the data, the statistical analysis is unable to further elucidate the cause of the relationship or any trends. To evaluate the different government systems, the means of each system were analyzed, where countries with a Military Junta system of government had the lowest level of climate change adaptation and countries in the
Commonwealth Realm the highest (on average).
This trend led the author to further analyze the political dynamics with respect to their, for lack of a better qualifying term, level of democracy. The results indicate that countries with high levels of democracy have, on average, the highest level of adaptation. Although the UNFCCC does not categorize countries by their government systems or their levels of democracy, there appears to be a trend that strongly democratic government systems are more likely to respond with adaptation to climate change than authoritarian or hybrid regimes.
Figure 17 combines four data sets (level of adaptation, GDP per Capita, level of democracy, and economic classification) to allow for a comparative analysis of whether economics or government systems play a stronger role in influencing the level of climate change adaptation. As displayed in the graph, in this analysis the highest level of adaptation was associated with strongly democratic government systems, outranking
217 economics. Countries with the lowest levels of adaptation were autocracies, followed closely by emerging economies.
It is important to note, that while there is a notable difference in the level of adaptation among different countries, these countries are adapting nonetheless; the poorest and most vulnerable nations are creating progress on adaptation.
Figure 17. Political and Economic Influences on Climate Change Adaptation
(Combined Graph)
90 50000
80 45000
40000 70
35000 60 30000 50 25000 40 20000 30 15000 GDP per Capita USD) ($
20 10000 National Level of Climate Change Adaptation Change Climate of Level National 10 5000
0 0
Adaptation Level GDP
218 7.10.2 Geographic influence on Climate Change Adaptation
Land type, given its categorical nature, was poorly represented in the multivariate
statistical analysis section. Therefore, simple statistics analyses were completed to look
for any relationships between the level of adaptation and land type. From the analysis of
the mean the central land type hypothesis holds true, that countries with a greater
coastline to land area ratio (i.e., islands) have almost double the rate of response to
climate change than the less exposed landlocked nations.
Like land type, region was poorly represented in the biplot. The mean level of
adaptation was calculated for six geographic regions with the Middle East and North
Africa yielding the lowest levels of adaptation and South Asia with the highest levels.
North America, comprised of only 3 countries was not added to the list, as each of the other regions has far more countries to calculate a mean statistical representation of the region.
Figure 18 combines the three geopolitical components (economic, political, and
geographic) with potential direct influence on the level of climate change adaptation into
one graph. If there is a proximate assumption of linearity between GDP and ability to
respond to climate change the graphs can then be analyzed from a secondary
perspective, one where there are points of inflection or deflection.
From this vantage point, there are three prominent points of deflection in the graph
where countries are over-performing relative to level of GDP per Capita: LDCs,
Developing Countries, and South Asia. It could be suggested that institutional
(multilateral or bilateral) and financial co-operation have strengthened these countries to
219 respond to climate change with greater intensity than their economic systems would have allowed.
Figure 18. Geopolitical Influences on Climate Change Adaptation (Combined
Graph)
90 50000
80 45000
40000 70 35000 60 30000 50 25000 40 20000 30
15000 GDP per Capita USD) ($ 20 10000
10
National Level of Climate Change Adaptation Change Climate of Level National 5000
0 0
Level of Adaptation GDP per Capita
Further, there are three points of inflection where countries are underperforming based on their economic capability: Europe and Central Asia, Middle East & North Africa, and, on average, OECD countries. Admittedly, there are most likely mitigating circumstances
(e.g., conflict, competing political priorities) that play a role as to the level of prominence climate change holds on a national agenda. Additional studies are needed to explore these observations.
220 7.10.3 Indirect Influences on Climate Change Adaptation
Thus far, several inferences have been made about how various geographic, political
and economic features of a nation may influence their respective level of climate
change adaptation. The fourth geopolitical component encompasses the tertiary (or
even quaternary) impacts of climate change: the climate-conflict and climate-
displacement dynamics. Section 8.3 highlighted multiple potential levels of the climate-
conflict dynamic, ranging from climate change increasing fragility and leading
communities into conflict, to the adaptation responses themselves creating or
intensifying pre-existing conflict; and, additionally, how both climate and conflict can
prompt or exacerbate displacement of populations. While this fourth component may not
directly influence the level of climate change adaptation, geopolitical tensions
undoubtedly influence a nation’s response to climate change. Responses in these
regions require an additional conflict-sensitive knowledge component to ensure that climate change adaptation does not inadvertently turn maladaptive.
7.10.3.1 On Conflict Table 36 established a linear relationship between levels of democracy and levels of
climate change adaptation. Using level of democracy as a proxy for adaptation, Figure
19 delineates a trend between the level of adaptation and peace. As responses to
climate change decrease, so too does the amount of peace experienced by a nation.
While ‘correlation does not equal causation’ (these two datasets are theoretically
unrelated), this trend emphasizes that countries that are advancing levels of adaptation
are also experiencing greater levels of peace. And thus, the opposite is likely the case.
221
The multiplicity of climate change impacts may come into effect where countries experiencing high levels of conflict are not implementing adaptation actions, whether by the inability to devote resources or because of the lack of institutional support. This can then become a compounding problem; whereby countries susceptible to conflict are not able to, or choose not to, implement responses to climate change. Conflict-sensitive adaptation actions can improve livelihoods, increase income stability and decrease state fragility, thereby stabilizing a community or region, and potentially inhibit the birth of conflict.
Figure 19. Climate Change Adaptation and the Global Peace Index60
90 0
80 -0.2
70 -0.4 60
50 -0.6
40 -0.8 Peace Index
30 -1 20
National Level of Climate Change Adaptation Change Climate of Level National -1.2 10
0 -1.4 Strong Democracy Weak Democracy Hybrid Regime Autocracy
Adaptation Level Peace Index
60 Inversed rankings displayed for the Global Peace Index, high values indicate higher levels of peace
222 7.10.3.2 On Displacement
Climate change is expected to increase the numbers of displaced persons, both
internally and as forced migrants. With greater levels of adaptation being established in
countries with higher levels of peace, countries prone to conflict will potentially be
creating high numbers of both refugees fleeing conflict zones and forced migrants
leaving climate-impacted zones.
Figure 20. Climate Change Adaptation and Refugees
90 450000
80 400000
70 350000
60 300000
50 250000
40 200000 Number of Refugees 30 150000
20 100000 National Level of Climate Change Adaptation Change Climate of Level National
10 50000
0 0 Strong Democracy Weak Democracy Hybrid Regime Autocracy
Adaptation Level 2015 Refugees (incl. refugee-like situations)UNHCR
An unsettling trend is seen in Figure 20, where there is an inverse relationship between the number of refugees in a country and the level of adaptation. Those countries with high levels of refugees already have established problems other than future impacts from a changing climate. Thus, changes in precipitation and temperatures leading to
223
droughts, floods or extreme events will only exacerbate their already pre-existing
troubles. Countries with high numbers of refugees require even greater levels of
support to respond to climate change. Over time, there is an expected increase in the
number of “climate refugees”9, and the current refugee data does not capture any existing or forthcoming displaced persons from a changing climate.
7.11 Summary of Findings
Applying mixed methods research, with both qualitative and quantitative data;
employing grounded theory and statistical analysis, including Spearman’s Rank
correlation coefficient, PCA Biplot analysis, and analysis of the mean, the following
conclusions can be drawn:
• No linear relationship between the national level of climate change adaptation
and GDP per Capita was found, therefore co-operation (bilateral, multilateral and
institutional) between rich and poor countries is suggested as the cause for non-
linearity allowing for both LDCs and developing nations to move forward on an
adaptation agenda with a greater level of intensity than their economies would
predict.
• Both rich and poor countries are responding to climate change; both rich and
poor countries are not responding.
• Emerging economies are falling behind LDCs and other developing nations.
Limited amounts of institutional guidance for these nations at the international
level may be at fault. Their failure to adapt to climate change signals a
224
potentially catastrophic consequence as emerging economies contain upwards of
80% of the world’s population. Greater levels of support and emphasis should be
given to emerging nations.
• Countries with very high levels of GDP are failing to respond to climate change.
• Political systems of government influence a nation’s response to climate change
more than their economic capacity; with Commonwealth Realms, on average,
having the highest levels of climate change adaptation.
• Strong democracies are responding the most to climate change, with autocratic
regimes responding the least.
• A geographic component to climate change response exists. More exposed
island nations are responding with higher levels of adaptation than landlocked
nations. By region, countries in South Asia have higher levels of adaptation
compared with countries in the Middle East and North Africa.
• Interconnected and cascading impacts are visible. Countries with high levels of
conflict have low levels of adaptation, with a similar trend seen in countries with
high numbers of refugees, also exhibiting low levels of response creating a
potentially compounding impact.
225
Using mixed methods research to explore the intersection between the geopolitical
factors, this study examined geographic, economic, political and security influences on
climate change adaptation. The central question - do geopolitical determinants of
climate change adaptation exist? - can be firmly answered, yes. This preliminary
analysis highlights several correlations between geopolitical factors and climate change adaptation. In doing so, this research, stresses the importance of the need for comprehensive understanding and future research into underdeveloped areas of knowledge about the impacts of geopolitics on the advancement of climate change adaptation.
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748-765.
Theisen, O. M., Gleditsch, N. P., & Buhaug, H. (2013). Is climate change a driver of
armed conflict? Climatic Change, 117(3), 613-625.
UNFCCC. (2011). Cancun Adaptation Framework. FCC/CP/2010/7/Add.1
unfccc.int/resource/docs/2010/cop16/eng/07a01.pdf
230 Chapter 8 The Progress of Multilateral Funding in Overcoming Barriers to Climate Change Adaptation Introduction to this Chapter
This Chapter discusses an unexpected finding from Chapter 7, Identifying Determinants of Climate Change Adaptation from National Documents. Following the application of grounded theory and quantitization to the data of 192 countries, a graph of the number of barriers that countries faced when responding to climate change was plotted against the national level of climate change adaptation (see methodology Chapter 6). It was within this analysis that an anomaly stood out: Twenty-two countries with high levels of barriers were unexpectedly able to make progress in advancing climate change adaptation. An examination of these 22 countries identified that multilateral funding, especially funding through the World Bank, facilitated the circumvention of these barriers and helped to move adaptation forward. This Chapter discusses these findings.
***
8.1 Setting the Foundation for Multilateral Aid
The story of funding for climate change begins prior to the 1992 United Nations
Convention on Climate Change (UNFCCC, the Convention), where Bretton Woods unknowingly set the stage for multilateralism in climate finance. The 1944 monetary conference laid out the framework for two of the most prominent financial institutions today: the World Bank and the International Monetary Fund. Following the Resolution of the Common Aid Effort of 1961, funding through multilateral channels flourished and has continued to do so over the past five decades (Hynes & Scott, 2013). In 2015,
Official Development Assistance (ODA) achieved nearly USD$132 billion, with
231
approximately 40% of ODA flowing through multilateral channels61. Similarly,
multilateral funding for climate change has dramatically increased. In 2014, the top
multilateral banks62 provided USD $28 billion for climate change; this number more than
doubled to USD $81 billion in 2015 (Joint-MDB, 2016).
8.1.1 Multilateral Funds for Climate Change Adaptation
Since 2001, multiple funds have been established under the Convention through the
Global Environment Facility (GEF) and independently in the World Bank to channel
climate funding from developed to developing countries; most notably, the Least
Developed Countries Fund (LDCF), Special Climate Change Fund (SCCF), Strategic
Priority Fund (SPA), the Adaptation Fund (AF), Pilot Program for Climate Resilience
(PPCR) and, recently, the Green Climate Fund (GCF). A summary of these funds and
pledged amounts are provided in Table 38.
8.1.2 Is Adaptation Funding Working?
While multilateral finance has a long history dating back to Bretton Woods in 1944, the
first climate funds for adaptation were established just over 15 years ago. Nevertheless,
literature is already suggesting multilateral climate funding is failing or ineffective. Baudoin
& Ziervogel (2016) argue there is a “failure of top-down approaches” to adaptation
funding, and Fenton and colleagues (2014) state that this “deficienc[y] of top-down
approaches has led to the increased need for bottom up (community based) action”.
61 OECD http://www.oecd.org/dac/development-aid-rises-again-in-2015-spending-on-refugees- doubles.htm 62 African Development Bank (AfDB), European Bank for Reconstruction and Development (EBRD), the European Investment Bank (EIB), Inter-American Development Bank Group (IDBG), and World Bank Group (WBG)
232
There is also an overarching belief that, “financial institutions are largely ineffective in achieving environmental goals” (McLean 2015). Additionally, there is a concern that,
“funds have been created in multilateral processes, only to be abandoned by Northern donors.” (Krasner, 1985 as cited in Ciplet, 2013).
Table 38. Multilateral Funds for Climate Change Adaptation ($ USD millions)63
Multilateral Fund Note about the Fund Pledged
($ USD millions)
Adaptation Fund (AF) Founded in 2009; Started to 569.2 commit funding in 2010
Green Climate Fund (GCF Established in 2010, adopted 10255.0 in 2011,
Least Developed Countries Established in 2001, became 1250.2 Fund (LDCF) operational in 2002
Pilot Program for Climate Approved in 2008 1200.0 Resilience (PPCR)
Special Climate Change Fund Established in 2001 and 367.31 (SCCF) operational in 2002
Strategic Priority on Adaptation In operation from 2004 to 2010 50.0
(SPA) (+ 649 million from partners)
Interestingly, the literature critical of adaptation finance echoes the foreign aid narrative.
Boone, (1996) writes about a number of ways that foreign aid has not been “substantially”
63 Note: this is an updated and simplified version of the table created by the Author for Smith et al., 2011
233 effective. Kosack (2003) supports these sentiments, concluding that the “general finding, however, seems to be that aid is not effective”. Perhaps the criticism of top-down multilateral funding in climate change adaptation is rooted in the failure of foreign aid narrative rather than actual proofs.
As this Chapter will show, over a decade timescale, multilateral funding has had a marked positive impact on national level climate change adaptation.
8.2 Data and Methodology 8.2.1 Data Analysis: Barriers to Climate Change Adaptation
Thirty-five variables (themes) were identified during the grounded theory process, one of the variables, Barriers to Climate Change Adaptation, was coded 558 times during analysis. Table 39 provides examples from coded text delineating several barriers discussed by nations that would impede their ability to adapt to climate change. The data from the Barriers to Climate Change Adaptation node was exported using a Matrix Coding
Query, which quantitizes qualitative data, by converting the codes into frequency counts
(Morse, 1990) for each of the 192 country case nodes.
Table 39. Examples of Coded References for Barriers to Climate Change
Adaptation
Country Example barrier identified Reference
Cook Island “a lack of knowledge of, and respect for, traditional (Cook customs and practices was recognised as potentially Island, impeding climate change adaptation” 2012)
234
Fiji “Poor and sluggish economic performance over the past (Republic decade, coupled with political turmoil and uncertainty of Fiji, means a lot more emphasis is placed on economic 2014) recovery and very little attention is paid to environmental concerns.”
Kiribati “Limited skilled human resources and tools, limited and (Kiribati, unreliable means of communication at national level…lack 2013) of highly qualified professionals and limited and over- worked qualified professionals to do training of stakeholders, limited number of research institutions”
Philippines “…a shortage and inaccessibility of non-climatic data (Philippines, (such as socioeconomic data, environment data, and other 2014) related information) from diverse sectors, which makes it difficult to make a full assessment on vulnerability to climate change and analysis of adaptation measures.”
The “lack of an enabling environment for effective climate (Gambia, Gambia change management; lack of skills for vulnerability and 2012) adaptation assessment; low level of scientific and technical capacity for effective climate change management; inadequate national policy- and decision- making processes for climate change management; …and inadequate, weak and ineffective research bodies and programmes.”
Uganda “The low level of income, high poverty levels, and high (Uganda, population is affecting the ability of most households to 2014) adapt to climate change.”
235 8.3 Primary Analysis
Pearson’s correlation was applied to statistically analyze the level of barriers to climate
change adaptation against the national level of climate change adaptation. It was
expected that there would be a strong correlation between adaptation and the level of barriers; a higher level of adaptation would correspond with a fewer number of barriers.
Unexpectedly, the resulting correlation coefficient was low, r = 0.360, with the results being highly significant (P < 0.0001) at an α of 0.05. Thus, there was an observed weak correlation between the level of barriers in a country and the country’s ability to adapt to climate change. Nonetheless, the value (r = 0.360), when compared with the critical values of the Pearson product moment correlation coefficient, (where the dataset has a degree of freedom of 190), is said to have significance when the coefficient (r) is higher than 0.195. A log plot of the data is provided in Figure 21. The outliers are highlighted in a box.
8.3.1 Initial Finding
Although a strong linear correlation was lacking, potentially warranting dismissal of the
finding, the observation that several countries with higher levels of barriers also had
higher levels of climate change adaptation prompted additional analysis.
236
Figure 21. Log plot of National level of barriers and climate change adaptation 1000 ) 10
100
10
1 National Level Level National of Change Climate (log Adaptation
0.1 0 2 4 6 8 10 12 14 16 Level of Barriers to Climate Change Adaptation
8.4 Secondary Analysis
The twenty-two countries identified as outliers were: Armenia, Bangladesh, Cape Verde,
Cook Islands, Egypt, Fiji, The Gambia, India, Kazakhstan, Kiribati, Maldives,
Mozambique, Namibia, Philippines, Samoa, Serbia, Seychelles, St Lucia, Sudan, Tonga,
Uganda and Yemen. Figure 22 shows a re-plot of Figure 21 with only the 22 countries.
237
Figure 22. Countries with Higher Levels of Barriers and Higher Levels of
Adaptation 1000 ) 10
Namibia Samoa Seychelles Gambia Maldives Cook Island Kazakhstan St Lucia Kiribati Philippines Armenia Mozambique Yemen Fiji 100 India Uganda Sudan Serbia Cape Verde Tonga (> Level 60) Egypt Bangladesh National Level Level National of Change Climate (log Adaptation
10 2 4 6 8 10 12 14 16 National Level of Barriers to Climate Change Adaptation (> Level 5)
8.4.1 Analysis of Progress on Climate Change Adaptation
Raw qualitative and quantitative data for each of the twenty-two countries was re- analyzed for new themes and indicators. It was observed that each of the countries was experiencing at least five or more different types of barriers. Additionally, each of the countries displayed high levels of adaptation (all over a level of 60). Achievements included: Adaptation Policy Creation, provision of Examples of Climate Change
Adaptation, and, or enacted Legislation, Laws & Acts, and Standards & Codes. Figure 23 provides a quantitative summary of the various adaptation actions that have been achieved by the countries to increase their level of climate change adaptation, including
238
116 adaptation project examples, 61 instances of funding for adaptation, and 73 implemented measures.
Figure 23. Frequency count of actions and measures of climate change adaptation for the 22 countries
140
120 116
100
80 80 75 74 73 67 61 60 49 47
Frequency Count Frequency 39 40 26
20 13 3 3 0
Increasing Level of Climate Change Adaptation
8.4.2 Relatively Stable Parameters
Relatively stable parameters are key pieces of information about a nation that remain unchanged over several years to centuries (Weible & Sabatier, 2006). An initial hypothesis for why these 22 countries had both high levels of climate change adaptation and high levels of barriers may be found within an identifiable difference in one or more relatively stable parameters. Therefore, a database of relatively stable parameters
(Sabatier & Jenkins-Smith, 1993) for the 192 countries was searched to identify any
239 similarities or differences. Table 40 shows a segment of the database for each of the
22 countries where the categories include: land type, geographic region, current system of government, UNFCCC Annex designation, UNFCCC group designation, economic classification, group affiliation and World Bank lending category.
For the land type parameter, all classifications are present in the database (landlocked, coastal, and island). Similarly, six regions are present (Europe, South Asia, sub-Saharan
Africa, Oceania, Middle East and North Africa). Thus, the ability of a country to circumvent a high level of barriers is not a component of geographical determinism. Further, all 22 countries are parties to the UNFCCC as Non-Annex I, i.e., countries with lower levels of development. This means that these countries have fewer resources than developed countries to apply to adaptation, a circumstance that should act as a barrier to adaptation action.
Comparing the systems of government, a diverse array of forms is present; as are differing economic classifications from least developed, developing to emerging economies, and one OECD country. Hence, the non-annex I designation becomes less significant when the economic component includes all possible categories. Additionally,
41% of the countries in the outlier group belong to the Alliance of Small Island States
(AOSIS), and 81% are members of the G77 & China Group. However, membership in either of these groups does not provide further explanation because many other countries in AOSIS and the G77 group are not a part of this outlier group.
240
Table 40. Relatively Stable Parameters for Countries with higher levels of barriers and higher levels of climate change adaptation
System of Annex UNFCCC Economic Group Lending Land Region Country Government Designation Designation Classification Affiliation Category Armenia Landlocked Europe & Central Republic Non-Annex I DC IBRD Asia Bangladesh Coastal South Asia Parliamentary Non-Annex I G77 & China LDC IDA Democracy Cape Verde Island Sub-Saharan Non-Annex I G77 & China AOSIS Blend Africa Cook Islands Island Oceania Self-governing Non-Annex I AOSIS Parliamentary Democracy Egypt Coastal Middle East & Republic Non-Annex I G77 & China DC IBRD North Africa Fiji Island East Asia & Pacific Republic Non-Annex I G77 & China DC AOSIS IBRD Gambia Coastal Sub-Saharan Republic Non-Annex I G77 & China LDC IDA Africa India Coastal South Asia Federal Republic Non-Annex I G77 & China EME G20 IBRD Kazakhstan Landlocked Europe & Central Republic; Non-Annex I IBRD Asia Authoritarian Presidential Rule Kiribati Island East Asia & Pacific Republic Non-Annex I G77 & China LDC AOSIS IDA Maldives Island South Asia Republic Non-Annex I G77 & China AOSIS IDA
241
Mozambique Coastal Sub-Saharan Republic Non-Annex I G77 & China LDC IDA Africa Namibia Coastal Sub-Saharan Republic Non-Annex I G77 & China IBRD Africa Philippines Island East Asia & Pacific Republic Non-Annex I G77 & China IBRD Samoa Island East Asia & Pacific Parliamentary Non-Annex I G77 & China AOSIS IDA Democracy Serbia Landlocked Europe & Central Republic Non-Annex I IBRD Asia Seychelles Island Sub-Saharan Republic Non-Annex I G77 & China OECD AOSIS IBRD Africa St Lucia Island Latin America & Parliamentary Non-Annex I G77 & China DC AOSIS Blend Caribbean Democracy and a Commonwealth Realm Sudan Coastal Sub-Saharan Federal Republic Non-Annex I G77 & China LDC IDA Africa Tonga Island East Asia & Pacific Constitutional Non-Annex I G77 & China DC AOSIS IDA Monarchy Uganda Landlocked Sub-Saharan Republic Non-Annex I G77 & China LDC IDA Africa Yemen Coastal Middle East & Republic Non-Annex I G77 & China LDC IDA North Africa
242
8.4.3 World Bank Lending Category
The most prominent classification in Table 40 was the World Bank Lending Category.
While not a climate change related grouping, it led the author to investigate whether there were any noteworthy connections between these countries and multilateral funding that they had received for climate change adaptation. Reflecting back on Table 40,
Multilateral Funds for Climate Change Adaptation, the author evaluated annual reports from the World Bank to identify any linkages between the 22 countries and the original multilateral funds created for climate change adaptation. Table 41 provides a summary of adaptation funds, established during the years 2002-2009, which were accessed by the 22 identified nations.
8.5 Interpretation
From the table of relatively stable parameters (Table 40), it is apparent that several parameters provide no further understanding for the observation that several Non-Annex
I countries with high levels of barriers were able to attain higher levels of adaptation.
Neither geographical determinism, political affiliation, government structure, nor economic classification could provide additional insight. The main commonality between the countries that may have accounted for this ability - to make advancements on adaptation in the face of severe barriers - is their access to historical climate change adaptation funds. An analysis of the funds identified that 50% of the countries obtained funding from SCCF (2002), 41% had access to funding from PPCR (2008), 32% had achieved funding from the Adaptation Fund (2009), and 64% from SPA (2004). In total,
21 out of the 22 countries with high levels of barriers and higher levels of climate change adaptation had received funding through the very first climate change adaptation funds.
243
At this point it is perhaps of value to note that the World Bank acts (or acted) as the trustee for the SCCF and the Adaptation Fund, in addition to being the trustee and administrating unit for PPCR, and an implementing agency for the SPA (under the GEF). As a trustee, the World Bank commits and transfers funds to implementing agencies. Of the funds accessed by the countries, 19 of the 22 countries receiving funding had the World Bank acting as either a fund trustee or the fund’s implementing agency.
Thus, of the countries with the highest numbers of barriers that are successfully moving forward on climate change adaptation, 95% of them received access to one or more of the original funds for climate change adaptation which were established between 8 - 15 years ago. The only country within this group (Sudan) that did not obtain funding through one of these funds, accessed funding through the United Nations Development
Programme (UNDP) and the United Kingdom’s Department for International
Development (DFID).
This finding highlights three important features of progress on climate change adaptation:
(1) access to funding, (2) early access to multilateral funding, as, on average, these funds were established 12 years ago, and (3) establishment of multilateral partnerships.
Additionally, it may also highlight the importance of the World Bank acting as a fund trustee and implementing agency (including under the GEF) for climate change adaptation.
244
Table 41. Original Climate Change Adaptation Funding of 22 identified countries SCCF (2002) SPA (2004) PPCR (2008) Adaptation Fund (2009) Other Armenia SPA/UNDP Bangladesh SPA/UNDP PPCR Cape Verde SPA/UNDP Cook Island SCCF Adaptation Fund Egypt SCCF Adaptation Fund Fiji SCCF SPA/ADB Gambia SPA/UNDP PPCR India SPA/World Bank Adaptation Fund Kazakhstan SCCF SPA/UNDP Kiribati SPA/World Bank Maldives Adaptation Fund Mozambique SCCF SPA/WB/UNEP PPCR Namibia SCCF SPA/UNDP Philippines SCCF SPA/ADB PPCR Samoa SCCF SPA/UNDP PPCR Adaptation Fund Serbia SCCF Seychelles Adaptation Fund St Lucia SCCF SPA/World Bank PPCR Sudan UNEP/DFID Tonga SCCF PPCR Uganda PPCR Adaptation Fund Yemen SPA/World Bank PPCR
245
8.6 Current Status of Climate Change Adaptation Funds
Examining the current five multilateral funding programs (PPCR, LDCF, AF, SCCF and
GCF), Figure 24 summarizes the most current data (as of January, 2017) for funding which has been pledged by countries, received by the fund, approved by the board, and disbursed to the country (i.e., cash transfers from the implementing agency).
Table 42. Multilateral Funds: from Pledged to Disbursed ($ USD millions)
Multilateral Fund64 Pledged Received Approved Disbursed
Pilot Program for Climate Resilience 1200 1117 972.5 185.5
Least Developed Countries Fund 1250.2 1077 981.2 482.2
Adaptation Fund 569.2 564.66 375.36 200.45
Special Climate Change Fund 367.31 362.31 324.47 207.21
Green Climate Fund 10255 9896 1174 0
64 Sources of Funding Data https://www.adaptation-fund.org http://www.climatefundsupdate.org/data https://www.thegef.org/ http://www-cif.climateinvestmentfunds.org/ ttps://www.cbd.int/financial/
246
Figure 24. Multilateral Adaptation Funds: Pledged, Received, Approved, Disbursed
247
From this most recent data, in the order of the fund’s launch date, the LDCF (operational in 2001) has disbursed 44.77% of funding received; SCCF (operational in 2002) has paid out 57.19%; PPCR (approved in 2008) has spent 16.61% of funding received; the
Adaptation Fund (founded in 2009) has distributed 35.50% of funding received; and the most recent fund GCF, adopted in 2011, has yet to disburse funding (other than providing a small sum for administration purposes). Table 42 and Figure 24 therefore provide both a quantitative and visual confirmation of the substantial disconnect between the level of funding approved and the level of funding disbursed to countries for climate change adaptation. Note the GCF on Figure 32 is on a secondary axis.
8.7 Conclusions and Recommendations
By examining the barriers for climate change adaptation in 192 nations and identifying 22 countries with higher levels of barriers and higher levels of adaptation, this chapter discusses a number of Non-Annex I countries of island, coastal and landlocked nations that were able to “adapt” to their barriers and move forward on a climate change adaptation agenda. They were able to do so by accessing funding from four of the original adaptation funds (established between the years 2002-2009). This research shows that historical funding for adaptation through multilateral agencies increased the ability of vulnerable countries to circumvent their barriers and to make progress on adaptation.
Additionally, it highlights the importance of adaptation funding and of establishing multilateral partnerships. Furthermore, the chapter underscores the need for fast disbursement of funding once approved, as it may take upwards of a decade for the impacts of the funding to be realized. 248
The research presented in this chapter also suggests that the following recommendations will increase the level of climate change adaptation and decrease exposure to impacts by the most vulnerable populations:
a) establish a greater balance between funding for mitigation and adaptation
b) decrease the adaptation funding gap between approved and disbursed levels of
funding
c) the GCF should identify ways to streamline climate finance and allow for fast
transfer of funds, especially to countries with high levels of barriers to climate
change adaptation
d) continue to strive for the global goal of achieving USD$100bn per year in funding
for climate change, including identifying new public and private resources
e) developing countries should persist in championing the importance of Article 9.1
of the Paris Agreement, calling for, “Developed country Parties [to] provide
financial resources to assist developing country Parties with respect to both
mitigation and adaptation”.
Although this chapter focuses on the importance of top-down multilateral funding, it should not be misinterpreted as an argument against community-based adaptation or bottom-up local level adaptation. All levels of government from federal to community level are vital to the success of adaptation. This chapter provides evidence that multilateral funding is a key component on that road to success, especially for countries that are experiencing high levels of barriers.
249 8.8 References
Baudoin, M. A., & Ziervogel, G. (2017) What role for local organisations in climate
change adaptation? Insights from South Africa. Regional Environmental Change,
1-12.
Boone, P. (1996). Politics and the effectiveness of foreign aid. European economic
review, 40(2), 289-329.
Ciplet, D., Roberts, J. T., & Khan, M. (2013). The politics of international climate
adaptation funding: Justice and divisions in the greenhouse. Global
Environmental Politics, 13(1), 49-68.
Cook Island. (2012). Cook Islands Second National Communication under the United
Nations Framework Convention on Climate Change. National Environment
Service. Tu’Anga Taporoporo. 112p.
Fenton, A., Gallagher, D., Wright, H., Huq, S., & Nyandiga, C. (2014). Up-scaling
finance for community-based adaptation. Climate and Development, 6(4), 388-
397.
Gambia, The. (2012). The Gambia’s Second National Communication under the United
Nations Framework Convention on Climate Change. Banjul. 113p.
Hynes, W., & Scott, S. (2013). The Evolution of Official Development Assistance:
Achievements, Criticisms and a Way Forward", OECD Development Co-
operation Working Papers, No. 12, OECD Publishing, Paris.
250
Joint-MDB. (2016). 2015 Joint Report on Multilateral Development Banks’ climate
finance. http://pubdocs.worldbank.org/en/740431470757468260/MDB-joint-
report-climate-finance-2015.pdf
Kiribati Government. (2013). Second national communication to the United Nations
Framework Convention on Climate Change. Environment and Conservation
Division, with assistance of Climate Change Study Team Ministry of
Environment, Lands and Agricultural Development. 196p
Kosack, S. (2003). Effective aid: How democracy allows development aid to improve the
quality of life. World Development, 31(1), 1-22.
Krasner, Stephen D. (1985). Structural Conflict: The Third World against Global
Liberalism. Berkeley: University of California Press.
McLean, E. V. (2015). A strategic theory of international environmental assistance.
Journal of Theoretical Politics, 27(2), 324-347.
Philippines. (2014). Section National Communication of the United Nations Framework
Convention on Climate Change. 110p
Republic of Fiji (2014). Second national communication to the United Nations
Framework Convention on Climate Change. Suva, Fiji, Ministry of Foreign
Affairs. 176p
Sabatier, P., & Jenkins-Smith, H. (1993). The Advocacy Coalition Framework:
Assessment, Revisions and Implications for Scholars and Practitioners. In P.
251
Sabatier and H. Jenkins-Smith (eds), Policy Change and Learning: An Advocacy
Coalition Approach. Boulder, CO: Westview Press, 211–35.
Uganda. (2014). Section National Communication of the United Nations Framework
Convention on Climate Change. Ministry of Water and Environment. Kampala.
213p.
UNFCCC. (1992). The United Nations Framework Convention on Climate Change.
http://unfccc.int/essential_background/convention/items/2627.php
Weible, C. M., & Sabatier, P. A. (2006). A guide to the advocacy coalition framework.
Tips for researchers. In F. Fischer, G. J. Miller & M. S. Sidney (Eds.), Handbook
of public policy analysis: Theory, politics, and methods (pp. 123-136). New York:
CRC Press.
252 Chapter 9 Overview of Theoretical Findings, Limitations and Future Research Advancing Climate Change Adaptation: Review of Research
Guided by a pragmatic theoretical framework, this research applied a mixed methods
approach to analyze climate change adaptation in 192 parties to the United Nations
Framework Convention on Climate Change (UNFCCC).
Grounded theory analysis of 403 qualitative documents, with 6744 coded references,
underpinned this research.
Using both qualitative and quantitative analysis, the data transformation model directed
the development of two quantitative indexes: the Adaptation Index (Index-1) and the
Level of Vulnerability (climate risk) (Index-2).
Following this analysis, 35 variables of climate change adaptation were identified, 15 of which were classified as determinants and ranked by between-groups multivariate
principal component analysis.
Sixty-eight pages of theoretical memos combined with statistical analysis of the 35
identified variables led to the development, classification and ranking of the Stages of
the Climate Change Adaptation Process.
Relatively stable parameters were then analyzed against the Adaptation Index (Index-1)
to understand the direct and indirect nature of geopolitical factors on the advancement
of climate change adaptation.
253 9.1 Key Elements of Adaptation Based on Research Contained in this Dissertation
The research in this thesis provides a window into several different elements that support successful adaptation. Based on this new knowledge, the following key elements of adaptation were identified. Adaptation should:
1. Be complementary to mitigation.
2. Decrease vulnerability.
3. Seek to avoid maladaptation; where maladaptation may cause increases in conflict,
emissions, vulnerability, or destroy culture.
4. Where possible, promote innovation and take advantage of beneficial opportunities.
5. Be inherently interdisciplinary.
6. Take climate change into account rather than repurposing existing actions as climate
change.
7. Be proactive.
8. Agree with the 17 Goals of Sustainable Development for 2030.
9. Maintain or increase quality of life.65
10. Maintain or increase standard of living.
11. Apply cultural and conflict-sensitive approaches.
12. Consider not only primary and secondary impacts of climate change, but also
“interconnected and cascading” impacts.
65 Not be palliative adaptation (See Dickinson & Burton, 2014 for discussion)
254 9.2 Theory: Optimal Environment for Climate Change Adaptation
Analysis from 192 countries on climate change adaptation confirms the initial observation that advancement of adaptation is not uniform across nations. Thus, while this study focuses on the factors that advance, or limit the advancement of, climate change adaptation, the question remains whether an optimal environment for climate change adaptation exists; and if such an optimal environment exists, what factors would encourage adaptation to thrive? Based on this research, Box 1 provides a summary statement of the factors that would lead to a theoretical optimal environment for climate change adaptation.
Box 1. FACTORS THAT LEAD TO AN OPTIMAL ENVIRONMENT FOR ADAPTATION Countries with the highest levels of adaptation have key commonalities: (1) they are strongly democratic; (2) they involve all levels of government; (3) they proactively implement reviews and evaluations of adaptation strategies and plans; (4) they develop and use tools specifically for climate change adaptation; (5) they provide (or timely receive) substantial amounts of funding for climate change adaptation; (6) as weather- related events or disasters occur they reactively make changes that take climate change into consideration.
9.3 Summary of Theoretical Findings
The following Table (43) summarizes the theoretical findings from Chapters 7, 8 and 9, providing an overview of 20 direct and indirect determinants and their influence on the advancement of climate change adaptation.
255
Table 43. Summary of Dissertation Findings
Variable Theoretical Findings
Barriers A weak correlation exists between the level of barriers to climate change adaptation in a country and that country’s ability to adapt to climate change.
Multilateral funding allows vulnerable countries to overcome barriers to climate change adaptation.
Climate Shock Nations making greater advancement on climate change adaptation proactively and reactively respond to climate change related events.
Democracy Strong democracies are responding the most to climate change, with autocratic regimes responding the least.
Economic Emerging economies are falling behind least developed countries Classification (LDCs) and developing nations.
Limited amounts of institutional guidance for these nations at the international level may be at fault.
Funding Countries with increased access to funding display a higher level of adaptation.
Maladaptation and misappropriation of adaptation funds are possible.
Ways to ensure fast disbursement of funding for approved projects and programmes need to be identified.
GDP per Capita Both rich and poor countries are responding to climate change; both rich and poor countries are not responding.
GDP does not directly guide the level of adaptation.
Countries with very high levels of GDP are failing to respond to climate change.
Geography A geographic component to climate change response exists.
256
Island nations are responding with higher levels of adaptation than landlocked nations.
By region, countries in South Asia have higher levels of adaptation compared with countries in the Middle East and North Africa.
Government International and national level conferences plans and strategies should be inclusive and require the involvement of delegates from all levels of government.
National Plans National strategies and plans have been a successful intervention in the climate change adaptation community.
However, national adaptation plans are not a current determinant of climate change adaptation.
Partnerships Partnerships have been successfully established at all levels of government in select countries with a high and statistically significant correlation to the advancement of adaptation.
Peace & On average, countries with high levels of conflict have low levels of Conflict climate change adaptation.
Policy Policy creation does not equal implementation.
Refugees Interconnected and cascading impacts are visible.
Countries with high numbers of refugees also exhibit low levels of adaptation response, creating a potentially compounding impact.
Research Climate change adaptation research has a lower level of influence on the advancement of climate change adaptation.
Continued research on climate change adaptation is necessary.
The call for ‘more research needed’ may be a way to delay action.
Reviews Adaptation plans, strategies and programmes should include predetermined review periods depending on duration (e.g., review every 1yr, 3yr, 5yr intervals).
257
Proactive review periods advance climate change adaptation
System of Political systems influence a nation’s response to climate change Government more than the country’s economic capacity.
Tool The development and use of tools for climate change adaptation is Development & highly correlated with the advancement of climate change Usage adaptation.
An (active and updateable) online platform should be created to house all available tools for climate change adaptation.
Transparency The current international climate community has been successful at promoting public transparency on progress made on adaptation.
Workshops & Workshops and training have marginal influence on advancing Training action on climate change adaptation.
9.4 Key Epistemological and Methodological Findings
During this research several propositions of pragmatism were tested. From this research a preliminary conclusion can be made that pragmatism readily lends itself to adaptation research. Therefore, climate change adaptation researchers66:
v. can apply both quantitative and qualitative assumptions when engaging in
research vi. are free to choose methodologies, techniques and procedures to best meet the
needs and purpose of their research vii. may look to many sources and approaches for collecting and analyzing data
66 Tested in this research, based on Creswell (2013) defined elements of pragmatism
258
viii. can apply mixed methods research to provide the best understanding of a
research problem.
Further, the application of mixed methods research was successfully applied to climate
change adaptation research. Thus, Mixed Methods Research allows for a climate
change adaptation researcher to67:
i. Use both top-down and bottom-up methodologies for hypothesis testing, and
knowledge and theory generation
ii. Identify both numerical and descriptive objectives
iii. Use numerical data and variables, documents, text data, categorical data: words,
themes; multiple languages
iv. Identify statistical relationships and correlations; patterns and themes
v. Transform qualitative data into quantitative data through the process of
quantitizing
vi. Create statistical and narrative based reports
Further, this research highlights that there is untapped potential in mixed methodological research for climate change adaptation research. Using a mixed methodology may enhance a researcher’s output.
67 Tested in this research, based on Johnson & Christensen (2010)
259 9.5 Limitations of Methods 9.5.1 Limitations of Mixed Methods Research
Mixed methods research inherently aims to decrease the limitations from single-method studies based solely in qualitative or quantitative methodology. Limitations and difficulties still exist however which must be overcome or acknowledged. Using both qualitative and quantitative methods may result in conflicting research findings. This research found the opposite; viz., that the mixing of methodologies aided in the confirmation of findings from one step of analysis. Additionally, an example is provided where memos from grounded theory provided additional information to principal component analysis, allowing for the understanding of the data matrix. Generally, mixed methods research is time consuming and requires that the researcher know both qualitative and quantitative methodologies; however, these are more difficulties rather than pure limitations of the method. As mixed methods research is a “third research paradigm” the “classical researcher” using purely qualitative or quantitative methodologies may not be familiar with both methods and may have difficulty comprehending the methodology, output or results. This limitation may always be present and can be decreased by the researcher devoting extra time to ensuring that all the mixed methods components of the study are fully explained when the target audience contains persons unfamiliar with the methodologies selected. Finally, it should be noted that a mixed method research study is not always superior to a purely qualitative or quantitative study. The research objectives must necessitate or underscore the need for mixed methods.
260 9.6 Limitations in Grounded Theory
Grounded theory, despite the name, is not a theory, but a methodology to generate
theory from qualitative data. It therefore does not ‘test a hypothesis’, which may be viewed as a disadvantage or limitation of the method by positivists. However, this study aimed to generate theory and understanding about the factors or determinants that advance adaptation. Hypothesis testing within this study was completed using mixed methods with a strong quantitative component (Chapter 6). Grounded theory is an excellent way to produce data. This may lead to two disadvantages i) an untrained researcher using grounded theory for the first time may assume the methodology is simple because they are able to generate vast amounts of data, and ii) there is the potential for ‘data overload’, where the trained researcher must understand when to stop coding. This latter point requires that the researcher continues to code the data until theoretical saturation is achieved. These limitations are surpassed with training, practice and a strong knowledge of the methodology grounded in the literature and empirical application.
Several researcher biases are possible in qualitative research. These are not full limitations, but a researcher must be aware that these biases exist. Two will be explained in relation to this study and to grounded theory: i) the confirmation bias, and ii) the social desirability bias. In grounded theory a confirmation bias would see the researcher coding only (or mostly) text that confirmed the researchers’ predicted opinion or belief. Theoretical saturation helps to decrease this bias, as the researcher codes all text - relevant or not - to the researchers’ preconceived understanding. Further, as previously stated, grounded theory generates theory, it does not test hypotheses, thus
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the researcher should begin the analysis with open acceptance of any and all
outcomes. Chapter 9 is an example of an output/finding that was the opposite of the
author’s previously held beliefs. The existence of this chapter substantiates that
confirmation bias was not a limiting factor in the analysis. The social desirability bias
has similar tendencies to the confirmation bias but seeks external validation to confirm
societal norms about the research. Thus, if undesirable results were found they must
still be included to avoid this bias, even if these findings are not socially accepted.
Chapter 7 identified 15 determinants of climate change adaptation. The top 5
determinants were a) unexpected and b) less socially acceptable (within the adaptation
community) than the other 10 determinants that did not factor into the advancement of
adaptation as much as the top five. It would be more desirable to have national adaptation plans and strategies, training and workshops, vulnerability and barriers having greater influence on adaptation than this research suggests, because much time and financial support is dedicated to those determinants. Instead, the research pointed to less desirable factors such as the development of tools, or the influence of strong democracy, or the necessity of funding - which may be a more desirable outcome for
least developed countries than for the developed nations who may bear the financial
responsibility.
As previously mentioned, grounded theory produces vast amounts of data, which can be hard to manage. Additionally, this data may produce ‘abstract knowledge’ with less practical application. This study used both qualitative and quantitative methodologies, where, in this study, findings from qualitative research (relativistic findings) were combined with quantitative results (realistic findings) to ensure a greater level of practical application. While grounded theory is cited as producing large amounts of
262 data, there is also the potential for generalization if only a few ‘respondents’ (in this study, countries) have a theme present. To illustrate, the node (also termed theme or determinant) Costing Climate Change had a limited number of countries contributing data. While from a fully qualitative perspective conclusions could be drawn from the data gathered, this mixed methods research applied statistical analysis (Pearson’s correlation coefficient) to analyze the significance of each variable found during grounded theory. The result was a coefficient greater than 0.05 and therefore the findings in that category were deemed ‘not statistically significant’ and no conclusions were drawn from the node.
9.6.1 Note on Statistical Significance Discussions
In a recent special issue of The American Statistician, 43 “innovative and thought- provoking papers” discussed the implications of reliance on p-Values and statistical significance (Wasserstein et al., 2019). In this research, the variable Costing Climate
Change was determined to be ‘not statistically significant’ and therefore no further analysis was taken. In 2016, The American Statistical Association release a “Statement on p-Values” calling on the scientific community to disengage from statistical significance. The 2019 publications rephrase this statement to suggesting researchers
‘embrace uncertainty’. While this has broad implications for the global scientific community, this research was underpinned by the inclusion/exclusion of variables based on ‘statistical significance’.
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Wasserstein et al., (201968) outlines five avoidances that should be made during research:
1. Do not base conclusions solely on an association or effect found to be
“statistically significant” (i.e., the p-Value passed some arbitrary threshold such
as p < 0.05).
2. Do not believe that an association or effect exists just because it was statistically
significant.
3. Do not believe that an association or effect is absent just because it was not
statistically significant.
4. Do not believe that your p-value gives the probability that chance alone produced
the observed association or effect or the probability that your test hypothesis is
true.
5. Do not conclude anything about scientific or practical importance based on
statistical significance (or lack thereof)
9.7 Limitations in Principal Component Analysis
There are a few assumptions required for principal component analysis (PCA); these include normality and linearity. Thus, nominal or categorical data are not suitable for analysis by PCA. This study included qualitative categorical groupings (e.g., region, land type) that when quantitized did not yield a linear data set. This limitation was
68 Wasserstein RL, Schirm AL, Lazar NA.(2019). Moving to a world beyond “P<0.05.”. Am Stat;73:1-19.
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overcome: Data unsuitable for PCA was analyzed using other statistical methods
including analysis of the mean or biplot analysis (Chapter 8).
The data matrix (or the covariance matrix) produced during PCA may pose a challenge
to interpret, even to PCA statisticians. Principal components may not reflect the data in
ways that lend to easy interpretation. This was observed in Chapter 3 where PCA was
applied to differentiate between stages in the adaptation process and determinants of
climate change adaptation. In this case, qualitative data informed the quantitative
analysis. Memoing (a component of grounded theory) was used to help guide the
researchers’ interpretation of each principal component. This limitation was only
overcome because the study used mixed methods research.
9.8 Limitations Using Atypical Datasets
This research relied upon an atypical dataset: qualitative (text-based) reports from national governments submitted to the United Nations. Limitations exist using this type of data, including:
i. Possible errors in submitted reports (intentional or unintentional)
ii. Exaggeration of the contribution to climate change adaptation
iii. Omission of damaging or critical information
Despite these limitations, there are several reasons that offset or decrease the limitations posed by these types of qualitative data sources. Specifically, these limitations are decreased or offset by:
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i. the UNFCCC, “require[ing] all parties to submit National Communications
approximately every four years, with a standardized format mandating the
inclusion of climate change adaptation” (Chapter 3)
ii. the UNFCCC requires submissions to be, “subject to in-depth review conducted
by an international team of experts and coordinated by the secretariat” (Chapter
3)
iii. the researcher, if questioning authenticity or factualness of the data, can
triangulate the information via other publically available sources, peer-reviewed
literature, contacting officials in office in the country of question, or interviewing
academic or independent experts. iv. an underlying assumption that all countries will want to appear in the most
positive light, thus, this limitation may prove to be an advantage as countries may
include a greater number of projects and examples that provide a better view of
progress being made within the country
v. the fact that there are very few resources available that provide a uniform source
of information on climate change adaptation for all Parties under the UNFCCC vi. and that this is a new area of research; therefore, as the global goal for
adaptation (GGA) and stocktaking under the Paris Agreement progresses, so too
will the availability of adaptation data for analysis.
9.9 Knowledge Mobilization and Future Research
The original aim of this research was to provide new insights about the factors that influence and motivate adaptation policy development, and to determine whether an optimal environment for adaptation implementation existed. The underlying research
266 objective was to contribute to a greater understanding of the factors that advance climate change adaptation. This thesis therefore sought to provide researchers, policy makers, decision makers and practitioners with original knowledge on how to advance adaptation plans, policies and measures. Knowledge mobilization could therefore be generated by the following avenues:
Peer-reviewed publications Popular book
Joint papers with country experts Host the National Level of Climate
Special issues Change Adaptation Database as an
National & international conferences online platform
Graduate level courses Continue to update the Adaptation
Textbooks Database as new information becomes
Workshops available
9.10 Potential Papers or Projects
The following section provides 10 examples of papers or projects (or future dissertation topics) that would continue to further the understanding of ways to advance climate change adaptation and to continue progressing and mobilizing knowledge about adaptation as a discipline. Many of these concepts originated during the grounded theory portion of the research, more specifically, during memoing.
9.10.1 Adaptation: The Idea of Progress (Textbook Chapter)
This dissertation established that progress on adaptation is occurring in nations around the world. A potential paper would review the history of adaptation, an overview of the current baseline of adaptation from a national level perspective and methods to move adaptation forward.
267 9.10.2 Revisiting Climate Change Adaptation and Vulnerability Dynamic (Paper, Workshop)
Peer-reviewed literature and the international impacts, adaptation, and vulnerability (IAV) community closely link climate change adaptation and vulnerability. Vulnerability serves as the basis for adaptation funding and preferred project approval. However, this fascination with vulnerability as a key indicator is not translating into quantifiable results.
This research indicates that there is a limited correlation between the level of vulnerability
to climate change and the level of adaptation occurring in a country (Pearson correlation
coefficient, 0.211, p<0.005). Questions therefore remain as to whether focusing on
vulnerability as the basis for funding or preferential project approval is a suitable approach
for advancing adaptation.
9.10.3 Historical Adaptation Intervention through Bilateral and Multilateral Projects (Project, Thesis topic)
Observation: Historical studies and partnerships on climate change adaptation appear to
be correlated with an increase in climate change adaptation and climate policy (r = 0.698,
p <0.0001). Research questions include the following: Are historical adaptation projects
a key influence in promoting the advancement of climate change adaptation? When
looking at specific historical projects (e.g., Caribbean Planning for Adaptation to Global
Climate Change (CPACC) Project in 1997) do the Caribbean countries involved in the
Caribbean Planning for Adaptation to Climate Change (CPACC) project show greater
advancement than those who did not participate?
9.10.4 Partnerships & Climate Change Adaptation (Project, Thesis topic)
“Uganda recognises the crucial role that private [partnerships] can play in the
development [of climate change adaptation]”
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This project would continue from the NVivo qualitative analysis of the node “partnerships” captured during grounded theory. Through literature and interviews with stakeholders involved in climate change adaptation the following questions would be analyzed:
1. What partnerships are being established in climate change adaptation? (e.g.,
public, private, community-based, institutional, bilateral, multilateral, NGOs,
insurance, others, etc.)
2. Why are these partnerships being established?
3. How are these partnerships being established (formal international channels,
or informal channels, other ways?)
4. Do nations with a greater number of international partnerships make greater
advancements in adaptation? (Preliminary results of this project suggest yes,
with a high statistically significant correlation)
5. Which partnerships are most effective at advancing adaptation?
9.10.5 Bilateral and Multilateral Bank Involvement (Project, Paper)
"This work has only been possible with the financial and technical support from the GEF-
LDCF and UNDP-Sudan."
This project/paper would be a second part to Chapter 9, looking at multilateral and bilateral funding (as opposed to looking specifically at the 22 identified countries with high levels of barriers and high levels of adaptation). Research questions would centre on whether multilateral intervention moves adaptation forward. (Chapter 9 suggests yes).
The research would analyze funding from a quantitative perspective, comparing available donor information with the national level of climate change adaptation.
269 9.10.6 Current Institutional Support (Project, Workshop)
International/institutional community is vital to the Adaptation Process. The National
Adaptation Programmes of Action (NAPA) process seems to have strengthened the Non-
Annex I country level adaptation. This project would require direct contact with
stakeholders in the NAPA development process. Several questions remain unanswered
around current institutional support for climate change adaptation. These include:
1. At what level of advancement are countries that have NAPAs?
2. How have NAPAs contributed to advancement of climate change adaptation?
3. Without NAPAs would LDCs have made the advancements they have? (How
important have NAPAs been in LDC progress?)
4. Are any trends visible in counties with NAPAs?
5. Would any of the ‘adaptation priorities’ have been established without these
institutions or institutional frameworks, mechanisms/programmes?
9.10.7 The Slow Down: Warming Pause and the Adaptation Lag (Project, Paper)
Observation: the countries Luxemburg, Netherlands, Norway, and Slovenia all made
significant progress on adaptation and then stopped. Question: What happened? Is there
a correlation between the “warming pause” and a change in policy momentum? This
project/paper would require direct contact (interviews) with stakeholders in each country.
9.10.8 Gender (Project/Paper)
Post-disaster analysis reports and peer-reviewed literature cite women as being disproportionally affected by weather-related events (including climate change). It was noted during memoing that gender was largely absent from the qualitative data analyzed.
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This research would begin with a reanalysis of the qualitative documents gathered (and/or updated documents available) to look specifically for ‘gender’. And then seek to understand (through interviews with stakeholders) why gender, with such a large presence in the UNFCCC negotiations and discussions, is lacking in reporting to the
Convention. The research would then analyze how (or whether) the relative absence of gender serves to hinder the advancement of adaptation.
9.10.9 Culture, Climate Change and Climate Dispossession (Thesis topic, Papers, Projects)
“A lack of knowledge of, and respect for, traditional customs and practices was recognised as potentially impeding climate change adaptation.” Several observations were made during memoing:
• Importance of indigenous • Challenges to current terminology:
involvement ‘traditional knowledge’; cultures are
• Connection between land and not stagnant, they can and do adapt
identity • Oppression, dispossession and
• Learning from indigenous prejudice are key elements in the
communities ability to make (or not make)
• Lack of indigenous methodological progress on adaptation in may
approaches nations
This project would require an indigenous principal investigator.
9.10.10 Repurposing Adaptation (Paper)
Several countries have plans, strategies and actions that were not developed for climate change purposes but are being submitted as ‘climate change adaptation’. However, these
271 repurposed measures do not always take climate change into account. The question then changes: Is the repurposing of already implemented polices and plans as ‘climate change adaptation’ actually maladaptation? Several examples are present in the data captured during grounded theory. This research would seek to analyze this data to a) determine if climate change was taken into consideration b) evaluate if any of these repurposed adaption measure have been exposed to climate-related events and c) evaluate if there is any evidence that repurposing adaptation increases risk to climate change.
272 9.11 In Closing – Personal Reflection The journey to this dissertation began unknowingly in December 2006.
Hired by Environment Canada, I spent several months working for the
Adaptation and Impacts Research Division (AIRD). At the time, I was
completely unaware that this short-term contract would lead me on the
pursuit of a lifetime’s worth of knowledge.
In the six years that followed those months at AIRD, I consulted for
community and municipal partners, public and private sectors, non-profit
organizations, national governments, and international agencies and
organizations. I administered multi-disciplinary consultancy and research
projects, reviewed proposals for funding from multilateral institutions and
national governments. I wrote extensively, edited and reviewed papers,
books and publications targeting local, national and international issues
and audiences.
But, in those many years I had not pursued a research project that was
entirely my own vison and undertaking.
Engaging in this doctoral research, while extremely challenging, has
been a gift. I have had the privilege of chasing after the questions that I
had been contemplating. Through this dissertation I was given the luxury
of being able to ask my own questions, and to find, if not all the answers,
the pathways to the knowledge I had been seeking.
This doctoral process has changed me both as an academic and as a
professional. My approach to research has a far greater theoretical
273 foundation than when I first sat before this task, and, perhaps paradoxically, this thesis supported my willingness to take more chances with my research – pushing methodological boundaries and inquiries.
While this is the end of my doctoral dissertation, it is the beginning of a new journey. Just as I was unaware of how my life would unfold in 2006,
I am similarly unknowing of where this new path will lead me.
But one thing is for certain, the path will have climate change adaptation written all over it.
~ Thea Dickinson, June 6th, 2017
274 Appendices
Additional Information 10.1 NVivo Case Nodes
During qualitative analysis of national level documents, 35 case nodes (units of
observation or ‘themes’) were discovered by grounded theory using software NVivo 11
Qualitative Data Analysis Software. The following is a list of all 35 case nodes:
1. Adaptation Policy Creation
2. Adaptation Tool Development & Usage
3. Assessment Phase (Impact and Adaptation)
4. Barriers to climate change adaptation
5. Climate Change Adaptation Modelling
6. Climate Change Adaptation Research
7. Climate Shock (Examples of policy)
8. Communication
9. Community Based Adaptation (CBA)
10. Costing Climate Change Adaptation
11. Culture
12. Disaster Impact Assessment & Preparedness
275 13. Establishing Partnerships
14. Evaluation of the Adaptation Outcome
15. Examples of Adaptation
16. Funding: Provided Delivered Received
17. Gender
18. Implementation of the adaptation action
19. Insurance & Financial Instruments
20. Legislation, Laws & Acts, Standards & Codes
21. Maladaptation
22. Migration (Forced)
23. Multi-Level of Government Involvement
24. National Adaptation Strategy or Plan
25. Project Example, or Program and Programme Development
26. Public Transparency
27. Recommendations, Priorities and Options & Regulations
28. Relocation of Population
276 29. Repurposing Development (Type I Adaptation) for Climate Change Adaptation
(Type II Adaptation) without taking climate change into account69
30. Reviews & Evaluations of Programs Plans
31. Risk Management, Assessment & Reduction
32. Role of Government
33. Small Island Developing States (SIDS)
34. Underdeveloped Themes
35. Workshops, Public Education & Training (Adaptation focused)
10.2 Methodological Summary Diagrams
The following figures (25-30) provide a diagrammatic overview of the methodological
design of the research contained in Chapters 5, 6, 7 and 8.
69 Type I Adaptation refers to past and current adaptation strategy, policy, and measures without considering climate change. Type II Adaptation is adaptation to climate change. (See Burton, I. (2004). Climate change and the adaptation deficit. Adaptation and Impacts Research Group, Meteorological Service of Canada, Environment Canada.)
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Figure 25 Methodological Development of Index-1
278
Figure 26. Methodological Development of Index-1 (continued)
279
Figure 27 Methodological Development of Index-2
280
Figure 28 Methodological Identification of Determinants of Climate Change Adaptation
281
Figure 29 Methodological Identification of Direct and Indirect Geopolitical Determinants
282
Figure 30 Methodological Identification of Multilateral Funding in Overcoming Barriers
283