TRANSITIONS PATHWAYS AND RISK ANALYSIS FOR CLIMATE CHANGE MITIGATION AND ADAPTATION STRATEGIES
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy
Project Coordinator: SPRU, Science Policy Research Unit, (UoS) University of Sussex
Work Package 5
Leader Organisation: NTUA, Energy Policy Unit, National Technical University of Athens
Lead Authors: Alexandros Nikas, Haris Doukas
Contributing authors: Eleftherios Siskos, John Psarras
Other contributors: All partners from ETHZ, UniGraz, SEI, and SPRU
July 2018 (updated December 2018)
TRANSrisk
Transitions pathways and risk analysis for climate
change mitigation and adaptation strategies
GA#: 642260
Funding type: RIA
Deliverable number 5 (relative in WP) Multi-criteria consideration of risk and uncertainty for climate Deliverable name: policy
WP / WP number: 5
Delivery due date: Month 35
Actual date of submission: 31st August 2018
Dissemination level: Closed until end of project, then public
Lead beneficiary: Energy Policy Unit, National Technical University of Athens (NTUA)
Alexandros Nikas, Haris Doukas, Eleftherios Siskos, John Psarras Responsible scientist/administrator: (NTUA)
Estimated effort (PM): 9 PMs
Other Contributor(s): All partners from ETHZ, UniGraz, SEI, and SPRU
Estimated effort contributor(s) (PM): 0.5 PMs
Internal reviewer: Chara Karakosta (UPRC), Eise Spijker and Wytze van der Gaast (JIN)
D.5.5 Multi-criteria consideration of risk and uncertainty for climate policy ii
Preface
Both the models concerning the future climate evolution and its impacts, as well as the models assessing the costs and benefits associated with different mitigation pathways face a high degree of uncertainty. There is an urgent need to not only understand the costs and benefits associated with climate change but also the risks, uncertainties and co-effects related to different mitigation pathways as well as public acceptance (or lack of) of low-carbon (technology) options. The main aims and objectives of TRANSrisk therefore are to create a novel assessment framework for analysing costs and benefits of transition pathways that will integrate well-established approaches to modelling the costs of resilient, low-carbon pathways with a wider interdisciplinary approach including risk assessments. In addition TRANSrisk aims to design a decision support tool that should help policy makers to better understand uncertainties and risks and enable them to include risk assessments into more robust policy design. PROJECT PARTNERS
No Participant name Short Name Country code Partners’ logos
Science Technology Policy Research, 1 SPRU UK University of Sussex
2 Basque Centre for Climate Change BC3 ES
3 Cambridge Econometrics CE UK
4 Energy Research Centre of the Netherlands ECN NL
Swiss Federal Institute of Technology (funded 5 ETH Zurich CH by Swiss Gov’t)
6 Institute for Structural Research IBS PL
7 Joint Implementation Network JIN NL
8 National Technical University of Athens NTUA GR
9 Stockholm Environment Institute SEI SE, KE
10 University of Graz UniGraz AT
11 University of Piraeus Research Centre UPRC GR
12 Pontifical Catholic University of Chile CLAPESUC CL
D.5.5 Multi-criteria consideration of risk and uncertainty for climate policy iii
Executive Summary
The aim of Task 5.5 is to evaluate climate policy-related risks, from the point of view of stakeholders, based on the principles of Multiple-Criteria Decision Analysis (MCDA). MCDA is a sub- discipline of operations research, aiming to support decision making in complex problems where multiple points of view must be taken into account before reaching a meaningful solution. It is involved in various stages of decision making, including problem structuring, preference modelling, construction of criteria aggregation models, and design of interactive solution procedures; and has been considerably evolving since it first appeared.
This task, includes the creation of a consistent set of evaluation criteria; capturing stakeholders’ preferences with regard to the assessment of the identified implementation and/or consequential risks against the selected criteria, as well as to the importance of these criteria; and carrying out an MCDA analysis to finally rank the risks. As stakeholders, we consider members of the groups that were targeted by the TRANSrisk project, who were engaged throughout the project for sharing their knowledge of the project’s case study work and of risks and uncertainties hindering the successful promotion of low-carbon transition pathways in the countries considered. These stakeholder groups include policymakers, government officials, industry representatives, NGOs, researchers, the civil society, and others, in an effort to capture an all-encompassing view of the society, in line with the objectives of the Talanoa dialogue. Driven by their own and their specific group’s interests, motives, expertise and strategies, engaged stakeholders only express part of the society, and thus the purpose of this deliverable is not only to capture multiple views but also, when possible, to identify the differences among these individual or group views.
A secondary objective of this task is to also gain insights into how differently the various stakeholders view each risk, in terms of each individual evaluation dimension (criterion) and of the overall criticality as resulting from the MCDA analysis.
Finally, Task 5.5 aims to explore the literature of climate policy-related studies, in order to assess the applicability of MCDA in this problem domain, select an appropriate methodology and develop a framework in support of climate policy.
Starting from the latter, an extensive review of such studies is carried out, providing (a) an overview of their use of MCDA with regard to application area, sector and explicit policy implications; (b) a survey of the selected MCDA framework and the type and nature of evaluation criteria and stakeholder engagement; (c) a mapping of their geographic scope; and (d) an assessment of the tool’s integration capacity with other methodologies, including modelling frameworks, a dimension lying at the heart of the TRANSrisk project. Drawing from this review, an appropriate MCDA methodology, TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), is selected to be the basis of the developed and presented methodological framework and tool, which apart from carrying out the MCDA analysis also allows for supervising the consensus on both the input and the output, among the stakeholders.
D.5.5 Multi-criteria consideration of risk and uncertainty for climate policy iv
In this report, seven case studies are explored, modelled and solved:
1. Case study 1: Evaluating risks associated with a pathway enhancing energy efficiency in Greece 2. Case study 2: Evaluating risks associated with the promotion of power generation from solar photovoltaics in Greece 3. Case study 3: Evaluating risks associated with a low-carbon transition of the Austrian iron and steel sectors 4. Case study 4: Evaluating risks underlying a nuclear phase-out in Switzerland 5. Case study 5: Evaluating risks associated with a low-carbon transition of the Indonesian power generation sector, through communal biogas digesters 6. Case study 6: Evaluating implementation risks hindering a low-carbon transition of the UK power sector, through nuclear expansion and/or shift to renewables 7. Case study 7: Evaluating implementation and consequential risks associated with the low- carbon transition of the Chinese built environment.
For the purposes of carrying out the MCDA analysis in these case studies, led throughout the project by NTUA (1 & 2), UniGraz (3), ETHZ (4), SEI (5) and SPRU (6 & 7), different stakeholder engagement processes were used. However, all of these were based on a dedicated semi- structured interview framework, designed for the purposes of Task 5.5 and used throughout Task 5.4, where the engaged stakeholders were kindly asked to assess a group of risks associated to their country’s transition pathways. Furthermore, all cases involved the assessment of implementation risks, while some also included consequential risks, with the latter not always discerning the two categories. However, all risks across all case studies were evaluated against their likelihood to manifest, their impact on the transition or the impact of the transition on them, the capacity to mitigate them and the level of concern, as perceived by the involved stakeholders.
D.5.5 Multi-criteria consideration of risk and uncertainty for climate policy v
Table of Contents
1 EC Summary Requirements ...... 7 1.1 Changes with respect to the DoA ...... 7 1.2 Dissemination and uptake ...... 7 1.3 Short Summary of results ...... 7 1.4 Evidence of accomplishment ...... 8
2 Introduction ...... 9 2.1 Rationale ...... 9 2.2 Research Questions ...... 10 2.3 Relation to other tasks ...... 10
3 Multiple-Criteria Decision Making in Support of Climate Policy ...... 12
4 Methodological Framework ...... 29 4.1 MCDA Method Selection ...... 29 4.2 Data Unification ...... 31 4.3 Consensus Analysis ...... 36
5 Assessment of Climate Policy Risks in TRANSrisk ...... 39 5.1 Evaluating risks associated with a low-carbon transition of the Greek economy39 5.1.1 Context ...... 40 5.1.2 Enhancing energy efficiency in Greece ...... 42 5.1.3 Boosting power generation from solar photovoltaics ...... 52 5.2 Evaluating risks in the Austrian steel sector towards a low-carbon transition 62 5.2.1 Context ...... 62 5.2.2 Stakeholder input ...... 66 5.2.3 Multicriteria analysis results ...... 68 5.2.4 Consensus ...... 71 5.3 Evaluating risks in the Swiss energy sector towards a nuclear phase-out ..... 74 5.3.1 Context ...... 74 5.3.2 Stakeholder input ...... 76 5.3.3 Multicriteria analysis results ...... 80 5.3.4 Consensus ...... 81
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 1
5.4 Evaluating risks associated with a low-carbon transition of the Indonesian power generation sector, through communal biogas digesters ...... 84 5.4.1 Context ...... 84 5.4.2 Stakeholder input ...... 86 5.4.3 Multicriteria analysis results ...... 90 5.4.4 Consensus ...... 91 5.5 Evaluating risks associated with the UK nuclear and RES expansion pathways93 5.5.1 Context ...... 93 5.5.2 Stakeholder input ...... 96 5.5.3 Multicriteria analysis results ...... 99 5.5.4 Consensus ...... 100 5.6 Evaluating risks associated with a low-carbon Chinese built environment .. 102 5.6.1 Context ...... 102 5.6.2 Stakeholder input ...... 104 5.6.3 Multicriteria analysis results ...... 106 5.6.4 Consensus ...... 108
6 Conclusions ...... 109
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 2
Figures
Figure 1 Evolution trends of the MCDA methodological frameworks in the studied problem domain, 2003-2017 ...... 23
Figure 2 Consensus analysis in MACE-DSS ...... 38
Figure 3 Implementation risks jeopardising the low-carbon transformation of the Greek built environment, as derived through stakeholder interviews ...... 43
Figure 4 Stakeholder evaluation of the risk associated with the lack of public awareness of climate change and the need to mitigate it, against the four evaluation criteria ...... 44
Figure 5 Consequential risks associated with the low-carbon transformation of the Greek built environment ...... 46
Figure 6 Stakeholder evaluation of the consequential risk of economic impacts on the government budget due to poorly designed financial support mechanisms, against the four evaluation criteria ...... 47
Figure 7 Most critical uncertainties and risks associated with a transition to an energy-efficient Greek building sector ...... 48
Figure 8 Final MCGDA results of the significance of the ten implementation risks ...... 49
Figure 9 Final MCGDA results of the significance of the four consequential risks ...... 50
Figure 10 Stakeholder analysis for each implementation and consequential risk, based on the MCGDA results ...... 52
Figure 11 Implementation risks, threatening the boost of power generation from solar photovoltaics, as they were assessed by the stakeholders...... 53
Figure 12 Stakeholder evaluation of the risk associated with political inertia, against the four evaluation criteria...... 54
Figure 13 Consequential risks associated with the boost of power generation from solar photovoltaic ...... 55
Figure 14 Stakeholder evaluation of the consequential risk of poverty, against the four evaluation criteria ...... 56
Figure 15 Most critical uncertainties and risks associated with the boost of power generation from solar photovoltaics ...... 57
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 3
Figure 16 Final MCGDA results of the significance of the twelve implementation risks ...... 58
Figure 17 Final MCGDA results of the significance of the four consequential risks ...... 59
Figure 18 Stakeholder analysis based on the results of the multicriteria analysis ...... 61
Figure 19 Risk clusters for the transition in the iron & steel sector and the energy sector ...... 64
Figure 20 Assessment, across the four evaluation criteria, of the risk associated with an unstable grid, due to low investments and/or high costs, by the 10 experts interviewed ...... 67
Figure 21 Assessment, across the four evaluation criteria, of the risk associated with the inexistence of a clear climate and energy strategy ...... 68
Figure 22 Final ranking of risks per risk category (colours explained in the legend). The horizontal axis corresponds to the numeric identifier of each risk...... 69
Figure 23 Experts' assessment of impact and mitigation capacity for [IA6] Timing for introducing new technologies, showing significant differentiation between Expert 8 and the rest of the group ...... 71
Figure 24 Experts' level of concern for [AA6] Lack of framework for investment and planning, showing significant differentiation between Expert 5 and the rest of the group ...... 72
Figure 25 Stakeholder analysis based on the results of the multicriteria analysis ...... 73
Figure 26 Assessment, across the four evaluation criteria, of the risk associated with the uncompetitive electricity generation, by the three expert groups interviewed ...... 79
Figure 27 Assessment, across the four evaluation criteria, of the risk associated with the damage to the natural environment ...... 80
Figure 28 Final MCGDA results of the significance of the examined risks ...... 81
Figure 29 Stakeholder analysis based on the results of the multicriteria analysis ...... 83
Figure 30 Implementation and consequential risks jeopardising the transition to communal biogas digester systems ...... 87
Figure 31 Stakeholder evaluation of the consequential risk of varied monitoring practices in different biogas programmes, against the four evaluation criteria ...... 88
Figure 32 Stakeholder evaluation of the risk associated with the poor maintenance of biogas digesters, against the four evaluation criteria ...... 89
Figure 33 MCGDA results on the significance of the risks ...... 91
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 4
Figure 34 Stakeholder analysis for each implementation and consequential risk, based on the MCGDA results ...... 92
Figure 35 Assessment, across the four evaluation criteria, of the risk associated with the damage to ecosystems, due to the transition pathway, by the ten experts interviewed ...... 98
Figure 36 Assessment, across the four evaluation criteria, of the risk associated with the social resistance to new RES development ...... 99
Figure 37 Final MCGDA results of the significance of the examined risks ...... 100
Figure 38 UK stakeholder analysis based on the MCGDA results ...... 102
Figure 39 Multicriteria analysis results on the significance of key implementation risks in China ...... 107
Figure 40 Multicriteria analysis results on the significance of key consequential risks in China 108
Tables
Table 1 Overview of climate policy related studies in the MCDA literature, with regard to their application area, sector and explicit policy implications...... 13
Table 2 Overview of the MCDA methodologies employed in the climate policy literature...... 17
Table 3 Overview of climate policy studies in which multiple criteria decision making is integrated with other methodologies...... 26
Table 4 Geographic scope of MCDA studies with climate policy implications ...... 27
Table 5 An overview of modelling-integrated TOPSIS applications used in the climate policy literature...... 30
Table 6 Summary of implementation and consequential risks, criteria and criteria weights ...... 47
Table 7 Summary of implementation and consequential risks, criteria and criteria weights ...... 56
Table 8 Risk classification for the Austrian iron and steel sector ...... 66
Table 9 Presentation of the most and least critical risks by risk group ...... 69
Table 10 Risk classification for the Swiss Energy Sector towards a nuclear phase out ...... 77
Table 11 Summary of implementation and consequential risks, criteria and criteria weights .... 90
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 5
Table 12 Risk classification for the UK’s energy transition ...... 97
Table 13 Description of implementation and consequential risks in China ...... 105
Table 14 Evaluation Criteria and criteria weights in China ...... 106
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 6
1 EC SUMMARY REQUIREMENTS
1.1 Changes with respect to the DoA
No changes with respect to the work described in the DoA.
Leadership of this task and deliverable was passed from ETHZ to NTUA.
1.2 Dissemination and uptake
The methodological framework proposed in this report, as well as the findings of its application to transition pathway risks in Greece, Austria, Switzerland, Indonesia, the UK, and China can prove beneficial to both the energy and climate policymaking and the wider research/academic community. While their disciplinary or sectorial background may be specific, their interest is informing and developing policies to promote low carbon transition pathways in an area that is inherently interdisciplinary and cross-sectoral. Parts of the findings for the Greece case study have already been used to enable policymakers to develop an effective and sustainable energy efficiency framework, through an integrative methodological approach, as presented in deliverable D7.2. In addition, the interested audience can include people working in consulting, policymakers, researchers, and international institutions, such as the United Nations, the EU, and the World Bank, development agencies and NGOs active at subnational, national and international level. It is noteworthy that representatives of all of the aforementioned groups have actively been involved in the processes carried out towards reaching the presented empirical findings.
1.3 Short Summary of results
Overall, risk rankings appear to greatly vary across the case studies, which is expected given the context of each country, the different range and expertise of the engaged stakeholders, as well as the different scope of the pathways for each case study.
In Greece, stakeholders, mainly comprising policymakers, researchers and technology manufacturers/importers/suppliers, appear to highlight the importance of political inertia, the adverse economic environment, bureaucracy and poor network infrastructure quality; they also urge caution on poorly designed energy efficiency and/or solar boosting financial support mechanisms, and are somewhat concerned with potential impacts on utility bill costs, energy security and grid-related problems. In Austria, stakeholders in the iron and steel sectors appear to be equally concerned with implementation and consequential risks, thus highlighting the importance of considering
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 7
both barriers and potential negative consequences when designing appropriate policy strategies. They agree that financial risks are the most critical to consider when designing the decarbonisation of the iron and steel sectors. In Switzerland, the most dominant risks from the stakeholders’ perspective, who mainly comprised academics and environmental protection groups, regard barriers hindering the nuclear phase-out, rather than potential negative consequences. They orientate on protective actions by environmental groups against renewable energy projects and on social opposition to interventions from the government, as well as the variation of priorities among layers of government. In Indonesia, on the other hand, social risks are not perceived to be of paramount importance by stakeholders. Rather, they appear to be primarily concerned over collective management issues with large-scale biogas systems, time requirements for feedstock and waste collection as well as little consideration of local context. In the UK stakeholders, primarily academics and consultants, judge the lack of massive investments in renewables and CCS appears to be the most critical risk, followed by low prices of carbon and hydrocarbon. Other financial risks, such as unbalanced markets and rising electricity prices, are considered of medium criticality, while risks of societal, environmental, and political/regulatory nature appear to be less significant, when considering the transition of the UK power sector, through nuclear and/or RES expansion. In China, the engaged stakeholders appear to stress investment risks for energy efficiency, difficulties in monitoring and policy enforcement, technological innovation risks, and low market expectations of technical application returns. At the same time, they appear to be concerned over potential negative consequences of the decarbonisation of the Chinese built environment, revolving around increased land requirements as well as climate poverty and social injustices.
Consensus analyses across all case studies, suggest that members of the academic and research community appear to significantly diverge from the other stakeholder groups, highlighting for example energy security, societal and environmental risks, rather than financial barriers and consequences.
1.4 Evidence of accomplishment
This deliverable; a series of book chapters to be published in Routledge Studies in Energy Transitions (Hanger-Kopp et al., in press); one publication in Heliyon, featuring the developed tool (Nikas et al., 2018); one manuscript submitted (at the revision stage) in the European Journal of Operational Research, featuring the main body of the literature review (Doukas and Nikas, in revision); one manuscript submitted in Energy Policy, featuring results from the Greek case study (Forouli et al., submitted); and an acknowledged contribution to the study in the 4th National Energy Efficiency Action Plan of Greece, which was largely based on the presented framework and tool (Ministry of Environment and Energy, 2017).
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 8
2 INTRODUCTION
2.1 Rationale
Parts of this subsection have been published in (Nikas et al., 2018).
Promoting transition pathways towards low carbon societies can be mainly supported through robust climate policy making processes that take into account the various types of risk and uncertainty. These are intertwined with climate change and respective policy design, implementation and acceptance. Climate policy support almost exclusively comes in the form of climate-economy modelling activities, by means of Integrated Assessment Models (IAMs). These have largely contributed to understanding the complex interactions between energy, climate, economy and all dimensions of human activity as well as the impacts of potential policy strategies on each one of these modules.
In order to capture these interactions, the structure of these models has grown inevitably complex, and data unavailability is compensated for by using a number of assumptions. As a result of this complexity, data-driven procedure, there is little room for policy makers (as well as other key stakeholder groups) to provide their insights and experience in order to bridge knowledge gaps. In turn, exclusion of the human factor from the equation, in combination with said complexity, makes policy makers reluctant to trust and use their results. There is an evident need for appropriate decision support frameworks that bridge the gap between policy experts and modellers.
In this direction, stakeholder engagement and participation has been gaining growing attention in environmental and climate policy studies (van Vliet et al., 2010), as has the implementation of different expert-driven decision support approaches (Nikas et al., 2017). One such approach can be found in Multiple Criteria Decision Analysis (MCDA), which is a sub-discipline of Operational Research focusing on supporting policy and decision making in multi-dimensional problem domains. Here, different alternatives must be assessed against a range of evaluation criteria, across different dimensions of said domains. Despite a late start, due to lack of appropriate guidelines (Borges and Villavicencio, 2004), MCDA has been gaining increasing attention in the climate policy domain. This is primarily due to the need for determining parts of scenario inputs (e.g. technological preferences, distinct values for uncertain parameters, possible future socioeconomic developments, climate- and economy-related requirements, etc.), as well as the ever-growing popularity of MCDA frameworks in studies on energy policy (Doukas, 2013). Energy policy is overwhelmingly the core aspect of climate policy: the energy system is the largest driver of greenhouse gas emissions (Bruckner et al., 2014) and lies at the heart of all economic activities.
MCDA encompasses a diverse range of different methodologies, with varying features, substantially different approaches, and implementation across a large number of applications and problem domains. A preliminary objective of this Deliverable, therefore, is to critically review climate policy related studies in the multicriteria decision making literature, in order to assess their scope
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 9
and explore the applicability of different frameworks in the climate policy domain. Additionally, given the need for integration with other modelling frameworks and tools, the assessment of their capacity to integrate with different approaches is also pursued.
Drawing from the findings of this review, an appropriate MCDA approach is selected. It should be noted, however, that a significant aspect of MCDA, on which the selected framework largely depends, lies in the available capacity to support a number of stakeholders. Given the desired participation of different stakeholder groups with different types and levels of knowledge (Xu e al., 2015), it is of vital importance that the group decision making aspect of MCDA be highlighted. The core aim of this paper, therefore, is to develop a decision support tool that can both support climate policy making by means of an appropriate MCDA methodology, and emphasise the desired group decision making aspect. In this context, a TOPSIS-oriented methodological framework is introduced and a dedicated spreadsheet-based tool that can support this process, with the capacity of enabling disagreement-driven consensus control and building, is developed and presented. The analytical framework is finally applied in seven case studies. This aims to evaluate the implementation and/or consequential risks associated with low-carbon transition pathways in a range of situations.
2.2 Research Questions
The work carried out in this task and deliverable aims to answer the following questions:
(i) For what types of decision-making problems in climate policy can MCDA be useful? (ii) What are the risks (and the uncertainties potentially giving rise to those risks) that are most important to consider in the course of climate policy design? (iii) How can we evaluate these risks, in terms of how likely, impactful, easy to mitigate and worrisome they are perceived to be by stakeholders? (iv) What do the individual and collective preference models tell us about the different points of view as well as about the consensus among stakeholders?
To answer these questions, seven different case studies are explored.
2.3 Relation to other tasks
Given that one of TRANSrisk’s core aims is to carry out risk analysis for climate policy, both Work Package 5, on Risk and Uncertainty, and Task 5.5, on multi-criteria evaluation of risk and uncertainty for climate policy, lie at the heart of the project. As a result, Task 5.5 is intertwined with numerous other tasks and activities in the project.
In particular, risks identified in the literature (Task 5.1), associated with TRANSrisk case studies (Task 5.2, and in the context of WP3), and analysed in modelling activities (Task 5.3) constitute
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 10
the input of Task and Deliverable 5.5. Furthermore, the elicitation framework in Task 5.4, was constructed to consider the needs and requirements of the current study.
In addition, Task 5.5 provided input to a series of Work Package 7 activities, including some of the case studies in the fuzzy cognitive mapping (Task 7.1) and portfolio analysis (Task 7.2) tasks. Finally, the outcomes of Task 5.5 have extensively been used in the final part of the case study work (Deliverable 3.3), within the TRANSrisk special edition, “Narratives of low carbon transitions: Understanding risks and uncertainties”, in Routledge Studies in Energy Transitions.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 11
3 MULTIPLE-CRITERIA DECISION MAKING IN SUPPORT OF CLIMATE POLICY
Multiple Criteria Decision Making (MCDM), sometimes referred to as Multiple Criteria Decision Aid or Analysis (MCDA) (Roy, 1990), is a sub-discipline of operations research. It aims to support decision making in complex problems where multiple points of view must be taken into account before reaching a meaningful solution (Govindan and Jepsen, 2016). MCDA is involved in various stages of decision making, including problem structuring, preference modelling, construction of criteria aggregation models, and design of interactive solution procedures (Doumpos and Zopounidis, 2011). It has evolved considerably since it first appeared (Roy and Vanderpooten, 1997) and has lately been receiving growing attention (Behzadian et al., 2010).
Climate policy studies initially lacked the knowledge and guidelines regarding the employment of MCDA approaches (Borges and Villavicencio 2004). However, in the last decade these approaches have attracted increasing attention in studies related to climate mitigation and adaptation as well as to policy making in various economic sectors where GHG emissions reduction is among the main evaluation criteria. This remarkable boom can be partly attributed to the growing proliferation of respective methodological frameworks; the increasing engagement, collaboration and participation of expert decision makers in modern modelling activities (Voinov and Bousquet, 2010); and the need for developing integrated methodologies for addressing problems in this domain (in which MCDA can significantly contribute). Another major driver of their ever-growing use can be found in their large popularity in energy policy problems, combined with the fact that decarbonising the power sector lies at the heart of climate policy (Doukas, 2013). Another factor contributing to said increase can be found in international and EU policy efforts for sustainable development. In particular, sustainability and respective implications for the environment are intertwined with decarbonisation and climate change mitigation. At the same time, sustainable development has been framed in reference to its multiple dimensions or pillars (economy, environment, society), as outlined in the Brundtland report of the World Commission on Environment and Development (Brundtland Commission, 1987); and has long been studied by means of multi-criteria approaches.
In general, a multitude of MCDA frameworks have been employed in energy planning and sustainable development studies (Diakoulaki et al., 2005; and Munda, 2005); a selection of applications in these two areas can be found in Part VII of Greco et al., (2016). When reviewing the MCDA literature since 2003 (in Google Scholar, Scopus and ScienceDirect for keywords relevant to climate policy and MCDA), 73 studies were found to be relevant to the climate policy field (Table 1). In these studies, multicriteria analyses were involved either directly in policy evaluation or indirectly in other areas, in which climate mitigation or adaptation criteria were included. Most studies focused on the assessment of different technologies, closely followed by those revolving around climate policy instrument or strategy comparison and evaluation, while a limited number of researchers used multicriteria processes for the purposes of selecting projects among various alternatives or analysing scenarios with climate policy implications. Finally, only one study performed risk assessment (Branco et al., 2012), one aimed at emissions-driven EU country
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 12
comparisons (Dace and Blumberga, 2016), and two focused on the identification and prioritisation of evaluation factors and indicators for the assessment of energy projects (Heo et al., 2010; and Luthra et al., 2015). As expected, given their rather wide use in the energy policy domain, the vast majority of reviewed MCDA frameworks were employed in power sector applications. The transportation sector also appeared to be popular among multicriteria studies; this can be attributed to the very large number of technological assessment studies. Only a small number of MCDA applications with climate policy implications focused on the agriculture, building and industry sectors or environmental management. It is noteworthy that one study, focusing on the evaluation of alternative mitigation instruments in different countries across Europe, employed a cross-sectoral approach (Konidari and Mavrakis, 2007).
The proposed categorisation is economic-sector-oriented and, acknowledging that different approaches for sorting the reviewed literature may be used, significant effort has been put in securing consistency; for example, publications assessing energy efficiency measures are sorted in respective (e.g. buildings or transportation) sector categories. It should also be noted that while the majority of the reviewed literature had climate policy implications, nine of the reviewed studies were explicitly conducted for the purpose of climate policy support (seven for mitigation, one for adaptation, and one for both).
It should be noted that the publications reviewed for the purposes of this paper were identified primarily on the basis of searches on the Scopus and Google Scholar academic search engines, for various keywords (e.g. “climate policy” AND “multiple criteria”, “climate change” AND “multicriteria”, “mitigation” AND “MCDM” OR “MCDA”, etc.). They included scientific journal articles, book chapters and conference papers. Furthermore, broader searches were performed in specific journals and books, after reviewing the number and thematic focus of the associated papers retrieved in the initial results.
The study of this section constitutes part of research that has been submitted for publication in (Doukas and Nikas, submitted) and part of research that has been published in (Nikas et al., 2018).
Table 1 Overview of climate policy related studies in the MCDA literature, with regard to their application area, sector and explicit policy implications.
Application Contributors Sector Explicitly Area about climate Agriculture Buildings Environment Industry Power Transport policy Sector
Policy (Alsabbagh et al., 2016) Mitigation evaluation (Batubara et al., 2016)
(Blechinger & Shah, 2011) Mitigation
(Borges & Villavicencio, 2004) Mitigation
(Chen & Pan, 2015)
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 13
Application Contributors Sector Explicitly Area about climate Agriculture Buildings Environment Industry Power Transport policy Sector
(Cristóbal, 2012)
(de Bruin et al., 2009) Adaptation
(Georgopoulou et al., 2003) Mitigation
(Javid et al., 2014)
(Konidari & Mavrakis, 2007) Mitigation
(Michailidou et al., 2016) Mitigation; Adaptation
(Mourhir et al., 2016)
(Neves et al., 2008)
(Oliveira & Antunes, 2004)
(Onu et al., 2017)
(Shiau & Liu, 2013)
(Streimikiene & Baležentis, 2013b) Mitigation
(Theodorou et al., 2010)
(Tsoutsos et al., 2009)
(Vaillancourt & Waaub, 2004)
Project (Diakoulaki et al., 2007) selection (Le Téno & Mareschal, 1998)
(Montanari, 2004)
(Perkoulidis et al., 2010)
(Ramazankhami et al., 2016)
(Vahabzadeh et al., 2015)
(Xu et al., 2016)
Risk (Branco et al., 2012) assessment
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 14
Application Contributors Sector Explicitly Area about climate Agriculture Buildings Environment Industry Power Transport policy Sector
Scenario (Baležentis & Streimikiene, 2017) analysis (Biloslavo & Dolinšek, 2010)
(Biloslavo & Grebenc, 2012)
(Jayaraman te al., 2015)
(Jun et al., 2013)
(Papadopoulos & Karagiannidis, 2008)
Technology (Almaraz et al., 2013) assessment (Antunes et al., 2004)
(Brand & Missaoui, 2014)
(Büyüközkan & Güleryüz, 2017)
(Büyüközkana & Karabulutb, 2017)
(Chang et al., 2012)
(Cowan et al., 2010)
(Cutz et al., 2016)
(Doukas et al., 2006)
(Fozer et al., 2017)
(Ghafghazi et al., 2010)
(Karakosta et al., 2009)
(Kaya & Kahraman, 2011)
(Klein & Whalley, 2015)
(Madlener et al., 2009)
(Maimoun et al., 2015)
(Mohamadabadi et al. 2009)
(Onar et al., 2015)
(Paul et al., 2015)
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 15
Application Contributors Sector Explicitly Area about climate Agriculture Buildings Environment Industry Power Transport policy Sector
(Pilavachi et al., 2009)
(Promentilla et al., 2014)
(Ren & Lützen, 2015)
(Ribeiro et al., 2013)
(Rojas-Zerpa & Yusta, 2015)
(Roth et al., 2009)
(Sadeghi et al., 2012)
(Sakthivel at al., 2015)
(Şengül et al., 2015)
(Shmelev & van den Bergh, 2016)
(Streimikiene et al., 2016)
(Streimikiene & Baležentis, 2012)
(Streimikiene & Baležentis, 2013a)
(Talaei et al., 2014)
(Ulutaş, 2005)
(Volkart et al., 2016)
(Yap & Nixon, 2015)
Other (Dace & Blumberga, 2016) Mitigation
(Heo et al., 2010)
(Luthra et al., 2015)
MCDA can be carried out in different ways, regardless of the economic sector or problem (e.g. climate policy mitigation or adaptation) it is applied to. There currently exist a very large number of methodological frameworks, based on different approaches and paradigms, each one with benefits and drawbacks depending on the application scope and purpose.
Among the 26 different approaches that were found to have been used in this domain, three methods appear to be the most popular:
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 16
Analytic Hierarchy Process (AHP) (Saaty, 1990), a structured approach to organising and analysing complex decisions that draws from mathematics and psychology and is based on pairwise comparisons and stakeholder judgment; PROMETHEE (Brans et al., 1986) family of methods, another pairwise comparison approach that is based on partial or complete ranking of alternatives; TOPSIS (Lai et al., 1994), a distance-based method that calculates how far each alternative is from the ideal solution.
These are followed by the Weighted Sum Method, the outranking ELECTRE (Roy et al., 1986) family of methods, the compromise-oriented VIKOR (Opricovic and Tzeng, 2004) method and the fuzzy versions of AHP and TOPSIS. Multiple-objective programming approaches were also found in the literature, mostly including multi-objective linear programming and goal programming. Table 2 summarises the frameworks found in the review, along with the applications in which they were employed and the nature of the evaluation criteria against which the alternatives were assessed. Italic formatting indicates studies in which different multicriteria methodologies were employed, either in different stages of an integrated approach or for the purpose of comparing them with each other and enhancing robustness.
Table 2 Overview of the MCDA methodologies employed in the climate policy literature.
MCDA Authors Evaluation Criteria1 Decision Sensitivity makers Analysis Methodology ECO ENE ENV REG SOC TEC OTH
AHP (Alsabbagh et al., 2016) Group
(Biloslavo & Dolinšek, 2010) Group
(Biloslavo & Grebenc, 2012) Group
(Blechinger & Shah, 2011) Group
(Borges & Villavicencio, 2004) Group
(Branco et al., 2012) Individual
(Büyüközkana & Karabulutb, 2017) Group
(Cowan et al., 2010) Group
(Javid et al., 2014) Group
(Konidari & Mavrakis, 2007) Group
(Montanari, 2004) Group
(Paul et al., 2015) Group
(Pilavachi et al., 2009) Individual
1 ECO = Economic, ENE = Energy-related, ENV = Environmental and climate-related, REG = Regulatory, SOC = Social, TEC = Technological, OTH = Other D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 17
MCDA Authors Evaluation Criteria1 Decision Sensitivity makers Analysis Methodology ECO ENE ENV REG SOC TEC OTH
(Rojas-Zerpa & Yusta, 2015) Group
(Shiau & Liu, 2013) Group
(Streimikiene et al., 2016) Group
(Talaei et al., 2014) Group
(Theodorou et al., 2010) Individual
(Yap & Nixon, 2015) Group
ANP (Büyüközkan & Güleryüz, 2017) Group
(Sakthivel at al., 2015) Group
(Ulutaş, 2005) Group
APIS (Shmelev & van den Bergh, 2016) Individual
ARAS (Baležentis & Streimikiene, 2017) Individual
(Streimikiene et al., 2016) Group
DEMATEL (Büyüközkan & Güleryüz, 2017) Group
ELECTRE (Georgopoulou et al., 2003) Group
(Karakosta et al., 2009) Group
(Madlener et al., 2009) Individual
(Michailidou et al., 2016) Group
(Neves et al., 2008) Group
(Papadopoulos & Karagiannidis, Individual 2008)
(Perkoulidis et al., 2010) Individual
(Theodorou et al., 2010) Individual
Fuzzy AHP (Heo et al., 2010) Group
(Kaya & Kahraman, 2011) Group
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 18
MCDA Authors Evaluation Criteria1 Decision Sensitivity makers Analysis Methodology ECO ENE ENV REG SOC TEC OTH
(Luthra et al., 2015) Group
(Onar et al., 2015) Individual
(Ren & Lützen, 2015) Group
(Sadeghi et al., 2012) Individual
Fuzzy ANP (Promentilla et al., 2014) Group
Fuzzy MCDM (Chang et al., 2012) Group
(Cutz et al., 2016) Group
Fuzzy PROMETHEE (Chen & Pan, 2015) Group
Fuzzy TOPSIS (Jun et al., 2013) Group
(Kaya & Kahraman, 2011) Group
(Onu et al., 2017) Group
(Sadeghi et al., 2012) Individual
(Şengül et al., 2015) Individual
Fuzzy VIKOR (Vahabzadeh et al., 2015) Group
MAUT (Konidari & Mavrakis, 2007) Group
MAVT (Fozer et al., 2017) Individual
MOORA (Paul et al., 2015) Group
MULTIMOORA (Streimikiene & Baležentis, 2012) Individual
(Streimikiene & Baležentis, 2013b) Individual
Multi-Objective Goal (Cowan et al., 2010) Group Programming (Cristóbal, 2012) Individual
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 19
MCDA Authors Evaluation Criteria1 Decision Sensitivity makers Analysis Methodology ECO ENE ENV REG SOC TEC OTH
(Jayaraman te al., 2015) Individual
Multi-Objective (Antunes et al., 2004) Individual Linear Programming (Oliveira & Antunes, 2004) Individual
(Ribeiro et al., 2013) Group
Point Allocation (Xu et al., 2016) Group Method
PROMETHEE (Batubara et al., 2016) Individual
(Borges & Villavicencio, 2004) Group
(Diakoulaki et al., 2007) Individual
(Doukas et al., 2006) Group
(Ghafghazi et al., 2010) Group
(Le Téno & Mareschal, 1998) Individual
(Mohamadabadi et al. 2009) Individual
(Paul et al., 2015) Group
(Theodorou et al., 2010) Individual
(Tsoutsos et al., 2009) Group
(Vaillancourt & Waaub, 2004) Group
(Xu et al., 2016) Group
SAW (Maimoun et al., 2015) Individual
SMART (Blechinger & Shah, 2011) Group
(Konidari & Mavrakis, 2007) Group
TOPSIS (Almaraz et al., 2013) Individual
(Baležentis & Streimikiene, 2017) Individual
(Brand & Missaoui, 2014) Group
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 20
MCDA Authors Evaluation Criteria1 Decision Sensitivity makers Analysis Methodology ECO ENE ENV REG SOC TEC OTH
(Büyüközkan & Güleryüz, 2017) Group
(Dace & Blumberga, 2016) Individual
(Jun et al., 2013) Group
(Maimoun et al., 2015) Individual
(Montanari, 2004) Group
(Mourhir et al., 2016) Group
(Ramazankhami et al., 2016) Individual
(Sakthivel at al., 2015) Group
(Streimikiene & Baležentis, 2012) Individual
(Streimikiene & Baležentis, 2013a) Individual
VIKOR (Büyüközkana & Karabulutb, 2017) Group
(Ramazankhami et al., 2016) Individual
(Ren & Lützen, 2015) Group
(Rojas-Zerpa & Yusta, 2015) Group
(Sakthivel at al., 2015) Group
WASPAS (Baležentis & Streimikiene, 2017) Individual
Weighted Sum (de Bruin et al., 2009) Group Method (Jun et al., 2013) Group
(Klein & Whalley, 2015) Individual
(Pilavachi et al., 2009) Individual
(Ribeiro et al., 2013) Group
(Roth et al., 2009) Group
(Volkart et al., 2016) Individual
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 21
It should be noted that, from a methodological point of view, Table 2 captures MCDA studies in the peer-reviewed literature with climate policy implications, excluding methodologies that have not been used in such studies but could nonetheless be effectively applied in climate policy. For example, the disaggregation-aggregation paradigm (Jacquet-Lagrèze and Siskos, 2001) has not been exploited in the climate policy domain so far. Due to its complex nature, however, climate policy making could significantly benefit from such approaches, and they have started to gain attention in the energy policy domain (e.g. Papapostolou et al., 2016; Papapostolou et al., 2017).
A very interesting outcome of this analysis can be found in the observation of the MCDA evolution trends in the problem domain, by identifying the frequency of adoption of each methodological framework during the past fifteen years. Figure 1 also reflects the growing attention that MCDA, as a sub-discipline of Operational Research, has been gaining lately. It should be noted that this analysis was concluded in May 2017, i.e. more MCDA studies may have been carried out, completed and published later in 2017.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 22
20 Weighted Sum Method
WASPAS
VIKOR
UTASTAR
TOPSIS
SMART
15 SAW PROMETHEE
Point Allocation Method
Multi-Objective Linear Programming Multi-Objective Goal Programming MULTIMOORA
MOORA 10 MAVT
MAUT
Fuzzy VIKOR
Fuzzy TOPSIS
Fuzzy PROMETHEE
Fuzzy MCDM
5 Fuzzy ANP
Fuzzy AHP
ELECTRE
DEMATEL
ARAS
APIS
ANP 0 AHP
Figure 1 Evolution trends of the MCDA methodological frameworks in the studied problem domain, 2003-2017
Source: Nikas et al. (2018)
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 23
Another significant aspect of multiple criteria analysis, on which the selection of the appropriate MCDA method largely depends, is the number of engaged decision makers it can support. Decision making in complex problems usually involves the selection of one or more solutions among multiple alternatives, evaluated against multiple criteria, by a large number of decision makers or stakeholder groups. The latter’s knowledge of the problem domain may largely vary, and their interests may not coincide (or even conflict) with each other but should nevertheless be taken into account before reaching a collectively acceptable solution. Stakeholder engagement in climate policy making, in particular, is especially important; as a complex, multi-dimensional and multi-disciplinary process that must result in robust, socially acceptable outcomes, it is a problem domain that would significantly benefit from the involvement of various stakeholder groups in the process. MCDA approaches are designed to include at least one decision maker. Table 2 also indicates which studies appear to exclude, potentially support or even promote group decision making.
Depending on the nature (problematic, application area, sector, geographic scope, etc.) of the problem, the criteria of the evaluation are very carefully selected. Modelling a consistent family of evaluation criteria — which are assumed to be non-redundant, exhaustive and cohesive — is a critical process that, according to Roy (1985), takes place at a very early stage, following the determination of the type of problematic (choice, sorting, ranking or description) and the set of different alternatives (Siskos et al., 2005). In MCDA studies with climate policy implications, the selected criteria can be classified into the following dimensions: economic (e.g. debt, economic efficiency, net present value, incentives and subsidies, etc.); energy (e.g. energy intensity, energy efficiency, consumption, contribution to energy independence or security, etc.); environmental and climate-related (e.g. greenhouse gas emissions reduction, mitigation of negative effects to the environment, etc.); regulatory (e.g. accordance or compatibility with the legal framework); social (e.g. societal acceptance, creation of jobs, etc.); technological (e.g. autonomy, reliability, technology transfer capacity, fuel flexibility, etc.); and other criteria (e.g. cooperation capacity, visual impact, etc.). Most of the studies used criteria from multiple dimensions. Only a limited number of studies, however, used criteria across all of the above listed dimensions (Ulutaş, 2005; and Biloslavo and Grebenc, 2012), while others only focused on the environmental and climate mitigation aspects of their problem ignoring other dimensions.
It is noteworthy that both multicriteria decision making and the problems in which it is employed usually feature significant uncertainties. There are multiple ways in which uncertainty is incorporated and dealt with in MCDA applications, with the most prominent one being sensitivity analysis. In fact, a little less than half of the applications reviewed, appeared to perform some kind of sensitivity analysis, as shown in Table 2. Only a limited number of these, however, explicitly referred to the robustness of their results, which was usually studied through the implementation of different MCDA frameworks in the aim of enhancing robustness (Rojas-Zerpa and Yusta, 2015; and Streimikiene and Baležentis, 2012).
Climate policy, on the other hand, features both risks and uncertainties that are not only limited to the methodological frameworks used for supporting policy making, but include others that are
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 24
relevant to the problem domain. In other words, they are part of ‘external’ uncertainties and risks that refer to conditions of the problem domain and are outside the control of the decision maker or the modeller. In the MCDA literature, however, these are hardly ever taken directly into account.
One popular way of dealing with such uncertainties is the use of different scenarios: Jun et al. (2013) quantified the flood risk vulnerability in South Korea by considering climate change impacts and comparing various climate change scenarios, while Michailidou et al. (2016) explored a climate change mitigation and adaptation strategy in tourist areas by means of different what-if scenarios. In a different setting, with the aim of dealing with the multi-criteria nature of IAMs’ outputs, Baležentis and Streimikiene (2017) attempted to rank different energy policy scenarios in the European Union. Interestingly, only one study used multicriteria analysis to perform risk assessment: specifically, Branco et al. (2012) used AHP in order to quantify the exposure level of a selected set of oil companies in the EU to carbon risk. This finding indicates that multicriteria risk assessment is largely underexploited in climate policy studies, even when we consider the fact that quantitatively assessing risks and uncertainties is only feasible in a limited number of relevant aspects of climate policy.
Although the vast majority of applications appear not to directly assess these climate change (or policy-related) risks and uncertainties, risk management has been indirectly incorporated in certain studies, either as risk-based policies in the set of alternative actions or in the form of risk indices as evaluation criteria (e.g. de Bruin et al., 2009). In other cases, other risk-oriented frameworks were integrated with the overall approach, such as the Political, Economic, Social, Technological, Legal, and Environmental (PESTLE) risk analysis framework or the Benefits- Opportunities-Costs-Risks (BOCR) framework. More insights regarding the different methods, models, frameworks and approaches that have been integrated with MCDA models in the reviewed pieces of literature can be found in Table 3.
An interesting finding of our review concerns the evolution trends of MCDA in the reviewed literature: some traditional approaches (such as ELECTRE and PROMETHEE) have remained popular in the literature, while others have gained more attention over the years (e.g. AHP and TOPSIS). More importantly it appears that newer methodologies, such as VIKOR and fuzzy versions of traditional techniques, have been gaining ground in the climate policy literature.
As already discussed, most applications do not solely base their results on one or multiple MCDA frameworks but rather employ more integrated approaches by combining multicriteria analysis with other methodologies at different stages. Among the many frameworks, models or methodologies that are displayed in Table 3, the most prominent ones are the different communication techniques, like Delphi (e.g. Cowan et al., 2010; and Xu et al., 2016) and Input- Output Analysis (e.g. Jayaraman et al., 2015). Another finding is that the other two decision support systems reviewed in this paper – Fuzzy Cognitive Maps (e.g. Shiau and Liu, 2013) and Portfolio Analysis (Almaraz et al., 2013) - are integrated with MCDA in different configurations. Most importantly, however, existing approaches integrating MCDA with IAMs, such as WITCH and TIAM (Baležentisa & Streimikiene, 2017), AIM and TIMES (Vaillancourt and Waaub, 2004), and
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 25
MARKAL (Shmelev and van den Bergh, 2016), prove that climate-economy modelling can benefit from MCDA frameworks, in various integration settings.
Table 3 Overview of climate policy studies in which multiple criteria decision making is integrated with other methodologies.
Category Approach Applications
Communication Delphi (Biloslavo & Dolinšek, 2010)
(Biloslavo & Grebenc, 2012)
(Cowan et al., 2010)
(Jun et al., 2013)
(Roth et al., 2009)
(Xu et al., 2016)
Input-Output Analysis (Cristóbal, 2012)
(Jayaraman et al., 2015)
(Oliveira & Antunes, 2004)
Semi-Quantitative Fuzzy Cognitive Mapping (Biloslavo & Dolinšek, 2010) Modelling (Biloslavo & Grebenc, 2012)
(Mourhir et al., 2016)
(Shiau & Liu, 2013)
Quantitative Climate Models (Jun et al., 2013) Modelling Energy System Models (Shmelev & van den Bergh, 2016)
(Brand & Missaoui, 2014)
(Vaillancourt & Waaub, 2004)
Integrated Assessment Models (Baležentis & Streimikiene, 2017)
Portfolio Analysis (Almaraz et al., 2013)
Uncertainty Monte Carlo Analysis (Baležentis & Streimikiene, 2017) assessment (Shmelev & van den Bergh, 2016)
Other Benefits – Opportunities – Costs – Risks (BOCR) Framework (Ulutaş, 2005)
(Yap & Nixon, 2015)
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 26
Category Approach Applications
Data Envelopment Analysis (Madlener et al., 2009)
Driving forces – Pressures – State – Impact – Responses (DPSIR) Framework (Mourhir et al., 2016)
Life Cycle Analysis (Le Téno & Mareschal, 1998)
(Fozer et al., 2017)
(Roth et al., 2009)
(Volkart et al., 2016)
Finally, with regard to their geographic scope, most climate policy-related MCDA studies appear to have been carried out in Europe on a national level, with only a few on an EU- (Baležentisa and Streimikiene, 2017; and Branco et al., 2012) or regional level (Konidari and Mavrakis, 2007; and Xu et al., 2016). Only one study focused on two countries across different regions (Yap and Nixon, 2015). Table 4 summarises the geographic scope of the reviewed studies.
Table 4 Geographic scope of MCDA studies with climate policy implications Application Region of Case Study Area Africa North America Central and South Asia Europe America Evaluating (Mourhir et (Javid et al., 2014) (Blechinger and (Alsabbagh et al., (Cristóbal, 2012) policy al., 2016) Shah, 2011) 2016) (de Bruin et al., 2009) instruments and (Onu et al., (Borges and (Batubara et al., (Georgopoulou et al., 2003) strategies 2017) Villavicencio, 2004) 2016) (Konidari and Mavrakis, 2007) (Chen and Pan, 2015) (Michailidou et al., 2016) (Shiaua and Liu, (Oliveira and Antunes, 2004) 2013) (Streimikiene and Baležentis, 2013b) (Theodorou et al., 2010) (Tsoutsos et al., 2009)
Selecting (Ramazankhami et (Diakoulaki et al., 2007) projects al., 2016) (Montanari, 2004) (Perkoulidis et al., 2010) (Xu et al., 2016)
Assessing risks (Branco et al., 2012)
Evaluating (Jayaraman te al., (Baležentisa and disccrete 2015) Streimikiene, 2017) scenarios (Jun et al., 2013) (Papadopoulos and Karagiannidis, 2008)
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 27
Assessing (Brand and (Cowan et al., (Cutz et al., 2016) (Büyüközkan and (Almaraz et al., 2013) different Missaoui, 2010) (Rojas-Zerpa and Güleryüz, 2017) (Antunes et al., 2004) technological 2014) (Cutz et al., 2016) Yusta, 2015) (Büyüközkana and (Doukas et al., 2006) options (Karakosta et (Doukas et al., Karabulutb, 2017) (Ribeiro et al., 2013) al., 2009) 2006) (Karakosta et al., (Roth et al., 2009) (Fozer et al., 2017) 2009) (Shmelev and van den Bergh, (Ghafghazi et al., (Onar et al., 2015) 2016) 2010) (Paul et al., 2015) (Shmelev and van den Bergh, (Karakosta et al., (Promentilla et al., 2016) 2009) 2014) (Streimikiene et al., 2016) (Klein and Whalley, (Sadeghi et al., 2012) (Streimikiene and Baležentis, 2015) (Şengül et al., 2015) 2013a) (Maimoun et al., (Talaei et al., 2014) (Volkart et al., 2016) 2015) (Ulutaş, 2005) (Yap and Nixon, 2015) (Mohamadabadi et (Yap and Nixon, al. 2009) 2015)
Prioritising (Heo et al., 2010) factors (Luthra et al., 2015)
Evaluating (Papapostolou (Dace and Blumberga, 2016) countries et al., 2016) (Papapostolou et al., 2017)
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 28
4 METHODOLOGICAL FRAMEWORK
This section constitutes part of research published in (Nikas et al., 2018).
4.1 MCDA Method Selection
Drawing from the results of our literature review (Section 3), the Technique for Order of Preference by Similarity to Ideal Solution or TOPSIS (Hwang and Yoon, 1981) multicriteria analysis method was selected to be used as the principal component of the TRANSrisk methodological framework. TOPSIS was developed as an alternative to the ELECTRE family of methods and is a compensatory aggregation method. This is based on the principle that the selected action must feature the shortest geometric distance from the most positive ideal solution, and the largest from the most negative ideal solution. The TOPSIS approach includes: the formulation and normalisation of the decision (alternatives against criteria) table; the calculation of the weighted decision table, the determination of the positive (for benefit-associated criteria) and negative (for cost- associated criteria) ideal solutions; and the calculation of the distance of each alternative from these solutions, towards reaching a final ranking. TOPSIS was later extended by Chen (2000), who developed ‘Fuzzy TOPSIS’, which was further extended to handle different types of input data (Chen and Tsao, 2008; Chen and Lee, 2010).
As can be seen in Table 1, Table 2 and Table 4, as well as in Figure 1, there have been a large number of TOPSIS and Fuzzy TOPSIS applications in the climate policy literature. These show a diverse set of application areas over a range of economic sectors, and can evaluate alternative actions against criteria over a diverse set of evaluation dimensions. Part of the rationale behind selecting TOPSIS is summarised in Table 5. One of the reasons is that TOPSIS appears to feature the most balanced distribution of individual and group decision making applications, a fact that indicates its capacity to support any MCDA problem regardless of the number of stakeholders in the decision making process. Furthermore, sensitivity analysis has been carried out in many of these studies, leading to enhanced robustness of their results.
But, most importantly, and in line with the core objective of the TRANSrisk project, TOPSIS features the largest number of applications in which the multiple-criteria decision making method is integrated with a quantitative modelling framework. In fact, with the exception of two studies that used two different MCDA frameworks, namely APIS (Shmelev and van den Bergh, 2016) and PROMETHEE (Vaillancourt and Waaub, 2004), only TOPSIS has been integrated with purely quantitative modelling frameworks. More specifically, Almaraz et al. (2013) design a hydrogen supply chain through mixed integer linear programming and assess a number of solutions based on their cost, climate mitigation potential and safety risk, by integrating an ε-constraint portfolio analysis approach with TOPSIS as well as a modified version of TOPSIS (M-TOPSIS).
Baležentisa and Streimikiene (2017) ranked a number of energy policy scenarios in the European Union, by means of two integrated assessment models (WITCH and TIAM-WORLD) and three different multicriteria analysis methodologies (WASPAS, ARAS and TOPSIS), while also applying
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 29
Monte Carlo simulations for analysing the sensitivity of their results against changes to criterion weights. Brand and Missaoui (2014) referred to TOPSIS as featuring a logic closely representing the rationale of human choice and a simple computation process; they linked it with an electricity system model presented in Brand et al. (2012) in order to assess different electricity generation mixes in Tunisia. In a more climate-change-oriented approach, Jun et al. (2013) used two climate change models of the National Center for Atmosphere Research, namely the Community Climate System Model 3 and the MM5 mesoscale model, in order to develop 19 different climate change scenarios, which they ranked with Fuzzy TOPSIS. Last but not least, and outside the strictly quantitative climate and energy modelling framework, Mourhir et al. (2016) used the Driving Forces-Pressures-State-Impact-Response (DPSIR) Integrated Environmental Assessment framework to enable their stakeholders to select appropriate indicators. These were later used by their experts to qualitatively design and describe Fuzzy Cognitive Maps; the latter were then quasi- quantitatively simulated and the most optimal scenarios were then ranked in a TOPSIS-oriented MCDA framework.
Table 5 An overview of modelling-integrated TOPSIS applications used in the climate policy literature. Study Stakeholders Sensitivity Integration with modelling frameworks Analysis
(Almaraz et al., 2013) Individual Portfolio Analysis (ε-constraint) (Baležentisa and Streimikiene, Individual Integrated Assessment Modelling (TIAM, WITCH); Monte Carlo 2017) Analysis (Brand and Missaoui, 2014) Group Electricity System Modelling
(Büyüközkan and Güleryüz, 2017) Group (Dace and Blumberga, 2016) Individual (Jun et al., 2013) Group Climate System Modelling (CCSM3; MM5)
(Kaya and Kahraman, 2011) Group
(Maimoun et al., 2015) Individual (Montanari, 2004) Group (Mourhir et al., 2016) Group Fuzzy Cognitive Mapping; Integrated Environmental Assessment (Driving Forces–Pressures–State–Impact–Response) (Onu et al., 2017) Group
(Ramazankhami et al., 2016) Individual (Sadeghi et al., 2012) Individual (Sakthivel at al., 2015) Group
(Şengül et al., 2015) Individual (Streimikiene and Baležentis, Individual 2012) (Streimikiene and Baležentis, Individual 2013a)
Despite the fact that a plethora of MCDA approaches exists (e.g. PROMETHEE, ELECTRE, AHP, UTA, etc.) (Greco et al., 2016), the outcomes of the literature analysis summarise part of the logic behind selecting the TOPSIS multicriteria method as the heart of our methodological framework. Furthermore, as Kim et al. (1997) and Shih et al. (2007) note, TOPSIS features a sound logic that represents the rationale of individual choice; simultaneously considers both the ideal and the anti- ideal solutions; and employs a systematic, explicit and easily programmable computation
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 30
procedure. Unlike pairwise comparison methods, it also allows for a large number of both criteria and alternatives.
Aside from the recorded advantages and the domain popularity of the method, as indicated by the results of the literature review, the selection of TOPSIS was also motivated by the availability of its methodological extensions in the fuzzy environment. This facilitates the prospective enhancement of the tool, as well as its capacity to effectively tackle the problem of interest (ranking the alternatives). Finally, despite its 36-year presence in the MCDA discipline, and driven by the need to include the notion of loss aversion behaviour, the TOPSIS framework was recently further enhanced by part of the research team that originally introduced it, producing Behavioural TOPSIS (Yoon and Kyung, 2017).
The proposed approach comprises three stages: (a) unification of input data, (b) multi-criteria analysis, and (c) consensus control. In this respect, a spreadsheet-based tool, MACE-DSS, has been developed, with the following features:
Capacity to deal with problems of up to 12 alternatives, against up to 12 criteria, evaluated by up to 12 decision makers (or stakeholders); Assessment of two different linguistic term scales; Assignment of criteria and expert weights; Specification of consensus control thresholds.
The user is only required to fill in the input data in the first sheet, which includes:
Expert judgment for each alternative against each criterion; Number of alternatives, criteria and experts; Thresholds; Selected scale, criteria and expert weights.
These three stages are described in detail in the sub-sections below.
4.2 Data Unification
The original TOPSIS framework works with numerical data, however certain stakeholders may either be reluctant to provide their insights in a purely numerical scale or find it hard to express them in such a scale because of the qualitative nature of such opinions (Agell et al. 2012; Estrella et al., 2017). We, therefore, need to allow the option of providing input via linguistic variables, which has been commonly used in the literature. However, before proceeding to a multicriteria analysis, there must be consistency among the input data.
As a result, all data is initially transformed into a uniform numerical scale. The analyst may choose a specific numerical scale and an appropriate linguistic term scale, so that every decision maker is free to provide their input into whichever scale they feel more comfortable with. The terms of
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 31
the linguistic scale are matched with discrete numbers of the numerical scale, and the final input is numerical.
Given that numerical input can be continuous whereas linguistic variables are discrete, MACE-DSS also allows for assessing a hybrid input model, aimed at decision makers who wish to provide their input into the form of a linguistic term “and then some”, in order to compensate for the precision gap between the two types of input.
The multi-criteria analysis is largely based on the TOPSIS model (Hwang and Yoon, 1981), which includes the following steps:
1. Designing the decision matrix 퐴 (Equation 1), which comprises alternatives and evaluation criteria
C
C1 C 2 ... C n
A1 x 11 x 12 ... x 1n (1) A2 x 21 x 22 ... x 2n A Am x m1 x m2 ... x mn
Where 퐴1, 퐴2, … , 퐴푚 , 푖 = 1,2, … , 푚 are the alternatives, 퐶1, 퐶2, … , 퐶푛, 푗 = 1,2, … , 푛, are the criteria and 푥푖푗 xijis the score of alternative 퐴푖Ai against criterion 퐶푗Cj.
2. Calculating the normalised decision matrix 푅 (Equation 2), where each element can be calculated as follows:
푥ij 푟ij = 푚 2 (2) √∑ 푥푖푗 푖=1
Where 푟푖푗 represents the normalised score of 퐴푖 Aiagainst criterion 퐶푗Cj.
3. Calculating the weighted normalised matrix 푃, by multiplying the normalised matrix 푅 with the normalised weights.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 32
푝푖푗 = 푤푗 × 푟푖푗 (3)
Wj n Where wj = n , j = 1,2, … , n so that ∑j=1 wj = 1 and Wj = [W1, W2, … , Wn] is the original ∑k=1 Wk, weight vector for each criterion 퐶푗. 4. Determining the positive ideal 푃+ (positive impact criteria) and negative ideal 푃− (negative impact criteria) solution vectors (Equations 4 and 5, respectively), by calculating the positive and negative ideal solutions for each criterion (Equations 6 and 7, respectively).
+ + + + 푃 = (푝1 , 푝2 , … , 푝푛 ) (4)
+ − − − 푃 = (푝1 , 푝2 , … , 푝푛 ) (5)
+ 푝푖 = {(max 푝푖푗 , 푗 휖 퐽) 표푟 (min 푝푖푗, 푗 휖 풥′)} (6)
− 푝푖 = {(max 푝푖푗 , 푗 휖 퐽) 표푟 (min 푝푖푗, 푗 휖 퐽′)} (7)
Where 퐽 represents positive impact criteria and 퐽′ represents negative impact criteria.
5. Calculating the distance of each alternative from the positive ideal solution (Equation 8) and the negative ideal solution (Equation 9).
푛 + + 2 푆 휄 = √∑(푝푖푗 − 푝푗 ) (8) 푗=1
푛 − − 2 푆 휄 = √∑(푝푖푗 − 푝푗 ) (9) 푗=1
6. Finally, calculating the relative closeness 퐷푖 Cito the ideal solution for each 퐴푖, as shown in Equation 10.
− 푆 푖 퐷 푖 = + − (10) 푆 푖 + 푆 푖
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 33
In group decision making processes, aggregating input is one of the most critical aspects (Lan et al., 2013). For the purposes of developing a group decision making decision support tool, the proposed approach involves utilising the TOPSIS methodological framework twice: once for aggregating the individual preference models of the stakeholders into a global model, and then again for assessing the global model towards reaching a final ranking. This approach is presented in (Krohling and Campanharo, 2011), although that particular study used the Fuzzy TOPSIS approach based on triangular fuzzy numbers.
In this approach, after eliciting all decision makers’ and/or stakeholders’ knowledge and unifying it into a consistent numerical scale, the TOPSIS model is used for each one of the 푙 decision makers. A relative closeness matrix (Global Closeness, or 퐺퐶) is then formulated, so as to incorporate all individual preference models (Equation 13).
1 푙 퐶 1 퐶 1 퐺퐶 = [ ] (13) 1 푙 퐶 푚 퐶 푚
Furthermore, if there have been weights determined for each of the stakeholders and based on the respective weight vector 푊퐸 = (푤푒1, 푤푒2, … , 푤푒푙), one can then calculate the weighted global model matrix (Equation 14).
1 푙 푤푒1퐶 1 푤푒1퐶 1 푊퐺퐶 = [ ] (14) 1 푙 푤푒1퐶 푚 푤푒1퐶 푚
Alternatively, the user may select a different TOPSIS-oriented approach, in which the weighted sum method (WSM) is implemented towards reaching one single score for each alternative against each criterion, thereby aggregating the scores of all decision makers, and then the TOPSIS framework is implemented once towards reaching the final ranking. This approach has been suggested by (Chen, 2000), for the original TOPSIS method.
For the sake of enhancing diversity and robustness of results, a substantially different approach based on an aggregation and translation model, as presented in (Herrera et al., 2005), is also modelled in the tool. This considers that the final evaluation of alternative actions must be expressed in comprehensible, easy-to-digest linguistic terms. This approach essentially uses the weighted sum method and the 2-tuple linguistic computational model (Martínez and Herrera, 2012), a symbolic model that improves other linguistic modelling approaches in several ways (Rodríguez and Martínez, 2013). It translates aggregated data into comprehensible information while ensuring that no loss of data occurs in the process:
The linguistic computational model based on linguistic 2-tuples carries out linguistic computational processes easily and without loss of information.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 34
The linguistic domain can be treated as continuous, whilst in the classical linguistic models it is treated as discrete. The results of the computational processes are always expressed in the initial linguistic domain extended to a pair of values that include the linguistic label and additional information.
To represent the linguistic information, this model uses a pair of values called linguistic 2-tuple (s, α), where s is a linguistic term and α is a numeric value representing a symbolic translation.
Definition 1. Let 푆 = {푠0, … , 푠푔} be a linguistic term set and β be the result of an aggregation of the indexes of a set of labels assessed in a linguistic term set S, i.e., the result of a symbolic aggregation operation. β in [0,g], being g + 1 the cardinality of S. Let i = round(β) and α = β - i be two values, such that i in [-0.5,0.5), then α is called a symbolic translation.
The symbolic translation of a linguistic term si is a numerical value within [-0.5, 0.5) indicating the difference of the information between the calculated value 훽 ∈ [0, 푔], and its closest element within {푠0, … , 푠푔} indicating the content of the closest linguistic term 푆 (푖 = 푟표푢푛푑(훽)).
This 2-tuple linguistic representation model extends the use of indexes modifying the fuzzy linguistic approach adding a new parameter, the so-called symbolic translation, and representing the linguistic information by means of a linguistic 2-tuple (si,α) ∈ S×[-0.5,0.5), being s ∈ S a linguistic term and α ∈ [−0.5,0.5) a numerical value representing the symbolic translation (Equation 15).
[−0.5, 0.5), 푖푓푠푖 ∈ {푠1 , 푠2, … , 푠푔−1} 푎 = { [0, 0.5), 푖푓 푠푖 = 푠0 (15) [−0.5, 0), 푖푓 푠푖 = 푠푔
The 2-tuple linguistic model defines a set of functions between linguistic 2-tuples and numerical values that facilitates the accurate computations with linguistic information.
Definition 2. Let 푆 = {푆0, … , 푠푔} be a linguistic term set and 훽휖 [0, 푔] a value supporting the result of a symbolic aggregation operation. Then the 2-tuple that expresses the equivalent information to 훽 is obtained with the function of Equation 16.
∆: [0, 푔] → 푆 × (−0.5, 0.5) (16) 푠 푖 = 푟표푢푛푑 (훽) ∆ (훽) = (푠 , 훼), 푤푖푡ℎ { 푖 푖 훼 = 훽 − 푖 훼휖[−0.5, 0,5)
Where round is the usual round operation, 푠푖 has the closest index label to 훽 and 훼 is the value of the symbolic translation.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 35
For example, for a 5-term scale {푁표푛푒, 퐿표푤, 푀푒푑푖푢푚, 퐻푖푔ℎ, 푃푒푟푓푒푐푡} and 훽 = 3.8, the 2-tuple representation of this information would be ∆(3.8) = (푃푒푟푓푒푐푡, −0.3).
Proposition 1. Let 푆 = {푆0, … , 푠푔} be a linguistic term set and (푆푖, 훼푖) be a linguistic 2-tuple. There is always a Δ−1 function, such that, from a 2-tuple it returns its equivalent numerical value 훽휖 [0,g] in the interval of granularity of S (Equation 17).
Proof. It is trivial, we consider the following function:
∆−1: 푆 × {−0,5, 0.5) → [0, 푔] (17) −1 ∆ (푠푖, 훼) = 푖 + 푎 = 훽
Remark 1. From Definitions 1 and 2 and Proposition 1, it is obvious that the conversion of a linguistic term into a linguistic 2-tuple consists of adding a value 0 as symbolic translation: 푠푖 휖 푆 ⇒ (푠푖, 0)
This model has a computational technique based on the 2-tuple linguistic representation model that has been widely described in (Martínez and Herrera, 2012).
4.3 Consensus Analysis
Although group decision making is an iterative process in which selection and consensus are intertwined (Choudhury et al., 2006), consensus control in MCDA is in principal a separate process (Dong et al., 2010). MACE-DSS allows for consensus control, an outlier detection process based on a classical statistical approach, which enables the analyst to easily detect potential disagreements among stakeholders across different stages of the process, in order to polish these disagreements and increase consensus. As a result, the input from participating decision makers (i.e. in our case stakeholders) can be evaluated to reach valuable insights into their behaviour. If there is perceived bias, weights can be modified accordingly.
At a very early stage and regardless of the chosen MCDA framework, an analysis per decision maker is carried out based on their input. A horizontal analysis calculates the mean value and standard deviation for each alternative and against all criteria; the standard score is calculated and compared against a set of user-defined thresholds, one for slight and one for large deviation, in which cases the corresponding cell turns yellow and red respectively. The standard score expresses the number of standard deviations by which a value is above or below the mean value. In this respect, the user may select thresholds of their own preference; for example, a threshold of 1.0 reflects value deviation from the mean value by one standard deviation. A standard score above the user-defined upper threshold indicates large deviation, while a standard score between the two thresholds indicates slight deviation.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 36
This type of analysis helps to identify whether the expert has provided extreme scores for specific criteria, for each alternative. A vertical analysis calculates the mean value and standard deviation of all alternatives for each criterion; again, by comparing the standard score to the user-defined thresholds, the user can determine whether extreme scores have been elicited for specific alternatives, in each criterion. Additionally, a vertical analysis is also carried out per alternative, in which for every alternative the input of all stakeholders is compared against the mean value and standard deviation of the group for each criterion. This constitutes MACE-DSS’s most significant early level analysis feature, since it allows for identifying whether the overall behaviour of a decision maker is in line with the other stakeholders. Such an approach can lead to penalising a stakeholder, by modifying the weight of their input. Stakeholder penalisation is sometimes used for cases where stakeholders appear less knowledgeable in a subject than others or where stakeholders employ strategies towards optimising their individual payoff resulting from the process (Yager, 2001). It is not uncommon in the MCDA literature (e.g. Yager, 2002; Quesada et al., 2015); however, the present study only features an analysis of divergence among stakeholders (and/or stakeholder groups) and no penalisation is carried out.
Finally, for each methodological framework, a post-MCDA analysis is carried out. In this type of analysis, the scores for each criterion have been aggregated—as per respective MCDA framework. As a result, two analyses take place at the Alternatives Stakeholder table, similar to the horizontal and vertical early-level analyses but instead comparing each stakeholder against the others.
The aforementioned consensus control options are displayed in Figure 2.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 37
Figure 2 Consensus analysis in MACE-DSS
Source: Nikas et al. (2018)
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 38
5 ASSESSMENT OF CLIMATE POLICY RISKS IN TRANSRISK
5.1 Evaluating risks associated with a low-carbon transition of the Greek economy
The European Union (EU) has committed to actions focusing on diffusing renewables and enhancing energy efficiency, at both the community and the Member State leveI. This is in pursuit of mitigating greenhouse gas (GHG) emissions and enhancing security of energy supply and socio- economic sustainability. Drawing from its respective national commitments, as well as the need to respond to the European and global efforts in the climate change front, Greece too has recently been striving to design and implement an effective and sustainable energy policy framework.
These efforts have been a dynamic, learning process, through which the policy framework is redesigned along the way. Such a framework encompasses policy instruments, measures and interventions, including financial incentives and tax breaks, in the renewable energy and energy efficiency areas. These actions primarily regard power generation as well as the built environment, but to some extent are also focused on the energy efficiency of the transport sector. These two areas are strongly intertwined: energy efficiency in the building sector also includes initiatives for micro-generation, and thus has energy policy implications and synergies with further diffusion of renewables. So far, the energy policy framework has oriented on financial incentives for the diffusion of renewable energy as well as building renovation across all scales: residential, public and private.
In respect to progress in achieving near-term energy efficiency targets, Greece appears to lag behind its goals (Nikas et al., 2019a). On the renewable energy front, the country appears not to be on track either (Capros et al., 2016); this has implications for energy efficiency in buildings, since various energy-efficient actions are focused on electricity generation from building integrated photovoltaics. Significant potential exists for exploiting renewable sources as well as improving energy efficiency in the country. Greece undoubtedly has a large potential for electricity generation from renewable energy sources (RES-E), specifically solar and wind energy (Tigas et al., 2015); due to favourable weather conditions and adequate uncultivated land. The building sector, in particular, has significant room for decarbonisation, as about 25-30% of final energy is consumed in the residential sector (Centre for Renewable Energy Sources and Saving, 2015). Considering its predominantly poor energy performance, with about six out of ten buildings having been constructed before 1980 (Hellenic Statistical Authority, 2011), the current building stock is in need of immediate renovations.
Given this potential, it is evident that current difficulties in delivering on the national energy efficiency and climate action-related commitments can, at least in significant part, be attributed to the underlying risks and uncertainties that essentially did not allow for the successful implementation of the past policy framework. At the same time, there has been little consideration of potential negative outcomes from relevant policies, thereby allowing for adverse
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 39
consequences to manifest. For example, despite their positive impacts (HELAPCO, 2016), policies aimed at further developing the solar power sector have had detrimental economic side effects (Tselepis, 2013), and a series of amending regulatory efforts have created an uncertain investment environment.
Until recently, the national policy framework for solar power development and energy efficiency has comprised actions, incentives and interventions, the design of which had been based on quantitative modelling outcomes. By looking at the inadequacy of this framework, as reported in the latest national energy efficiency action plan (Ministry of Environment and Energy, 2017), it seems that scientific and policymaking processes may have ignored certain implementation risks and uncertainties. In addition, they failed to foresee the manifestation of negative consequences as a result of the policy framework implementation. These risks and uncertainties, however, if overlooked, can jeopardise both the national 2030 energy policy framework and overall efforts towards long-term decarbonisation. Consequently, resulting failure to accomplish the near-term national goals on the energy efficiency front raises questions not only on the country’s capacity to realise long-term decarbonisation visions but also on the existence of such visions in the first place. In this context, it is necessary to carefully examine the risks hindering the promotion of a sustainable transition pathway, in the longer-term, based on energy efficiency and solar power diffusion.
Parts of this sub-section have been published in (Nikas et al., 2019a).
5.1.1 Context
The Greek energy policy framework is uncertain per se: an overview of the past pathway indicates that, at least as far as energy policy is concerned, the country has shown significant delays in adopting Community Directives in the national policy framework. On the energy efficiency front, after the early determination of thermal insulation requirements for buildings in 1979, the first notable policy in the country was the late adoption (in 1998) of the 1993 EU directive on limiting carbon emissions by improving energy efficiency. Respectively, the National Law 3661/2008 brought Directive 2002/91/EC in effect, by defining minimum energy performance requirements; introducing energy performance certificates, and referring to qualified and accredited energy inspectors. It was later amended in 2010, so as to provide for the gradual implementation of energy management systems in all public buildings. Although the adoption of the Regulation on the Energy Performance of Buildings (KENAK) in 2010 is considered to have set a milestone in national energy efficiency policy and has since been updated based on Community regulatory advancements - it was the belated Law 4342/2015 that adopted the Union’s 2020 objectives (Directive 2012/27/EU) in Greece. This record of significant delays in adopting European Directives in the national policy framework can itself be considered as an uncertainty for future developments in both energy and climate policy.
For renewable energy, which could prove instrumental in accomplishing targets regarding both energy efficiency and renewable energy, large investments took place in a period of strong fiscal incentives for boosting solar power development (2008-2012), which eventually led to an excessive
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 40
burden to the Operator. Due to the ever-growing deficit, the government implemented a 12.5% cut in the support scheme for solar power coming online from February 2012, and a larger (44%) cut on feed-in tariff (FIT) rates for solar PV plants installed after January 2013. In fact, it was only the attractive FIT contracts that sustained the momentum of solar power growth in 2013, despite the economic recession and the unfavourable changes to the incentive system. The latter, in combination with a freeze on the receipt and processing of new solar power investment applications from August 2012 to April 2014 as well as an unexpected shift towards new lignite- fired plants, constitute the background of an ever-changing regulatory environment and consequent caution and mistrust in the policy framework.
Moreover, public perception of climatic change and of the urgency of the need to mitigate its impacts also appears to change in a period of economic crisis. In particular, Greek citizens seem to perceive the problems associated with climate change as a matter of priority. Although the vast majority of the Greek people are aware of environmental problems and, at the break of the recession, would consider climate change as the ultimate global problem (European Commission, 2008), trends were reversed five years later: the issues of poverty and the economic situation dramatically gained on climate change, in terms of prioritisation (European Commission, 2013). At the same time, citizens now appear to have very low confidence in authorities and big enterprises with regard to their capacity and willingness to deal with climate change (Papoulis et al., 2015). Uncertain societal cohesion adds to the already volatile context of the Greek efforts towards energy efficiency and decarbonisation, since a lack of public acceptance could potentially halt introduction and diffusion of technically and economically feasible technological options as well as the successful implementation of otherwise prominent policy instruments. Especially with regard to enhancing energy efficiency in the residential and commercial sectors, many of the measures considered, depend on behavioural change and rely heavily on initiatives that decision makers, at the small scale, are free to implement or disregard.
Last but not least, Greece also faces challenges and respective uncertainties in the political and economic axes. It is the country hit hardest by the 2008 crisis, which led to a cumulative output loss of 30% since the beginning of the recession; huge poverty levels (45%, when considering a poverty threshold anchored to 2008 in real terms) with consequent inequality and decline in average living standards; and implications for long-lasting societal consequences (Kaplanoglou and Rapanos, 2018). As a result, Greece also went through significant political instability and is still underway a crisis of political representation (Stavrakakis and Katsambekis, 2018). A by-product of this instability was an unexpected approval of new lignite-fired power plants, namely Ptolemaida V and Meliti II, of combined capacity of more than 1 GW (Simoglou et al., 2018). This gave rise to doubts on how determined the Greek government is to actually deliver on its energy and climate commitments.
Based on these conditions, many critical questions emerge: will Greece be politically and financially capable of incentivising long-term transition strategies? Even if this is the case, will citizens have the economic capacity and adequate trust in the stability of the policy framework to take advantage of the support mechanisms? It is evident that these uncertainties can foster the
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 41
manifestation of severe implementation risks to any policy framework or allow for negative consequences of the latter to realise.
5.1.2 Enhancing energy efficiency in Greece
5.1.2.1 Stakeholder input
A diverse group of ten stakeholders were interviewed in order to elicit their knowledge and assess the risks against specific evaluation criteria:
Likelihood to manifest; Impact on/from policy framework and/or transition pathway; Capacity to mitigate, and level of stakeholders’ concern.
The engaged stakeholder group comprised three policymakers from the Ministry of Environment and Energy (PM1, PM2 and PM3), two researchers (R1 and R2), one stakeholder from the financial sector (F1), one representative of electric utilities and regulators (U1), one member of associations involved in the provision of GHG emissions (A1), and two technology suppliers, including manufacturers and importers (T1 and T2). The stakeholder engagement process included one round of detailed discussions on the topic; the assembly of the identified risks; and a second round featuring semi-structured questionnaires for the purposes of carrying out the multicriteria analysis.
Among the various sectors discussed in the context of a long-term decarbonisation vision, it was acknowledged by all stakeholders that the Greek building sector features the largest potential for easy-to-implement improvements in energy consumption, along with power generation. For a country with the solar potential of Greece, there are many opportunities in promoting solar heating and cooling systems. Rational energy use can also be promoted on the installation and management of optimised building energy management systems (BEMS), replacement of old devices and motors, investments in building shells and insulation, diffusion of other renewable energy sources in the built environment (e.g. geothermal power), and wide-scale development of building-integrated photovoltaics. These constitute both available and efficient technological solutions, but can, however, be expensive. Especially in the Greek islands, where demand varies significantly throughout the year, use of large solar power storage batteries could potentially help overcome current challenges in promoting sustainable energy, without heavy investments in linking the mainland and non-interconnected networks or new power plants.
This sectoral preference, however, concerned not only the energy efficiency front but the vision of the overall low-carbon transition of the Greek economy in general. This can be partly explained by the perceived infrastructural challenges associated with the transformation of the transport sector; the limited financial capacity to invest in decarbonising practices in an otherwise small heavy and a practically non-existent light industrial sector (with negligible potential for emission reductions); and the limited interest from those involved in the agricultural sector.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 42
Implementation risks
With regard to existing barriers, posing direct or indirect threats to the successful design, funding and implementation of a sustainable and effective energy efficiency policy framework, stakeholders agreed on ten major implementation risks (Figure 3).
•Lack of public awareness Societal dimension •Distrust of government/institutions and societal opposition
•Poor political prioritisation/political inertia Political axis
•Lack of financial capacity Economic environment •Absence of economic incentives, tax breaks, subsidies
•Fuzziness of regulatory and policy framework Regulatory framework •Complex bureaucratic processes
•Current lack of storage technologies Technological axis •Geographic barriers •Inexperienced personnel
Figure 3 Implementation risks jeopardising the low-carbon transformation of the Greek built environment, as derived through stakeholder interviews
On the social axis, stakeholders appeared to consider that social participation in decarbonisation actions in the building sector is the major barrier to achieving an energy-efficient economy; however, they disassociated the lack of public awareness (Figure 4) from societal opposition created by distrust in government or institutions. From a political perspective, instability in the Greek political scene appears to be a severe implementation risk in the short term, but associated with longer-term uncertainties such as the economic environment. This instability is reflected in high abstention levels, frequent movements of Members of Parliament among elected parties, inability to form single-party governments, as weak ruling majorities for coalition governments, and a consequent record of snap elections. In the longer run, the main concern of stakeholders appears to be the poor prioritisation of climate change actions and political inertia. Amidst the ongoing economic crisis, and in light of the respective national commitments, all political parties’ priorities are perceived to be mainly of socio-economic nature and orient on addressing the recession rather than climate change mitigation and sustainable energy use. This is why policymaking in the country is believed to be carried out in a short-term perspective without a clear and solid strategy.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 43
Evaluation of [IR1] Lack of public awareness/knowledge Extreme
High
Medium
Low
None Expert R1 Expert F1 Expert U1 Expert A1 Expert T1 Expert T2 Expert R2 Expert PM1 Expert PM2 Expert PM3 Likelihood to manifest Impact on policy framework Lack of mitigation capacity Level of concern
Figure 4 Stakeholder evaluation of the risk associated with the lack of public awareness of climate change and the need to mitigate it, against the four evaluation criteria
With regard to the Greek economy, two major risks are identified. Evidently, all interviewed stakeholders are significantly concerned with the adverse economic environment in Greece and the respective lack of financial capacity. This recognises the difficulties in overcoming what appeared to be a short-term recession at a global scale, but is still in effect in the country and is estimated to have long-lasting economic implications for the average household. This concern refers not only to the capacity of the State to incentivise a low-carbon transition in the longer run but also to the capacity of citizens to make use of available incentives and invest in energy upgrades of their dwellings. Additionally, this particular implementation risk is even more complicated in the context of the built environment, since the economic situation is also a key driving force for the construction sector. When looking at the transition pathway itself, rather than a number of policy strategies designed specifically in the aim of promoting such a pathway, stakeholders are also concerned with the possibility of a general lack of economic incentives, subsidies and tax breaks. The latter, however, is deemed to be of lower importance, since incentives and other financial mechanisms are considered to be primarily dependent on the will, determination and financial capacity of the government, rather than on a multitude of exogenous factors.
From a regulatory perspective, the main reservations regarded the fuzziness of the regulatory and policy framework. This is closely related to the instability of the political scene, but mostly in terms of an ever-changing framework as a result of the deficit-amending modifications and the hitherto short-term nature of energy and climate action planning.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 44
On the technological axis, the main concerns expressed, revolved around the current lack of storage technologies; poorly equipped, inexperienced personnel; and the geographic barriers. The latter include the difficulties in implementing actions in city centres as well as poor infrastructure risks, such as the saturation and security level of the power grid and the absence of interconnections necessary for proper penetration of renewables in the built environment.
Other implementation risks only infrequently referred to, i.e. considered by one or two stakeholders, concerned the inadequacy of business models for the residential sector, and the limited liberalisation of the internal electricity market and respective operators. An expectedly interesting instance lies in the limited reference to the old age of the Greek building stock, which could be considered either as a risk hindering the diffusion of energy-efficient technologies or overburdening the transformation costs, or as an opportunity given the large underlying potential, for the success of the transition pathway, depending on the perspective.
From the elicitation of stakeholder knowledge, the identified implementation risks can also have synergistic effects. For example, poor prioritisation of climatic change and action, political instabilities, frequent changes to a consequently fuzzy regulatory environment can all be intertwined, as well as linked to a sceptic, distrustful society and even opposing society. Drawing from the unique characteristics of the Greek economy, another example can be found in the financial capacity. An ongoing recession can have detrimental impacts on all other dimensions and significantly expand the ground for the manifestation of the remaining implementation risks. This adds not only to the likelihood of their occurrence but also on the level of their impact and the capacity to mitigate either.
Consequential risks
As far as potentially negative consequences of a low-carbon transition pathway, and an energy efficiency-oriented policy framework aimed at promoting this pathway, are concerned (Figure 5), discussions mainly oriented on the potentially negative economic impact of poorly designed financial incentives and support schemes. This focus can, to a large extent, be attributed to the sum of legislative mistakes made in designing the financial support mechanisms of the past. This refers to the pace of renewable energy penetration into the power generation mix causing an equally high pressure of liquidity demands for compensating producers, based on the design of the feed-in tariff mechanism. Coupled with delays of relevant payments, these liquidity gaps created a large deficit in the Renewable Energy Sources Fund. Further legislative advances in an effort to reduce the deficit mostly revolved around tariff cuts and heavy taxation (Dusonchet and Telaretti, 2015). These eventually managed to freeze licensing applications and connection requests, at the cost of solar power development and of public trust in the policy framework.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 45
Social impacts
•Further impoverishing Greek households •Implications for employment
Economic consequences
•Deficits associated with financial incentives and support mechanisms •Discouraging investments
Figure 5 Consequential risks associated with the low-carbon transformation of the Greek built environment
This case is an illustrative example of the lifecycle of a barrier and the interplay between its implementation and consequential nature. Poor policy design in the past, gave rise to a large deficit in the renewables fund that in turn led to an ever-changing policy framework. Eventually, mistrust of the government developed around its ability to vote and retract policies and mechanisms, which is now largely considered as a significant implementation risk to any relevant action in the future. And, at the same time, the knowledge of such a consequential risk among policymakers and relevant stakeholder groups creates cognitive barriers to introducing mechanisms that can, potentially, allow such a risk to manifest. In other words, the perception and consideration of the consequential risk of developing a deficit in a support fund may also act as an implementation risk, hindering the design of appropriate support mechanisms. The evaluation of this consequential risk, from the stakeholders’ perspective, is illustrated in Figure 6.
Another risk that evidently concerns the stakeholders lies in potential implications for poverty as a by-product of climate and energy efficiency actions. Although an adverse economic environment is considered to be one of the main exogenous barriers to realising a low-carbon transition, stakeholders are largely worried about the possibility of such a transition further impoverishing Greek households. For example, according to an expert coming from the private sector energy industry, largely-funded yet poorly-designed infrastructure and building renovation projects may lead to the opposite results and widen inequalities. Funding programs and financial support must be strictly prioritised and planned over the long-term, in order for the transition to boost the economy instead of leading to another crisis. Furthermore, the stakeholder coming from the banking sector acknowledged that, so far, financial incentives aimed at the residential sector primarily targeted lower income households. In practice this enables them to apply for loans of low interest rate, that may be hard to pay in a time of recession. A similar approach may overburden the middle class and further impoverish the working class.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 46
Evaluation of [CR1] Deficits due to financial support mechanisms Extreme
High
Medium
Low
None Expert R1 Expert F1 Expert U1 Expert A1 Expert T1 Expert T2 Expert R2 Expert PM1 Expert PM2 Expert PM3 Likelihood to manifest Impact from policy framework Lack of mitigation capacity Severity of concern
Figure 6 Stakeholder evaluation of the consequential risk of economic impacts on the government budget due to poorly designed financial support mechanisms, against the four evaluation criteria
Finally, two consequential risks that came up during the discussions regard employment and investments. In fact, some stakeholders mentioned the possibility of a low carbon transition bearing implications for unemployment. This is especially with regard to the energy transformations brought about by further development of the solar power sector, e.g. in the fossil fuel extraction and transformation sector. Experts also agreed on the possibility that an energy- efficient transformation of the Greek economy might discourage new investments.
In order for the stakeholders to evaluate the alternative risks against the four evaluation criteria, a 5-term linguistic scale was used: {None, Low, Medium, High, Extreme}. The objective of this process is to reach a meaningful ranking from the best (least significant) to the worst (most significant) risk. The table below summarises the risks and evaluation criteria, along with their weights, based on the stakeholders’ assessment.
Table 6 Summary of implementation and consequential risks, criteria and criteria weights
Risk Groups Risks Evaluation Criteria Weights
IR1. Lack of public awareness/knowledge C1. Likelihood to manifest 0.275 IR2. Political inertia/other priorities C2. Impact on/from policy 0.275 Implementation IR3. Lack of economic incentives/subsidies/tax breaks C3. Lack of mitigation capacity 0.24 Risks IR4. Complicated bureaucratic processes C4. Level of concern 0.21 IR5. Lack of storage technologies IR6. Lack of financial capacity
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 47
IR7. Lack of human skills IR8. Distrust of government and institutions/social reluctance IR9. Fuzziness of regulatory framework IR10. Geographical barriers CR1. Economic impacts due to poor design of financial support
Consequential CR2. Discouraging private investment Risks CR3. Further impoverishing Greek households CR4 Unemployment
5.1.2.2 Multicriteria analysis results
Two multi-criteria group decision analysis (MCGDA) problems were modelled and solved, one for the implementation risks and one for the consequential risks associated with a low-carbon transition of the Greek building sector. Figure 7 summarises the most critical implementation and consequential risks, as a result of the multicriteria analysis. The main arrow represents the pathway, uncertainties are displayed outside the main frame, potentially allowing for the manifestation of implementation and consequential risks, as illustrated by the downwards and upwards arrows respectively.
Figure 7 Most critical uncertainties and risks associated with a transition to an energy-efficient Greek building sector
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 48
In the MCGDA analysis on the perceived performance of the ten implementation risks against their likelihood to occur, the level of their impact on the policy framework and the transition pathway, the capacity to mitigate their impacts and the level of the stakeholders’ concern, stakeholders appeared to be mostly worried by political inertia, closely followed by the lack of financial capacity and the bureaucratic complexity of the energy efficiency-associated processes. Following these, stakeholders appeared to feel that both of the perceived risks on the societal axis are of medium importance/relevance to the effective design of a sustainable and robust pathway to an energy-efficient and climate-resilient Greek building sector. Finally, non-provision for economic incentives, tax breaks and subsidies, along with risks of technological nature, were considered the least critical by the engaged stakeholders. Figure 8 summarises the results for the implementation risk analysis, as resulting from the application of the selected TOPSIS methodology in MACE-DSS.
Criticality of implementation risks: final ranking Lack of public awareness/knowledge
Low Geographical barriers Political inertia/other priorities
Medium
Fuzziness of regulatory Lack of economic framework incentives/subsidies/tax breaks High
Distrust of government and High Complicated bureaucratic institutions/social reluctance processes
Medium
Lack of human skills Lack of storage technologies Low
Lack of financial capacity
Figure 8 Final MCGDA results of the significance of the ten implementation risks
As far as consequential risks are concerned, results show that, from the stakeholders’ perspective, the most critical concern orients on the possibility of financial deficits arising as a result of poorly designed financial mechanisms for promoting energy efficiency and building renovation actions. This concern is followed by a potential negative effect on further impoverishing Greek citizens, especially with regard to low-income households in the current economic environment. Following these, stakeholders collectively found that other negative socioeconomic implications of actions
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 49
towards an energy-efficient economy for private investments and unemployment are both relatively insignificant (Figure 9).
Criticality of consequential risks: final ranking
Discouraging private investment
Low
Medium
High Adverse economic impacts due Further impoverishing Greek to poor design of financial households support High
Medium
Low
Unemployment
Figure 9 Final MCGDA results of the significance of the four consequential risks
5.1.2.3 Consensus
By now focusing on the stakeholder component and carefully observing the stakeholders’ assessments of each risk against each criterion, we can draw useful conclusions with regard to the consensus levels. By observing deviations between each stakeholder’s preference model and the derived collective model, we can form a behavioural pattern through which stakeholders may be evaluated on their perception of key risks.
Regarding the implementation risks, early-level analysis suggests that the three engaged policymakers almost rejected the possibility of a lack of economic incentives actually happening and found its potential impact on the pathway negligible. This was not the case with a perceived probable continuation of the economic recession and the consequent lack of financial capacity.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 50
Furthermore, what appeared to matter equally as much to stakeholders, in terms of severity and impact on the transition pathway, is the inherent bureaucracy of the processes necessary for incentive applications, energy upgrade certifications, etc. However, complicated bureaucratic processes appear to be critical, given that all stakeholders deem that the capacity to mitigate its impacts is significantly better, compared to a fuzzy regulatory framework. This is why, with the exception of the representatives of the research community and of electric utilities, all stakeholder groups appeared to be much more concerned with regulatory bureaucracy than fuzziness.
On the consequential risk front, the overall consensus appeared to be stronger; nevertheless, there were instances in which stakeholder groups stood out. Specifically, with regard to potential implications for employment, the interviewed experts from the policymaking group practically rejected any notable impact, but nevertheless expressed their concern of such a probability. In addition, although most experts agreed on the possibility that an energy-efficient transformation of the Greek economy might discourage new investments, about half of the engaged stakeholders considered that such an impact would be virtually inexistent. Besides, as most of the involved representatives of the banking and policymaking community noted, the capacity to mitigate such an effect is relatively high, provided that the policy strategies promoting the required transformations are effectively and sustainably designed.
By delving into each individual stakeholder’s preference model, based on the results of the MCGDA analysis, we can use the consensus control capacity featured in MACE-DSS and draw comparisons between each individual model and the collective model. In particular, we notice that, across both implementation and consequential risks, there is large consensus observed in the final preference model and respective rankings. In fact, for three implementation risks, there is exactly one stakeholder showing significant divergence from the collective preference model; the same can be observed for two consequential risks (Figure 10). The largest deviation can be observed for the expert coming from the Utilities and Regulators stakeholder group and, for the potential negative impact of the pathway on poverty levels, researcher (R2) is consistently far away from the consensus.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 51
Deviations from the collective model
LARGE DEVIATIONLARGE
SLIGHTDEVIATION
DEVIATION NEGLIGIBLE
IR1 IR2 IR3 IR4 IR5 IR6 IR7 IR8 IR9 IR10 CR1 CR2 CR3 CR4 Expert R1 Expert F1 Expert U1 Expert A1 Expert T1 Expert T2 Expert R2 Expert PM1 Expert PM2 Expert PM3 Figure 10 Stakeholder analysis for each implementation and consequential risk, based on the MCGDA results
5.1.3 Boosting power generation from solar photovoltaics
5.1.3.1 Stakeholder input
The stakeholders assessed both the implementation and the consequential risks against specific evaluation criteria:
Likelihood to manifest; Impact on/from policy framework and/or transition pathway; Capacity to mitigate; Level of stakeholders’ concern;
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 52
Ten stakeholders with vastly different backgrounds were interviewed. They comprised of two representatives of electric utilities and regulators (U1 and U2), four policy makers form the Ministry of Environment and Energy (PM1, PM2, PM3 and PM4), one technology user including manufacturers and importers (T1), one representative from private and public sector industries associations that are involved in the provision of GHG emitting services, and two scientists and researchers (R1 and R2). The interview included a detailed discussion on the PV sector in Greece and the assembly of the identified risks, while in the elicitation process a second round took place that included a semi-structured questionnaire for use in the multicriteria analysis.
Implementation risks
The term implementation risks, is used to describe the existing barriers that could pose a direct or indirect threat to the successful implementation, design and funding of the proposed framework regarding photovoltaics. The stakeholders agreed on twelve major implementation risks (Figure 11).
•Lack of public awareness Societal dimension •Distrust of government/institutions and societal opposition
•Political inertia/other priorities Political axis •Limited liberalisation of internal electricity market •Lack of long-term energy planning
•Lack of economic incentives/ tax breaks/ subsidies Economic environment •Economic crisis and poor investment framework
•Fuzziness of regulatory and policy framework Regulatory framework •Complicated buraucratic processes
•Lack of storage technologies Technological axis •Lack of human skills •Poor interconnections & grid/infrastructure quality
Figure 11 Implementation risks, threatening the boost of power generation from solar photovoltaics, as they were assessed by the stakeholders.
Regarding the societal dimension, the stakeholders consider that lack of public awareness (Figure 12) is one of the main implementation risks. They also added as a barrier the distrust of the public in government and institutions as well as gave emphasis on the risk of societal opposition. The aforementioned risks (societal opposition and distrust), can be associated with regulatory mistakes of the past. There has already been an unsuccessful effort to establish the widespread usage of photovoltaics, given that the climate in Greece is more that suitable for the production of this kind of renewable energy.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 53
In the political axis, the stakeholders narrowed down barriers to the following three: political inertia; limited liberalisation of internal electricity market; and the lack of a comprehensive and concise long-term energy plan. Political inertia, can be observed in the fact that renewables are not within the priorities set by each elected government, and can be associated with the lack of a long-term energy strategy. As for barriers related to the economy, the stakeholders believe that there are not enough incentives, tax breaks or subsidies given. The ongoing economic crisis and a poor investment framework is also considered to be a barrier. Fuzziness of regulatory and policy framework along with complicated bureaucratic processes are considered to be the two major barriers in the regulatory axis. Finally, regarding the technological barriers the stakeholders assessed that lack of storage technologies and human skills along with poor interconnections and grid or infrastructure quality to be the main issues of impediment.
Evaluation of [IA1] Lack of public awareness Extreme
High
Medium
Low
None Expert U1 Expert U2 Expert Expert T1 Expert R1 Expert R2 Expert Expert Expert Expert A1 PM1 PM2 PM3 PM4 Likelihood to manifest Impact on policy framework Lack of mitigation capacity Level of concern
Figure 12 Stakeholder evaluation of the risk associated with political inertia, against the four evaluation criteria.
The above figure presents an evaluation of the stakeholders on the criterion lack of public awareness. As can be observed, the vast majority believe that the lack of mitigation capacity is of medium importance, and about half of them assess that the level of concern is of low importance. On the other hand, the vast majority assess the likelihood to manifest to be of high importance regarding the criterion of public awareness. A similar trend is observed in the criterion ‘likelihood to manifest’; the stakeholders assess it to be of high importance with regard to the implementation risk lack of public awareness.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 54
Consequential risks
Consequential risks are those that could potentially impose negative consequences in boosting power generation from solar photovoltaics. Discussions regarding the consequential risks emphasised the economic and societal dimensions. Regarding the economic environment, the stakeholders concluded that the most prominent issues are the potential high electricity costs for the final consumer, the discouragement of private investment, and the tariff deficits. As with the energy efficiency case study, for the societal axis, the stakeholders believe that unemployment and poverty are two potential risks. These two derive from a combination of past legislative mistakes, the fact that there is a common belief that everything comes as a tax in disguise along with the ongoing financial crisis. On the energy security front, the stakeholders concluded that an increased dependency on natural gas may also emerge; as for the technological front, the risk of grid connection and power shortages is eminent. These risks are summarised in Figure 13.
Social impacts •Unemployment •Poverty Economic consequences •High electricity costs •Discouraging private investment •Tariff deficits Energy Security •Increased dependency on Natural Gas Technological •Grid congestion and power shortages
Figure 13 Consequential risks associated with the boost of power generation from solar photovoltaic
Figure 14 presents how the stakeholders perceive the consequential risk of poverty in respect to the evaluation criteria. It can be observed that all of the stakeholders are concerned with this risk. On the other hand, they do not think that poverty is associated with the lack of mitigation capacity.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 55
Evaluation of [CR6] Poverty Extreme
High
Medium
Low
None Expert U1 Expert U2 Expert Expert T1 Expert R1 Expert R2 Expert Expert Expert Expert A1 PM1 PM2 PM3 PM4 Likelihood to manifest Impact on policy framework Lack of mitigation capacity Severity of concern
Figure 14 Stakeholder evaluation of the consequential risk of poverty, against the four evaluation criteria
Similarly, to the analysis of the building sector in Greece, in order for the stakeholders to evaluate the alternative risks against the four evaluation criteria, a 5-term linguistic scale was used: {None, Low, Medium, High, Extreme}. The objective of this process is to reach a meaningful ranking from the best (least significant) to the worst (most significant) risk. The table below (Table 7) summarises the risks and evaluation criteria, along with their weights, based on the stakeholders’ assessment.
Table 7 Summary of implementation and consequential risks, criteria and criteria weights
Risk Groups Risks Evaluation Criteria Weights IR1. Lack of public awareness/knowledge C1. Likelihood to manifest 0.275 IR2. Political inertia/other priorities C2. Impact on/from policy 0.275 Lack of mitigation IR3. Lack of economic incentives/subsidies/tax breaks C3. 0.24 capacity IR4. Complicated bureaucratic processes C4. Level of concern 0.21 IR5. Lack of storage technologies Implementation IR6. Lack of human skills Risks IR7. Distrust of government and institutions/social reluctance IR8. Fuzziness of regulatory framework IR9. Lack of long-term energy planning IR10. Economic crisis and poor investment framework IR11. Poor interconnections and grid/infrastructure quality IR12. Limited liberalisation of the internal electricity market CR1. High electricity costs
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 56
CR2. Increased dependency on Natural Gas CR3. Grid congestion and power shortages
Consequential CR4. Tariff deficits Risks CR5. Discouraging private investment CR6. Poverty CR7. Unemployment
5.1.3.2 Multicriteria analysis results
Two MCGDA problems were modelled and solved, one for the implementation risks and one for the consequential risks. In Figure 15 the most critical implementation and consequential risks are summarised, as they resulted from the multicriteria analysis. The main arrow represents the pathway, uncertainties are displayed outside the main frame, potentially allowing for the manifestation of implementation and consequential risks, as illustrated by the downwards and upwards arrows respectively.
Figure 15 Most critical uncertainties and risks associated with the boost of power generation from solar photovoltaics
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 57
In the MCGDA analysis on the perceived performance of the twelve implementation risks against their likelihood to occur, the level of their impact on the policy framework and the transition pathway, the capacity to mitigate their impacts, and the level of the stakeholders’ concern. Stakeholders are mostly worried about poor interconnections and grid infrastructure and quality as well as, political inertia, closely followed by distrust of government and institutions and social reluctance. The stakeholders appear to be somewhat worried about the fuzziness of the regulatory framework, the lack of human skills, and the lack of economic incentives, subsidies and tax breaks. Also somewhat important from the stakeholders’ point of view are the economic crisis and poor investment framework, and the lack of public awareness. What seems to least worry the stakeholders, on the other hand, is the lack of storage technologies. The results of the implementation risks as they occurred from the application of the selected TOPSIS methodology in MACE-DSS are presented in Figure 16 below.
Criticality of implementation risks: final ranking
Lack of public awareness/knowledge
Limited liberalisation of the Political inertia/other internal electricity market Low priorities
Lack of economic Poor interconnections and Medium incentives/subsidies/tax grid/infrastructure quality breaks
High
Economic crisis and poor Complicated bureaucratic investment framework processes
High
Lack of long-term energy Lack of storage technologies planning Medium
Fuzziness of regulatory Lack of human skills framework Low
Distrust of government and institutions/social reluctance
Figure 16 Final MCGDA results of the significance of the twelve implementation risks
Regarding the consequential risks, the stakeholders assess that most of them are somewhat critical. Specifically, the risk ‘tariff deficits’ is considered more critical, in relation to high electricity costs, increased dependency on Natural Gas, and grid congestion and power shortages. The risk ‘discouraging private investment’ is considered the least critical by the stakeholders. The
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 58
final ranking of the consequential risks with regard to their criticality as a result of TOPSIS methodology in MACE-DSS are presented in Figure 17 below.
Criticality of consequential risks: final ranking
High electricity costs
Low
Increased dependency on Unemployment Natural Gas Medium
High
Grid congestion and power Poverty High shortages
Medium
Low Discouraging private Tariff deficits investment
Figure 17 Final MCGDA results of the significance of the four consequential risks
5.1.3.3 Consensus
By carefully observing stakeholders’ assessment of each risk against each criterion, some conclusions can be drawn regarding the consensus level. By comparing the differences between each stakeholder’s assessment and the collective model, a behavioural pattern can be formulated. We can therefore observe how each stakeholder’s assessment differs in relation to the rest of the group.
Concerning implementation risks, early level analysis indicates that most stakeholders agree that lack of storage technologies is not a significant issue. On the other hand, most of them also agree that the most pressing issues that are political inertia, lack of economic incentives and the lack of a long-term energy planning.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 59
The two representatives of the electric utilities and regulators, unanimously believe that the most pressing issue is poor interconnections and grid quality, along with political inertia, whilst also believing that the least important issue is the lack of storage technologies. The policy making experts, on the other hand, do not agree on the most eminent risk, however they agree that the least concerning risk is the lack of storage technologies.
Regarding the consequential risks, there appears to be a stronger consensus. Specifically, regarding the most significant consequential risk, the majority of the stakeholders agree on tariff deficits, closely followed by the discouragement of private investments. However, there appear to be some stakeholder groups that stand out. These are the two scientists and researchers, who disagree with the rest of the stakeholders and with each other. One of them considers the high electricity costs to be the most severe risk, while the other believes that discouraging private investment is the most significant risk.
By looking into each individual stakeholder’s preference, based on the results of TOPSIS, the consensus control capacity embedded in MACE-DSS can shed light to differences between each individual and the collective opinion (Figure 18). Particularly, both for the implementation and the consequential risks, there seem to be some experts whose assessment is significantly divergent from the rest of the group. Specifically, the largest deviation comes from one of the academics. One of the policy makers and the representative of public or private sector industries seem to also have different assessments in relation to the rest of the stakeholders.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 60
Deviations from the collective model
LARGE DEVIATION LARGE
SLIGHT DEVIATION SLIGHT NEGLIGIBLE NEGLIGIBLE DEVIATION
IR1 IR2 IR3 IR4 IR5 IR6 IR7 IR8 IR9 IR10 IR11 IR12 CR1 CR2 CR3 CR4 CR5 CR6 CR7
Expert U1 Expert U2 Expert PM1 Expert T1 Expert R1 Expert R2 Expert PM2 Expert PM3 Expert PM4 Expert A1
Figure 18 Stakeholder analysis based on the results of the multicriteria analysis
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 61
5.2 Evaluating risks in the Austrian steel sector towards a low-carbon transition
Parts of this sub-section have been published in (Wolkinger et al., 2019).
5.2.1 Context
The EU’s 2050 decarbonisation goals require steeper emission reductions after 2030 than before. By decarbonisation, we normally think of energy-related measures such as higher energy efficiency, electrification of demand, zero-carbon fuels and a zero-carbon electricity supply in order to move towards net-zero CO2 emissions. While such measures could set, say, the building and transport sectors onto an emissions pathway compatible with the Paris Agreement’s 2 °C long term warming limit, this is not so easy for industry, especially in steel production. The International Energy Agency (IEA) suggests that, by 2050, direct emissions from industry need to be 24% lower than those in 2007. By the same time, demand for manufactured goods is expected to at least double. Given the large disparity between growing demand and the requirement for reducing industrial carbon emissions, the adoption of low-carbon technologies in all energy intensive sectors is required.
Industrial production is often strongly interrelated with other sectors of the domestic and international economy, implying a relatively wide range of possible risks and indirect effects when decarbonising these sectors. This section aims to reveal and assess risks that emerge from the implementation of climate-neutral steel production technologies as well as from the interrelated transition of the energy supply sector. For this reason, the most important stakeholder groups participating in this study come from industry (iron and steel; cement; petrochemical; and innovative technologies for renewable energy), power supply companies, the Chamber of Labour (as part of the ‘social partnership’, which is part of the Austrian consensus-based political system), the Ministries of Environment and Finance, political parties (only from the Green Party, other parties were invited but did not attend the workshop due to elections) and NGOs.
Sectors and countries differ in their previous efforts as well as in the scope of low-carbon technologies. According to an IEA assessment for energy intensive industries, the deployment of best available technology in iron and steel production would contribute about 70% of global industrial energy savings, whereas only 30% would be contributed by cement, pulp and paper, and aluminium industries.
Considering Austria’s national GHG inventory, iron and steel production has a large emission profile - about 15.5% of total national GHG emissions. A low-carbon transition requires ambitious energy efficiency and materials efficiency solutions. It also requires radical innovation and shifts in processing technologies and feed-stocks enabling renewables and energy infrastructures, as well as rethinking the role of cooperation and competitiveness within the industrial value chain— changes. Energy efficiency policy, as well as industrial, research, innovation, trade, energy and climate policies, need to be aligned to reach zero emissions in industry. As a result, the blast
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 62
furnace technologies used in the steel sector need to be replaced by breakthrough process technologies such as zero-carbon electricity supply, electrification of residual demand and zero- carbon fuels, over the next 30 years.
Steelmaking is likely to still take place in Austria in the same quantities as today, but based on a hydrogen-based technology, through electrolysis. This would have the potential to almost eliminate CO2 emissions, as long as zero carbon electricity is used. Furthermore, electrolysis generates O2 as off-gas, which can be sold for profit. It is a high-risk-high-reward technology, which could be very promising in the long-term, but is currently only available at laboratory scale in the EU. Risks mainly include costly electrolysers, fluctuating renewable energy generation preventing electrolysis from being widely and exclusively used, and lack of expertise regarding its smooth operation. The large quantities of electricity that Austria would already need in the midterm, if production is based on hydrogen, cannot be served internally. South Eastern Europe, with its large potentials regarding renewables, could provide electricity for export. In the expansion of renewable electricity production, NGOs play (and will play) an important role. While there is an EU strategy for an Energy Union, that is rather indicative, there are few binding requirements for action. The transition towards decarbonisation of economic activities, or a ‘carbon management’, in Austria in 2050 needs a deliberate institutional framework.
Hydrogen-based steel production is now the ‘holy grail’ for steelmakers looking to decarbonise. That's because, following electricity generation, iron and steelmaking produce the most greenhouse gases, with cement a close third. Research cooperation on hydrogen production and storage between the main Austrian steel producer, the largest energy supply company and the regulator ensuring Austrian electricity supply is working successfully, thus making steel production with gas (methane) a bridging technology.
Transitioning to circular economy strategies can further reduce steel demand, thus suppressing the resulting CO2 emissions. This could include implementing measures to increase material efficiency, such as lightweight product design, increasing product lifespans, using products more intensively, and better manufacturing processes. In addition, transitioning to circular product designs that enable the re-use of steel without melting and the substitution of steel with lower emissions-intensive materials, such as aluminium in vehicles, can also contribute towards meeting the desired emissions reduction target.
The Austrian government has been implementing various policies concerning the GHG emission reduction target, although Austria is not projected to reach its 2020 emission reduction target with the existing measures. Nevertheless, climate change is an important topic in Austria’s political debate. There is a focus on the use of renewable energy sources (RES), as the Austrian government offers multiple instruments to support the build-up of RES. However, when it comes to the industrial sector, most political parties fear to discuss and promote climate change issues, as the threat of de-industrialisation in Austria is highly present, and so are short planning horizons driven by budgetary constraints. This fear results in stagnation and lack of awareness regarding the degree of reform needed. Introducing new climate-friendly technologies such as electric vehicles require significant reforms in spatial planning legislation, which are not routed for the near future, as the political resistance for change is great.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 63
Striving for a swift and serious transformation to a carbon-neutral economic system requires a closely coordinated approach across sectors, with new types of institutional cooperation in an inclusive climate policy. The individual climate mitigation strategies in the various economic sectors and related areas are not sufficient. Other types of transformation should also be taken into account, such as those of the energy system, because decentralised production, storage and control systems for fluctuating energy sources and international trade are gaining in importance.
Throughout the process of transition pathways development, many risks and uncertainties have been identified by stakeholders and the UniGraz research team in an iterative co-creative process. These have been clustered for further processing (Figure 19). These clusters comprise implementation risks (barriers) as well as consequential risks (impacts). While some clusters intrinsically focus on implementation risks only, like cluster 2, others contain mainly consequential risks like cluster 3. The main reason behind clustering risks in terms of dimension, rather than in implementation and consequential risks, is that the TRANSrisk framing was adopted after the respective stakeholder workshop in Austria took place. Although this framing can be adjusted to the TRANSrisk framework by sorting risks into implementation and consequential risks, this might not reflect stakeholders’ assessments of risks in the subsequent MCGDA analysis correctly, given that participating stakeholders were provided this particular framing; if a different risk classification framework was discussed, this may have had an impact on their original assessments.
4 2 Consumers/ Political and institutional acceptance framework
Coordinated political NIMBY framework 1 Energy Legal framework Social justice infrastructure 6 Innovation and Behavioral International technology choice cooperation changes Demand
Lock-in effects Grid 3 5 Environment Conditions of competition and Syngergy and Flexibility of (financial) markets conflicts system Resources Risikmanagement Predictability Technologies Autarky and Timing international trade Linkages to climate change Pricing and financing
Figure 19 Risk clusters for the transition in the iron & steel sector and the energy sector
Changing demand for electricity from fluctuating RES energy generation, challenges for the grid (stability) and the issue of flexibility of the energy system raise many debates in the Energy
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 64
Infrastructure cluster. A well-designed energy infrastructure, centralised or otherwise, is a prerequisite for the energy transition, and therefore involves many implementation risks.
An integrative and constructive climate policy framework significantly contributes to managing climate change mitigation. However, no comprehensive national energy and climate strategy, with broad political support and clear instruments for implementation has been developed so far, thus emerging the Political and Institutional Framework cluster. Furthermore, national transition depends on future international cooperation and developments, which are uncertain and bear risks.
Consequential risks occur mainly in the Environment cluster. Due to the intermittency implied in the renewable energy production, storage becomes a crucial factor. This can lead to increasing demand for resources, and inevitably environmental consequences during extraction or disposal (e.g. for lithium which is used in batteries for storage of electricity).
The Consumers/Acceptance cluster includes mainly implementation risks. A strong and massive involvement of civil society in the decision-making processes can accelerate necessary measures. Relevant knowledge gaps should be addressed because they also delay further action, however they are not the most important factors. The ‘Not-In-My-Back-Yard’ (NIMBY) effect, for instance, refers to the risk of civilians opposing renewable energy projects. For example, in Austria, there is not much potential left to construct large hydro power plants, but if the number of small power plants was increased it may lead to opposition from nature conservationists against these projects. The neglect of social equity issues, and the role of behavioural change along the transition pathways, are mainly consequential risks that should be considered.
The Financial cluster includes mainly consequential risks, and the lack of planning security for investors as a prevalent implementation risk. A switch to new technologies in the European iron and steel industry will be influenced by either increased tariffs or cheap gas imports, making it less competitive to switch from conventional blast furnace routes to currently more expensive (hydrogen-based) routes. Approaches to overcoming these barriers include a comprehensive administrative reform with a view to the challenges at hand, or pricing products and services according to their climate impacts. Key factors in this regard include an abolition of environmentally harmful financing and subsidies, such as the exploration of new fossil reserves. A major indicator for planning further climate mitigation projects is the development of the CO2 price, which is currently uncertain.
Risks in the Innovation and Technology Choice cluster take into consideration that major investments in infrastructure with long lifespans limit the degree of freedom in the transformation to sustainability, if greenhouse gas emissions and adaptation to climate change are not considered. If all projects were subjected to ‘climate-proofing’, which considers integrated climate change mitigation and appropriate adaptation strategies, this would avoid the so-called ‘lock-in effects’, which create long-term emission-intensive path dependencies. Path dependency and lock-in effects (e.g. through capacity mechanisms) are consequential risks along the transition pathway, while timing is considered as an implementation risk; it is very difficult to predict the right time
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 65
for installation of the hydrogen-based technology, i.e. when it will be competitive against other technologies.
5.2.2 Stakeholder input
Taking into consideration the numerous implementation and consequential risks associated with the country’s low-carbon transformation, especially with regard to the iron and steel sector, Austrian stakeholders ended up considering twenty-five risks classified in five major categories. The risk classification is thematic, including the following categories: energy sector, political/institutional framework, environment/acceptability, financial, and innovation and technology. This also means that the proposed classification does not discern between implementation and consequential risks, since each category may encompass both implementation and consequential risks, as well as others that can be perceived as both. The risks are evaluated against: their likelihood to manifest; the level of the perceived impact that they can have on the climate mitigation policy framework (for implementation risks) or that the latter can have on the environment and human systems (for consequential risks); the capacity of the society and/or state to mitigate them; as well as the involved stakeholders' level of concern over them. The weights regarding the four evaluation criteria were determined by the research team, as shown in Table 8. Eventually ten stakeholders participated in the process, as equally weighted decision makers.
Table 8 Risk classification for the Austrian iron and steel sector
Group Alternatives Evaluation Criteria Weights
EA1. Lack of transparency for consumers C1. Likelihood to manifest 1 Energy EA2. Instability of grid due to low investments C2. Impact on/from policy 1 Infrastructure EA3. Administration and approval procedure too complicated C3. Lack of mitigation capacity 2 EA4. Lack of storage C4. Level of concern 4 Political/ PA1. Missing climate and energy strategy Institutional PA2. Missing political leadership Framework PA3. Market distortion in the electricity industry
PA4. Missing regulatory evidence based framework
PA5. Inadequate and unpredictable CO2 prices AA1. Resource consumption is not considered in projects AA2. Resistance against investment projects Environment/ AA3. Play-off between climate mitigation and social justice Acceptability AA4. Behavioural change not sufficiently considered in transition AA5. Lack of framework for investment and planning FA1. Price risk when implementing new technologies FA2. Too narrow consideration of competitive conditions Financial FA3. No coordinated European energy policy FA4. Risk from financial markets FA5. Households bear a too large part of costs for transition IA1. Lock-ins due to capacity mechanisms IA2. Missing cross sectoral integration and use of synergy effects Innovation and IA3. Lack of information flows and infrastructure Technology IA4. Funding shortfall IA5. Imperfect picture of transition
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 66
IA6. Timing for introducing new technologies
In order for the stakeholders to evaluate the alternative risks against the four evaluation criteria, a five-term linguistic scale was used: {None, Low, Medium, High, Extreme}. The objective of this process is to reach a meaningful ranking from the best (least significant) to worst (most significant) risk. After eliciting the information from the stakeholders (for example, see Figures 20 and 21), we use MACE-DSS to calculate the rankings for each implementation risk.
Evaluation of [EA2] Grid instability
Extreme
High
Medium
Low
None Expert1 Expert2 Expert3 Expert4 Expert5 Expert6 Expert7 Expert8 Expert9 Expert10
Likelihood to manifest Impact on policy framework Lack of mitigation capacity Level of concern
Figure 20 Assessment, across the four evaluation criteria, of the risk associated with an unstable grid, due to low investments and/or high costs, by the 10 experts interviewed
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 67
Evaluation of [PA1] Missing climate and energy strategy
Extreme
High
Medium
Low
None Expert1 Expert2 Expert3 Expert4 Expert5 Expert6 Expert7 Expert8 Expert9 Expert10
Likelihood to manifest Impact on policy framework Lack of mitigation capacity Level of concern
Figure 21 Assessment, across the four evaluation criteria, of the risk associated with the inexistence of a clear climate and energy strategy
5.2.3 Multicriteria analysis results
Figure 22 depicts the collective results exported from the implementation of the TOPSIS method in MACE-DSS, with respect to each risk category separately. The risks are ranked according to their proximity to the ideal solution; therefore, the ones being close enough to the x axis are the most critical, while as we move further from the x axis, the significance of the risks decreases. In the horizontal axis of the graph, each number corresponds to the unique identifier for each risk, per risk category (colours are explained in the graph legend), as obtained in Table 8. For example, the risk depicted in dark red along the 5th major gridline corresponds to PA5 - inadequate and unpredictable CO2 prices - which also appears to be the closest to the horizontal axis among the political/institutional risks and is therefore perceived to be the most critical. The most and least significant risks for each category, as perceived by the ten involved stakeholders and based on the TOPSIS results, are presented in Table 9.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 68
Criticality of risks, final ranking
Energy Infrastructure Political and Institutional framework Environmental/Acceptability Financial
Innovation and Technology
)
-
( Significance
1 2 3 4 5 6 Risk identifier for each category
Figure 22 Final ranking of risks per risk category (colours explained in the legend). The horizontal axis corresponds to the numeric identifier of each risk.
Table 9 Presentation of the most and least critical risks by risk group Political and Energy Environmental/ Innovation and Risk Institutional Financial Infrastructure Acceptability Technology framework Most critical EA1 PA5 AA2 FA4 IA4 Least critical EA2 PA1 AA1 FA1 IA3
On the Energy Infrastructure axis, stakeholders appear to consider that the lack of transparency for consumers is the major barrier to achieving a low-carbon transition of the Austrian steel and iron sectors. The second most critical energy-related risk appears to be the lack of storage technologies, for example through the use of intelligent networks integrating intermittent renewables and chemical storage, in order to address fluctuations. On the other hand, low investments coupled with renewable energy volatility leading to grid instability constitute the risk considered the least by stakeholders, mainly due to the availability of intelligent digital systems.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 69
This is closely followed by bureaucratic and complicated administration and approval procedures, which may lead to significant time delays, hindering the timely transition of Austrian industry.
With regard to Political and Institutional Framework-related risks, stakeholders highlighted five barriers: the lack of a clear climate and energy strategy, inadequate political leadership, market distortions in the electricity market, a missing regulatory evidence-based framework and unpredictable CO2 prices. However, based on their assessment of these risks against the evaluation criteria, and the TOPSIS analysis, these appear to vary greatly in terms of significance to the desired low-carbon transition. Uncertainty over the development of CO2 prices is perceived to be by far the most important risk; carbon prices largely depend on international cooperation, and if this is inadequate, focus may shift towards less restrictive policy regions. On the contrary, the inexistence of an integrated climate and energy strategy in Austria, with clear milestones and targets, is ranked almost as a negligible risk, according to the involved stakeholders. The concern most probably draws on the fact that no comprehensive energy and climate strategy has been developed so far for Austria. Stakeholders realise that this risk can easily be mitigated in the near future as it is less dependent on exogenous factors and, despite it being widely considered as a likely event with huge impacts, it therefore worries them the least. Among the other three risks, only inadequate political leadership appears to stand out as a significant barrier, but even that is largely outranked by unpredictable CO2 prices.
From a Social (acceptability) perspective, the main concerns oriented on societal resistance against large investment projects and the construction of new overhead high-voltage power lines. Insufficient focus on behavioural change and play-off between mitigation policy and social justice are equally considered as intermediately critical risks, closely followed by the risk of not developing a sufficient investment framework. On the other hand, and along the lines of the latter, overlooking resource consumption in project planning appears to be the risk that is least considered as a serious threat to the transition.
On the Financial axis, evidently all stakeholders are significantly concerned with the adverse economic environment as well as strong lobbying hindering transparency, and the respective risk from financial markets. This encompasses misleading market rules and poor market design, over- regulations and subsidies on fossil fuels. Other less critical risks include a narrow consideration of competition conditions in the market, and unclear regulations and financial incentives associated with a lightly-coordinated European energy policy framework. These are followed by the risk of uneven distribution of transition costs, with a focus on the possibility of households bearing disproportionate share of these costs, and finally the consequential risk of price fluctuations as a result of implementing new technologies.
Finally, with regard to the Innovation and Technology axis, the majority of the interviewed experts agree that the lack of information flow is the least critical, along with the inexistence of cross-sectoral integration and use of synergy effects. On the other hand, the implementation risk of inadequate funds targeted at technological innovation appears to worry them the most.
In the multicriteria analysis on the perceived performance of the twenty-five risks against their likelihood to occur, the level of their impact on the policy framework and the transition pathway
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 70
(or vice versa), the lack of capacity to mitigate their impacts, and the level of the stakeholders’ concern, financial risks in general appear to be the most critical. On the contrary, the most optimistic assessments concern the category of risks associated with the political and institutional framework of Austria.
We may also conclude that, drawing on the nature of the most critical risks across all axes, stakeholders are concerned about both implementation and consequential risks. This further highlights the importance of considering both barriers and potential negative consequences, when designing appropriate policy strategies for promoting a sustainable transition of the Austrian iron and steel sector.
5.2.4 Consensus
By now focusing on the stakeholder component, and carefully observing the stakeholders’ assessments of each risk against each criterion, we can draw useful conclusions with regard to the consensus levels. By observing deviations between each stakeholder’s preference model and the derived collective model, we can form a behavioural pattern through which stakeholders may be evaluated on their perception of key risks. As an indicative example, Figure 23 and Figure 24 present the major variations noticed for Experts 5 and 8, for selected evaluation criteria, compared to the rest of the experts.
Evaluation of [IA6] Timing for introducing new technologies
Extreme
High
Medium
Low
None
Impact on policy framework Lack of mitigation capacity
Figure 23 Experts' assessment of impact and mitigation capacity for [IA6] Timing for introducing new technologies, showing significant differentiation between Expert 8 and the rest of the group
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 71
Evaluation of [AA5] Lack of framework for investment and planning
Extreme
High
Medium
Low
None
Level of concern
Figure 24 Experts' level of concern for [AA6] Lack of framework for investment and planning, showing significant differentiation between Expert 5 and the rest of the group
By delving into each individual stakeholder’s preference model, based on the results of the MCGDA analysis, we can use the consensus control capacity featured in MACE-DSS and draw comparisons between each individual model and the collective model. In particular, we notice that, across the five risk categories, Expert 7 appears to exhibit the broadest disagreement with the collective opinion, then followed by Expert 2 (Figure 25).
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 72
Deviations from collective model
LARGE
DEVIATION
DEVIATION
SLIGHT NEGLIGIBLE DEVIATION NEGLIGIBLE
EA1-EA4 PA1-PA5 AA1-AA5 FA1-FA5 IA1-IA6 Expert1 Expert2 Expert3 Expert4 Expert5 Expert 6 Expert 7 Expert 8 Expert 9 Expert 10
Figure 25 Stakeholder analysis based on the results of the multicriteria analysis
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 73
5.3 Evaluating risks in the Swiss energy sector towards a nuclear phase-out
Parts of this sub-section have been published in (van Vliet, 2019).
5.3.1 Context
Over the last decade the energy debate in Switzerland has been dominated by climate change. Thus, decarbonisation and energy conservation have been pillars of the modern Swiss energy policy ever since the ‘Energy 2000’ program was introduced. Since electricity in Switzerland is overwhelmingly generated from zero-carbon sources, electrification seemed to push Switzerland towards carbon neutrality. However, after the disaster at Fukushima Daiichi in March 2011 the perception of nuclear energy changed drastically in many Western countries, including Switzerland. After the shock, the Swiss federal council opted for a nuclear phase-out. The Energy Strategy 2050 initiative drawn up by the Federal Council calls for a gradual withdrawal from nuclear energy. It also foresees expanded use of renewables and hydro power but anticipates increased reliance on fossil fuels and electricity imports as interim measures. No construction licenses will be issued for new nuclear power reactors under the revised energy law and no basic changes to existing nuclear power plants will be permitted. The country's five existing reactors will be allowed to remain in operation as long as the Federal Nuclear Safety Inspectorate considers them safe to do so. In 2003, Switzerland imposed a moratorium on the export of used fuel for reprocessing until 2020. The Energy Strategy 2050 extends this ban indefinitely.
In 2015, nuclear power supplied a third of Switzerland’s electricity, along with 35% hydropower from storage dams, 25% run-of-river hydropower and 4% non-hydro renewables. The Swiss therefore have more than 98% low-carbon energy on their grid already. The remaining two percent is oil and gas in cogeneration. Outside of the cement sector, the Swiss use almost no coal. A lot of electricity is imported from France, Austria and Germany and up to 25 TWh/yr exported to Italy, with total exports and imports largely balanced. To ensure security of supply during winter months, Swiss utilities have long-term contracts to import 2,500 MWe of French nuclear power, at a price premium. Existing nuclear plants should be used until they expire and no new plants will be built. This proposal is equivalent to a gradual nuclear phase-out until 2034 based on a reactor life span of 50 years. This current development has confronted the Swiss energy policy with new challenges. If nuclear power is phased out, short falling capacities will raise electricity prices. Although this will lead to decreased demand compared to a business-as-usual one, increasing electrification will still likely lead to at least stable demand for electricity. This means that abandoned supply capacities have to be replaced in one way or the other.
While the simplest way of securing electricity supply would be via increased imports, decreased exports, gas-fired plants, or renewable generation. All four options have major disadvantages. To begin with, additional imports are feasible from a grid perspective: Switzerland already has the transmission lines and interconnections to move large amounts of electricity; however, these imports would stem from either nuclear plants in France or coal-fired plants in Germany.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 74
Furthermore, decreasing exports may be very costly, since Swiss electricity producers are making profits from arbitrage by buying and storing cheap base load electricity from abroad and selling at expensive peak load. On top of that, having a negative annual net balance on electricity trade may be politically undesirable. Gas-fired plants (integrated gasification combined cycle, or IGCC) are both considered as a transitional solution on the way to a renewable power mix, and are economically feasible in the short to mid-term. But IGCC leads to elevated CO2 emissions, thus preventing the phase-out of all remaining fossil fuels. Finally, another option for replacing nuclear electricity would be generation from renewables including wind and solar. However, they are associated with disadvantages via two channels: first, these technologies are linked to high levelised average lifetime cost and, second, the huge variance in electricity supply from those sources may end up in using more of the storage capacity the hydro plants which will hinder their use for trade gains on the European markets. A third renewable technology option is a large-scale geothermal power plant. While engineers think that geothermal generation could replace nuclear base load power at low cost, incalculable risks of earthquakes and their monetary consequences may make this option unattractive for potential investors.
Debate on Switzerland’s ‘Energy Strategy 2050’ has focused on what customers and taxpayers will pay for the measures and whether a four-fold rise in solar and wind power by 2035, as envisaged in the law, can deliver reliable supplies. Critics warn that Switzerland might have to pay more (say a family of four would pay 3,200 Swiss francs in extra annual costs) and that more intermittent wind and solar energy would mean a greater reliance on imported, dirtier electricity from neighbouring countries, especially Germany. Foreign demand for German electricity is overwhelmingly met by fossil fuels, because nuclear reactors run at maximum possible output to meet domestic base load and renewable energy already has priority dispatch.
Widespread deployment of various renewables, combined with energy efficiency measures in both production and consumption, are widely touted by environmental organisations as the only appropriate pathway for replacing global consumption of fossil fuels and nuclear power. The ‘energy efficiency measures’ term mainly implies urban and rural housing improvements by installing solar panels. In fact, a large number of PV panels could fit on existing rooftops in built- up area owned by individuals and companies. The new energy law includes a feed-in tariff to support expansion of renewables, but the Swiss home solar industry is still in its infancy compared to, e.g., Germany. However, it is unlikely that energy efficiency alone will have much impact on greenhouse gas emissions because of increasing energy demand associated with the economic growth of developing countries, continued growth of the human population and the well- documented ‘rebound effect’ in more developed economies whereby gains in efficiency are offset by increased consumption or new uses for energy.
As for the renewables in Switzerland, Energy Strategy 2050 calls for subsidies to bolster the country’s hydroelectric sector. Switzerland currently produces about 60% of its energy from hydro, but it is unsubsidised, making it expensive compared to wind and solar energy generated across the border in Germany. Energy Strategy 2050 also calls for large-scale investment in renewable sources of energy like wind and solar, both of which Switzerland notably lacks and intends to boost. However, the potential for those technologies in Switzerland is limited. Estimates of the
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 75
potential capacities report 12 TWh of photovoltaic and 4 TWh of wind electricity. Those estimates are very optimistic, and more conservative numbers may be appropriate. While annual production from these two sources is relatively predictable, the hourly and daily outputs are uncertain. This causes additional cost as peak load generating units may be activated to cover short-falling generation from new renewables. On the other hand, photovoltaics produce electricity especially during summer, when traditionally electricity production is high from run-of-river hydro plants and demand is low. This would harden the systemic inefficiency of the annual Swiss electricity production schedule. Wind, despite its much lower potential in Switzerland, would be cheaper and would produce more energy in winter months. This would be desirable, and thus wind power seems to be a good complement to run-of-river hydro. It seems that, although there is a theoretical potential for renewable energy resources to supply global energy demand, the real-world limitations of renewables, such as economic cost, land transformation, intermittency, scalability, energy storage, long-distance transmission, geographical distribution and social acceptance issues, put serious constraints on the plausibility of high renewable penetration.
The second favourable pathway is to import renewable electricity from abroad. This would require a build-up of renewable capacity outside Switzerland to feed into the Swiss grid. Based on the current situation, there are two possible options for this: wind power, most likely from the west coast of Europe like the North Sea, or concentrated solar power (CSP), from sunny regions like southern Spain or North Africa. This would allow use of the most abundant renewable resources in and around Europe without having to build in populated areas, making for cheaper electricity. Essentially imports could provide a simple means of providing renewable electricity. However, there are three important shortcomings associated with this option. First, importing electricity may be viewed as being critical, since imported power would mainly stem from nuclear plants or fossil fuel combusting technologies. Second, it may be politically infeasible to become dependent on net imports on an annual basis, as the Swiss public are generally opposed to foreign control over power supply. Finally, in the generating countries public acceptability of power plants and transmission lines for foreign benefit is an unknown quantity. While increased electricity trade over the last decades has helped Switzerland to increase systemic stability and to raise revenues, politicians and voters may still feel obliged to have a net balanced electricity trade on an annualised basis.
The most recommended option for mitigating the consequences arising from a nuclear phase-out would be for Switzerland to combine the domestic renewable pathway with the imported renewable pathway. The most promising combination seems to be Swiss rooftop PV, offshore wind from the North Sea, and Swiss hydropower. Such a mix would also be acceptable to the Swiss public. This is especially important, given the Swiss political system in which policies and projects can be challenged in local, cantonal and national referenda.
5.3.2 Stakeholder input
The decision to phase out nuclear energy is of strategic importance for Switzerland. Providing a clean, economic, and climate-friendly supply of energy is a major political and societal challenge. Since the parliament’s decision to phase out nuclear energy, the Federal Council has rapidly
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 76
advanced plans for ensuring Switzerland’s power supply without nuclear energy. A number of ongoing developments in Europe pose further obstacles to a transformation of Switzerland’s energy supply. Renouncing nuclear energy, which is used around the world, creates unnecessary problems - particularly if it is to happen on a short timescale. The Swiss economy currently faces tough competitive conditions, thus making its energy sector an unwelcoming environment for risky experiments. If the Federal Council’s ‘Energy Strategy 2050’ is to be implemented successfully, a consensus on burden-sharing must be reached. Based on a scenario analysis, the risks associated to a nuclear-free Switzerland were identified and specified. A survey of a range of academic, consultant and environmental stakeholders ended up identifying ten major risks linked to a rapidly changing Swiss electricity sector (Table 10).
Table 10 Risk classification for the Swiss Energy Sector towards a nuclear phase out
Alternatives Evaluation Criteria Weights
A1. New infrastructure permission blocking C1. Likelihood to manifest 2.63 A2. Different priorities between governance layers C2. Impact on/from policy 2.88 A3. Perception of green as economically damaging C3. Lack of mitigation capacity 3.13 A4. Opposition to government intervention C4. Level of concern 3.14 A5. Protective actions against RES infrastructures A6. Damage to natural environment A7. Electricity imports vulnerable to grid disruptions A8. Policies paralysing market development A9. Uncompetitive electricity generation A10. Negative effects in the quality of nearby life
In particular, Switzerland’s options in energy policy after a nuclear phase-out and the possible risks of such a step for the society, energy security, economic efficiency, and carbon footprint of Switzerland’s energy supply depend to a considerable extent on the international environment in which the country’s energy policy is formulated.
The nuclear phase-out will remove one of the pillars of Switzerland’s low carbon energy supply. Also, a nuclear exit would make it necessary to boost investments to accommodate increased imports, as well as strengthen connections from hydropower plants to areas currently served by reactors. That means higher energy bills for consumers, not to mention the risk of increasing grid instabilities (and ultimately blackouts) in such a case. Consequently, the main current challenges for the electric power industry concern the power transmission network, which is in urgent need of being expanded and overhauled in order to ensure security of supply. However, Switzerland’s integration into the EU power market significantly enhances its security of supply; the lengthy bureaucratic procedures and governmental permissions required in order to expand the existing network are therefore an implementation barrier.
Over the last two decades, Swiss governments have encouraged investments in renewable generation, mostly in variable renewable energies (VRES), i.e. solar and wind. However, these technologies create a new challenge for the sector. Their availability factors are significantly lower than those of, for example, thermal generation, and their production is subject to inherent
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 77
variability that needs to be balanced in real-time (Lise et al., 2013). Additionally, their nearly zero variable cost alongside with the large penetration in the power grid, has a price-lowering effect, which decreases the profitability of other generators. Consumers may not benefit from these lower prices since they are charged the cost of subsidising renewable technologies (Osorio and van Ackere, 2016), but the risk of these technologies being perceived as economically damaging raises concerns. The inherent uncertainty, along with the lack of political will, concerning new investments and rising tariffs due to RES subsidies, affects numerous energy markets. This can cause great instabilities and uncompetitive electricity generation, thus paralysing the whole energy market. Many countries, Switzerland included, are thus facing the challenge of providing increasing amounts of affordable green electricity in the right place and at the right time.
The most important risk in the spectrum of electricity supply will be the expansion of renewables, which are being subsidised through feed-in tariffs, and therefore the reinforced intermittency of electricity supply. Due to its (limited) suitability for supplying base loads and its low lifetime CO2 emissions, hydropower is a desirable candidate for expansion, but the potential for further expansion here is extremely limited (4 TWh). Solar power, on the other hand, is to play a major role, with plans to expand photovoltaic power by 2050. Wind energy is expected to contribute another 4 TWh a year, to be generated by 800 turbines. Wind power projects in Switzerland are of extreme significance among the stakeholders, since they often become systematically jammed at some point, due to local protective actions against new RES infrastructure on principle. The idea of wind power is largely supported in the country, but very few people want a wind turbine in their neighbourhood (‘not-in-my-back-yard’ phenomenon). PV panels are quite costly - and energy-intensive - in their production, resulting in relatively high electricity prices. But security of supply is perhaps the biggest problem due to the intermittent nature of wind and solar power. So, in order to achieve the steady flow of electricity necessary to match demand and guarantee network stability, large RES investments are required combined with storage technology. In addition to the further network costs arising from the latter, local populations and environmental groups can be expected to offer resistance against large-scale RES projects due to negative impacts on the natural environment and quality of the nearby life in the surrounding areas.
The Swiss government tries to promote transparency when it comes to policymaking, in order to foster a competitive investment climate. Proposed laws and regulations are open for public comment (including interested parties, interest groups, cantons, and cities) then discussed within the bicameral parliamentary system. They may then be subject to facultative or automatic referenda that allow the Swiss voters to reject or accept the proposals. Only in very rare instances are regulations not reviewed on the basis of scientific analysis, rather than political preferences. This kind of political transparency is the main reason why stakeholders seem to care little about the risk of increased complexity arising from intense political intervention in support of specific renewable technologies. However, gathering all the documentation, working out the nuclear deactivation project, getting decommissioning approval and configuring a common framework to promote the nuclear power phase-out through an integrated pre-defined pathway, will require a lot of time, fruitful coordination among all governance layers and mutual respect. It will be necessary to take into consideration the framework conditions and involve all parties - from direct
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 78
interests to the general population, the cantons and the federal authorities - thus making the alignment of priority needs associated to each municipality a major challenge.
The risks are evaluated against their likelihood to manifest, the level of the perceived impact they can have on the climate mitigation policy framework, the capacity of the society and/or State to mitigate them, as well as the involved stakeholders' level of concern over them. The weights regarding the four evaluation criteria were determined by the authors (Table 10). Eventually eight stakeholders, as equally weighted decision makers, participated in the process. They can be clustered into three categories (environmental protection groups; academia & consulting; and others).
In order for the stakeholders to evaluate the alternative risks against the four evaluation criteria, a 5-term linguistic scale was used: {None, Low, Medium, High, Extreme}. The objective of this process is to reach a meaningful ranking from the best (least significant) to worst (most significant) risk. After eliciting the information from the stakeholders (for example, see Figure 26 and Figure 27), we use MACE-DSS to calculate the rankings for each implementation risk.
Evaluation of [A9] Uncompetitive electricity generation Extreme
High
Medium
Low
None Others Environmental protection groups Academia & consulting
Likelihood to manifest Impact on policy framework Lack of mitigation capacity Level of concern
Figure 26 Assessment, across the four evaluation criteria, of the risk associated with the uncompetitive electricity generation, by the three expert groups interviewed
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 79
Evaluation of [A6] Damage to natural environment Extreme
High
Medium
Low
None Expert1 Expert2 Expert3
Likelihood to manifest Impact on policy framework Lack of mitigation capacity Severity of concern
Figure 27 Assessment, across the four evaluation criteria, of the risk associated with the damage to the natural environment
5.3.3 Multicriteria analysis results
The MCGDA analysis examined the perceived performance of the ten risks against their likelihood to occur, the level of their impact on the policy framework and the transition pathway, the lack of capacity to mitigate their impacts, and the level of the stakeholders’ concern. Stakeholders appeared to be mostly worried by three implementation risks, namely: (a) the environmental- friendly groups and their active opposition to new RES infrastructures; (b) societal opposition to government intervention; and (c) the different prioritisation of needs among the different governance layers. Furthermore, the involved stakeholders appeared to feel that the challenges caused by the government’s intervention, and its fostering policies, are of high importance and relevance to the effective design of a sustainable and robust pathway to a climate-friendly Swiss energy sector. They slightly outperform the significance of the risks’ economic perspective, in terms of criticality, as expressed by fear of uncompetitive electricity generation. This divergence is even greater when looking at concerns over the end-consumers’ perception that ‘green’ essentially equates to economically damaging. Finally, societal perception over damage that new RES development and infrastructures may cause to both the environment and the energy market seems to be perceived as the least critical by the engaged stakeholders. Figure 28 summarises the results of the risk analysis, as resulting from the application of the selected TOPSIS methodology in MACE-DSS.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 80
Criticality of risks: final ranking
New infrastructure permission blocking None Negative effects in the Different priorities between quality of the nearby life governance layers Low
Medium
Uncompetitive electricity Perception of green as generation High economically damaging
Extreme
Policies paralyzing market Opposition to government development intervention
Electricity imports Protective actions against vulnerable to grid RES infrastructures disruptions
Damage to natural environment
Figure 28 Final MCGDA results of the significance of the examined risks
Given that this analysis was carried out for all of the identified risks, we can also observe that there appears to be a balance between implementation and consequential risks, with respect to the resulting significance, from the stakeholders’ perspective. That is despite implementation barriers dominating the three most critical risks. It is also noteworthy that, contrary to other case studies, the bulk of the identified risks are perceived as highly important. Aside from potential negative consequences to the market and the natural environment as well as public’s perception of green as expensive, all other risks are deemed of medium significance or worse.
5.3.4 Consensus
By now focusing on the stakeholder component, and carefully observing the stakeholders’ assessments of each risk against each criterion, we can draw useful conclusions with regard to the consensus levels. By observing deviations between each stakeholder group’s preference model and the derived collective model, we can form a behavioural pattern through which stakeholders may be evaluated on their perception of key risks.
RES support schemes, in particular the feed-in tariff, typically create market distortions in operational decisions, due to limited exposure and/or response of VRE generators to market
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 81
signals. Moreover, such schemes often grant priority dispatch and, sometimes, exemption of balancing responsibilities to VRES generators, regardless of price signals that reflect their negative impacts on system operation. These all contribute to increased residual system costs and thus increased integration costs, affecting the price paid by end-consumers and therefore raising concerns. However, this concern appears to be of extreme significance to stakeholders coming from environmental groups, while those coming from academic institutions and consulting entities appeared to care little about this risk, with their interest focused on governance issues.
On the other hand, both academic institutions and environmental organisations seem to care little about the bureaucratic procedures that block the permission granting, emphasising more practical issues. However, other stakeholders involved in the study raise time-consuming procedures holding back a renewable and sustainable future, as a key challenge to the success of the transition. Finally, we notice that mainly academics and consultants highlight the significance of the energy import risk.
By delving into each stakeholder group’s preference model, based on the results of the MCGDA analysis, we can use the consensus control capacity featured in MACE-DSS and draw comparisons between each model and the collective model. As expected, we notice that, across all risks, the ‘academics & consultants’ category appears to exhibit the smallest disagreement with the collective opinion (Figure 29). This should first and foremost be attributed to the fact that this group encompasses more than half of the individuals involved, and is thus significantly more influential to the global preference model. This is also why the ‘Other’ stakeholder, comprising one individual with a different background from the rest of the stakeholders, appears to deviate most from the collective model, across all risks. It is also expected that, given the nature of the problem and the clustering of the stakeholders into three groups, no significant deviations are observed. Note that in the horizontal axis of the graph, each number corresponds to the unique identifier for each risk, as shown in Table 10.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 82
Deviations from the collective model NEGLIGIBLE DEVIATION NEGLIGIBLE
1 2 3 4 5 6 7 8 9 10
Other (n=1) Environmental protection groups (n=2) Academia & consulting (n=5)
Figure 29 Stakeholder analysis based on the results of the multicriteria analysis
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 83
5.4 Evaluating risks associated with a low-carbon transition of the Indonesian power generation sector, through communal biogas digesters
Parts of this sub-section have been published in (Takama et al., 2019).
5.4.1 Context
Indonesia is an ethnically and culturally diverse country with a population of almost 260 million, spread across more than 17000 islands. Along with significant economic growth over the past decade, energy consumption has steadily increased, together with the emissions of greenhouse gases. Indonesia is now the 7th largest emitter in the world. Without any proper responses from policy makers, this continuous rise in energy consumption and emissions is likely to continue. Indonesia’s climate policies are rated as ‘inadequate’ to meet the Nationally Determined Contribution (NDC) on fulfilling the Paris Agreement, which is set to a 26% reduction by 2020, compared to business as usual (BAU) (Climate Action Tracker, 2016). While a target of national energy mix complemented with other interim targets exists, there are no clear plans from the government on how the country will fulfil the goal, with several factors acting as a possible hindrance.
On the policy side, the main strategies for the development goals are defined in the National Long- Term Development Plan (RPJPN), which is divided into several National Medium-term development plans (RPJMN). While the current RPJMN (up to 2019) includes commitments to performing a transition towards a green economy and sustainable development (Nachmany et al., 2015), other policies, for example in the energy sector, may work counter to this framework. Increasing the portion of renewable energy from the baseline of 4% in 2014 to 26% in 2025 is one of the targets made by the Indonesian government, directly correlating with the national target for reducing emissions, as stipulated in the National Energy Policy 2014 (NEP 14). Moreover, Indonesia has recently sought to shift energy supply from international markets to domestic ones, in order to meet rising domestic demand, while fossil fuels still dominate the energy mix, according to the NEP 14 and RPJPN.
The fossil fuel sector plays a prominent role in these sub-optimal developments. It is estimated that roughly 30% of total government revenues stem from the fossil fuel sector with a state-owned company, PT Pertamina, controlling the distribution and refining of crude oil and petroleum products. This is also the case for the gas sector, where PT Pertamina Gas controls the distribution and transmission facilities. In addition, the country was the world’s largest coal exporter in 2014 (PWC, 2016).
With Indonesia also focused on maintaining momentum for economic reform, the transition to a sustainable economy and energy production unveils many barriers - economic growth is currently prioritised over environmental sustainability. This is verified by the development of non-
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 84
renewable power plants, which continuously exhibit high revenues and production, to the detriment of the environment. Other problems, such as low tax revenue and low commodity prices combined with complex bureaucratic procedures and a lack of transparency, also hinder the investment in clean energy infrastructures. As fossil fuel played a dominant role for the country, a domestic approach to identifying the solution is taken by analysing Indonesia’s sectorial energy consumptions and emissions.
Using 2012 figures, CO2 emissions are distributed in each sector, with power generation accounting for roughly 39% of emissions; 29% stems from the transport sector, while the industrial sector is responsible for approximately 26% of emissions (Republic of Indonesia, 2015). Additionally, while it is estimated to contribute only as much as 4% to the country’s total emissions, the residential sector is accountable for the largest share of energy consumption (35%). Consequently, CO2 emission reductions are highly prioritised in the country’s political agenda and these numbers are acting as the baseline, with sustainable electricity and cleaner domestic energy use potentially being the critical points of the mitigation effort. In addition, the new and innovative technologies have to compete with this fossil fuel-based ‘regime’ (Geels and Schot, 2007). Facing these existing risks and several other barriers, the alignment of climate and energy policy with the mitigation goal is a prerequisite for the Indonesian government to achieve its NDC.
As stated in the previous paragraph, the sustainable energy policy framework in Indonesia is lagging behind, being uncertain and jeopardised by several different risks. Specifically, the adoption of transition pathways towards a more sustainable future is sorely needed by the Indonesian government, since the country is struggling to meet pledges and targets, including a mainly unilateral reduction of emissions by 29% below BAU.
Starting from 2014, the government of Indonesia has focused on increasing electricity access throughout the nation, reaching out to rural parts of the country that previously did not have a reliable source of electricity. But most of the resources deployed are coal power generators, which come in the form of both permanent and semi-permanent plants. As a result, since the government currently aims at both decreasing CO2 emissions and improving the electrification ratio, multiple efforts were taken and new targets were determined. Currently, one of the most notable actions comes from Perusahaan Listrik Negara (PLN), a state-owned electricity company. Under the policy of MEMR, PLN is opening its doors to private companies to buy its service area in order to generate and sell electricity.
However, the government’s initiatives have still not reached their targets and the electrification ratio through renewable energy is not growing as rapidly as the government intended to. Stakeholders have identified that the current climate and energy policy framework is not totally supportive, since it is hard to reach the government’s electricity price standard. This is caused mainly by the high level of subsidy, both direct and indirect, received by PLN for electricity generation. The current state of the subsidy system is understandable, considering it is an effort to maintain the country’s economic stability.
Nevertheless, the private sector, also attending the policy dialogue with the policy makers, has been orienting on environmentally friendly initiatives, such as solar energy, hydro-powered
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 85
generators and (recently) biogas systems. The focus on generating electricity from communal biogas plants stems from the high price for developing other renewable energy power plants, such as solar, hydro, and ocean energy, and the fact that this also helps in addressing another big problem for the country, namely waste management.
The aim of this case study is to analyse the risks associated with a low-carbon transition of the Indonesian power sector. Bali and East Java are selected as the focus locations for the case study, mainly due to the region’s potential in producing bioenergy, justified by its high feedstock production and the local government initiatives on clean energy. Furthermore, biogas, and specifically the deployment of large communal biogas digester systems, is selected as the bioenergy option due to the availability of feedstock, the potential technological and managerial improvement, and its suitability with the critical points mentioned before - sustainable electricity generation and clean domestic energy consumption.
In the following section, a risk analysis of bioenergy pathway is conducted, based on the input provided by stakeholders from Bali and East Java. The implementation and consequential risks are identified and assessed over a set of four evaluation criteria. The risks are evaluated and ranked based on their magnitude, using the TOPSIS multiple criteria analysis method. Finally, the consensus degree of the stakeholders is assessed, with regard to the evaluation of the potential risks and the magnitude of the criteria, in order to increase the robustness of the results.
5.4.2 Stakeholder input
A diverse group of nine stakeholders, all climate and energy experts, were interviewed as part of this case study. During the interviews, the evaluation criteria, under which the alternative risks are evaluated, were defined and finalised. In addition, the experts were asked to articulate their knowledge with regard to assessing the risks against the aforementioned criteria, and ranking the risks based on their severity.
Specifically, four evaluation criteria were selected, namely: (i) likelihood to manifest; (ii) extent of impact; (iii) lack of capacity to mitigate; and (iv) level of stakeholders’ concern. The stakeholder engagement process included one round of detailed discussions on the topic, followed by assembly of the identified risks. A second round of stakeholder engagement then took place, featuring semi-structured questionnaires for the purposes of assigning linguistic values over the criteria, to implement subsequently the multicriteria analysis.
Among the various sectors discussed, it was acknowledged by all stakeholders that the power generation sector features significant potential for improvement towards cleaner energy production, along with the promotion of renewables. However, this transition is hindered by three domains of uncertainty, as identified by the experts: (i) the unclear role of public and private sectors; (ii) the changing focus on each political term; and (iii) the lack of a national biogas target. On the risk perceptions, the transition to a greater percentage of power generation from biogas generators is highly affected by certain technological and economic barriers, for example the feedstock issue, technological choices and leakages, as well as the lack of available public
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 86
investment. Another potential risk expressed by the experts is associated with farmers, who may, instead of using biogas, return to using firewood or LPG, aggravating air pollution. On the other hand, the biogas-to-electricity pathway would rather create positive economic impact, either commercially (on the market) or at the national level. Stakeholders perceived that policy makers will play an important role, as most of the identified risks and uncertainties stem from the political system.
With regard to barriers, posing direct or indirect threats to the successful design, funding and implementation of sustainable and effective communal biogas digester systems, stakeholders agreed on six major implementation and consequential risks (Figure 30). These risks are further categorised along three separate axes, namely, the social, operational and technological dimensions.
•Collective management issues with larger biogas systems (Implementation) Social dimension •Time-requirement imbalance between men and women (Consequential)
•Time consuming process of feedstock/waste collection (Consequential) •Poor maintenance of biomas digester Operational issues (Implementation) •Varied monitoring practices in different biogas programmes (Implementation)
•Choice of technology not always made based on Technological axis good knowledge of local conditions (Consequential)
Figure 30 Implementation and consequential risks jeopardising the transition to communal biogas digester systems
The stakeholders involved in the Indonesian narratives have a tendency to focus on the implementation risks rather than the consequential ones, since biogas development has not been the main focus of the government, and has not been therefore evenly spread across the country. Several activities and programmes now target biogas technology, but they have both diverse management (government, provincial governments, and NGOs) and objectives (such as environmental, economic, and energy security concerns).
Biogas plays different roles in these different programmes, either as part of a bigger programme or as part of the main aim to support renewable energy development. For example, one biogas project’s objectives was to reduce deforestation and forest degradation by encouraging the farmers to switch from firewood to biogas. Meanwhile, another programme aimed at implementing the national policy mandate on the renewable energy deployment. Unclear national targets on
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 87
biogas installation to support emission reductions is the main uncertainty playing a significant role on this risk. When the biogas target is set, programmes should become better aligned around the same objective, rather than existing as scattered independent programmes.
Different motivations were one of the causes of diverse schemes on the monitoring processes, as there was no specified consensus on the biogas monitoring procedures (Figure 31). Most of the identified rural biogas digesters were abandoned, as investment in follow-up and evaluation were lacking. Consequently, when farmers faced hardship and constraints in operating the plant, they gave up using it. Inadequate monitoring procedures are followed by the poor maintenance (Figure 32). When maintenance services are lacking, there has been a higher tendency to abandon the digesters as the farmers did not receive any trainings on their maintenance. Moreover, highly- subsidised digesters with little investment on maintenance and monitoring may create a bad precedent and therefore create some cognitive barriers on the biogas deployment. These cases were mostly run by the government’s programmes.
Evaluation of [CR2] Varied monitoring practices in different biogas Extreme programmes
High
Medium
Low
None Expert 1 Expert 2 Expert 3 Expert 4 Expert 5 Expert 6 Expert 7 Expert 8 Expert 9
Likelihood to manifest Impact from policy framework Lack of mitigation capacity Severity of concern
Figure 31 Stakeholder evaluation of the consequential risk of varied monitoring practices in different biogas programmes, against the four evaluation criteria
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 88
Evaluation of [IR2] Poor maintenance of biogas digesters Extreme
High
Medium
Low
None Expert 1 Expert 2 Expert 3 Expert 4 Expert 5 Expert 6 Expert 7 Expert 8 Expert 9 Likelihood to manifest Impact on policy framework Lack of mitigation capacity Level of concern
Figure 32 Stakeholder evaluation of the risk associated with the poor maintenance of biogas digesters, against the four evaluation criteria
Further, as stated by the representative of the Ministry of Energy and Mineral Resources, infrastructure is a part of maintenance-related risk; it should therefore be taken into consideration together with the main biogas technology. For instance, poor waste separation may damage biogas infrastructure and take a few months to repair. In addition, the government-run programmes also pose a risk in terms of the biogas technology distribution to beneficiaries, since they have been described as heavily bureaucratic and time-consuming.
Accessibility of the feedstock is also considered as another major risk by the farmers and the policymakers. It is quite a confusing statement, as one of the main motivation for biogas development is the abundance of organic waste for the feedstock. However, the farmers claimed that collecting feedstock could be time-consuming, not only due to the time necessary to collect the scattered cow manure, but also the time needed to process it for use in the digesters. Similarly, the risk concerning feedstock availability is also expressed by the policy-makers. Apart from small-scale biogas, they also showed great interest in large-scale biogas systems to serve a large number of residents with electricity, and they expressed concern about the large amount of organic waste needed to feed biogas plants of this scale.
Another risk, arising from the collective installations in Indonesia, is the management of the biogas digesters. Unsuitable and ineffective management systems potentially create risks in collective biogas deployment, as in many cases the farmers did not seem to be managing the systems as a team. This may be correlated to the lack of familiarity with a relatively new technology. The farmers also do not seem to use the biogas as it was intended (i.e. to compensate and to secure the conventional energy use at the household level); instead they often use it to cover other needs, such as water heating. Apart from these technological constraints, the users are not
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 89
encouraged to carry out activities that would generate more income, as suggested by the researchers and Bappenas.
On the social aspect, women in rural Bali had more responsibilities under the dependency on the firewood. They have significant roles in collecting the firewood and cooking. Nevertheless, it was identified by the stakeholders that the exchange of roles has reduced the women’s working time, while increasing the men’s. During that debates on this perception, it was mainly raised as an issue by the male farmers; other stakeholders, such as the policy-makers and some researchers, do not view it as a significant issue. The policy makers suggested that this gender-based issue is either insignificant or does not represent the situation across the whole of Indonesia. However, this gender-role division is embedded in local customs, and the introduction of biogas technology might cause disruptions, which is why this is included as a perceived consequential risk.
Table 11 summarises all six risks associated with the implementation of communal biogas systems, and separates them into those that hinder the successful implementation of the systems and the risks that may arise as a consequence of the deployment of biogas. The table also presents the weights of the four evaluation criteria, as expressed by the stakeholders when completing the questionnaire. Specifically, all four criteria are of high importance according to the input from the experts, however, it is apparent that the likelihood to manifest and the lack of mitigation capacity are two factors that worry them to a greater degree.
Table 11 Summary of implementation and consequential risks, criteria and criteria weights
Risk Groups Risks Evaluation Criteria Weights IR1. Collective management issues with larger biogas systems C1. Likelihood to manifest 0.278 Poor maintenance of biomass digesters Implementation IR2. C2. Impact on/from policy 0.247 Risks Varied monitoring practices in different biogas C3. Lack of mitigation capacity 0.268 IR3. programmes C4. Level of concern 0.206 CR1. Time-requirement imbalance between men and women
Consequential CR2. Time consuming process of feedstock/waste collection Risks Choice of technology not always made based on good CR3. knowledge of local conditions
5.4.3 Multicriteria analysis results
After the identification of the six risks, as stated by the stakeholders, and the elicitation of the criteria weights, a MCGDA problem was modelled and solved to rank the risks.
The multicriteria analysis examined the perceived performance of the risks against their likelihood to occur, the level of their impact on the policy framework and the transition pathway, the capacity to mitigate their impacts and the level of the stakeholders’ concern. Stakeholders appeared to feel that both risks on the societal axis are of least importance and relevance. In fact, stakeholders are primarily concerned over collective management issues with large-scale biogas systems, closely followed by time requirements for feedstock and waste collection as well
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 90
as little consideration of local context. Figure 33 summarises the results of the MCGDA analysis, as resulting from the application of the selected TOPSIS methodology in MACE-DSS.
Criticality of all risks: Final ranking
Collective management issues with larger biogas systems Low
The choice of technology Time-consuming process of is not always made based Mediu feedstock/waste on good knowledge of m collection local conditions High
High Time-requirement Varied monitoring pracices imbalance between men in different biogas and women Medium programmes
Low Poor maintenance of biogas digester
Figure 33 MCGDA results on the significance of the risks
5.4.4 Consensus
A thorough look on the stakeholder component, and a careful observation of the stakeholders’ assessments of each risk against each criterion, helps to draw useful conclusions with regard to their consensus levels. By observing deviations between each stakeholder’s preference model and the derived collective model, a behavioural pattern can be formed, through which stakeholders are evaluated on their perception of key risks.
Regarding the implementation risks, early-level analysis suggests that the nine engaged stakeholders almost rejected the possibility of collective management issues actually threatening the success of the biogas pathway. However, the experts agreed that the operational implementation risks need to be carefully accounted for, and especially the gender imbalances with regard to time-requirements, with regard to its likelihood to manifest and the lack for mitigation capacity.
On the consequential risk front, although the overall consensus appeared to be equally strong, a couple of stakeholders stood out from the average viewpoints. In addition, although most experts
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 91
agreed on the possibility that the implementation of communal biogas digesters might result in varied monitoring practices, over half of the engaged stakeholders considered that there will be high capacity to mitigate it. The rest of the consequential risks were considered by the majority of the experts as less critical, and they do not seriously jeopardise the success of the pathway.
By exploring each individual stakeholder’s preference model, based on the results of the MCGDA analysis, we can use the consensus control capacity featured in MACE-DSS and draw comparisons between each individual model and the collective model (Error! Reference source not found.).
Figure 34 Stakeholder analysis for each implementation and consequential risk, based on the MCGDA results
In particular, we notice that, across both implementation and consequential risks, there is large consensus among the stakeholders, observed in the final preference model and the occurring rankings. However, for the societal risks (and the first of the operational ones) there is exactly one stakeholder showing significant divergence from the collective preference model; the same can be observed for the societal and operational consequential risks. The rest of the risks are evaluated with a larger degree of consensus among stakeholders, a fact that supports the robustness and soundness of the evaluation results.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 92
5.5 Evaluating risks associated with the UK nuclear and RES expansion pathways
Parts of this sub-section have been published in (Alvarez-Tinoco et al., 2019).
5.5.1 Context
The United Kingdom (UK) has set itself on a transition to a low-carbon economy and society, through the imposition of a goal, under the 2008 Climate Change Act, of reducing its greenhouse gas (GHG) emissions by 80% by 2050 (against a 1990 baseline) and the creation of an institutional framework in order to secure this target. Much attention has been given to long-term pathways for emission reduction in the electricity system, because there exist a range of options for decarbonising electricity generation and supply. This drive has been built upon two major pillars: the first to reduce emissions and ensure safe low carbon sources of energy; and the second to ensure secure energy supplies from within the UK’s own borders.
Nuclear energy is currently providing most of the low-carbon electricity consumed in the EU. Some Member States consider the risks related to nuclear energy as unacceptable. Since the accident in Fukushima, public policy on nuclear energy has changed in some Member States, while others continue to see nuclear energy as a secure, reliable and affordable source of low-carbon electricity generation. Nuclear power involves multiple risks, such as safety (including cyber-security), radioactive waste management and nuclear weapon proliferation, thus causing an extremely large dichotomy of opinion within the EU. Nevertheless, the UK has remained committed to nuclear new build, whilst also ensuring that the industry revisits the way it provides assurance in terms of safety standards and demonstrating transparency in its operations. At present, the UK has around nine GW of operational nuclear capacity, providing about 21% of UK electricity demand (DUKES, 2017). All but one of the nuclear plants are expected to close before 2030. However, the UK Government has ambitions for a future build-up of nuclear capacity unlike anywhere else in Europe. These plans reflect the UK Government's belief that nuclear power has a role to play in the future UK energy mix. They mainly seek to encourage energy utility companies to invest in new nuclear build. In order to do so, there are several risks to be overcome and an emerging need for clear support by both the public and government. Regulatory certainty (regarding the acceptability of reactor design) and market certainty (giving operators’ confidence that they will see a return over the lifetime of the project) are also needed.
Overcoming the conflicting policies and regulations, while strengthening the determination of communities, governments and businesses to take decisive action, is one of the main transition challenges. The nuclear power sector is embedded in a wide-ranging, trans-sectoral institutional framework, which brings significant complexity in the development and application of regulations for new nuclear. Consequently, courageous moral leadership is an essential precondition for the rapid implementation of a nuclear low-carbon economy transition strategy, in terms of setting long policy directions while mobilising the investment required to drive the commercialisation and deployment of social and technological innovations. In addition to the corrosive influence of denial
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 93
campaigns and the lobbying of vested interests, other obstacles standing in the way of decisive climate change leadership include competing and more immediate economic and political demands, the pressure not to seem politically naïve or unrealistic and the sense that the transformational change required is simply not possible.
Furthermore, the high capital costs of new nuclear power stations raise great burdens that need to be tacked, particularly when taking into account the so-called ‘back end’ costs, such as for decommissioning and waste disposal. However, negotiation between investors and the UK Government over a long-term fixed price for electricity well above current wholesale prices could counteract such risks. In addition, continuing public concern over safety, waste management and nuclear proliferation remain significant barriers. Numerous studies carried out emphatically point out that social acceptance of nuclear transition is widely diffused, with past experience showing that even distant accidents have radically shifted countries’ positions towards nuclear.
Last but not least, lack of technology and expertise is considered among the most likely and potentially harmful risks to the success of nuclear power in the UK. Building, collecting, transferring, sharing, maintaining, preserving and utilising knowledge is essential in developing and keeping the necessary technical expertise and competences required for nuclear power programmes and other nuclear technology. Lack of skills and capacity among regulators, alongside the upcoming ‘Brexit’, may lead to a slow-down in technological interconnections with traditional UK nuclear partners such as France, thus further increasing both the likelihood and magnitude of such risk.
As for the renewable resources in the UK, their adoption within the electricity mix depends on the failure of the current policy of major nuclear expansion and has significantly increased over the last ten years. Their overall contribution to UK electricity production has more than tripled between 2008 and 2016, reaching a total share of more than a quarter of the total domestic installed capacity (DUKES, 2017). The government has made clear its commitment to increase the amount of renewable energy deployed in the UK. This will make the UK more energy secure, will help towards protecting consumers from fossil fuel price fluctuations, drive investment in new jobs and businesses in the renewable energy sector, as well as keep us on track to meet their carbon reduction objectives for coming decades. The UK’s goal is to ensure that 15% of our energy demand is met from renewable sources by 2020 in the most cost effective way. However, its ambition extends beyond 2020. Recent independent advice from the Committee on Climate Change (CCC) has made clear the long term role for renewable energy. The CCC concluded that there is scope for renewable energy penetration to reach 30 - 45% of all energy consumed in the UK by 2030. The CCC also recognised that achieving this level of growth would require resolution of current uncertainties and cost reductions.
The recent cost reductions in renewable technologies continue, and make renewables highly competitive in market conditions. The UK has the largest potential wind energy resource in Europe. With a presence in the UK spanning some 20 years, onshore wind is one of the most established, large-scale sources of renewable energy in the UK. Large onshore wind farms and smaller-scale distributed and community wind energy projects will continue to contribute to meeting the UK’s renewable energy targets. The UK is also demonstrating considerable
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 94
international leadership in the development of offshore wind. The last decade has seen UK offshore wind progress from an immature technology into a proven technology that is expected be a significant contributor to achieving EU renewables targets. Solar PV is widely deployed, both in the form of solar farms and integrated into building design. As with other renewable technologies such as tidal and biomass, they face some barriers – financial and non-financial – in maximising the potential opportunities for development. However, the Committee on Climate Change has suggested that renewable generation could be a major source of electricity in the UK. The Government is pressing forward with policies to maximise the available opportunities from onshore wind deployment.
As the renewable industry grows, the expansion of existing technologies and the development of new and innovative solutions to renewable energy production bring with them new risks, including environmental impairment. Suitable renewable energy sites are a limited resource. The UK Government, Devolved Administrations and local authorities have a key role to play in facilitating strategic spatial planning, informed by robust strategic environmental assessment, to steer development towards the least sensitive sites. Furthermore, investment in closing ecological data gaps, especially relating to marine wildlife, could enable renewable industries to move forward more cost-effectively. Understanding impacts could be improved through more standardised monitoring and systematic storage of environmental information.
Nowadays, many renewable decarbonisation technologies are already available, such as onshore wind and solar PV. However, in the longer term, meeting emission targets will require investments in innovation. This includes developing a smarter grid, energy storage options, and technologies to unlock renewable energy, particularly in the marine environment, such as floating wind turbines. However, at the same time Britain lacks expertise and technological ownership with respect to most of these renewable technologies. As a consequence, renewables’ implementation continues to rely on technology imports, leaving a possible energy security issue.
Promoting a transition to low-carbon energy through renewable sources requires economic incentives to shape the energy mix. The UK Government should continue to support onshore wind and solar, which provide opportunities for rapid decarbonisation. Perverse subsidies for fossil fuels also need to be phased out in order to create a more level playing field. In addition to government subsidies, access to private funds for renewable investment is imperative. Nevertheless, the latter in the UK is determined by a multiplicity of factors, such as costs of the adopted technologies, general volatility of market prices for electricity and variations in prices of fossil fuels and carbon, leading to relatively high degrees of risk and uneven expectations.
Conflicting conditions between sub-national initiatives and the UK national framework for renewables have systematically characterised the sector’s dynamics. Devolution in the UK and, with it, the reallocation of certain energy-related powers to the new sub-national governments have certainly impacted the evolution of renewable energy development. The role of sub-national government has previously been rather under-recognised, which can be seen as reflective of blind spots in transition theory, stemming from the emphasis on technological innovation and the lack of an appropriate legal framework explicitly stating the transition regime. While the devolved governments have all given support and protection to newer, innovative technologies, the main
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 95
material effects of devolved government on renewable energy outcomes to date have only been faster deployment of mature technologies, especially onshore wind. The innovations that have most affected development outcomes are thus more in the fields of land use planning and consenting along with measures that help to align investors with the availability of sites.
5.5.2 Stakeholder input
The UK has been a leader in legislating long-term climate mitigation targets, crucially setting a binding institutional and reporting framework to ensure that a series of 5-year carbon budgets are adhered to. However, there is a number of key risks to transitioning to a low-carbon energy system that are evident from the range of analyses undertaken, which are being recognised (to differing extents) by UK stakeholders. A survey of a range of industrial, academic and consultant stakeholders identified twelve major risks associated with a rapidly changing UK electricity sector (Table 12).
In particular, the starting point for achieving wide decarbonisation is considered to be a strong legislative basis. The fundamental reforms to energy, transport, industrial, agricultural and fiscal policy that will follow need statutory legitimacy. Adoption of a climate change law is a great challenge, and a way of forging the broad political consensus that will be needed during implementation. It is striking how many of the climate change laws in major economies have been bipartisan efforts (Townshend et al., 2011). A key purpose of the legislation is to make a statement of intent that can subsequently guide policy delivery and reduce uncertainty for decision makers. Building a low-carbon economy will take decades, much longer than policymakers can credibly commit to. This creates problems for businesses, which may mistrust the long-term validity of the plan and hedge their behaviour. However, in the UK, most climate change laws are unifying laws that bring together existing strands of regulation (e.g. on energy efficiency), express a long-term objective and create a platform for subsequent action. As a result, risks implying conflicts in policy regulations, and insufficient legal framework and political support for RES expansion, raise a few concerns among the interviewees.
Any combination of renewables, nuclear and CCS will succeed in bringing down the carbon intensity of power production. The choice is determined by cost, environmental considerations and issues of system operation. Each country makes its own decisions. Germany, for example, is resisting nuclear energy, but has invested heavily in solar energy. The UK is emphasising wind power over solar PV and has so far accepted nuclear power. It is putting particular emphasis on off-shore wind, which is more expensive than on-shore wind, but raises fewer concerns around local acceptability and environmental damage. For this reason, implementation risks such as damage to the ecosystem and social resistance to new RES development are identified, but not considered the most impactful or probable to materialise.
Furthermore, there is an issue considering system operation. An intermittent power-generation system with a high wind load will require increased levels of storage and back-up capacity to ensure system security and stability. Despite the increasing need for energy storage, and the substantial progress that has been made, utility-scale development of new electric energy storage
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 96
technologies has not kept pace with the advent of variable renewable generation. The complex interplay between the power system, market structures, and generally low wholesale market prices has complicated the fair valuation of energy storage, and are thus considered as a serious risk.
On the other hand, fear of limited development, deployment and investment in low-carbon RES technologies appears to concern all involved stakeholders, mainly because many of these technologies are still emerging and lack the necessary innovation as well as the knowledge arising from the ‘learning-by-doing’ techniques. Power sector decarbonisation is critical for meeting the carbon budgets out to 2030, and requires strong roles for wind, nuclear and CCS (Pye et al., 2015). Starting to ramp up the deployment of other low-carbon technologies in end-use sectors through the 2020s will also be critical to ensure market-readiness and supply capacity.
The incremental investment in low-carbon technologies will mean higher energy costs, at least in the near term. Many low-carbon technologies are highly capital-intensive, requiring increased levels of upfront capital, whilst significant changes in the system of energy delivery will also require engagement and ‘buy-in’ from a range of stakeholders. Engagement with people is essential not just for individual technologies but for the whole system; a focus of engagement should be on how a transition is organised and paid for. This includes acceptance of siting specific technologies in local communities, and how energy services are provided in the future. Because of the energy market’s insufficiency and instability, there are also affordability concerns, with consumers paying for this transition through direct purchases of low-carbon technologies or through their energy bills.
These risks are evaluated against their likelihood to manifest, the level of the perceived impact they can have on the climate mitigation policy framework, the capacity of the society and/or State to mitigate them, as well as the involved stakeholders' level of concern over them. The weights regarding the four evaluation criteria were determined by the authors. Ten stakeholders participated in the process, as equally weighted decision makers and clustered in ‘industry’ (n=2), ‘consultants’ (n=3), ‘academia’ (n=4) and ‘others’ (n=1).
Table 12 Risk classification for the UK’s energy transition
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 97
Alternatives Evaluation Criteria Weights
A1. Damage to ecosystems C1. Likelihood to manifest 4 A2. Rising electricity prices C2. Impact on/from policy 4 A3. Inability to enhance power distribution C3. Lack of mitigation capacity 1.5 A4. Inability to provide backup electricity storage C4. Level of concern 2 A5. Low hydrocarbon prices A6. Low carbon prices A7. Inefficient and unbalanced markets A8. Limited investment in RES and CCS technologies A9. High cost of RES and CCS technologies A10. Social resistance to new RES development A11. Conflicts in every policy and regulation A12. Insufficient political support for RES expansion
In order for the stakeholders to evaluate the alternative risks against the four evaluation criteria, a 5-term linguistic scale was used: {None, Low, Medium, High, Extreme}. The objective of this process is to reach a meaningful ranking from the best (least significant) to worst (most significant) risk. After eliciting the information from the stakeholders (for example, see Figure 35 and Figure 36), we use MACE-DSS to calculate the rankings for each implementation risk.
Evaluation of [A1] Damage to ecosystems Extreme
High
Medium
Low
None Others Industry Consultant Academia
Likelihood to manifest Impact on policy framework Lack of mitigation capacity Level of concern
Figure 35 Assessment, across the four evaluation criteria, of the risk associated with the damage to ecosystems, due to the transition pathway, by the ten experts interviewed
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 98
Evaluation of [A10] Social resistance to new RES development Extreme
High
Medium
Low
None Others Industry Consultant Academia
Likelihood to manifest Impact on policy framework Lack of mitigation capacity Severity of concern
Figure 36 Assessment, across the four evaluation criteria, of the risk associated with the social resistance to new RES development
It is interesting to note the differences among the stakeholder groups, prior to any analysis. For the example of the risk of the transition being detrimental to ecosystems, industry representatives are not as concerned as consultants and academics - the latter especially fear that the likelihood of this happening is relatively high. For the case of societal resistance, academics again appear to be extremely worried despite, alongside consultants, but do not consider the probability of this occurring as likely.
5.5.3 Multicriteria analysis results
The MCGDA analysis explored the perceived performance of the twelve risks against their likelihood to occur, the level of their impact on the policy framework and the renewable energy transition pathway (or vice versa), the lack of capacity to mitigate their impacts, and the level of the stakeholders’ concern. Financial and energy market-related risks appear to be of extreme criticality and importance to the effective design of a sustainable and robust pathway to a climate- resilient UK energy sector (Figure 37). More specifically, stakeholders seem to be significantly concerned about the lack of massive investment in renewable energy and carbon capture technologies, mainly due to the current low cost of coal in combination with the low cost of hydrocarbon utilisation. Furthermore, stakeholders appeared to feel less concerned about challenges for electricity price fluctuations resulting from renewable energy sources integration into the grid, closely followed by the environmental damage to the ecosystem implied by the extensive RES investment.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 99
On the contrary, the most relatively optimistic stakeholders’ assessments concern the insufficient electricity storage capacity, followed by the inability to externally intervene into the grid, towards developing the latter in order to efficiently support the ‘new’ renewable electricity distribution. Despite the turmoil and the turbulent political landscape in the UK due to the upcoming ‘Brexit’, stakeholders seem to care little about the risk of insufficient political support, in terms of fostering new RES investments. Finally, the societal (acceptability) perspective of risks oriented on social resistance towards large investment projects are, according to the results of our study, considered of medium significance among the involved stakeholders.
Criticality of risks: final ranking
Damage to ecosystems Insufficient political support None Rising electricity prices for RES expansion
Low Conflicts in every policy and Inability to enhance power regulation Medium distribution
High
Social resistance to new RES Extreme Inability to provide backup development electricity storage
High
High cost of RES and CCS Medium Low hydrocarbon prices technologies
Low Limited investment in RES Low carbon prices and CCS technologies None Inefficient and unbalanced markets
Figure 37 Final MCGDA results of the significance of the examined risks
5.5.4 Consensus
By now focusing on the stakeholder component, and carefully observing the stakeholders’ assessments of each risk against each criterion, we can draw useful conclusions with regard to the consensus levels. By observing deviations between each stakeholder group’s preference model and the derived collective model, we can form a behavioural pattern through which stakeholders may be evaluated on their perception of key risks.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 100
For example, industrial and academic stakeholders significantly worry about the limited investments in renewable and carbon capture technologies. However, consultants and other stakeholders participating in the MCGDA study appeared not to care much about this risk: consultants shift their concerns to more energy-related issues such as the low cost of hydrocarbons exploitation, whilst other stakeholders seem to care about more governance-related issues, such as insufficient political support and complexities in every policy and regulation. In addition, there is a great deal of divergence among stakeholders’ opinions regarding the high investment cost of renewable energy and CCS technologies, as industrial and consulting stakeholders consider this challenge to be extremely important for the success of the UK nuclear phase-out initiative, while academics and other stakeholders seem to disregard this issue.
Finally, all of the participating stakeholders tend to agree that insufficient electricity storage capacity is one of the least significant risks to be overcome, whilst the fluctuating electricity prices due to the extensive RES investments is a more important one. Their views are equally akin around social resistance to the development of new forms of RES, which is regarded as a risk of low significance. On the other hand, their opinions seem to differ significantly on the environmental destruction triggered by the massive RES installation, since industrial and consulting stakeholders assess it as of low criticality, whilst academic stakeholders consider this to be a risk of great importance.
By delving into each individual stakeholder group’s preference model, based on the results of the MCGDA analysis, we can use the consensus control capacity featured in MACE-DSS and draw comparisons between each individual model and the collective model. In particular, we notice that, across the risks, consultants appear to exhibit the broadest disagreement with the collective opinion, followed by academic stakeholders (Figure 38). We can also observe that, as with the Swiss case discussed before, in general little deviation can be observed amongst the four stakeholder groups. Note that in the horizontal axis of the graph, each number corresponds to the unique identifier for each risk, as obtained in Table 12.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 101
Deviations from collective model
SLIGHTDEVIATION
DEVIATION NEGLIGIBLE
1 2 3 4 5 6 7 8 9 10 11 12 Others Industry Consultant Academia
Figure 38 UK stakeholder analysis based on the MCGDA results
5.6 Evaluating risks associated with a low-carbon Chinese built environment
Parts of this sub-section have been published in (Song et al., under review).
5.6.1 Context
Green transition studies are increasingly highlighting the interplay and co-evolution of socio- technical systems in a geographical scale, considering relational turn (Geels, 2016; Yeung, 2005; Hammer et al., 2011; ), where socio-technical changes can occur in specific urban contexts (Turnheim et al, 2015; Bulkeley et al, 2010, Fastenrath and Braun, 2018; Hodson et al., 2017). Green transition is conceptualised as a configuration of system elements that include technology, policy, infrastructure and culture, in support of a sustainable trajectory (Smith, 2007, Smith et al., 2005, Geels, 2004). These system configurations are embedded in local and regional political, economic and social systems (Scott and Storper, 2015). Therefore, the co-evolution of system configurations can differ from one location to another, by multiple changes in different urban transition histories, economic structure, cultural preferences, natural endowments and various
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 102
stakeholder groups. However, there is a limited analysis of how these system configurations have impacts on transition processes (Kirby et al., 2018; Geels, 2012).
In particular, the impacts from policy transformation at national or other (e.g. local and city) levels receive limited attention. The mix of policy responses may vary across countries and cities, due to different stakeholders, institution structures and transition governance ways (Wittmayer et al., 2016). Even in the same context of national or local carbon control regimes, different political and policy choices would result in distinct low-carbon transition pathways and outcomes.
An increasing number of studies on green transitions are exploring the drivers for transition pathways (Fastenrath and Braun, 2018; Holtz et al., 2018). However, barriers to or consequences of transition pathways have been underexplored due to the multiple-level dynamics (Murphy, 2015). Risks of green transitions are complex, because these are not only derived from path dependencies of technological hardware, institutions, social culture and multiple actors (Van den Bergh et al., 2011); they also exist in the mismatch of configuration elements in multiple-level governance and the specific interplay of urban regime (Jacobsson and Bergek, 2011). Even through single risks in such transition pathways may be discussed in the literature, such as financial risks (Thoma and Chenet, 2016), behaviour change impacts (Niamir et al., 2018), local low-carbon governance capability (Westley et al., 2011) and cognitive barriers (Olazabal and Pascual, 2015), few studies have explored risks in depth from a view of multiple-level dynamics. Furthermore, most approaches lack a certain concept of ‘transition risk’, which has system-wide effects on a transition process. Here, we draw from a classification of transition risks into two different types: implementation risks and consequential risks. Implementation risk is defined as the “potential for diverse causes to affect the design, implementation or success of a given policy”, which introduces potential failure of policy implementation to the idea of barriers or challenges; while consequential risk refers to the “potential for a certain policy to cause diverse negative consequences” (Hanger-Kopp et al., 2019). Grounded on these definitions, this paper seeks to contribute to closing the knowledge gap in low- carbon transition risks in urban areas, by providing empirical insight from a case study in the Chinese residential building sector, with specific references to Beijing and Shanghai. Increasing urban population and density have fuelled the rapid development of the building industry and have driven energy consumption growth in China. The construction industry contributed 26.4% of the country’s GDP, and building energy use accounted for 33% of total energy use in the country in 2015 (Zhang et al. 2017). Total carbon emissions from China’s building sector have increased from
984.69 million tons of CO2 in 2005 to 3,753.98 million tons of CO2 in 2014 (Jiang and Li, 2017). Rapid urbanisation and economic development have driven the demand for higher-quality living spaces, including improved indoor comfort, with a corresponding increase in energy consumption. Therefore, energy conservation in the building stock, especially residential buildings in urban areas, has become one of the largest challenges for China’s energy conservation and emissions reduction action (IEA and Tsinghua, 2015). Green transition in the building sector thus plays an important role in ensuring emissions peak before 2030, as committed by the Chinese government in the country’s first Nationally Determined Contribution (NDC).
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 103
The green transition pathway in the Chinese built environment can be viewed from the perspective of the life cycle of a building, including energy consumption during the building planning, design and construction processes, and energy consumption of the building in operation (Chen and Luo, 2008). ‘Green buildings’ are commonly understood as the practice of creating resource-efficient and healthier approaches for the design, construction, renovation, operation and maintenance of buildings (Fastenrath and Braun, 2018). When the green building concept is narrowed down to energy saving and carbon reduction throughout the building’s life cycle, the transition to greener buildings can potentially be achieved by the adoption of ‘green’ architectural principles (e.g. solar, passive or low-energy design); the application of ‘low-carbon’ building technologies, such as prefabricated construction or energy efficiency technologies; the renovation of existing buildings with exterior windows or windows shading; the installation of on-site renewable energy generation systems, and green or vegetated roofs and walls; and other energy saving measures in the building sector ( Lewis, 2007; UNEP, 2011; Akadiri and Chinyio, 2012; Fastenrath, 2018).
Given the context of China’s low-carbon transition policies in urban areas, this case study focuses on risks associated with two directions for the transition to green building: ‘energy efficiency in the building sector’ and ‘green energy in the building sector and the electricity system”, such as on-site renewable energy installations and renewable energy supply in the electricity system.
A low-carbon transition in the residential built environment in China is more complicated than other building categories. The increased complexity can be attributed to the wide range of housing types, differences in household demographics, varying climate conditions across the country that affect the ability of households to regulate indoor temperature, as well as different socioeconomic contexts that include the housing rental market, social customs and culture. It is hard to conceptualise the dynamics of socio-technical change in a single pathway, considering the different building categories, locations, climates and natural endowments (Bauer et al., 2009). Even if urban cities in different climate zones provide similar innovation potential for a low-carbon transition, their pathways would face different mismatches with the pre-existing socio-economic and technological structures, the developments of which are associated with different uncertainties and risks. The case study of a low-carbon transition in the residential building sector provides an opportunity to explore the risks that may exist in the implementation of policies and the possible negative consequences of a transition process. This case study aims to capture stakeholders’ perceptions of the risks associated with the processes of a low-carbon transition and evaluate these risks based on these perceptions.
5.6.2 Stakeholder input
Two multiple-criteria group decision making problems are designed and assessed, one for implementation (Figure 2) and another for consequential risks (Figure 3) that are associated with the envisaged transition. Considering the complexity of green transition processes and the knowledge gaps associated with our understanding of the risks associated with climate action, we mobilise the tacit knowledge embedded in stakeholders and attempt to identify the potential barriers to and consequences of the transition of the Chinese built environment based on their perceptions. Stakeholder engagement was carried out over July to August, 2018. A group of eleven
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 104
stakeholders was formed to identify, discuss and evaluate the list of risks, comprising four policymakers from green building authority agencies in Beijing and Shanghai; five researchers in the field of energy policy and climate change economics; one stakeholder from the construction firm; and one stakeholder from an NGO involved in promoting actions to reduce pollution emission. The resulting list of risk included fifteen implementation and five consequential risks (Table 13), which are evaluated against four evaluation criteria: (C1) their likelihood to occur; (C2) the level of their impact on the policy framework and the transition pathway (for implementation risks) or the level of the transition pathway’s impact on them (for consequential risks); (C3) the perceived capacity to mitigate them; and (C4) the level of the involved stakeholders’ concern (Table 14).
Table 13 Description of implementation and consequential risks in China
Implementation Risks
R1. Lack of integrated systemic planning with broader resource use (e.g. water use, waste management) for new green buildings
R2. Lack of action and willingness from construction firms to build green buildings
R3. Weak monitoring and policy enforcement in the operation of green buildings
R4. Immature green certification for operating green buildings
R5. Lock-in behaviours from end-user side that counteract energy efficiency measures
R6. Investment risks for energy efficiency technologies
R7. Insufficient planning priorities for retrofitting existing buildings
R8. Insufficient supporting policy for regional retrofitting
R9. Lack of market financing channel for retrofitting existing buildings
R10. Lack of awareness/information/experience in retrofitting buildings from the public
R11. Lack of innovative policies for integrated grid
R12. Lack of information of renewable energy installation for the public
R13. Lack of financial support for renewable energy
R14. Low returns compared to renewable energy installation costs
R15. Intermittent heating from renewable energy for households
Consequential Risks
R16. Increasing land demands with more constructions of new buildings
R17. Social justice and climate/energy poverty
R18. Increase the living cost such as the price of real estates
R19. Lower resilience to climate change such as heat waves or cold spells
R20. Increased unemployment in building sectors due to higher construction costs related to green buildings
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 105
Stakeholders evaluated the performance of these risks against these four evaluation criteria using a five-term linguistic scale: {None, Low, Medium, High, Extreme}. We then used the MACE-DSS tool for assessing multiple stakeholders’ perceptions of climate policy-related risks against multiple criteria (Nikas et al., 2018). MACE-DSS is based on a variation of the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) multiple-criteria decision aid method for group decision making (Krohling and Campanharo, 2011) and modified to incorporate the engaged stakeholders' behavioural tendency to avoid loss (Yoon and Kim, 2017).
Using MACE-DSS and based on the mean value of the evaluation criteria weights (Table 14) as well as the individual evaluations of each risk against the four criteria, as provided by the eleven stakeholders, we calculate the final ranking of the fifteen implementation (Figure 39) and five consequential risks (Figure 40). In both figures, the lower the final score of a risk is, the more significant that risk is perceived to be.
Table 14 Evaluation Criteria and criteria weights in China
Evaluation Criteria Weight
C1. Likelihood to manifest 3.36
C2. Impact on/from policy 3.91
C3. Mitigation capacity 3.36
C4. Level of concern 3.09
5.6.3 Multicriteria analysis results
As seen in Figure 39, stakeholders considered the intermittency of renewable energy sources (RES) used for heating in the residential sector as the most critical barrier to the effective design of a sustainable and robust pathway. This implementation risk was closely followed by the perceived capacity to effectively enforce and monitor policy instruments in the built environment. Certain risks of financial nature associated with the envisaged low-carbon transition of the Chinese building sector are also considered of high significance among the participating stakeholders: limited expectations for returns on renewable installation investments, especially compared to the respective costs, as well as a multitude of perceived investment risks implied in the implementation of energy efficiency technologies, constitute a very critical component of the envisioned low-carbon transition.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 106
Significance of implementation risks
Lack of integrated systemic planning with broader… Intermittent heating from 0.8 Lack of action and renewable energy willingness from… Low returns compared to 0.6 Weak monitoring and policies renewable energy… enforcement 0.4
Lack of financial support Immature green certification 0.2
0 Lack of information of Lock-in behaviours from end- renewable energy… users
Lack of innovative policies Investment risks
Lack of awareness in Insufficient planning retrofitting buildings priorities Lack of market financing Insufficient supporting policy
Figure 39 Multicriteria analysis results on the significance of key implementation risks in China
This analysis then stresses other risks across the political and regulatory axes, such as the lack of integrated systemic planning with broader resource use and insufficient planning prioritisation. Apart from the fear of behavioural lock-ins, other societal risks were ranked lower in terms of significance, including the levels of public awareness in building retrofitting; the quality and adequacy of information flow on RES; limited focus on innovative policies; and the reluctance of the construction sector to comply with the new paradigm.
Results of the multicriteria analysis of the consequential risks are depicted in Figure 40. Stakeholders mostly highlighted the negative impacts that a transition to a low-carbon building sector may have on social justice as well as on energy and climate poverty. These findings reflect the stakeholders’ concerns that not all citizens may be able to afford the retrofitting and energy efficiency actions associated with a decarbonisation pathway of the built environment.
These concerns are followed by the potential negative effect to land use planning, due to the expected increase in land demands for greening the building sector, which is then closely followed by the risk of lower resilience to climate change: for example, renewable energy intermittency in heating and cooling may increase vulnerability to heat waves or cold spells. Other consequential risks in the social dimension such as higher living costs and increased unemployment, although important, are considered of less significance compared to the other perceived potential consequences of the envisaged transition.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 107
Significance of consequential risks
Increasing land demands 0.8
0.6
0.4 Increased unemployment Social justice and due to higher costs for climate/energy poverty green buildings 0.2
0
Lower resilience to climate Increase the living cost change
Figure 40 Multicriteria analysis results on the significance of key consequential risks in China
5.6.4 Consensus
Using the built-in capacity of MACE-DSS to evaluate the consensus of the engaged stakeholders by means of statistical indices, we also noticed certain deviations between the preferences of each stakeholder group. For instance, the policymaker group placed particular emphasis on the economic aspect of renewable energy installations, highlighting investment risks, low expected returns on respective investments and the potential lack of financial support for renewables. Policymakers also perceived risks on behavioural lock-ins and intermittency in cooling and heating as more important than how the other stakeholder groups did. On the consequential risk front, the same stakeholder group appeared to highlight the potential negative impacts of a transition pathway of the building sector on social justice and on energy and climate poverty. Experts coming from the research community, however, placed more emphasis on investment return risks and the social implementation risks of the transition, such as the lack of information and public awareness. Furthermore, what appeared to matter nearly equally to all engaged stakeholders are the lock-in behaviours from the end-user side that negate the effect of energy efficiency measures as well as the political risk associated with poor planning priorities for retrofitting the existing building stock.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 108
6 CONCLUSIONS
In this report, we develop a methodological approach, and dedicated tool, based on Multiple- Criteria Decision Aid, to support the process of evaluating risks associated with low-carbon transitions.
First, a thorough survey of the literature is carried out, in order to explore the contributions of all reported MCDA methodologies to the problem domain of climate policymaking, especially focusing on their group decision making capacity. Through this task, we conclude that the TOPSIS method appears to be the appropriate framework for this domain. In this respect, a TOPSIS-oriented methodological framework and associated tool is developed, featuring three components: gathering and unifying data; carrying out the analysis; and controlling consensus. Finally, the methodological framework and tool are implemented in seven TRANSrisk case studies: energy efficiency in Greece; boosting solar power in Greece; decarbonising the Austrian iron and steel sectors; achieving a nuclear phase-out in Switzerland; greening Indonesian power generation with communal biogas; achieving a low-carbon transition of the UK energy sector; and decarbonising the Chinese built environment.
From a methodological point of view, the use of linguistic variables enables the stakeholders involved to easily and meaningfully provide their knowledge, when evaluating the alternative options, and the capacity to include hybrid information can provide a sense of precision regarding stakeholder input. Secondly, the multicriteria analysis component allows for dealing with real- world problems by supporting the assessment of multiple alternative options against multiple evaluation criteria, while involving multiple decision makers (stakeholders). They comprise three different group of decision making multicriteria analysis methodologies, thereby enhancing the robustness of results through the application of a diverse set/combination of methods. Finally, the tool features the capacity to gain insights into the stakeholders’ preference models, by means of a number of consensus control analyses. The tool also allows for imposing weight penalties based on these insights, in order to balance the identified collective bias or even re-engage stakeholders in the aim of eliciting a feedback-driven round of input (note that we did not use this feature for the purposes of this Deliverable and the TRANSrisk project).
The empirical findings of the seven case studies suggest that:
Greece
Stakeholders engaged in the risk assessment stakeholder engagement activities appear to highlight the importance of political inertia, closely followed by the economic environment and the consequent lack of financial capacity to support a low-carbon transition, as well as the bureaucratic complexity of the regulatory framework and the poor grid/infrastructure quality. Social risks (including public awareness and/or reluctance) are perceived to be of medium importance (although slightly more critical in the case of solar projects). However, drawing from lessons recently learnt, they urge caution on poorly designed energy efficiency and/or solar boosting financial support mechanisms giving rise to adverse economic consequences, and are
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 109
somewhat concerned with potential impacts on utility bill costs, energy security and grid-related problems. Consensus was not very high in either case, with members from the academic and research communities showing the largest observed deviations from the collective model in both cases.
Austria
Engaged stakeholders appear to be equally concerned with implementation and consequential risks, thus highlighting the importance of considering both barriers and potential negative consequences when designing appropriate policy strategies. In particular, stakeholders agreed that risks of a financial nature (adverse economic conditions, limited transparency due to lobbying, poor or misleading market rules and design, and over-regulations) are the most critical to consider when designing the decarbonisation of the iron and steel sectors. Risks associated with the political and institutional framework were considered the least important, with the exception of uncertainty over future CO2 prices. Although there were numerous observed deviations from the collective model, insights into each stakeholder group could not be gained, since there was no information on the stakeholder’s expertise, field and occupation.
Switzerland
Again, overall results showed a balanced concern between implementation and consequential risks; however, the most dominant risks from the stakeholders’ perspective regard barriers hindering the nuclear phase-out, rather than potential negative consequences. In particular, concerns strongly orient on protective actions by environmental groups against renewable energy projects and on social opposition to interventions from the government, as well as the variation of priorities among government layers. Regarding the observed differences among stakeholder groups, it is noteworthy that environmental groups appear particularly worried about increased costs for end-users, as opposed to academics and consultants who highlight the importance of the energy import and security risks. All of these groups, however, appear to disregard risks associated with bureaucratic procedures that block the permission granting, emphasising on more practical issues.
Indonesia
In this case study on communal biogas, an interesting finding concerns the identification of a gender-related risk, regarding the time requirements imbalance between men and women. However, neither this nor any other social risk are perceived to be of paramount importance by stakeholders, who rather appear primarily concerned over collective management issues with large-scale biogas systems. This is closely followed by time requirements for feedstock and waste collection as well as poor considerations of local context. Although agreement levels were higher in this case study, limited insights can be gained from consensus analysis, since little information was available on the background of the stakeholders interviewed.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 110
UK
According to UK stakeholders the lack of massive investments in renewables and CCS, mainly due to the low cost of coal, appears to be the most critical consequential risk, this in combination with low prices of carbon and hydrocarbon. Other financial risks, such as unbalanced markets and rising electricity prices, are considered of medium criticality, while risks of a societal, environmental, and political/regulatory nature appear to be of lesser significance. Several insights can be gained into how each stakeholder group views the identified risks. For example, industrial and academic stakeholders stress the importance of limited investments in green technologies, while consultants centre their concerns around energy cost-related issues, and other stakeholders on governance- related ones. Consensus can be observed in less critical risks, such as storage capacity and social resistance.
China
The most critical risks, as defined by stakeholders, included four barriers or implementation risks that oriented on investment risks for energy efficiency, difficulties in monitoring and policy enforcement, technology innovation risks, and low market expectations of technical application returns; and two consequential risks, revolving around increased land requirements as well as climate poverty and social injustices.
Final remarks
Overall, risk rankings appear to greatly vary across the case studies, which is expected given the context of each country as well as the completely different scope of the examined pathways for each of the case studies. Consensus analyses across all case studies, on the other hand, suggest that members of the academic and research community appear to significantly diverge from the other stakeholder groups, highlighting for example energy security, societal and environmental risks, rather than financial barriers and consequences.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 111
References
Agell, N., SáNchez, M., Prats, F., & Roselló, L. (2012). Ranking multi-attribute alternatives on the basis of linguistic labels in group decisions. Information Sciences, 209, 49-60. DOI: 10.1016/j.ins.2012.05.005
Akadiri, P. O., Chinyio, E. A., & Olomolaiye, P. O. (2012). Design of a sustainable building: a conceptual framework for implementing sustainability in the building sector. Buildings, 2(2), 126- 152. Almaraz, S. D. L., Azzaro-Pantel, C., Montastruc, L., Pibouleau, L., & Senties, O. B. (2013). Assessment of mono and multi-objective optimization to design a hydrogen supply chain. International journal of hydrogen energy, 38(33), 14121-14145. DOI: 10.1016/j.ijhydene.2013.07.059 AlSabbagh, M., Siu, Y. L., Guehnemann, A., & Barrett, J. (2016). Integrated approach to the assessment of CO 2 e-mitigation measures for the road passenger transport sector in Bahrain. Renewable and Sustainable Energy Reviews. DOI: 10.1016/j.rser.2016.12.052
Alvarez-Tinoco, R., Stua, M., & MacKerron, G. (2019, in press). United Kingdom: Pathways towards a low-carbon electricity system: nuclear expansion versus nuclear phase out. In Hanger-Kopp, S. Lieu, J., & Nikas A. (eds.). Narratives of low-carbon transitions: Understanding risks and uncertainties. Routledge, Abingdon. Antunes, C. H., Martins, A. G., & Brito, I. S. (2004). A multiple objective mixed integer linear programming model for power generation expansion planning. Energy, 29(4), 613-627. DOI: 10.1016/j.energy.2003.10.012
Axelrod, R. (Ed.). (1976). Structure of decision: The cognitive maps of political elites. Princeton university press. Baležentis, T., & Streimikiene, D. (2017). Multi-criteria ranking of energy generation scenarios with Monte Carlo simulation. Applied Energy, 185, 862-871. DOI: 10.1016/j.apenergy.2016.10.085 Batubara, M., Purwanto, W. W., & Fauzi, A. (2016). Proposing a decision-making process for the development of sustainable oil and gas resources using the petroleum fund: A case study of the East Natuna gas field. Resources Policy, 49, 372-384. DOI: 10.1016/j.resourpol.2016.07.010
Bauer, M., Mösle, P., Schwarz M. (2009) The Motivation behind the Green Building Idea. In: Green Building, 8-21. Springer, Berlin, Heidelberg Behzadian, M., Kazemzadeh, R. B., Albadvi, A., & Aghdasi, M. (2010). PROMETHEE: A comprehensive literature review on methodologies and applications. European journal of Operational research, 200(1), 198-215.
Beijing Municipal Commission of Housing, Urban-Rural Development and Management (MCOHURDM). 2017. The actions in Beijing Green Buildings Development
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 112
Biloslavo, R., & Dolinšek, S. (2010). Scenario planning for climate strategies development by integrating group Delphi, AHP and dynamic fuzzy cognitive maps. Foresight, 12(2), 38-48. DOI: 10.1108/14636681011035771 Biloslavo, R., & Grebenc, A. (2012). Integrating group Delphi, analytic hierarchy process and dynamic fuzzy cognitive maps for a climate warning scenario. Kybernetes, 41(3/4), 414-428. DOI: 10.1108/03684921211229497 Blechinger, P. F. H., & Shah, K. U. (2011). A multi-criteria evaluation of policy instruments for climate change mitigation in the power generation sector of Trinidad and Tobago. Energy Policy, 39(10), 6331-6343. DOI: 10.1016/j.enpol.2011.07.034 Borges, P. C., & Villavicencio, A. (2004). Avoiding academic and decorative planning in GHG emissions abatement studies with MCDA: The Peruvian case. European Journal of Operational Research, 152(3), 641-654. DOI: 10.1016/S0377-2217(03)00063-8 Branco, D. A. C., Rathmann, R., Borba, B. S. M., de Lucena, A. F. P., Szklo, A., & Schaeffer, R. (2012). A multicriteria approach for measuring the carbon-risk of oil companies. Energy Strategy Reviews, 1(2), 122-129. DOI: 10.1016/j.esr.2012.06.002 Brand, B., & Missaoui, R. (2014). Multi-criteria analysis of electricity generation mix scenarios in Tunisia. Renewable and Sustainable Energy Reviews, 39, 251-261. DOI: 10.1016/j.rser.2014.07.069 Brand, B., Stambouli, A. B., & Zejli, D. (2012). The value of dispatchability of CSP plants in the electricity systems of Morocco and Algeria. Energy Policy, 47, 321-331. DOI: 10.1016/j.enpol.2012.04.073 Brans, J. P., Vincke, P., & Mareschal, B. (1986). How to select and how to rank projects: The PROMETHEE method. European journal of operational research, 24(2), 228-238. Bruckner T., Bashmakov, I. A., Mulugetta, Y., Chum, H., de la Vega Navarro, A., Edmonds, J., Faaij, A., Fungtammasan, B., Garg, A., Hertwich, E., Honnery, D., Infield, D., Kainuma, M., Khennas, S., Kim, S., Nimir, H. B., Riahi, K., Strachan, N., Wiser, R., & Zhang, X. (2014). Energy Systems. In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., Pichs-Madruga, R., Sokona, Y., Farahani, E., Kadner, S., Seyboth, K., Adler, A., Baum, I., Brunner, S., Eickemeier, P., Kriemann, B., Savolainen, J., Schlömer, S., von Stechow, C., Zwickel, T., & Minx, J.C. (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Brundtland Commission (1987). Our common future: The World Commission on Environment and Development. Oxford University Press. United Nations. ISBN: 019282080X.Diakoulaki et al., 2005;
Bulkeley, H., Broto, V. C., Hodson, M., & Marvin, S. (Eds.). (2010). Cities and low carbon transitions. New York: Routledge. Büyüközkan, G., & Güleryüz, S. (2017). Evaluation of Renewable Energy Resources in Turkey using an integrated MCDM approach with linguistic interval fuzzy preference relations. Energy, 123, 149- 163. DOI: 10.1016/j.energy.2017.01.137
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 113
Büyüközkan, G., & Karabulut, Y. (2017). Energy project performance evaluation with sustainability perspective. Energy, 119, 549-560. DOI: 10.1016/j.energy.2016.12.087 Capros, P., De Vita, A., Tasios, N., Siskos, P., Kannavou, M., Petropoulos, A., Evangelopoulou, S., Zampara, M., Papadopoulos, D., Nakos, C. and Paroussos, L., 2016. EU Reference Scenario 2016- Energy, transport and GHG emissions Trends to 2050. Centre for Renewable Energy Sources and Saving, 2015. Energy efficiency trends and policies in Greece. September 2015. Chang, P. L., Hsu, C. W., & Lin, C. Y. (2012). Assessment of hydrogen fuel cell applications using fuzzy multiple-criteria decision making method. Applied energy, 100, 93-99. DOI: 10.1016/j.energy.2016.12.087 Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy sets and systems, 114(1), 1-9. DOI: 10.1016/S0165-0114(97)00377-1 Chen, L., & Pan, W. (2015). A BIM-integrated Fuzzy Multi-criteria Decision Making Model for Selecting Low-Carbon Building Measures. Procedia Engineering, 118, 606-613. DOI: 10.1016/j.proeng.2015.08.490 Chen, S. M., & Lee, L. W. (2010). Fuzzy multiple attributes group decision-making based on the interval type-2 TOPSIS method. Expert systems with applications, 37(4), 2790-2798. Doi: 10.1016/j.eswa.2009.09.012 Chen, T. Y., & Tsao, C. Y. (2008). The interval-valued fuzzy TOPSIS method and experimental analysis. Fuzzy Sets and Systems, 159(11), 1410-1428. DOI: 10.1016/j.fss.2007.11.004
Chen, W.K., Luo, F. (2008). Research on building energy consumption based on whole life cycle theory. Building Science, 10, 23-27. Choudhury, A. K., Shankar, R., & Tiwari, M. K. (2006). Consensus-based intelligent group decision- making model for the selection of advanced technology. Decision Support Systems, 42(3), 1776- 1799. DOI: 10.1016/j.dss.2005.05.001 Climate Action Tracker, 2016. Indonesia: Emission Reduction Pledges [WWW Document]. URL http://climateactiontracker.org/countries/indonesia.html (accessed 9.14.17). Cowan, K., Daim, T., & Anderson, T. (2010). Exploring the impact of technology development and adoption for sustainable hydroelectric power and storage technologies in the Pacific Northwest United States. Energy, 35(12), 4771-4779. DOI: 10.1016/j.energy.2010.09.013 Cutz, L., Haro, P., Santana, D., & Johnsson, F. (2016). Assessment of biomass energy sources and technologies: The case of Central America. Renewable and Sustainable Energy Reviews, 58, 1411- 1431. DOI: 10.1016/j.rser.2015.12.322 Dace, E., & Blumberga, D. (2016). How do 28 European Union Member States perform in agricultural greenhouse gas emissions? It depends on what we look at: Application of the multi- criteria analysis. Ecological Indicators, 71, 352-358. DOI: 10.1016/j.ecolind.2016.07.016 de Bruin, K., Dellink, R. B., Ruijs, A., Bolwidt, L., van Buuren, A., Graveland, J., ... & Tassone, V. C. (2009). Adapting to climate change in The Netherlands: an inventory of climate adaptation
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 114
options and ranking of alternatives. Climatic change, 95(1), 23-45. DOI: 10.1007/s10584-009-9576- 4 Diakoulaki, D., Georgiou, P., Tourkolias, C., Georgopoulou, E., Lalas, D., Mirasgedis, S., & Sarafidis, Y. (2007). A multicriteria approach to identify investment opportunities for the exploitation of the clean development mechanism. Energy Policy, 35(2), 1088-1099. DOI: 10.1016/j.enpol.2006.02.009 Dong, Y., Zhang, G., Hong, W. C., & Xu, Y. (2010). Consensus models for AHP group decision making under row geometric mean prioritization method. Decision Support Systems, 49(3), 281- 289. DOI: 10.1016/j.dss.2010.03.003 Doukas, H. (2013). Modelling of linguistic variables in multicriteria energy policy support. European Journal of Operational Research, 227(2), 227-238. DOI: 10.1016/j.ejor.2012.11.026 Doukas, H., & Nikas, A. (submitted). Decision Support Models for Climate Policy Making: A Critical Review and Future Directions. European Journal of Operation Research. Doukas, H., & Psarras, J. (2009). A linguistic decision support model towards the promotion of renewable energy. Energy Sources, Part B, 4(2), 166-178. DOI: 10.1080/15567240701620432 Doukas, H., Karakosta, C., & Psarras, J. (2010). Computing with words to assess the sustainability of renewable energy options. Expert Systems with Applications, 37(7), 5491-5497. DOI: 10.1016/j.eswa.2010.02.061
Doukas, H., Nikas, A., González-Eguino, M., Arto, I., & Anger-Kraavi, A. (2018). From integrated to integrative: Delivering on the Paris Agreement. Sustainability, 10(7), 2299. Doukas, H., Patlitzianas, K. D., & Psarras, J. (2006). Supporting sustainable electricity technologies in Greece using MCDM. Resources Policy, 31(2), 129-136. DOI: 10.1016/j.resourpol.2006.09.003 Doumpos, M., & Zopounidis, C. (2011). Preference disaggregation and statistical learning for multicriteria decision support: A review. European Journal of Operational Research, 209(3), 203- 214. DUKES. (2017). Digest of United Kingdom Statistics, July 2017. Department for Business, Energy & Industrial Strategy [Online]. Department for Business, Energy & Industrial Strategy. Available at: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data /file/643414/DUKES_2017.pdf [Accessed April 30 2018]. Durbach, I. N., & Stewart, T. J. (2012). Modeling uncertainty in multi-criteria decision analysis. European Journal of Operational Research, 223(1), 1-14. Dusonchet, L. and Telaretti, E., 2015. Comparative economic analysis of support policies for solar PV in the most representative EU countries. Renewable and Sustainable Energy Reviews, 42, pp.986-998. Estrella, F. J., Cevik Onar, S., Rodríguez, R. M., Oztaysi, B., Martínez, L., & Kahraman, C. (2017). Selecting firms in University technoparks: A hesitant linguistic fuzzy TOPSIS model for
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 115
heterogeneous contexts. Journal of Intelligent & Fuzzy Systems, 33(2), 1155-1172. DOI: 10.3233/JIFS-16727 European Commission, 2008. Europeans’ Attitudes towards Climate Change, Special Eurobarometer 300/Wave 69.2. Brussels, Belgium. European Commission, 2013. Climate Change, Special Eurobarometer 409/Wave EB80.2. Brussels, Belgium.
Fastenrath, S. (2018). Urban Sustainability Transitions in the Building Sector. Insights from contrasting contexts: Freiburg and Brisbane (Doctoral dissertation, Universität zu Köln).
Fastenrath, S., & Braun, B. (2018). Ambivalent urban sustainability transitions: Insights from Brisbane's building sector. Journal of Cleaner Production, 176, 581-589. Forouli, A., Gkonis, N., Nikas, A., Siskos, E., Doukas, H., Tourkolias, C. (Submitted). Energy Efficiency promotion in Greece in light of risk: evaluating policies as portfolio assets. Energy Policy (submitted). Fozer, D., Sziraky, F. Z., Racz, L., Nagy, T., Tarjani, A. J., Toth, A. J., Haaz, E., Benko, T., & Mizsey, P. (2017). Life cycle, PESTLE and Multi-Criteria Decision Analysis of CCS process alternatives. Journal of Cleaner Production. DOI: 10.1016/j.jclepro.2017.01.056
Geels, F. W. (2004). From sectoral systems of innovation to socio-technical systems: Insights about dynamics and change from sociology and institutional theory. Research policy, 33(6-7), 897-920.
Geels, F. W. (2012). A socio-technical analysis of low-carbon transitions: introducing the multi- level perspective into transport studies. Journal of transport geography, 24, 471-482. Geels, F. W., & Schot, J. (2007). Typology of sociotechnical transition pathways. Research policy, 36(3), 399-417.
Geels, F. W., Kern, F., Fuchs, G., Hinderer, N., Kungl, G., Mylan, J., Neukirch, M., & Wassermann, S. (2016). The enactment of socio-technical transition pathways: a reformulated typology and a comparative multi-level analysis of the German and UK low-carbon electricity transitions (1990– 2014). Research Policy, 45(4), 896-913. Georgopoulou, E., Sarafidis, Y., Mirasgedis, S., Zaimi, S., & Lalas, D. P. (2003). A multiple criteria decision-aid approach in defining national priorities for greenhouse gases emissions reduction in the energy sector. European Journal of Operational Research, 146(1), 199-215. DOI: 10.1016/S0377-2217(02)00250-3 Ghafghazi, S., Sowlati, T., Sokhansanj, S., & Melin, S. (2010). A multicriteria approach to evaluate district heating system options. Applied Energy, 87(4), 1134-1140. DOI: 10.1016/j.apenergy.2009.06.021 Govindan, K., & Jepsen, M. B. (2016). ELECTRE: A comprehensive literature review on methodologies and applications. European Journal of Operational Research, 250(1), 1-29.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 116
Greco, S., Ehrgott, M. & Figueira, J.R., 2016 (Eds). Multiple Criteria Decision Analysis: State of the Art Surveys, Volumes 1 & 2, Second Edition, International Series in Operations Research & Management Science, Springer. DOI: 10.1007/978-1-4939-3094-4
Groumpos, P. P. (2010). Fuzzy cognitive maps: basic theories and their application to complex systems. In Fuzzy cognitive maps (pp. 1-22). Springer, Berlin, Heidelberg.
Hammer, Stephen, Kamal-Chaoui, Lamia, Robert, Alexis, & Plouin, Marissa. (2011). Cities and Green Growth: A Conceptual Framework. OECD Publishing.
Hanger-Kopp, S., Nikas, A., Lieu, J. (2019). Framing risk and uncertainty associated with low- carbon pathways. In Hanger-Kopp, S. J. Lieu, and A. Nikas (eds.). Narratives of low-carbon transitions: Understanding risks and uncertainties. Routledge, Abingdon. HELAPCO, 2016. Statistics of the Greek photovoltaic market in 2015. Hellenic Association of Photovoltaic Companies, Athens, Greece. Retrieved from http://helapco.gr/wp- content/uploads/pv-stats_greece_2015_1Mar2016.pdf. Hellenic Statistical Authority, 2011. Buildings’ construction per time period. Retrieved from http://www.statistics.gr/documents/20181/1204362/A1601_SKT01_TB_DC_00_2011_05A_F_GR.x ls/b14a676e-3d69-49c3-ba5e-11665b23e536. Heo, E., Kim, J., & Boo, K. J. (2010). Analysis of the assessment factors for renewable energy dissemination program evaluation using fuzzy AHP. Renewable and Sustainable Energy Reviews, 14(8), 2214-2220. DOI: 10.1016/j.rser.2010.01.020 Herrera, F., Martınez, L., & Sánchez, P. J. (2005). Managing non-homogeneous information in group decision making. European Journal of Operational Research, 166(1), 115-132. DOI: 10.1016/j.ejor.2003.11.031 Herrera-Viedma, E., Alonso, S., Chiclana, F., & Herrera, F. (2007). A consensus model for group decision making with incomplete fuzzy preference relations. IEEE Transactions on fuzzy Systems, 15(5), 863-877. DOI: 10.1109/TFUZZ.2006.889952
Hodson, M., Geels, F. W., & McMeekin, A. (2017). Reconfiguring urban sustainability transitions, analysing multiplicity. Sustainability, 9(2), 299.
Holtz, G., Xia-Bauer, C., Roelfes, M., Schüle, R., Vallentin, D., & Martens, L. (2018). Competences of local and regional urban governance actors to support low-carbon transitions: Development of a framework and its application to a case-study. Journal of Cleaner Production, 177, 846-856. Hwang, C. L., & Yoon, K. P. (1981). Multiple attribute decision making: methods and applications. Berlin. Springer. DOI: 10.1007/978-3-642-48318-9
Hwang, C. L., & Yoon, K. P. (1981). Multiple attribute decision making: methods and applications. Berlin. Springer. DOI: 10.1007/978-3-642-48318-9
IEA (International Energy Agency) and Tsinghua University (2015). Building Energy use in China: Transforming construction and influencing consumption to 2050. OECD/IEA.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 117
Jacobsson, S. , & Bergek, A. (2011). Innovation system analyses and sustainability transitions: contributions and suggestions for research. Environmental Innovation & Societal Transitions, 1(1), 41-57. Jacquet-Lagreze, E., & Siskos, Y. (2001). Preference disaggregation: 20 years of MCDA experience. European Journal of Operational Research, 130(2), 233-245. Javid, R. J., Nejat, A., & Hayhoe, K. (2014). Selection of CO 2 mitigation strategies for road transportation in the United States using a multi-criteria approach. Renewable and Sustainable Energy Reviews, 38, 960-972. DOI: 10.1016/j.rser.2014.07.005 Jayaraman, R., Colapinto, C., La Torre, D., & Malik, T. (2015). Multi-criteria model for sustainable development using goal programming applied to the United Arab Emirates. Energy Policy, 87, 447- 454. DOI: 10.1016/j.enpol.2015.09.027
Jiang, R., Li, R. (2017). Decomposition and Decoupling Analysis of Life-Cycle Carbon Emission in China’s Building Sector. Sustainability 9(5), 793 Jun, K. S., Chung, E. S., Kim, Y. G., & Kim, Y. (2013). A fuzzy multi-criteria approach to flood risk vulnerability in South Korea by considering climate change impacts. Expert Systems with Applications, 40(4), 1003-1013. DOI: 10.1016/j.enpol.2015.09.027 Kaplanoglou, G. and Rapanos, V.T., 2018. Evolutions in consumption inequality and poverty in Greece: The impact of the crisis and austerity policies. Review of Income and Wealth, 64(1), pp.105-126. Karakosta, C., Doukas, H., & Psarras, J. (2009). Directing clean development mechanism towards developing countries' sustainable development priorities. Energy for Sustainable Development, 13(2), 77-84. DOI: 10.1016/j.esd.2009.04.001 Kaya, T., & Kahraman, C. (2011). Multicriteria decision making in energy planning using a modified fuzzy TOPSIS methodology. Expert Systems with Applications, 38(6), 6577-6585. DOI: 10.1016/j.eswa.2010.11.081 Kim, G., Park, C. S., & Yoon, K. P. (1997). Identifying investment opportunities for advanced manufacturing systems with comparative-integrated performance measurement. International Journal of Production Economics, 50(1), 23-33. DOI: 10.1016/S0925-5273(97)00014-5
Kirby, P., & O’Mahony, T. (2018). Addressing the Problem: Understanding Low-Carbon Transition with the Social Sciences. In The Political Economy of the Low-Carbon Transition (pp. 57-86). Palgrave Macmillan, Cham. Klein, S. J., & Whalley, S. (2015). Comparing the sustainability of US electricity options through multi-criteria decision analysis. Energy Policy, 79, 127-149. DOI: 10.1016/j.enpol.2015.01.007 Kong, X., Lu, S., & Wu, Y. (2012). A review of building energy efficiency in china during “eleventh five-year plan” period. Energy Policy, 41(none), 624-635. Konidari, P., & Mavrakis, D. (2007). A multi-criteria evaluation method for climate change mitigation policy instruments. Energy Policy, 35(12), 6235-6257. DOI: 10.1016/j.enpol.2007.07.007
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 118
Kosko, B. (1986). Fuzzy cognitive maps. International journal of man-machine studies, 24(1), 65- 75. Krohling, R. A., & Campanharo, V. C. (2011). Fuzzy TOPSIS for group decision making: A case study for accidents with oil spill in the sea. Expert Systems with applications, 38(4), 4190-4197. DOI: 10.1016/j.eswa.2010.09.081
Krohling, R. A., & Campanharo, V. C. (2011). Fuzzy TOPSIS for group decision making: A case study for accidents with oil spill in the sea. Expert Systems with applications, 38(4), 4190-4197. Lai, Y. J., Liu, T. Y., & Hwang, C. L. (1994). Topsis for MODM. European Journal of Operational Research, 76(3), 486-500. Lan, J., Sun, Q., Chen, Q., & Wang, Z. (2013). Group decision making based on induced uncertain linguistic OWA operators. Decision Support Systems, 55(1), 296-303. DOI: 10.1016/j.dss.2013.01.030
Lewis, P. (2007). Climate change 2007: the physical science basis. South African Geographical Journal Being a Record of the Proceedings of the South African Geographical Society, 92(1), 86- 87.
Li, Y., Ren, J., Jing, Z., Lu, J., Ye, Q., & Lv, Z. 2017. The existing building sustainable retrofit in china: a review and case study. Procedia Engineering, 205, 3638-3645. Lise, W., van der Laan, J., Nieuwenhout, F., & Rademaekers, K. (2013). Assessment of the required share for a stable EU electricity supply until 2050. Energy Policy, 59, 904-913. Luthra, S., Mangla, S. K., & Kharb, R. K. (2015). Sustainable assessment in energy planning and management in Indian perspective. Renewable and Sustainable Energy Reviews, 47, 58-73. DOI: 10.1016/j.rser.2015.03.007 Maimoun, M., Madani, K., & Reinhart, D. (2016). Multi-level multi-criteria analysis of alternative fuels for waste collection vehicles in the United States. Science of the Total Environment, 550, 349-361. DOI: 10.1016/j.scitotenv.2015.12.154
Markantoni, M. (2016). Low carbon governance: mobilizing community energy through top‐down support? Environmental Policy and Governance, 26(3), 155-169. Martínez, L., & Herrera, F. (2012). An overview on the 2-tuple linguistic model for computing with words in decision making: Extensions, applications and challenges. Information Sciences, 207, 1- 18. DOI: 10.1016/j.ins.2012.04.025 Martínez, L., Liu, J., Ruan, D., & Yang, J. B. (2007). Dealing with heterogeneous information in engineering evaluation processes. Information Sciences, 177(7), 1533-1542. DOI: 10.1016/j.ins.2006.07.005 Michailidou, A. V., Vlachokostas, C., & Moussiopoulos, Ν. (2016). Interactions between climate change and the tourism sector: Multiple-criteria decision analysis to assess mitigation and adaptation options in tourism areas. Tourism Management, 55, 1-12. DOI: 10.1016/j.tourman.2016.01.010
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 119
Ministry of Environment and Energy, 2014. 3rd National Energy Efficiency Action Plan of Greece, Athens, Greece. Ministry of Environment and Energy, 2017. 4th National Energy Efficiency Action Plan of Greece, Athens, Greece.
Mo, K., & Philanthropies, B. (2016). Financing Building Energy Efficiency: A Challenge and Opportunity for China’s Cities. Paulson Institute. Mohamadabadi, H. S., Tichkowsky, G., & Kumar, A. (2009). Development of a multi-criteria assessment model for ranking of renewable and non-renewable transportation fuel vehicles. Energy, 34(1), 112-125. DOI: 10.1016/j.energy.2008.09.004 Montanari, R. (2004). Environmental efficiency analysis for enel thermo-power plants. Journal of Cleaner Production, 12(4), 403-414. DOI: 10.1016/S0959-6526(03)00015-5 Mourhir, A., Rachidi, T., Papageorgiou, E. I., Karim, M., & Alaoui, F. S. (2016). A cognitive map framework to support integrated environmental assessment. Environmental Modelling & Software, 77, 81-94. DOI: 10.1016/j.envsoft.2015.11.018 Munda, G. (2005). Multiple criteria decision analysis and sustainable development. In Multiple criteria decision analysis: State of the art surveys (pp. 953-986). Springer New York.
Murphy, J. T. (2015). Human geography and socio-technical transition studies: Promising intersections. Environmental Innovation and Societal Transitions, 17, 73-91. Nachmany, M., Fankhauser, S., Davidová, J., Kingsmill, N., Landesman, T., Roppongi, H., ... & Sundaresan, J. (2015). The 2015 Global Climate Legislation Study: A review of climate change legislation in 99 countries: Summary for policy-makers.
Niamir, L., Filatova, T., Voinov, A., & Bressers, H. (2018). Transition to low-carbon economy: assessing cumulative impacts of individual behavioral changes. Energy Policy, 118(July 2018), 325- 345. Nikas, A., Doukas, H., & López, L. M. (2018). A group decision making tool for assessing climate policy risks against multiple criteria. Heliyon, 4(3), e00588. Nikas, A., Doukas, H., Lieu, J., Alvarez-Tinoco, R., Charisopoulos, V., & van der Gaast, W. (2017). Managing stakeholder knowledge for the evaluation of innovation systems in the face of climate change. Journal of Knowledge Management, 21(5), 1013-1034. DOI: 10.1108/JKM-01-2017-0006
Nikas, A., Gkonis, N., Forouli, A., Siskos, E., Arsenopoulos, A., Papapostolou, A., Kanellou, E., Karakosta, C., & Doukas, H. (2019a, in press). Greece: From near-term actions to long-term pathways: risks and uncertainties associated with the national energy efficiency framework. In Hanger-Kopp, S. Lieu, J., & Nikas A. (eds.). Narratives of low-carbon transitions: Understanding risks and uncertainties. Routledge, Abingdon. Nikas, A., Ntanos, E., & Doukas, H. (2019b, in press). A semi-quantitative modelling application for assessing energy efficiency strategies. Applied Soft Computing.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 120
O’Neill B.C. , Kriegler E, Ebi K.L. , Kemp-Benedict E. , Riahi K., Rothman D.S. , van Ruijven B., Birkmann J. , Kok K. , Levy M. , Van Vuuren D.P. (2017) The Roads Ahead: Narratives for Shared Socioeconomic Pathways Describing World Futures in the 21 st Century. Global Environmental Change, 42, 69-180. O’Neill, B. C., Kriegler, E., Ebi, K. L., Kemp-Benedict, E., Riahi, K., Rothman, D. S., van Ruijven, B.J., van Vuuren, D. P., Birkmann, J., Kok, K., & Levy, M. (2017). The roads ahead: narratives for shared socioeconomic pathways describing world futures in the 21st century. Global Environmental Change, 42, 169-180. DOI: 10.1016/j.gloenvcha.2016.05.009
O’Neill, B. C., Kriegler, E., Riahi, K., Ebi, K. L., Hallegatte, S., Carter, T. R., Mathur, R., & van Vuuren, D. P. (2014). A new scenario framework for climate change research: the concept of shared socioeconomic pathways. Climatic Change, 122(3), 387-400.
Olazabal, M., & Pascual, U. (2015). Urban low-carbon transitions: cognitive barriers and opportunities. Journal of Cleaner Production, 109, 336-346. Oliveira, C., & Antunes, C. H. (2004). A multiple objective model to deal with economy–energy– environment interactions. European Journal of Operational Research, 153(2), 370-385. DOI: 10.1016/S0377-2217(03)00159-0 Onar, S. C., Oztaysi, B., Otay, İ., & Kahraman, C. (2015). Multi-expert wind energy technology selection using interval-valued intuitionistic fuzzy sets. Energy, 90, 274-285. DOI: 10.1016/j.energy.2015.06.086 Onu, P. U., Quan, X., Xu, L., Orji, J., & Onu, E. (2017). Evaluation of sustainable acid rain control options utilizing a fuzzy TOPSIS multi-criteria decision analysis model frame work. Journal of Cleaner Production, 141, 612-625. DOI: 10.1016/j.jclepro.2016.09.065 Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European journal of operational research, 156(2), 445-455. Osorio, S., & van Ackere, A. (2016). From nuclear phase-out to renewable energies in the Swiss electricity market. Energy Policy, 93, 8-22.
Pachauri, R.K., Allen, M.R., Barros, V.R., Broome, J., Cramer, W., Christ, R., … Dubash, N.K. (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 (p. 151). IPCC. Papadopoulos, A., & Karagiannidis, A. (2008). Application of the multi-criteria analysis method Electre III for the optimisation of decentralised energy systems. Omega, 36(5), 766-776. DOI: 10.1016/j.omega.2006.01.004 Papapostolou, A., Karakosta, C., Marinakis, V., & Flamos, A. (2016). Assessment of RES cooperation framework between the EU and North Africa: A multicriteria approach based on UTASTAR. International Journal of Energy Sector Management, 10(3), 402-426. DOI: 10.1108/IJESM-12-2014-0007 Papapostolou, A., Karakosta, C., Nikas, A., & Psarras, J. (2017). Exploring opportunities and risks for RES-E deployment under Cooperation Mechanisms between EU and Western Balkans: A multi-
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 121
criteria assessment. Renewable and Sustainable Energy Reviews, 80, 519-530. DOI: 10.1016/j.rser.2017.05.190 Papoulis, D., Kaika, D., Bampatsou, C. and Zervas, E., 2015. Public Perception of Climate Change in a Period of Economic Crisis. Climate, 3(3), pp.715-726. Paul, S., Sarkar, B., & Bose, P. K. (2015). Eclectic decision for the selection of tree borne oil (TBO) as alternative fuel for internal combustion engine. Renewable and sustainable energy reviews, 48, 256-263. DOI: 10.1016/j.rser.2015.03.060 Perkoulidis, G., Papageorgiou, A., Karagiannidis, A., & Kalogirou, S. (2010). Integrated assessment of a new Waste-to-Energy facility in Central Greece in the context of regional perspectives. Waste Management, 30(7), 1395-1406. DOI: 10.1016/j.wasman.2009.11.021 Pilavachi, P. A., Stephanidis, S. D., Pappas, V. A., & Afgan, N. H. (2009). Multi-criteria evaluation of hydrogen and natural gas fuelled power plant technologies. Applied Thermal Engineering, 29(11), 2228-2234. DOI: 10.1016/j.applthermaleng.2008.11.014 Promentilla, M. A. B., Aviso, K. B., & Tan, R. R. (2014). A group fuzzy analytic network process to prioritize low carbon energy systems in the Philippines. Energy Procedia, 61, 808-811. DOI: 10.1016/j.egypro.2014.11.970 PwC. (2018). Oil and Gas in Indonesia: Investment and Taxation Guide 9th Edition (Investment and Taxation Guide). Jakarta: PT PricewaterhouseCoopers Indonesia. Pye, S. et al. (2015). Pathways to deep decarbonization in the United Kingdom, SDSN - IDDRI. Quesada, F. J., Palomares, I., & Martínez, L. (2015). Managing experts behavior in large-scale consensus reaching processes with uninorm aggregation operators. Applied Soft Computing, 35, 873-887. DOI: 10.1016/j.asoc.2015.02.040 Ramazankhani, M. E., Mostafaeipour, A., Hosseininasab, H., & Fakhrzad, M. B. (2016). Feasibility of geothermal power assisted hydrogen production in Iran. International Journal of Hydrogen Energy, 41(41), 18351-18369. DOI: 10.1016/j.ijhydene.2016.08.150 Ren, J., & Lützen, M. (2015). Fuzzy multi-criteria decision-making method for technology selection for emissions reduction from shipping under uncertainties. Transportation Research Part D: Transport and Environment, 40, 43-60. DOI: 10.1016/j.trd.2015.07.012 Republic of Indonesia, 2015. Indonesia First Biennial Update Reporty (BUR) under the United Nations Framework Convention on Climate Change. Directorate General of Climate Change, Jakarta. Ribeiro, F., Ferreira, P., & Araújo, M. (2013). Evaluating future scenarios for the power generation sector using a Multi-Criteria Decision Analysis (MCDA) tool: The Portuguese case. Energy, 52, 126- 136. DOI: 10.1016/j.energy.2012.12.036 Rodríguez, R. M., & Martínez, L. (2013). An analysis of symbolic linguistic computing models in decision making. International Journal of General Systems, 42(1), 121-136. DOI: 10.1080/03081079.2012.710442
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 122
Rojas-Zerpa, J. C., & Yusta, J. M. (2015). Application of multicriteria decision methods for electric supply planning in rural and remote areas. Renewable and Sustainable Energy Reviews, 52, 557- 571. DOI: 10.1016/j.rser.2015.07.139 Roth, S., Hirschberg, S., Bauer, C., Burgherr, P., Dones, R., Heck, T., & Schenler, W. (2009). Sustainability of electricity supply technology portfolio. Annals of Nuclear Energy, 36(3), 409-416. DOI: 10.1016/j.anucene.2008.11.029 Roy, B. (1990). Decision-aid and decision-making. European Journal of Operational Research, 45(2- 3), 324-331. Roy, B., & Vanderpooten, D. (1997). An overview on “The European school of MCDA: Emergence, basic features and current works”. European Journal of Operational Research, 99(1), 26-27. Roy, B., Présent, D. M., & Silhol, D. (1986). A programming method for determining which Paris metro stations should be renovated. European Journal of Operational Research, 24(2), 318-334. Saaty, T. L. (1990). How to make a decision: the analytic hierarchy process. European journal of operational research, 48(1), 9-26. Sadeghi, A., Larimian, T., & Molabashi, A. (2012). Evaluation of renewable energy sources for generating electricity in province of Yazd: a fuzzy MCDM approach. Procedia-Social and Behavioral Sciences, 62, 1095-1099. DOI: 10.1016/j.sbspro.2012.09.187 Sakthivel, G., Ilangkumaran, M., & Gaikwad, A. (2015). A hybrid multi-criteria decision modeling approach for the best biodiesel blend selection based on ANP-TOPSIS analysis. Ain Shams Engineering Journal, 6(1), 239-256. DOI: 10.1016/j.asej.2014.08.003 San Cristóbal, J. R. (2012). A goal programming model for environmental policy analysis: Application to Spain. Energy Policy, 43, 303-307. DOI: 10.1016/j.enpol.2012.01.007
Schot, J., Kanger, L., & Verbong, G. (2016). The roles of users in shaping transitions to new energy systems. Nature Energy, 1(5), 16054.
Scott, A. J. & Storper, M. (2015). The nature of cities: the scope and limits of urban theory. International Journal of Urban and Regional Research, 39(1), 1-15. Şengül, Ü., Eren, M., Shiraz, S. E., Gezder, V., & Şengül, A. B. (2015). Fuzzy TOPSIS method for ranking renewable energy supply systems in Turkey. Renewable Energy, 75, 617-625. DOI: 10.1016/j.renene.2014.10.045
Shanghai Municipal Commission of Housing, Urban-Rural Development and Management (MCOHURDM). 2017. Shanghai Green building Development Reprot 2016. Shiau, T. A., & Liu, J. S. (2013). Developing an indicator system for local governments to evaluate transport sustainability strategies. Ecological indicators, 34, 361-371. DOI: 10.1016/j.ecolind.2013.06.001 Shih, H. S., Shyur, H. J., & Lee, E. S. (2007). An extension of TOPSIS for group decision making. Mathematical and Computer Modelling, 45(7), 801-813. DOI: 10.1016/j.mcm.2006.03.023
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 123
Shmelev, S. E., & van den Bergh, J. C. (2016). Optimal diversity of renewable energy alternatives under multiple criteria: An application to the UK. Renewable and Sustainable Energy Reviews, 60, 679-691. DOI: 10.1016/j.rser.2016.01.100
Silaen, M., Yuwono, Y., Taylor, R., Devisscher, T., Thamrin, S., Ismail, C., & Takama, T. (2019, in press). Indonesia: Risks and uncertainties of biogas for cooking and electricity. In Hanger-Kopp, S. Lieu, J., & Nikas A. (eds.). Narratives of low-carbon transitions: Understanding risks and uncertainties. Routledge, Abingdon. Simoglou, C.K., Bakirtzis, E.A., Biskas, P.N. and Bakirtzis, A.G., 2018. Probabilistic evaluation of the long-term power system resource adequacy: The Greek case. Energy Policy, 117, pp.295-306. Siskos, E., & Tsotsolas, N. (2015). Elicitation of criteria importance weights through the Simos method: A robustness concern. European Journal of Operational Research, 246(2), 543-553.
Smith, A. (2007). Translating sustainabilities between green niches and socio-technical regimes. Technology analysis & strategic management, 19(4), 427-450.
Smith, A., Stirling, A., & Berkhout, F. (2005). The governance of sustainable socio-technical transitions. Research policy, 34(10), 1491-1510. Stavrakakis, Y. and Katsambekis, G., 2018. The populism/anti-populism frontier and its mediation in crisis-ridden Greece: from discursive divide to emerging cleavage?. European Political Science, pp.1-16. Streimikiene, D., & Baležentis, T. (2013a). Multi-criteria assessment of small scale CHP technologies in buildings. Renewable and Sustainable Energy Reviews, 26, 183-189. DOI: 10.1016/j.rser.2013.05.046 Streimikiene, D., & Balezentis, T. (2013b). Multi-objective ranking of climate change mitigation policies and measures in Lithuania. Renewable and Sustainable Energy Reviews, 18, 144-153. DOI: 10.1016/j.rser.2012.09.040 Streimikiene, D., Balezentis, T., Krisciukaitienė, I., & Balezentis, A. (2012). Prioritizing sustainable electricity production technologies: MCDM approach. Renewable and Sustainable Energy Reviews, 16(5), 3302-3311. DOI: 10.1016/j.rser.2012.02.067 Štreimikienė, D., Šliogerienė, J., & Turskis, Z. (2016). Multi-criteria analysis of electricity generation technologies in Lithuania. Renewable Energy, 85, 148-156. DOI: 10.1016/j.renene.2015.06.032 Talaei, A., Ahadi, M. S., & Maghsoudy, S. (2014). Climate friendly technology transfer in the energy sector: A case study of Iran. Energy Policy, 64, 349-363. DOI: 10.1016/j.enpol.2013.09.050 Theodorou, S., Florides, G., & Tassou, S. (2010). The use of multiple criteria decision making methodologies for the promotion of RES through funding schemes in Cyprus, A review. Energy Policy, 38(12), 7783-7792. DOI: 10.1016/j.enpol.2010.08.038
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 124
Thoma, J., & Chenet, H. (2016). Transition risks and market failure: a theoretical discourse on why financial models and economic agents may misprice risk related to the transition to a low- carbon economy. Journal of Sustainable Finance & Investment, 1-17. Tigas, K., Giannakidis, G., Mantzaris, J., Lalas, D., Sakellaridis, N., Nakos, C., ... & Alexandridis, A. T. (2015). Wide scale penetration of renewable electricity in the Greek energy system in view of the European decarbonization targets for 2050. Renewable and Sustainable Energy Reviews, 42, 158-169. Townshend, T., Fankhauser, S., Matthews, A., Feger, C., Liu, J., & Narciso, T. (2011). Legislating climate change on a national level. Environment: Science and Policy for Sustainable Development, 53(5), 5-17. Tselepis, S., 2015. The PV Market Developments in Greece. Net-Metering Study Cases. 28th EUPVSEC, Paris, France. Tsoutsos, T., Drandaki, M., Frantzeskaki, N., Iosifidis, E., & Kiosses, I. (2009). Sustainable energy planning by using multi-criteria analysis application in the island of Crete. Energy Policy, 37(5), 1587-1600. DOI: 10.1016/j.enpol.2008.12.011
Turnheim, B., Berkhout, F., Geels, F., Hof, A., Mcmeekin, A., & Nykvist, B., et al. (2015). Evaluating sustainability transitions pathways: bridging analytical approaches to address governance challenges. Global Environmental Change, 35, 239-253. Ulutaş, B. H. (2005). Determination of the appropriate energy policy for Turkey. Energy, 30(7), 1146-1161. DOI: 10.1016/j.energy.2004.08.009
UNEP (2011). Climate change and pops: predicting the impacts. Report of the UNEP/AMAP expert group. Arctic Monitoring & Assessment Program. Vahabzadeh, A. H., Asiaei, A., & Zailani, S. (2015). Green decision-making model in reverse logistics using FUZZY-VIKOR method. Resources, Conservation and Recycling, 103, 125-138. DOI: 10.1016/j.resconrec.2015.05.023 Vaillancourt, K., & Waaub, J. P. (2004). Equity in international greenhouse gases abatement scenarios: A multicriteria approach. European Journal of Operational Research, 153(2), 489-505. DOI: 10.1016/S0377-2217(03)00170-X
Van den Bergh, J., Truffer, B., Kallisa, G. (2011) Environmental innovation and societal transitions: introduction and overview. Environmental Innovation & Societal Transitions. 1 (1), 1–23. van Vliet O.P.R, Hanger S, Nikas A, Spijker E, Carlsen H, Doukas H, & Lieu J. (2019, under review). The importance of stakeholders in scoping risk assessments – lessons from low-carbon transitions, to be submitted to the stakeholders in scoping risk assessments. Environmental Innovation and Societal Transitions. van Vliet, M., Kok, K., & Veldkamp, T. (2010). Linking stakeholders and modellers in scenario studies: The use of Fuzzy Cognitive Maps as a communication and learning tool. Futures, 42(1), 1- 14. DOI: 10.1016/j.futures.2009.08.005
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 125
Van Vliet, O. P. R. (2019, in press). Switzerland: Risks of implementing a national energy strategy. In Hanger-Kopp, S. Lieu, J., & Nikas A. (eds.). Narratives of low-carbon transitions: Understanding risks and uncertainties. Routledge, Abingdon. Voinov, A., & Bousquet, F. (2010). Modelling with stakeholders. Environmental Modelling & Software, 25(11), 1268-1281. Volkart, K., Bauer, C., Burgherr, P., Hirschberg, S., Schenler, W., & Spada, M. (2016). Interdisciplinary assessment of renewable, nuclear and fossil power generation with and without carbon capture and storage in view of the new Swiss energy policy. International Journal of Greenhouse Gas Control, 54, 1-14. DOI: 10.1016/j.ijggc.2016.08.023
Westley, F., Olsson, P., Folke, C., Homer-Dixon, T., Vredenburg, H., & Loorbach, D., et al. (2011). Tipping toward sustainability: emerging pathways of transformation. AMBIO, 40(7), 762-780.
Wittmayer, J. M., Avelino, F., Steenbergen, F. V., & Loorbach, D. (2016). Actor roles in transition: insights from sociological perspectives. Environmental Innovation & Societal Transitions.
Wolkinger, W., Mayer, J., Tuerk, A., Bachner, G., & Steininger, K. (2019, in press). Austria: Co- Designing a low-carbon transition pathway focusing on energy supply for the iron and steel sector. In Hanger-Kopp, S. Lieu, J., & Nikas A. (eds.). Narratives of low-carbon transitions: Understanding risks and uncertainties. Routledge, Abingdon. Xu, B., Nayak, A., Gray, D., & Ouenniche, J. (2016). Assessing energy business cases implemented in the North Sea Region and strategy recommendations. Applied Energy, 172, 360-371. DOI: 10.1016/j.apenergy.2016.03.110 Xu, X. H., Du, Z. J., & Chen, X. H. (2015). Consensus model for multi-criteria large-group emergency decision making considering non-cooperative behaviors and minority opinions. Decision Support Systems, 79, 150-160. DOI: 10.1016/j.dss.2015.08.009
Xu, X., Anadon,L.D. and Lee, H.. 2016. Increasing Residential Building Energy Efficiency in China: An Evaluation of Policy Instruments. Discussion Paper, 2016-02, Energy Technology Innovation Policy Project, Belfer Center, April. Yager, R. R. (2001). Penalizing strategic preference manipulation in multi-agent decision making. IEEE Transactions on Fuzzy Systems, 9(3), 393-403. DOI: 10.1109/91.928736 Yager, R. R. (2002). Defending against strategic manipulation in uninorm-based multi-agent decision making. European Journal of Operational Research, 141(1), 217-232. DOI: 10.1016/S0377- 2217(01)00267-3 Yap, H. Y., & Nixon, J. D. (2015). A multi-criteria analysis of options for energy recovery from municipal solid waste in India and the UK. Waste Management, 46, 265-277. DOI: 10.1016/j.wasman.2015.08.002
Yeung, H. W. C. (2005). Rethinking relational economic geography. Transactions of the Institute of British Geographers, 30(1), 37-51.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 126
Yoon, K. P., & Kim, W. K. (2017). The behavioral TOPSIS. Expert Systems with Applications, 89, 266-272.
Yuan, X., Zhang, X., Liang, J., Wang, Q., & Zuo, J. (2017). The development of building energy conservation in china: a review and critical assessment from the perspective of policy and institutional system. Sustainability, 9(9), 1654.
Zhang, Y., Kang, J., & Jin, H. (2017). A Review of Green Building Development in China from the Perspective of Energy Saving. Energies, 11(2), 334.
Zhou, N., Khanna, N. Z., Fridley, D., & Romankiewicz, J. (2013). Development and implementation of energy efficiency standards and labeling programs in china: progress and challenges.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 127
Appendix
This section may include too large tables and figures, events agendas, list of participants, presentations, etc.
D5.5: Multi-criteria consideration of risk and uncertainty for climate policy Page 128