A Thesis Submitted to the Faculty of the Graduate School of Arts And

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THE ROAD AFTER PARIS: THE RELATIONSHIP BETWEEN CLIMATE CHANGE POLICY STRINGENCY AND ECONOMIC GROWTH AT THE COUNTRY LEVEL

A Thesis submitted to the Faculty of the Graduate School of Arts and Sciences of Georgetown University in partial fulfillment of the requirements for the degree of Master of Public Policy in Public Policy

David P. Allen, B.A.

Washington, DC April 12, 2016

THE ROAD AFTER PARIS: THE RELATIONSHIP BETWEEN CLIMATE CHANGE POLICY STRINGENCY AND ECONOMIC GROWTH AT THE COUNTRY LEVEL

David P. Allen, B.A.

Thesis Advisor: Adam T. Thomas, Ph.D.

ABSTRACT

Increased emissions of carbon dioxide and greenhouse gases (GHG) have exacerbated the effects of climate change and have led to intensified weather events and a steady rise in the average global temperature. Countries sought to outline an aggressive agenda for combatting climate change at the Conference of the Parties (COP 21) in Paris last year. In order to reach a common goal, countries released national action plans, known as Intended Nationally

Determined Contributions (INDCs) for reducing GHGs and CO2 emissions. However, a source of contention is the effect that limiting emissions might have on economic growth. In the context of the recently completed COP21, this paper examines the relationship between the stringency of climate policy implemented prior to 2015 and countries’ gross domestic product (GDP), as a proxy for economic growth. Because INDCs were only introduced in the lead up to COP 21, this paper instead uses the Climate Change Performance Index (CCPI) to measure the stringency of countries’ climate policies from 2010 through 2014. My results show that an increase in the

CCPI percentile score is positively associated with a small rise in a country’s GDP. Additionally, this analysis indicates that countries may experience higher increases in GDP for an improved

CCPI ranking, depending on the country’s level of development. Therefore, my paper provides some suggestive evidence that countries may be able to enact progressively stricter climate policies without impeding their economic growth.

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TABLE OF CONTENTS

Introduction ...... 1 Background ...... 4 Literature Review ...... 7 Economic Cost of Climate Change ...... 7 The Price Tag of Global Mitigation ...... 9 The Kyoto Protocol and Economic Impacts of Regional ETS ...... 10 Conceptual Framework & Hypothesis ...... 15 Economic Factors ...... 16 Demographic Factors ...... 17 Political Factors ...... 18 Data and Methods ...... 19 Dependent Variable ...... 19 Independent Variable ...... 20 Control Variables ...... 21 Model ...... 21 Descriptive Statistics ...... 25 Regression Results ...... 28 Discussion ...... 34 Appendix 1: CCPI Background and Methodology ...... 38 Appendix 2: List of CCPI Participating Countries ...... 40 References ...... 41

LIST OF FIGURES AND TABLES

Figure 1. Influences on a country’s economic growth ...... 16

Table 1. Definition of variables...... 23

Table 2. Descriptive statistics for dependent, key independent, and control variables...... 27

Table 3. Coefficients from OLS Models and Fixed-Effects Models Regressing GDP (USD$ billion) on the CCPI Percentile Score and Selected Control Variables ...... 32

Figure 2. Components of the CCPI ...... 39

INTRODUCTION

The exacerbated effects of climate change in the 21st century have led to steady increases in the global average temperature and increasingly intensified weather events (Coumou, 2012).

Symptoms of climate change such as extreme drought, rising sea levels, Arctic ice loss, desertification, and the spread of diseases have the potential to reshape the future of geopolitics and compel countries to reassess their economic priorities and interests. These natural phenomena are predicted to have an adverse impact on the ability of governments to maintain global infrastructure and ensure access to food and electricity for their populations (Harvey,

2011). As virtually every nation will be affected, climate related considerations are being incorporated into the planning, design, and operation of urban infrastructure and energy generation (Moss, 2010).

The recently completed Conference of the Parties (COP 21) in Paris this past December has set forth a global agenda on climate change for the next five years and beyond. In preparation for the conference, signatory countries to the United Nations Framework Convention on Climate

Change (UNFCCC) publicly released their post-2020 climate action plans, also known as

Intended Nationally Determined Contributions (INDCs) (Biru, 2015).1 Major greenhouse gas

(GHG) emitters, such as the European Union (EU), the United States (U.S.), China, and Russia, provided their INDCs well in advance. These early INDC submissions prompted developing states to pledge achievable, but ambitious reductions and led to the overall success of the conference. The resulting international agreement aimed to hold any increase in the global

1 The United Nations Framework Convention on Climate Change (UNFCCC) is an international agreement concluded in 1992 and which has been ratified by 197 Parties as of 2016. The agreement established a Secretariat, which is responsible for holding regular Conferences of the Parties (COPs) to coordinate global efforts to address climate change.

1 average temperature to below two degrees Celsius above pre-industrial levels (Leggett, 2015).

Additionally, participating countries agreed to “pursue best efforts” to limit temperature increases to 1.5 degrees Celsius. The articulation of these objectives makes the Paris Agreement one of the most robust climate change pacts to be negotiated (Davenport, 2015).

One of the biggest concerns leading up to the Paris agreement was whether a critical mass of countries would pledge sufficiently aggressive reduction targets. Many developing countries and newly emerging economies (including the BRICs) are concerned that limiting emissions will significantly curtail their economic growth trajectories (Tian, 2009).2 While mitigating climate change is perceived as an important issue, poverty eradication and socioeconomic development are higher priorities for underdeveloped states (The Economist,

2015). The European Union and U.S. submitted moderately strong INDCs in anticipation of the climate talks, but have substantial resources for investing in renewable energy and other clean power technologies, compared to developing countries. The level of development and economic strength of the EU and the U.S facilitate their ability to reduce GHG emissions in a relatively expedited and efficient manner (Tian, 2009).

Additionally, the Global South has largely been more reluctant to commit to the same level of emission reductions as advanced economies.3 As developed countries historically released high amounts of GHG emissions during periods of industrialization, developing countries have asserted that advanced economies have a greater responsibility for mitigating and facilitating adaptation to climate change (Adger, 2001). As a result of this contention, those countries deemed as non-industrialized economies by the UNFCCC (often referred to by the

2 BRICS is an acronym that refers to five major emerging economies: Brazil, Russia, India, China, and South Africa 3 The Global South is a broad term referring to developing countries (encompassing the great majority of countries south of Europe, Russia, and the United States).

Kyoto Protocol term: non-Annex 1 countries) have tended to offer weak or conditional emission reduction targets, and intend to rely on climate finance mechanisms such as the Green Climate

Fund to provide significant monetary support (Biru, 2015).

In the context of the recent climate talks, this paper will focus on the extent to which climate action plans and policies impact a country’s economic growth. Since the INDC model was only introduced for the most recent climate conference, this paper will use the Climate

Change Performance Index (CCPI) as a proxy to measure the stringency of countries’ climate polices from 2010 through 2014. Produced annually by Germanwatch and the Climate Change

Network-Europe, the CCPI evaluates the strength of climate polices for 58 countries which are collectively responsible for over 90% of global CO2 emissions (Burk, 2014).

The results of my analysis provide policymakers with evidence regarding the relationship between economic growth and climate change policy. Thus, this study will provide useful evidence for officials as they confront the quandary of attempting to expand economic opportunity for their citizens while addressing the impending repercussions of climate change.

BACKGROUND

Understanding the international climate policy framework can be difficult, because countries’ interests and positions change often. The UNFCCC is an international environmental treaty that has been highly successful in coordinating the climate change efforts of 197 countries

(UNFCCC, 2015). Parties to the convention have met annually since 1995 to assess progress and push for stronger standards to stabilize and reduce GHG concentrations in the atmosphere. Since the treaty entered into force in 1994, there have been twenty COPs held in different cities throughout the northern and southern hemispheres (UNFCCC, 2015). Two well-known accomplishments, in particular, have emerged from these multilateral summits. The first is the adoption of the findings produced by the Intergovernmental Panel on Climate Change (IPCC), an international scientific body established by the UN that synthesizes peer-reviewed reports on the state of, and trends in, climate change. The second is the Kyoto Protocol on Climate Change, an international treaty that committed parties to reduce GHG emissions through 2012. Additionally, numerous financial and technical mechanisms, such as Emissions Trading Systems (ETS), Joint

Implementation (JI), and Clean Development Mechanisms (CDM) were adopted in subsequent

COP rounds and implemented by different countries to varying degrees of success (UNFCCC,

2015).

Last December, delegates from 195 nations gathered in Paris for the 21st Conference of the Parties (COP 21) to discuss a new climate agreement framework that would include substantially all countries in the world. Besides the stated goal of limiting GHG emissions, the

Paris Agreement also established a framework in which all UNFCCC Parties would modify

INDCs every five years starting in 2018, contribute $100 billion collectively in financial support to developing countries, and submit progress reports for international review (Leggett, 2015).

Parties further pledged to reach zero net emissions of GHGs by 2050. The Paris summit deliberately abandoned the top-down approach of previous COPs, which had attempted to fairly distribute emissions reduction responsibilities by collectively determining each country’s obligations, but had failed to gain sufficient buy-in from countries to meaningfully reduce emissions (Meyer, 2015). Instead, the Paris agreement’s bottom-up approach invited countries to make their own commitments without any criteria imposed from above. The flexibility of this new approach proved much more successful, resulting in some 186 countries submitting pledges

(Davenport, 2015). This landmark agreement has been hailed as a major turning point in climate negotiations for uniting developed and developing countries in a common framework for the first time.

Despite the breadth of this agreement, measuring the stringency of an international climate policy regime is challenging. Measures used in existing literature often cannot reflect the various impacts climate change can have on agriculture, manufacturing, health, and sea levels

(Tol, 2009). There are numerous approaches to the empirical measurement of environmental policy stringency generally, but very few on climate policy specifically (Hille and Althammer,

2015). Five different approaches for measuring environmental stringency have been adopted to assess climate policy. These include: calculating private sector abatement costs; direct assessment of individual regulations (otherwise known as natural experiments); composite indices; measures based on pollution or energy use; and measures based on public sector efforts or costs (Levinson, 2013).

In this paper, I will be using the CCPI as a composite index that can compress the multidimensionality of climate regulation into a single estimate. Germanwatch, a non-profit non- governmental organization (NGO) in Bonn, Germany, has attempted to measure climate change

5 performance since 2010 using fifteen different indicators that are then pooled into a single composite index. These fifteen indicators are classified into five different categories: emissions level; development of emissions; efficiency; renewable energies; and climate policy. Together, they form a standardized baseline against which to examine climate change performance for all countries measured in the index (Burk, 2014). The fifth category in the CCPI, climate policies, rewards countries whose policies aim to protect the climate, and who are currently striving towards a more comprehensive action plan.4 Countries that pass stricter climate polices will first experience a rise in their CCPI climate policy score, which will then result in higher scores in the other four categories in subsequent years, if the enacted policy was effective. This interplay between the five CCPI categories provides a holistic perspective of the relative strength and effectiveness of implemented climate policies across countries, making the index a useful proxy for measuring stringency.

4 For a more detailed explanation of the Climate Change Performance Index (CCPI), please see Appendix 1.

LITERATURE REVIEW

Economic Cost of Climate Change

Several studies attempt to estimate the economic damage that climate change is likely to impose in the absence of mitigation efforts. The first analyses of the welfare effects of climate change in the U.S. were completed by Cline (1992), Nordhaus (1991), and Titus (1992). These and subsequent analyses are generally in agreement on the economic costs of climate change.

For a rise in global temperature to 2.5 degrees Celsius, most studies estimate the impact of climate change to be relatively small – between 1 and 2.5 percentage of global GDP

(Fankhauser, 1995; Tol, 1995; Nordhaus and Yang, 1996; Plambeck and Hope, 1996; Nordhaus and Boyer, 2000; Tol, 2002; and Hope, 2006). Some even estimate the impact to be less than one percent of GDP (Mendelsohn, Schlesinger, and Williams, 2000; Maddison, 2003; Hope, 2006; and Nordhaus, 2006). However, once scenarios model for a rise in average global temperatures above 3 degrees Celsius, cost estimates range widely from 1.3 percent of global GDP (Nordhaus,

1994b), to 2.5 percent (Nordhaus, 2008), and even 11.5 percent (Rehdanz and Maddison, 2005).

The difference in estimates is likely due to the two different methodological approaches used by economists: the enumerative approach and the statistical approach. Studies that use the enumerative approach (Fankhauser, 1994, 1995; Nordhaus, 1994a; and Tol, 1995, 2002a) obtain estimates of the physical impact of climate change from natural science papers, which are then given a price and added up. For example, papers studying the effects of climate change on agricultural products, first calculate how future crop harvests will be effected by rising temperatures (Tol, 2009). Economists then determine the diminished value in production through market price models. In contrast, studies that use the statistical approach estimate how income across the world (Nordhaus, 2006), household consumption (Maddison, 2003), and output per

7 sector (Mendelsohn, Morrison, Schlesinger, and Andronova, 2000) has varied over time.

Compared to the enumerative approach, statistical studies are based on observed real-world differences in climate and income.

Despite these different approaches, researchers broadly agree on three main points. First, under a scenario in which GHG emissions are doubled based on current levels, the loss to GDP is roughly equal to a year’s growth in the global economy (Tol, 2009). These estimates suggest that the economic loss from climate change over a century may not be that large. Yet, the damage is not inconsequential. Since climate change may cause permanent reduction of welfare, steps to reduce these causes would certainly be justified (Tol, 2009). Second, many of these estimates also highlight initial benefits for a modest increase in temperature, followed by substantial losses as temperatures continue to increase (Hope, 2006; Mendelsohn, Schlesinger, and Williams,

2000; and Tol, 2002). These initial benefits occur partly due to the output of the global economy being mainly concentrated in temperate zones. Modest global warming can reduce heating costs, improve crop yields and diminish cold-related health problems (Tol, 2009). However, past a 1.1 degree Celsius increase in global average temperatures, these economic benefits could turn negative. Finally, while GHG emissions per capita are higher in developed countries, the economic impacts of climate change would be greater for developing countries (Yohe and

Schlesinger, 2002). Since low income countries tend to be near tropical areas that are close to the equator, agricultural output would suffer more from higher temperatures. Developing countries are also less likely to be able to adapt to climate change due to a lack of resources and capable institutions (Adger, 2006; Smit and Wandel, 2006; and Tol and Yohe, 2007).

In light of these findings, several researchers conclude that a modest reduction of GHGs is immediately necessary and that the stringency of climate policy should accelerate over time

(Nordhaus, 1992, 1993; Nordhaus and Yang, 1996; and Tol, 2009). While the conclusion that climate policy should be progressively implemented is widely accepted by most researchers, the rate at which policy should be strengthened is highly controversial (Tol, 2013).

The Price Tag of Global Mitigation

Determining a global estimate of the price of mitigation policy is challenging because an accurate assessment depends on the baseline used, the method that the costs are modeled on, and assumptions regarding future government policy (Barker, 2007). The first cost benefit analysis examining a possible association between economic growth and mitigation policy was published by Stern (2007). Stern estimates that the cost of cutting total GHGs consistent with a carbon dioxide stabilization level of 550 parts per million (ppm) (450 ppm is considered equivalent to the Paris Agreement’s 2 degree Celsius scenario), would be in the range of a 1 percent decrease to a 3.5 percent gain in global GDP (Stern, 2007). On average, he predicts that reducing emissions may be associated with an increase in GDP by approximately 1 percent (Stern, 2007).

While the Stern review was positively received by some (Dietz, et al 2007), many economists take issue with the 1.4 percent discount rate used to calculate climate change damages, which is considerably lower than those used in previous economic studies on climate change (Nordhaus, 2007; Mendelsohn, 2009). Other researchers produce cost estimates much higher than Stern’s, which would indicate that mitigation policy would have a more substantial impact on economic growth. Fisher (2007) estimates that the macroeconomic costs of stabilizing

GHGs between 450 and 700 ppm by 2030 range from a 3 percent decrease in global GDP to a small increase. Using the same stabilization range, by 2050, the estimated cost to global GDP increases from a 1 percent gain to a 5.5 percent decrease. Barker (2007) finds similar results with

9 the cost to GDP under a 450 ppm scenario to be 3.1 percent on average and 0.5 percent for a 550 ppm scenario.

These cost estimates are supported by a moderate amount of evidence by the Fourth

IPCC Assessment Report, which reviews and assesses peer-reviewed literature (IPCC, 2007).

Out of 24 scenarios, the report estimates that to keep GHGs under 550 ppm by 2030, costs to global GDP would be less than 0.7 percent. Under a 450 ppm scenario, 11 out of 12 studies assessed by the IPCC predict mitigation costs to be just below 1 percent of global GDP (IPPC,

2007).

The Kyoto Protocol and Economic Impacts of Regional ETS

Prior to the Paris Agreement, the Kyoto Protocol served as the main international climate agreement that set binding emission targets for advanced industrialized countries and the

European Union as a whole. Adopted in 1997, the Protocol advanced the norm of ‘common but differentiated responsibilities’, which compelled developed countries to cut GHG emissions, while economies in transition and developing countries were exempted. (UNFCCC, 2015). As a result, these industrialized countries collectively committed to reducing GHG emissions by a global average of five percent between 2009 and 2012, based on 1990 levels (Böhringer and

Vogt, 2003). The Protocol primarily called on countries to prioritize reducing emissions domestically, but also provided parties flexibility in reaching these targets by allowing them to trade carbon credits through market-based mechanisms such as emission trading systems and joint implementation projects (Böhringer and Vogt, 2003).

In an attempt to reach its Kyoto target, the European Union instituted an Emissions

Trading System (EU ETS). The EU ETS limits the annual aggregate emissions of CO2 by

10 allocating a particular number of pollution allowances to each participating emitter. Firms must surrender their permits at the end of the year for each ton of CO2 emitted, but they can also buy additional permits or sell excess ones on an international allowance market (Böhringer and Vogt,

2003). The EU ETS is split into four different periods during which the cost of carbon increases while the number of allowances is significantly reduced (Martin, 2015). Phase I of the EU ETS ran from 2005 to 2007 and Phase II from 2008 to 2012. Phase III began in 2013 and is set to continue through 2020. A number of studies have focused on the possible impacts the EU ETS may have on economic indicators such as company profits and revenue, output, and employment

(Martin, 2015).

Analyses using firm-level data find that the EU ETS has no statistically significant impact on a firm’s value added or profit margins (Abrell, Ndoye, and Zachmann, 2011).

However, the same study also identifies that from 2004 to 2008, employment decreased by approximately 0.9 percent for firms covered under the EU ETS. Another study that uses firm level panel data for over 150,000 European firms from 1996 to 2007 finds that the EU ETS has a significant negative effect on return-on-capital, but that impact on employment, total factor productivity, and investment are negligible (Commins, et al., 2011). Studies looking at firms within individual countries under the EU find contrasting results. One study that examines 419

German firms discovers no relationship between the ratio of EU ETS permits allocated and employment between 2004 and 2005 (Anger and Oberndorfer, 2008). These results are reiterated by Petrick and Wagner (2014) who also find that there is no significant impact on the employment of French and German firms that are regulated under the EU ETS. In contrast,

Wagner, et al. (2013) finds a decrease in employment by 8 percent when analyzing French plant- level data during the second phase of the EU ETS.

In terms of trade, Costantini and Mazzanti estimate the impact of the first phase of the

EU ETS on net exports from 15 EU countries, to more than 100 destination countries, for a broad range of industries. Results of their study indicate that net exports decreased for all industries, except medium-low tech industries (Costantini and Mazzanti, 2012). However, they conclude that further disaggregation and longer time series were needed to acquire more reliable assessments. Reinaud (2008) adopts a similar approach by regressing the net imports of aluminum into the EU on emission permit prices from 1999 to 2007. While economic theory suggests that a higher carbon price will increase net imports from unregulated countries, Reinaud finds a negative association. However, the study notes that the relationship is not causal as the analysis does not differentiate between the impact of the ETS and a secular, upward trend in net imports.

Several surveys include interviews with managers at large manufacturing and refining companies that participate in the EU ETS. According to the managers, participation in the ETS neither results in significant costs (Kenber, Haugen, and Cobb, 2009; Lacombe, 2008), nor incentivizes a fundamental shift in business strategy such as plant relocation or reduction in the workforce (Kenber, Haugen, and Cobb, 2009). These results are reiterated in a later study that collected 761 interviews with managers of both participating and non-participating firms of the

EU ETS in six European countries. On average, the risk for a firm downsizing is low as most firms report that future carbon prices do not influence their location decisions (Martin, et al.,

2014a, 2014b). However, while the risk of downsizing is low, it is significantly higher for those firms participating in the EU ETS than those not participating, although it does in any case not exceed 10 percent reduction in employment.

Overall, evidence suggests that the EU ETS has a robust negative impact on emissions without having a strong detrimental effect on economic performance (Martin, 2015). For firms covered under the EU ETS, some studies find a possible reduction in employment, but others find no significant reduction on employment or turnover and no evidence of diminished trade flows.

In contrast to the EU, the United States rejected signing the Kyoto Protocol claiming that the U.S would have to bear a disparate share of the burden of adjustment (Ruth, 2008). In the absence of the agreement, some U.S. states chose to develop a regional cap-and trade program known as the Regional Greenhouse Gas Initiative (RGGI) (Ruth, 2008). Starting in 2009, RGGI included ten northeastern and mid-Atlantic states, becoming the country’s first market-based

5 program to reduce CO2 emissions from existing power plants. Similar to the EU ETS, emission allowances are dispersed every year through centralized regional auctions. In an analysis examining the economic impacts of the program, RGGI is found to have generated $1.3 billion

(net present value), or $31 per capita, in economic benefits across the region (Hibbard, 2015).

While the inclusion of the cost of CO2 allowances lead to increased retail electricity prices in the region, near-term price impacts are offset by states investing the auction proceeds into energy- efficiency and renewable energy programs. These investments lead to overall reduced electricity consumption and displace more expensive electricity generation sources (Hibbard, 2012).

Additionally, RGGI energy consumers – businesses, households, and government agencies – are estimated to save nearly $460 million as overall energy bills drop over time (Hibbard, 2015). In terms of employment, each of the participating states show net job additions. In the first three

5 The ten states participating in RGGI include Connecticut, Delaware, Massachusetts, Maryland, Maine, New Hampshire, New Jersey, New York, and Rhode Island. New Jersey participated in the RGGI program for three years before withdrawing in 2012.

13 years (2009-2012), RGGI is shown to have led to 16,000 job years, while the net effect in the second period from 2012 to 2015 is 14,200 new job-years (Hibbard, 2012, 2015). After six years, analysis suggests that RGGI provides net economic benefits to all participating states, including growth in economic output, electricity cost reductions, increased jobs, and successful emission reductions.

Both the EU ETS and RGGI provide anecdotal evidence that GHG emissions can be significantly reduced without detrimental effects to the economy. However, methodological challenges to identifying causality between ETS regimes, emission reductions, and economic performance still remain (Martin, 2015). These challenges are in part due to a lack of suitable data, as well as the fact that participation by firms in both programs is not random. Increasing understanding of the economic impacts of the EU ETS and other regional cap-and-trade programs can both help improve the design of future emissions trading schemes as well as inform the ongoing debate over the cost of climate mitigation policy (Martin, 2015).

CONCEPTUAL FRAMEWORK & HYPOTHESIS

I hypothesize that there is no relationship between the stringency of a country’s climate policies and its economic growth. In other words, I expect that countries will not experience a decrease in their GDP growth if they implement or strengthen new or existing climate policies.

Separately, if a positive relationship is found, I predict that there is a stronger, more positive association for developed countries than for BRICS or developing countries due to their ability to pay the high upfront costs required to implement renewable energy and energy efficiency technology. My model will account for economic conditions and climate policy changes as well as political and demographic factors. These characteristics are visually represented in Figure 1.

Figure 1. Influences on a country’s economic growth

ECONOMIC Previous economic growth Inflation Rate

Net Exports

DEMOGRAPHIC CLIMATE CHANGE

Population Size PERFORMANCE Population Growth Economic Climate Policy Population Density Growth Emissions Level Urban population Efficiency Education Renewable Energy Age Number of internet users

POLITICAL Democratic Government Age of Regime

Economic Factors

A country’s previous macroeconomic characteristics can also influence its future economic growth. Past GDP growth rates, inflation, and export rates are a strong indicator of a country’s future economic success (Roubini, 1991). Countries that maintain a low and stable inflation rate can experience consistent levels of economic growth (Barro, 1996). Higher inflation can lead to the erosion of purchasing power, devaluation of the local currency, and diminishing wealth, damaging a country’s GDP growth rate. Additionally, many newly emerging economies - such as Hong Kong, Taiwan, Singapore, and Korea - were able to develop quickly

16 through export-oriented growth (Kniivilä, 2007). Export promotion can prompt specialization in the manufacturing of export products, which can boost productivity, improve the skill levels of workers, and efficiently allocate resources (Giles and Williams, 2000).

Demographic Factors

Economic growth also varies based on the composition of a country’s population.

Countries with growing populations are more likely to report sluggish GDP growth rates and to not experience any sustained rise in income per capita, especially when technological change is slow (Cincotta and Engleman, 1997; Galor and Weil, 2000). The age distribution of a country’s population can also have significant effects on its economic performance. Nations with a large population of youth not of working age are likely to devote a high proportion of resources to education and other services, which can depress the pace of economic growth (Bloom and

Canning, 2001). In contrast, countries in which the majority of the population is of working age

(15 through 64) may experience a “demographic dividend” of economic growth due to the elevated productivity of this particular age group relative to those younger or older (Bloom and

Canning, 2001). Countries with a large proportion of elderly citizens may experience economic effects similar to those of a very young population. Additionally, countries with dense urban populations have greater economic output as manufacturing and industrial sectors become more centralized. Dense urban populations can also lead to greater energy efficiency and higher access to electricity, which can affect the climate policy decisions of a country (Carlino, Chatterjee, and

Hunt, 2006). Education is significantly related to economic growth, as countries that have high enrollment rates in college or university tend to experience greater economic returns (Barro,

2013). Finally, internet use is associated with a country’s economic growth and development, by

17 lowering transaction costs, improving business efficiency, and providing easier access to information (Grace, et al., 2004). Countries with higher rates of internet penetration also experience improved environmental and educational outcomes, as well as an increased awareness of the possible impacts of climate change, which may facilitate greater public support and pressure for climate policies (Schäfer, 2012).

Political Factors

A country’s political structure can influence its economic performance. More specifically, long-term democracies have higher economic growth rates than authoritarian regimes (Gerring, 2005). However, a newly democratic country will not likely experience any measureable change in its growth rate in the short term. Countries must also maintain democratic institutions over the course of several decades before economic growth can be experienced in subsequent years (Gerring, 2005).

DATA AND METHODS

This paper uses country-level climate change performance and economic data for 56 countries over a 5-year period, from 2010 to 2014. While the CCPI contains scores for 58 countries, my analysis will be limited to 56 because observations in the index for Taiwan and

Egypt had to be dropped from the dataset.6 For Taiwan, there was a lack of economic data in the

World Bank Development Indicators (WBDI) dataset. In the case of Egypt, the CCPI did not begin including the country in its annual survey until 2012. The CCPI dataset is used to measure the independent variable (climate policy stringency). The dependent variable of interest (GDP) and control variables are drawn from the WBDI and the Democracy and Dictatorship Revisited

(DDR) datasets.7

Dependent Variable

As highlighted in the literature review, this analysis focuses on a country’s GDP as a measure of economic growth. GDP is reported in the WBDI as a continuous variable, measured in U.S. dollars and adjusted for inflation at current market prices. This variable is available for all of the countries and years included in the analysis sample.

6 For a full list of countries that the CCPI includes in its rankings, please see Appendix 2. 7 Data were missing for the dependent variable (GDP), tertiary enrollment, and all three of the economic control variables. The missing GDP data values were all from 2014 and included Luxembourg, Malta, New Zealand, and Switzerland. Missing observations were inputted manually using GDP data from the UN Data Database. Out of the missing demographic controls, 101 data points were missing for the tertiary enrollment variable. For the economic data, I am missing four data points for the GDP growth rate, five for the inflation rate, and 18 for the export rate. All missing data for these controls were multiply imputed. My dataset contains 15 variables, with 280 state year observations (56*5), for a total of 4,200 data points. In all, these variables yielded a total of 128 missing values, or approximately 3% of the total number of data points in the analysis.

Independent Variable

The CCPI conceptualizes climate policy performance by combining and weighting five different components into one composite score: emissions level (30%), development of emissions (30%), renewable energies (10%), energy efficiency (10%), and national and international climate policies (20%). While I am most interested in the stringency of climate policy, the effectiveness of these policies are reflected in the other categories after a few years.

Countries that introduce more stringent policies will receive increases in their overall climate policy score. The direct effects of these policies are lagged, with increases only becoming observable in the other categories in subsequent years. These categories are then aggregated and used to quantify countries’ individual climate change efforts into a single score ranging from 0 to

100. Due to a change in methodology by Germanwatch in 2012, CCPI scores are transformed into percentiles (ranging from 1 to 99) in order to allow for comparisons across years.

Emissions data for the CCPI are taken from the annual “CO2 Emissions from Fuel

Combustion” edition produced by the International Energy Agency (IEA). These data allow for a yearly comparison of all energy related emissions for the countries included in the survey. The

CCPI also includes data on emissions from deforestation based on the UN Food and Agriculture

Organization (FAO) Global Forest Resource Assessment 2010. The qualitative data on countries’ climate policies are compiled through surveys of local climate change experts, who are usually representatives from environmental and climate NGOs. These experts outline the most important policy measures to promote renewable energies, increase energy efficiency, and reduce GHG emissions in different sectors. These policies are then evaluated in terms of their effectiveness in preserving the climate (Burk, 2015).

Control Variables

As discussed in the conceptual framework, past research supports the inclusion of control variables measuring countries’ political structures, previous economic growth, and population demographics. Information on countries’ political structures were gleaned from the DDR dataset, which classifies the regime types of 202 countries. The economic control variables, including

GDP growth rate, inflation, and exports of goods and services, were also taken from WDBI dataset. Data on the demographic controls, including population size, population growth, urban population density, school enrollment, population age, and internet use were collected from the

WBDI.

Model

In order to examine the relationship between a country’s economic growth and its climate policies, I will estimate a fixed-effects model, which controls for unobserved country- and time- specific factors that are related to a country’s economic growth. Country fixed effects specifically control for individual country characteristics that do not vary over the period of the study, such as unobserved legislative differences and cultural attitudes. Year fixed effects control for factors that change over time but are fixed across countries, such as global macroeconomic trends, the global average temperature, or rate of sea ice loss, which might influence a country’s decision to change its climate policies. In sum, I estimate the following model, with the country- year as the unit of analysis:

푒푐표푛푔푟표푤푡ℎ = 훽0 + 훽1퐶퐶푃퐼 + 훽2푑푒푣푒푙표푝푒푑 ∗ 퐶퐶푃퐼푖푡 + 훽3퐵푅퐼퐶푆 ∗ 퐶퐶푃퐼푖푡 + 훽4푝표푝푠𝑖푧푒푖푡

+ 훽5푝표푝푔푟표푤푡ℎ푖푡 + 훽6푢푟푏푎푛푝표푝푖푡 + 훽7푡푒푟푡𝑖푎푟푦푒푑푢푐푖푡 + 훽8푝표푝푎푔푒0_14푖푡

+ 훽9푝표푝푎푔푒15_64푖푡 + 훽10𝑖푛푡푒푟푛푒푡푢푠푒푟푠푖푡 + 훽11푔푑푝푔푟표푤푡ℎ푖푡 + 훽12𝑖푛푓푙푎푡𝑖표푛_푟푎푡푒푖푡

+ 훽13푒푥푝표푟푡_푟푎푡푒푖푡 + 훽14푟푒푔𝑖푚푒_푎푔푒푖푡 + 훽15푟푒푔𝑖푚푒_푎푔푒 ∗ 푑푒푚표푐푟푎푐푦푖푡 + 훾푡 + 훼푖

+ 푒푖푡, where i represents the country index, t is the year index, 훾푡 represents dummy variables for each year, 훼푖 captures country-specific time-invariant factors, and 푒푖푡 is the error term. The analytical sample contains 280 observations (56 states over 5 years). All variables included in the model are defined in Table 1.

Table 1. Definition of variables

Variable Source Definition Independent Variable Climate Change Performance CCPI A continuous variable that reflects the overall Score score of a country’s climate policies, restated as within-years percentile rankings (1-99) Dependent Variable GDP WBDI A continuous variable measuring a country’s annual gross domestic product (current U.S. $) Demographic Characteristics Population size WBDI A continuous variable measuring a country’s population size Population growth WBDI A continuous variable measuring the change in population from year to year (0-100%) Urban population WBDI A continuous variable measuring the proportion of a country’s population who live in urban areas (0-100%) School Enrollment, tertiary WBDI A continuous variable measuring the proportion of population who are enrolled in a four-year university/college or its equivalent (0-100%) Population Age WBDI A continuous variable measuring the proportion of the population between the ages of 0-14, 15-64, and 65+ (0-100%) Internet Users WBDI A continuous variable measuring the number of individuals who have used the Internet from any location in the last 12 months (per 100 people) Economic Characteristics GDP Growth Rate WBDI A continuous variable measuring the annual percentage growth rate of GDP at market prices from year to year, based on constant local currency (change in % terms between t-1 and t-2) Inflation Rate WBDI A continuous variable measuring the annual change in inflation (annual %) Exports of goods and services WBDI A continuous variable measuring a country’s exports of goods and services provided to the rest of the world per year (% of GDP)

Political Characteristics Age of Regime DDR A continuous variable that measures the age in years of a country Democratic Regime DDR A dichotomous variable that classifies a country’s type of government (0 = civilian, military, royal dictatorship; 1 = parliamentary, mixed, presidential democracy)

DESCRIPTIVE STATISTICS

Descriptive statistics for the dependent and key independent variables and for the demographic, economic, and political controls are provided in Table 2. Over the five years included in my study, the average CCPI percentile score for the 56 countries included in the dataset is 44.34. There is considerable variation within the CCPI score variable, which has a standard deviation of 30.52. Countries that score in the top quintile across all five years include the United Kingdom, Sweden, Switzerland, Portugal, Mexico, Germany, and France.

Conversely, Australia, Canada, China, Kazakhstan, Malaysia, Turkey, and the United States are consistently in the bottom quintile of the CCPI percentile scores. GDP also varies widely across countries included in the analysis. The average GDP during this timeframe is approximately

$4,439 billion with a minimum of $8.16 billion (Malta in 2010) and a maximum of $17,419 billion (United States in 2014).8

Values for the control variables also differ widely across countries. For example, the minimum urban population density (30.93 percent for India in 2010) is about a third of the maximum (100 percent for Singapore across all five years), and the proportion of internet users out of the total population ranges from 7.51 (India in 2010) to 98.16 (Iceland in 2014). For the economic controls, the average GDP growth rate from 2010 to 2014 is 2.32 percent with a minimum growth rate of -8.86 percent (Greece in 2011) and a maximum of 15.24 percent

(Singapore in 2010). Additionally, the variation in the export rates oscillates considerably given that some countries - such as Luxembourg, Hong Kong, and Singapore - rely heavily on trade.

8 Not included in the table is the correlation between the CCPI score and GDP, which is weak and negative (-0.418).

While the average export rate is 27.42 percent of GDP, the maximum rate is 203.32 percent

(Luxembourg in 2013) and the minimum is 10.74 percent (Brazil in 2010).

In regards to the political controls, 80 percent (or 45 countries) out of the 56 countries included in the analysis are coded as a democracy according to DDR dataset.9 As of 2014, the average age of these democratic regimes is 61.4 years with a minimum age of 7 years (Thailand) and a maximum age of 145 years (several countries).10 For countries coded as dictatorships, the average age of the regime is 59.37 years with a minimum age of 11 years (Singapore) and maximum of 69 years (Saudi Arabia). In terms of the entire sample, the average age of all regimes included in the analysis is 54.34 years.

9 The Democracy and Dictatorship Revisited (DDR) dataset extends only to 2008. In contrast, my CCPI scores and GDP data are from 2010 through 2014. Thus, the data for the political control variables, which included a country’s regime type and average age of the regime, were manually inputted for the subsequent six years. The only country that experienced a democratic transition from 2010-2014 within the dataset was Egypt. 10 The maximum age for several countries is 145 years as the DDR dataset began making observations starting from 1870.

Table 2. Descriptive statistics for dependent, key independent, and control variables11 Variable Mean Min Max SD. CCPI Score (out of 100) 54.99 24.5 75.23 8.43 CCPI Percentile Score 44.34 1 99 30.52 GDP ($ billion USD) 4,439.02 8.16 17,419.01 4,432.86

Demographic Population size (millions) 757.88 .32 1,364.27 592.59 Population growth (%) .82 -2.26 2.45 .54 Urban population (% of total 57.03 30.93 100 19.54 population) School enrollment, tertiary (% 39.74 14.32 116.62 24.33 gross) Population age (% of total population) 0-14 22.18 12.94 30.94 6.23 15-64 69.47 61.36 74.35 4.05 65+ 9.34 2.76 25.71 4.89 Internet Users (% of total 41.29 7.5 98.16 24.82 population)

Economic GDP growth rate (%) 5.51 -8.86 15.24 3.35 Inflation rate (%) 5.36 -1.42 59.22 4.44 Exports (% of GDP) 27.42 10.74 203.32 14.07

Political Age of regime (in years, as of 54.34 3 145 39.59 2014)

N=280

11 All estimates have been weighted using the average country population size over the five years of observation.

REGRESSION RESULTS

The results of my regression analyses are summarized in Table 3. Models 1 through 3 are estimated using OLS, while models 4 through 6 include country and year fixed effects. Model 1 is a simple OLS regression of the raw correlation between the dependent variable, GDP in billions of U.S. dollars, and the key independent variable, the CCPI percentile score. In Model 2,

I include dummy variables recording whether countries are categorized as developed economies or BRICS countries. These countries are allocated to separate categories as the growing strength of their economies render them different from developing countries (Pao, 2010). Model 3 introduces demographic, economic, and political control variables.

The results for Model 1 indicate a negative, statistically significant relationship between countries’ GDP and their CCPI percentile scores. However, despite the large, negative magnitude of this estimate, omitted factors that are associated with both economic growth and climate change performance significantly bias the model. A one-unit increase in the CCPI percentile score is associated with a decrease in a country’s GDP of $77.61 billion. In Model 2, the inclusion of the developed and BRICS dummy variables does little to change the key coefficient. In Model 3, the CCPI estimate is smaller and no longer statistically significant due to the addition of the demographic, economic, and political control variables. Remarkably, the

CCPI coefficient score becomes positive, although it is not significantly differentiable from zero.

The fact that the coefficient changes signs illustrates that, collectively, the demographic, economic, and political indicators are strongly predictive of both the CCPI scores and GDP.

Therefore, in both Model 1 and Model 2, my key coefficient of interest is downwardly biased due to the omission of these controls.

Model 4 includes the same control variables as Model 3 but also includes country and time fixed effects, which account for unobserved, time-invariant factors related to a country’s economic growth such as legislative differences and macroeconomic trends.12 The estimate produced in Model 4 is thus less affected by omitted variable bias, compared to the first three.

The results from this regression indicate that there is a positive, statistically significant relationship between a country’s CCPI score and GDP. For a one-unit increase in the CCPI percentile score, a country experiences an increase in GDP of $14.67 billion, on average. The magnitude of this relationship depends on the country in question. For the United States, with an annual GDP of approximately $18 trillion, this estimate would have little implication for its economic growth. More generally, the average GDP of a developed country in the CCPI is

$1,218 billion dollars, a one-unit increase in the CCPI score would correspond approximately to a 1.2 percent increase in GDP for developed countries.

But for developing countries in my sample, $14.67 billion could have a sizeable impact on their economy. For instance, Malta reported a GDP of $10.51 billion in 2014. If Malta were to improve its CCPI score by one percentile point, the country would experience an increase in

GDP of more than one hundred percent of its current economic output, according to the key coefficient in Model 4. However, the relationship between economic growth and climate change performance should be examined on a case-by-case basis, as the average GDP for developing countries is $1,118 billion. This indicates that, on average, developing countries would experience a similar rise (approximately 1.28 percent) in GDP as developed countries for a one- unit increase. To complicate these results further, the average GDP of BRICS countries is $2,977 billion. A one-unit rise in the CCPI percentile score would only represent a 0.49 percent increase

12 Please refer to the Data and Methods section for a more thorough discussion of country and time fixed effects.

29 in GDP. This is likely due to the small sample size for the BRICS dummy variable and the rapid economic growth of BRICS countries. However, on a per capita basis, GDP for BRICS countries

($5,526) is significantly lower than developed countries ($43,820) included in my sample.

In light of these considerations, I interact the CCPI percentile score with both the developed and BRICS dummy control variables in Model 5. The interactions between the CCPI and these dummy variables allow a more precise estimate of the key coefficient for each of my three country groups. In Model 5, my main coefficient of interest increases in magnitude, indicating that developing countries on average experience a rise in GDP by $20 billion for a one-unit increase in their CCPI percentile score. With the inclusion of the interactions terms, my key coefficient of interest now only represents the economic growth for developing countries. In order to observe the association between GDP and CCPI for developed and BRICS countries, the interaction coefficients must be added to the key coefficient of interest (20.18). The coefficients for the developed country and BRICS dummy variable interactions are $4.37 billion and negative

$13.16 billion, respectively. This translates into a rise in GDP of $24.55 billion for a one-unit increase in developed countries’ CCPI percentile score and a $7.02 billion rise in GDP for

BRICS. However, neither of these interactions is statistically significant. Therefore, I cannot confidently claim that the rise in GDP for a one-unit increase in the CCPI percentile score for developed and BRICS countries is different than for developing countries. Additionally, when testing for joint significance between the CCPI percentile score and the interaction for developed countries, no significant relationship is observed. This contradicts my earlier findings that the relationship between the CCPI and developing countries is positive and significant, and that there is no difference in the relationship for developed and developing countries.

Despite these inconsistencies, the results from these analyses generally suggest that there is a positive relationship between a country’s CCPI score and its GDP. Additionally, the inclusion of country and time fixed effects reduces the omitted variable bias found in the first three models, increases the magnitude of the estimate, and improves the accuracy of the coefficient. These results are surprising, as many assume that implementing climate policies, limiting GHG emissions, and improving energy efficiency would have a deleterious effect on economic output. In addition to the overall relationship between climate change performance and economic growth, I also find a statistically significant positive association for two out of the three country categories.

Table 3. Coefficients from OLS Models and Fixed-Effects Models Regressing GDP (USD$ billion) on the CCPI Percentile Score and Selected Control Variables

Variable Model 1 Model 2 Model 3 Model 4 Model 5

CCPI Percentile Score -77.61*** -77.23*** 12.30 14.67** 20.18*** (11.54) (12.43) (9.07) (7.07) (7.46)

Economic Indicators Developed 5,930.53*** -182.13 (1223.90) (995.96) CCPI & Developed 4.37 interaction (14.50) BRICS countries 3,931.41*** -3,327.94*** (609.31) (576.75) CCPI & BRICS interaction -13.16 (11.16) GDP growth rate -73.28 -24.24 -22.72 (83.19) (15.56) (14.85) Inflation rate -160.02*** 7.75 8.81 (36.58) (15.77) (14.91) Export rate -81.71*** -27.59* -27.50* (13.83) (14.65) (14.68)

Demographic Indicators Population size 4.97*** 9.68 6.57 (0.99) (6.87) (7.18) Population growth -1,687.13*** -230.76 -255.28 (652.44) (208.66) (274.22) Urban population 89.98*** 424.19*** 469.04*** (23.17) (117.90) (141.21) School enrollment, tertiary 53.29*** -23.34** -21.59** (17.92) (10.64) (8.91) Population age 0-14 256.28*** -536.52* -410.36 (83.27) (312.94) (291.83) 15-64 635.90*** -366.56 -329.96 (80.79) (298.31) (272.34) Internet Users -26.41 19.59 11.32 (21.92) (16.83) (17.40)

Political Indicators Age of regime 81.08*** 184.25** 178.44** (11.56) (90.48) (87.63) Age of regime & Democracy -8.71 -377.04*** -358.78*** interaction (10.80) (107.03) (98.26)

Variable Model 1 Model 2 Model 3 Model 4 Model 5

F-Statistics and p-values for Joint Hypotheses (a) Age of regime* Democracy interaction = 0 9.35*** 9.51*** (0.003) (0.003) (b) CCPI and CCPI*Developed interaction = 0 0.69 (0.409) (c) CCPI and CCPI*BRICS interaction = 0 3.83* (0.055)

Country fixed effects N N N Y Y Year fixed effects N N N Y Y R-squared .286 .466 .862 .879 .884 Number of Observations 280 280 280 280 280

Robust standard errors and p-values in parentheses *** p<0.01, ** p<0.05, * p<0.1

DISCUSSION

The objective of this analysis is to improve understanding of the relationship between climate change policy and economic growth. Given concerns about global warming and countries’ energy security, the results of this study can help to inform policymakers as they consider implementing climate policy, renewable technology, and energy efficiency programs to meet goals outlined in their countries’ INDCs. The results provide suggestive evidence that implementing climate change policy is positively associated with economic growth on average, particularly for developing and developed countries, while the estimated returns for economies in transition are also positive, but considerably lower. This smaller magnitude is potentially due to the need for these countries to continue to ramp up industrialization in order to grow their economies, resulting in greater amounts of GHG emissions. However, my results also suggest that, once economies in transition reach a certain state of development, the economic benefits from climate policy may increase considerably.

The signs of the estimated association between the CCPI index and GDP are substantively noteworthy. The results indicate that as countries improve their CCPI percentile score, their GDP may increase by a small, but significant amount, approximately 0.3 percent on average. Large leaps in CCPI percentile rankings are associated with moderate increases in GDP.

As discussed in the Literature Review, previous studies find the relationship between the mitigation of GHGs and economic growth to be either near zero or slightly negative (Stern,

2007; Mendelsohn, 2009; and Barker, 2007). My results contrast with previous literature by finding evidence of a positive relationship. A likely explanation for this difference is that previous studies used climate models to forecast the cost of mitigation policy based on scenarios that limit GHGs to a certain level. Other studies examine past policies for individual countries

(or regions, in the case of the EU), rather than examining the current climate policy framework as it stands globally. By using five years of data for 56 countries, this study is able to develop a contemporary measure of climate policy efforts immediately after the Global Financial Crisis and leading up to the 2015 Paris climate talks. However, my sample is self-selected. Only countries that have the capacity to measure and report their emission levels are able to be included in the CCPI. This limits the study’s generalizability by primarily examining the climate change performance of countries located in the Global North. Very few Middle Eastern, African, or Latin American countries are able to be included in the study.

This study uses a single composite index to measure countries’ efforts regarding domestic and global climate policy, emissions trends, renewable energy, and energy efficiency. Such an index cannot capture the nuances of countries’ climate regimes. Another limitation with using the CCPI is that the adjudication of the national and international climate polices had to be quantified by a panel of experts causing the scoring of this category to be subjective. In comparison, the other categories included in the index used measurable figures. Furthermore, the weighting of the five CCPI categories in relation to each other is likely to be ad hoc and based on the perceptions of the authors, potentially biasing the rankings

In addition, the reliability of the results is limited by the fact that this study endeavors to explain global climate and economic patterns that are likely affected by a variety of other factors that cannot be easily quantified. The relationship between the CCPI index and GDP may manifest disparately for different countries. A single coefficient may do a reasonable job of capturing the relationship between economic growth and variation in CCPI scores for all countries on average, but it may not accurately capture the relationship of these two variables for particular countries.

There are variety of factors not included in the analysis that may be correlated with both climate policy and economic growth. For example, a measure of the size of countries’ manufacturing sectors would likely be negatively correlated with climate change performance, but may be either positively or negatively correlated with economic growth, resulting in either an upward or downward bias in the estimated coefficient for GDP. Politics is a second factor that could contribute to further omitted variable bias. Because the analysis spans five years, this paper might miss turnovers in government administrations that may have platforms that are either significantly pro- or anti-climate change or that advance different economic policy approaches.

A study that measures past and present governments’ stance on climate change would remove this source of omitted variable bias. However, it is not clear whether such a measure would positively or negatively bias my estimate since economic policies would likely be implemented without consideration to GHG emissions. Finally, data quality is an issue for both the control variables and the dependent variable. My results may be less applicable for developing countries because they represent a small proportion of the sample size relative to the number of developed countries in the analysis. Furthermore, consistent data collection and accurate emissions reporting remain issues for a number of developing countries.

Future research on the relationship between economic growth and climate policy should examine whether or not the Paris climate negotiations have an observable effect on countries’ growth. A study that examines GDP growth before and after the Paris accord could assist in understanding how more stringent international climate policy contributes to economic growth.

Additionally, future research should also examine more closely the relationship between climate change performance and GDP at different levels of economic development. My results hint at a wide divide between developed, developing, and transitioning economies. This categorization

36 may allow a better understanding of best practices for countries at different levels of development. Lessons can then be drawn from developed countries that have implemented successful climate policies and applied to developing countries seeking to become increasingly industrialized.

The results of this study suggest that there is a meaningful relationship between climate change policy and economic growth. My analysis indicates that, on average, a country could implement increasingly stringent climate policies without hampering their economic growth.

This result could be helpful to policymakers considering reforms in their energy sectors, funding for renewable technology, improvements in energy efficiency, and reassessments in advance of future iterations of their INDCs. The limitations of my analysis prevent more specific policy recommendations, and additional analysis is needed to prompt more aggressive polices that prevent irreversible climate change. As countries begin to implement their INDCs, future research on the relationship between economic growth and climate change can help government officials better assess their reduction capabilities and reevaluate their targets.

APPENDIX 1: CCPI Background and Methodology

Developed by Germanwatch and the Climate Action Network-Europe, the Climate

Change Performance Index (CCPI) compares the climate policies of 58 countries that together are responsible for more than 90 percent of annual global carbon dioxide emissions. The index includes advanced industrial economies, countries that are in transition (which are Annex 1 parties to the UNFCCC), and countries that emit more than one percent of global CO2 emissions.

The CCPI draws from several different datasets, including emissions data provided by the

International Energy Agency (IEA), deforestation data provided by the UN Food and Agriculture

Organization (FAO) Global Forest Resource Assessment report, and information on climate policy adjudication taken from evaluations performed by more than 300 climate experts from around the world. Each year, the CCPI scores and ranks countries based on how far they have progressed in ensuring the prevention of climate change through evaluation of various types of public policy. The index is presented at each annual UN Climate Change Conference in order to serve both as a warning and a word of encouragement to participating countries.

The CCPI is measured using fifteen different indicators which are then combined into one single composite score. These indicators can be classified in to five different categories: emission levels, development of emissions, efficiency, renewable energies, and climate policy.13

Emission indicators constitute 60 percent of the composite CCPI score. Renewable energy and efficiency indicators each contribute ten percent to the overall score, while climate policy indictors make up the remaining 20 percent. For the breakdown of the CCPI scores by individual indicators and categories, please see Figure 2.

13 For further information on the way in which Germanwatch calculates scores and weights each of the five categories, please see “The Climate Change Performance Index: Background and Methodology.”

Figure 2. Components of the CCPI

The scores reported in the CCPI are not absolute. Rather, they allow for an inter-country comparison which reflects a country’s climate performance relative to other participating countries. Countries that introduce more stringent policies that reduce emissions generally receive increases in their climate policy score. Whether or not these policies are effective is reflected, after a few years, in the emissions, efficiency and renewable energies categories. The top three rankings in the CCPI remain empty as Germanwatch has yet to deem any country’s climate change performance to be sufficient.

APPENDIX 2: List of CCPI Participating Countries

Algeria Lithuania Argentina Luxembourg Australia Malaysia Austria Malta Belarus Mexico Belgium Morocco Brazil Netherlands Bulgaria New Zealand Canada Norway China Poland Croatia Portugal Cyprus Romania Czech Republic Russia Denmark Saudi Arabia Egypt* Singapore Estonia Slovakia Finland Slovenia France South Africa Germany South Korea Greece Spain Hungary Sweden Iceland Switzerland India Taiwan* Indonesia Thailand Iran Turkey Ireland Ukraine Italy United Japan Kingdom Kazakhstan United States Latvia

* indicates countries not included in the analysis

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