Political Economy of Climate Change Policy

Abstract

Anthropogenic climate change poses a threat to all people and governments, but the response to that threat varies enormously across countries. Some adopt politically costly and economically challenging climate change mitigation policies, while others deny that climate change is occurring. Why do some countries adopt effective climate change policies while others do not? To answer this fundamental question, this paper analyses the political economy determinants of climate change policy around the world. In order to measure climate change policy, we introduce a new index, the ‘Climate Laws, Institutions and Measures Index’ (CLIMI), the first systematic attempt to measure countries’ policy responses to the risk of climate change. CLIMI covers all the relevant institutions and sector-specific policies in 95 countries, representing 90% of the world’s GHG emissions. We then use CLIMI to examine the political and economic factors that determine countries’ choices to implement policies to tackle climate change. We find that the level of democracy alone is not a major driver of climate change policy adoption, but that public knowledge of climate change is. Not surprisingly, a high concentration of carbon-intensive industry in the economy hinders the adoption of climate change policy. Countries in which the citizenry has a better public awareness of climate change have more effective climate policies regardless of the presence of democratic institutions.

Full text

Electronic copy available at: http://ssrn.com/abstract=2456538
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Political Economy of Climate Change
Policy
Franklin Steves
Alexander Teytelboym
Working paper 13-02
October 2013
Smith School of Enterprise and the Environment
School of Geography and the Environment
www.smithschool.ox.ac.uk

Electronic copy available at: http://ssrn.com/abstract=2456538
2
Political Economy of Climate Change Policy∗
Franklin Steves1 and Alexander Teytelboym2
16 October 2013
Abstract
Anthropogenic climate change poses a threat to all people and governments, but the
response to that threat varies enormously across countries. Some adopt politically costly
and economically challenging climate change mitigation policies, while others deny that
climate change is occurring. Why do some countries adopt effective climate change
policies while others do not? To answer this fundamental question, this paper analyses
the political economy determinants of climate change policy around the world. In order
to measure climate change policy, we introduce a new index, the ‘Climate Laws,
Institutions and Measures Index’ (CLIMI), the first systematic attempt to measure
countries’ policy responses to the risk of climate change. CLIMI covers all the relevant
institutions and sector-specific policies in 95 countries, representing 90% of the world’s
GHG emissions. We then use CLIMI to examine the political and economic factors that
determine countries’ choices to implement policies to tackle climate change. We find
that the level of democracy alone is not a major driver of climate change policy adoption,
but that public knowledge of climate change is. Not surprisingly, a high concentration of
carbon-intensive industry in the economy hinders the adoption of climate change policy.
Countries in which the citizenry has a better public awareness of climate change have
more effective climate policies regardless of the presence of democratic institutions.
1 Introduction
There is a consensus in the scientific community that humanity will be adversely
affected by anthropogenic climate change unless worldwide emissions of
greenhouse gases are cut dramatically in the next 40 years (IPCC, 2007).
Although climate change has moved to the top of the global political agenda over
the past two decades, national mitigation policies remain a subject of intense
debate (Stern, 2007; Giddens, 2009).
Scientists are still uncertain about the exact size and distribution of the long-
term economic damages resulting from climate change (IPCC, 2007). As a result,
policymakers in some countries remain reluctant to introduce aggressive climate
change mitigation policy (Giddens, 2009). However, many countries have – often
unilaterally – cut their emissions over the past twenty years.
∗This paper draws heavily on the Chapter 4 of the Low Carbon Transition Report (EBRD, 2011),
which we co-authored with Daniel Treisman. We would like to thank Alex Chirmiciu, Grzegorz
Peszko, Jeromin Zettelmeyer and Robert Hahn, and all participants at the EBRD OCE and the Smith
School seminars for their invaluable comments, which greatly improved this working paper. We would
also like to express special thanks to Kamila Kavankova and Stefan Sulek for their tireless research
work during the preparation of the CLIMI.
1 European Bank for Reconstruction and Development; StevesF@ebrd.com
2 Department of Economics, University of Oxford and Smith School for Enterprise and the
Environment; alexander.teytelboym@smithschool.ox.ac.uk

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Given the scale of emission reductions the world needs to achieve to keep global
warming in check, it is important for policymakers and academics to focus on
how future emission reductions will be brought about. However, this is a public
goods problem, so voluntary reductions are unlikely to be sufficient. Moreover,
it is uninformative to measure a country’s commitment to emission reduction by
looking at its current emissions, which are affected by a variety of factors
including economic conditions and trade. Substantial long-term reductions in
emissions can only be achieved if most countries adopt effective emission
reduction policies.
As this paper shows, many countries have already introduced climate change
mitigation policies. However, measuring and comparing their quality and
effectiveness across countries is fraught with difficulties. First, no two policies
are the same because they usually arise through different legislative processes.
Second, it is unclear to what extent the laws in the book are actually
implemented in practice. Third, evaluating policies separately may miss out on
important synergies that make policy packages more effective.
This paper proposes a new and simple ranking of national legislative, fiscal and
institutional frameworks that can make a long-term impact on emission
reduction: the Climate Laws, Institutions and Measures Index, or CLIMI. Unlike
most climate policy indices CLIMI focuses on policy inputs – climate laws,
institutions and measures – rather than policy outcomes, viz., emissions. This is
the first index that takes into account all major sectoral and cross-sectoral
policies and measures as well as all government institutions focussed on climate
change for 95 countries. A country that scores highly on CLIMI is not only
committed to reducing emissions today, but is also building the institutional
capacity to reduce emissions in the future.
CLIMI, while imperfect, allows us to understand how and why climate change
policy is made.3
We first set out a stylised model of climate change policymaking,
drawing from the larger literature on the political economy of reform. We then
use this model to propose some hypotheses about the key obstacles to climate
change policy adoption, and test these hypotheses drawing on a variety of data
sources.
The rest of this paper is organised as follows. In Section 2 we introduce the
Climate Laws, Institutions and Measures Index (CLIMI). For the sake of brevity,
we relegate most of the methodology and sensitivity testing to the Appendix.
Section 3 sketches out our political economy approach to climate change policy
and proposes several hypotheses about the relationship between different actors
in the formation of national climate change policies. Section 4 presents the
empirical testing of the hypotheses from the model, including a number of
alternative specifications. Section 5 summarises the findings and suggests
several directions for further work.
3 In this paper we use the terms ‘climate change policy’ and ‘climate policy’ interchangeably. Unless
otherwise specified, we use these terms to denote policies designed to mitigate climate change (and
thus global warming), as opposed to policies for adaptation to the impacts of climate change.

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2 Measuring climate change policy
To understand the factors driving emissions outcomes around the world, it is
important to understand to what extent and in which ways climate change policy
varies across countries. A number of international measures of climate change
outcomes, such as CO2 emissions or carbon-intensity, already exist. However, the
transmission mechanism from a government intention to CO2 emissions
reductions is through climate change policies and measures. Policies and
measures are based on, and embodied in, laws and institutions. There are no
internationally comparative measures of climate change policies, which
motivated us to construct CLIMI: Climate Laws, Institutions and Measures Index.
2.1 Policy commitments versus outcomes
In order to tackle climate change, global cooperation is necessary. No single
country can cut its emissions quickly and deeply enough to prevent the
concentration of CO2 in the atmosphere from rising to dangerous levels. Yet
focussing solely on emissions, neglects how governments are trying to mitigate
climate change. The relationship between emissions and policy commitment is
not straightforward for several reasons:
• Emissions may rise despite good climate change policy due to economic
development objectives. This is particularly the case in some developing
countries with effective governments, which are struggling to contain
emissions produced by rapid fossil fuel-based electrification and
urbanisation despite some excellent mitigation measures.
• Climate change policy may not be well implemented because of overall
government ineffectiveness. This does not mean that the government is
shirking from climate change effort. Instead, improving overall institutional
capacity of the government will improve the implementation of climate
change policies and measures.
• Some countries have multiple objectives when they adopt emission reduction
policies. For example, some countries seek to influence climate change policy
in other countries through negotiations. On one hand, Costa Rica may lead by
example aiming to decarbonise the economy fully within 20 years. On the
other, during the 15th session of the Conference of Parties (COP) in
Copenhagen, China refused to negotiate unless the EU agreed on CO2
reduction of 20% by 2020, which was less ambitious than the 30% cut EU had
been prepared to accept (Guardian, 2009).
• There is a great variety of possible climate change mitigation policies. Policies
that relate directly or, more often, indirectly to CO2 emission reductions are
often adopted within other legislative documents, and the mandate for
promoting them may not lie with a single authority. A plethora of policies, in
areas such as energy security or urban transport sustainability, can have
positive climate change effects. It is therefore important to set out

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comparable benchmarks for all key carbon-related sectors and check to what
extent various government climate change policies have met them.
• Good policies have a long-run effect on emissions. Several economies with
high per capita emissions, such as Germany, have adopted aggressive carbon
reduction policies that are likely to lead to a significant reduction in
emissions in the next decade. Many developing countries do not even have a
long-term strategy even for reducing carbon intensity per unit of GDP and
their emissions will increase significantly, even though they may be easily
preventable in the context of rapid economic development.
These concerns identify a considerable gap in the measurement of climate
change policy commitments and outcomes, which the CLIMI is designed to fill. It
may be useful not only for policy-makers, but also for the analysis of the drivers
of emission trends: climate change policies and the political economy aspects of
climate change. It is important to underline that CLIMI is not merely an
improvement on an existing index; it offers an entirely new and objective
measure of climate change policies around the world.
2.2 National Communications and data sources
Comparing the quality, breadth and depth of climate policies, measures, laws and
institutions across a wide range of countries is neither a simple nor an
uncontroversial task. First, the range of government policies and measures that
can influence climate change is vast.4
It is therefore necessary to select, ex ante,
from the universe of government policies and measures those that are directed
towards and are most effective in reducing carbon emissions and therefore
mitigating global climate change.
A second major methodological problem relates to the availability of reliable
data on climate change policies and measures that are comparable across
countries. While there are a large number of country studies on the quality of
individual countries’ climate change policies, there are no available cross-
country comparative assessments of climate change policies with global
coverage.
We therefore chose to use the most systematic information on countries’ climate
change mitigation policies and measures that is publicly available: National
Communications to the United Nations Framework Convention on Climate
Change (UNFCCC). The National Communications include detailed accounts of
climate change mitigation and adaptation policies and measures adopted by
national governments.
National Communications must be submitted by all countries, which have signed
the UNFCCC. Under Article 4.1(b) “all parties… shall…formulate, implement,
publish and regularly update national and, where appropriate, regional
4 For example, minimum energy-efficiency standards in residential building regulations can have a
significant impact on carbon emissions, whether or not the consequence is intended.

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programmes containing measures to mitigate climate change by addressing
anthropogenic emissions by sources and removals by sinks of all greenhouse
gases not controlled by the Montreal Protocol…” All countries, which signed the
UNFCCC, except the United States, subsequently signed and ratified the Kyoto
Protocol5
(UNFCCC, 1998), which expired in 2012. The Protocol divided
countries into developed countries, which had legally binding emissions
commitments for the period 2008-2012 (Annex I), and developing countries,
which did not (non-Annex I).
The Protocol also elaborated the reporting requirements: Article 10(b) states
that “all parties…shall…formulate, implement, publish and regularly update
national and, where appropriate, regional programmes containing measures to
mitigate climate change … (i) [s]uch programmes would, inter alia, concern the
energy, transport and industry sectors as well as agriculture, forestry and waste
management.” Since, under Article 4 of the Protocol, “[e]ach Party included in
Annex I shall incorporate in its annual inventory of anthropogenic emissions by
sources and removals by sinks of greenhouse gases not controlled by the
Montreal Protocol”, Annex I countries also tend to update their policies and
measures every year. Non-Annex I countries publish their National
Communications considerably less regularly, but by January 2011 only 13 out of
153 non-Annex I countries had not submitted a National Communication.6
National Communications offer an excellent starting point for a comparative
understanding of the breadth and quality of climate change mitigation policies.
First, countries have a clear incentive to report all the policies and measures that
they are taking. Therefore it is unlikely that countries would intentionally omit
any of their significant legislation or programmes, which address climate change
mitigation. To prevent misreporting based on exaggeration, the relevant policies
were cross-checked with existing databases of climate change policies, using
national legislation as well as expert and UNFCCC country focal point
consultations (see Appendix 6.7 for a full list of sources). Second, National
Communications are systematic. UNFCCC prepared detailed and standardised
guidelines for reporting policies and measures in the National Communications
for Annex I (UNFCCC, 2000) and non-Annex I countries (UNFCCC, 2003, inter
alia). The sectoral structure of the guidelines is particularly reflected in two out
of the four parts of CLIMI.
2.3 Country coverage
We cover all countries, which submitted a National Communication to the
UNFCCC between 1 January 2005 (the year the Kyoto Protocol came into force)
and 15 January 2011.7
5 The Protocol became legally binding on all signatories on 16 February 2005 after Russia ratified it in
November 2004.
We also include China, India, South Africa, the Republic of
Korea, Turkey and Azerbaijan, in order to represent the largest and fastest
6 These are: Angola, Brunei Darussalam, Cyprus, Equatorial Guinea, Iraq, Kuwait, Liberia, Libya,
Myanmar, Oman, Qatar, Somalia, Timor-Leste.
7 We exclude Liechtenstein, Luxembourg, Monaco and San Marino.

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growing emitters.8
6.2
This allows CLIMI, unlike previous indices of its kind, to offer
extensive country coverage, including developing countries and small-island
states. Appendix includes a full list of countries.
CLIMI thus provides an objective comparative assessment of the breadth and
quality of climate change mitigation legislation, policies, measures and
institutions in 95 countries (including all countries in the EU, all post-communist
transition economies, all large developing countries, many least developed
countries and small island states), covering 91 per cent of global emissions and
73 per cent of the world’s population.
2.4 Structure
CLIMI measures the breadth and quality of four main policy areas: international
cooperation and policy; domestic institutions and national climate change
mitigation policy; sectoral policies; and cross-sectoral policies. The components
of CLIMI follow the standardised structure of the National Communications,
which was designed to highlight the most important areas of climate change
mitigation policies and measures. CLIMI therefore has 12 constituent variables
grouped into four key policy areas:
1. International cooperation: how quickly a government ratified the Kyoto
Protocol and whether it developed the institutional capacity to participate in
the flexible mechanisms (host projects under Joint Implementation (JI) or the
Clean Development Mechanism (CDM)).9
2. Domestic climate framework: this includes broad climate change laws and
targets, as well as the levels of institutional engagement in climate change
(ministerial level, independent committees, etc.).
3. Sectoral fiscal or regulatory measures or targets: these include targets
and regulations in each of the sectors identified in the reports of the
Intergovernmental Panel on Climate Change, apart from waste, as detailed in
Appendix 6.3.
4. Cross-sectoral fiscal or regulatory measures: these include carbon taxes
and emission-trading schemes.
Most scores are assigned on a three level scale: 0/0.5/1. There are two
exceptions within the International Cooperation policy area: Kyoto Protocol
ratification is assigned on a linear scale and JI/CDM project existence is a binary
measure. A score of 1 is supposed to signify worldwide best practice – not the
best conceivable policy. A score of 0.5 means that significant mitigation measures
have been intentionally taken, but they fall considerably short of best practice. A
score of 0 means the institution or policy is non-existent, insignificant or its
8 For these two countries, we used a large number of sources to obtain the information that is normally
provided in the National Communications.
9 See Dolsak (2009) for a similar approach.

8
stated function is deceptive. Appendix 6.3 describes in detail how the scores are
assigned and provides examples of typical policy measures.
Weights are used to reflect, broadly, the contribution of each of the sectoral
policy areas to possible carbon emission reductions. The weights are assigned in
the following way: half of the CLIMI score is assigned to national institutions and
nationwide policies and targets, and half of the score is assigned to sectoral and
cross-sectoral policies. Within the sectoral policies, different sectors are assigned
weights according to their contribution to worldwide emissions measured by
IPCC (2007). Subcomponents in other policy areas are weighted equally.
Appendix 6.4 summarises these key components and shows how they are
weighted.
Finally, it is worth noting that CLIMI has not introduced any controversial ways
of measuring climate change policy. As the references indicate, most
subcomponents have been used in other indices or as proxies for climate change
cooperation. The uniqueness of CLIMI is that it only takes into account policies
and measures, it does so objectively, and it does not mix policies with outcomes.
Before we describe the results of the CLIMI, it is worth reiterating what CLIMI is
not. Importantly, CLIMI does not include an assessment of outcomes (e.g.
emissions), implementation quality or adaptation measures. Thus, it is possible
that emissions may be on a rising trend in countries that have a high score on
CLIMI. For example, China’s industrial growth puts pressure on emissions, but its
mitigation policies (which limit emissions that would not have occurred anyway)
are increasingly ambitious. In addition, CLIMI measures the policies that
countries have adopted to mitigate climate change, but does not provide an
assessment of the quality of implementation of those policies. Instead, it relies on
an assessment of the extensiveness of policy measures. CLIMI does not claim to
be comprehensive to climate change policy coverage: for example, we do not
look at R&D policies due to serious data limitations. Finally, CLIMI looks only at
climate change mitigation; it does not look at either adaptation or broader
environmental policies, which are likely to have different political economy
mechanisms from those we identify.10
2.5 Relationship between CLIMI and outcomes
The countries that score best on CLIMI tend to be northern European countries,
mostly EU member states. The countries that score lowest on CLIMI tend to be
low-income countries, predominantly located in sub-Saharan Africa, which have
little pressure to reduce their relatively low emissions and low state capacity to
design what are often legally and economically complex policies and measures.
Indeed, there is a clear correlation between countries’ per capita income and the
adoption of good climate change policies, as highlighted in Figure 1.
10 CLIMI does not attempt to measure waste sector policies and measures, due to a practical difficulty
in their comparative ranking across countries. This is the smallest emissions sector in almost all
countries, accounting for only 2.8% emissions globally (IPCC, 2007).

9
There is little correlation between countries’ vulnerability to climate change and
the adoption of climate change mitigation policies and measures. This reflects
the fact that the countries that are most vulnerable to climate change tend to
contribute little to the problem – and hence tend to focus their efforts on
adaptation rather than mitigation.
Figure 1: Correlation between per capita income and CLIMI
The full results of CLIMI are reported in Appendix 6.1.
3 How climate policy is made: a political economy approach
Why do some countries adopt ambitious climate change policies while others do
not? The literature on the political economy of policy-making and reform
suggests four sets of factors that are likely to be important. These relate to the
international context, the structure of government, the degree of political
accountability, and the characteristics of interest groups. The model presented in
this section is highly stylized and focuses only on the national context. Its
purpose is to shed some light on the complex political economy of climate change
policy rather than make tight predictions.
First, the international context will affect how governments approach climate
policy. The making of such policy can be thought of as a two-level interaction (cf.
Putnam, 1988). At the higher level, the world’s governments interact
strategically, each seeking to benefit from the global climate change regime while
reducing their costs. Since there is no international authority with strong
sanctioning power, this can be considered a “game” of voluntary contributions to

10
a public good: climate stabilisation. At the lower level, climate policies are
formulated and implemented within each country by national governments once
the international level is settled.
While the international bargaining game is important, this paper focuses on the
domestic level. We take international agreements as given and ask why some
governments do far more than others to rapidly concretise and implement their
international commitments. Under international agreements such as the Kyoto
Protocol, countries do pledge to meet certain carbon-reduction targets. These
pledges then serve as the background rather than a credible commitment to the
game of domestic policy-making.
Domestic policy-making depends in the first instance on the structure of
government. Governments differ in the number of institutional veto players – or
actors whose agreement is necessary for policies to be enacted – that they
contain (Tsebelis, 2002). The number of veto players depends on whether the
parliament consists of two chambers, each with strong powers; whether there is
a president; and whether the constitution is federal in the sense of granting veto-
power over central policy to regional governments or their representatives. In
addition, the number of veto players will depend on the number of parties in the
ruling coalition, since defection by a coalition member can preclude a bill’s
passage. The more veto players there are and the more divergent their views, the
more difficult it is to change policy. One veto player, the agenda setter, gets to
make the proposals to which other veto players respond. Hence, the identity of
the agenda setter will also affect what policy is chosen.
The motivation of these veto players depends on the degree of political
accountability. In democracies, parties and individual politicians in the
government have reason to take into account the views of their constituents. The
more responsive the democracy, the more the preferences of the electorate will
matter. The degree of responsiveness will depend on the electoral rules, but also
on the degree of media freedom, which affects the accuracy and amount of
information available to voters. The ability of voters to extract accurate
information from the media and other sources will depend on their level of
education.
Finally, the characteristics of interest groups will also affect the outcomes of
domestic policy-making. In part, the landscape of interest groups will simply
reflect the underlying economic interests in the society, associated with the
economic structure. However, particular interest groups will be better organised
in some places than others. Classic contributions to this literature suggest that
the outcomes of policy will reflect the set of pressures – or bids – from competing
interest groups (Olson, 1965; Becker, 1983; Grossman and Helpman, 1994).
Figure 2 outlines the hypothesised relationships among the key actors who drive
the formation of climate policy by governments (represented as G1, G2...).

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Figure 2: Stylised model of climate change policy formation
Thinking about policy interactions in this way suggests a number of reasons why
one country might pursue climate policy more actively than another. First, some
countries are more dependent on carbon-intensive industries than others. If the
income of the majority of the electorate depends on such industries, then one
might expect democratic politicians to resist reforms that would threaten the
livelihood of their constituents (Kahn and Matsuaka, 1997). Of course, there are
local benefits, for example in terms of air quality. If the benefits of developing
clean industry exceed the costs of retiring heavy polluters, the voters could in
principle be compensated. However, promises to do so may not be credible and
voters may block support for clean industry.
Even if the majority of voters do not depend on carbon-intensive industry,
special interests working on behalf of the carbon-intensive industry can still
achieve political influence disproportionate to the share of votes it can mobilise,
as long as it is well organised. Thus, a strong presence of high-carbon industries
may result in the effective blocking of reform.
However, other interest groups and issue-oriented lobbies such as
environmental non-governmental organisations (NGOs) may balance the
pressures of carbon-intensive industry, informing both the public and politicians
about the benefits of climate policy (Botcheva, 1996). In addition, low-carbon
industries may lobby for policies that support their activities.
Indeed, the battle over climate change policies will in part be a battle of ideas.
Supporters and opponents of climate policy will seek to inform – and sometimes
to misinform – both the public and politicians on the causes of climate change
and the costs and benefits of mitigation measures. Given this conflicting
. . .
Level one: international
Private contribution to a
public good game
Public Carbon-
intensive
industry
Low-carbon industry
G1 G2 G3 G4
Media
NGOs
Other
actors
Policy
Policy Policy Policy
Funding
Political
influence
Information
Level two: domestic
Political game—
different in
different
countries
G2
Veto
player 1 Veto
player 2
. . .
Agenda
setter

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information, a lot may depend on the sophistication of the general public – which
in turn depends on the level of education – and on the extent to which the media
are free and motivated to pursue the truth rather than to represent corporate or
government interests.
Public beliefs will also be shaped by history. Many countries, which have an
abundance of fossil fuels, also have an energy-intensive and wasteful industrial
structure. In these countries, there tends to be a widespread belief that energy
use is less costly to society than it actually is. This may be another reason to
expect slower reforms in countries where the energy-intensive sector is larger.
If a government is not democratic, then the paths of influence will tend to go
directly from interest groups to government actors, with less influence by the
public along the way. If the energy-intensive industry is well organised, it may
succeed in blocking the implementation of climate policy commitments that
benefit the public but are costly to entrenched interests.
The nature of the political regime may affect reform in one other way: by
determining the time horizon of policy-makers. Reducing CO2 emissions has
potentially huge long-term benefits in terms of preventing climate change, but
also large short-term costs. If leaders are focused on winning the next election
(as in a democracy), or on avoiding an imminent coup (in an unstable autocracy),
their regard for the future may be lower than that of the broader society. By
contrast an (well-informed) autocrat who expects to remain in power for 20
years might take the threat of global warming more seriously.
As should be clear, most of the variables likely to affect climate policy – regime
type, press freedom, even the relative size of carbon-intensive industries – may
have conditional or even conflicting effects. How economic structure, the extent
of democracy and other factors influence countries’ performance in climate
change mitigation is therefore an empirical question. This is the subject of the
following section.
4 Empirical investigation of climate change policy adoption
In his section, we use CLIMI to analyse empirically the relationship between
climate change policies and measures – the outcome of interest in this paper –
and the various aspects of the stylised model of climate policymaking outlined
above.
In the real world, some governments will be constrained by overwhelming public
opposition to carbon-reduction policies – regardless of the hard economic facts –
while in other countries the tide of public opinion will leave political leaders with
little choice but to implement policy measures that are economically painful in
the short run. In some countries, the influence of the carbon-intensive industry
lobby will be channelled via opaque means or personal relationships, while in
other countries the debate between carbon-intensive and low-carbon industries

13
will take place in the public arena with open engagement by civil society and the
independent media.
Recognising this complexity, we estimate a reduced-form statistical model, based
on six major factors that the political economy literature identifies as likely to
drive climate change mitigation policy:
1. Public knowledge of the threat represented by climate change. Given the
extent to which the government responds to public pressure, one would
expect public knowledge of climate change to lead to stronger policies. The
data used to measure this are taken from a 2009 Gallup poll, conducted in
175 countries, which asked people whether they see climate change as a
threat, how much they know about climate change, and whether climate
change is caused by human activity or is a natural phenomenon. However,
because the public’s understanding of climate change will itself be influenced
by national climate policies, an instrumental variable approach is required to
understand this link (see below).
2. The level of democracy. The direct effect of democratic systems on climate
change mitigation policy could be either positive or negative. Democratic
political systems are designed to transmit popular concerns and priorities
into the policy-making process. In democratic countries where public
knowledge of the threats and causes climate change is pervasive, one may
expect climate policy to be ambitious. However, if the public is opposed to
climate policy because it may harm short-term economic prospects,
democratic political systems may inhibit the adoption of ambitious policy in
this area. We employed the widely used Polity IV regime characteristics
dataset for 2007 to measure the level of democracy.
3. The strength of the carbon-intensive industry lobby. The political weight
of the carbon-intensive industry lobby is simultaneously the most important
determinant of climate change policies and measures, and the most difficult
to measure. Carbon-intensive industry may hinder climate change policy
especially if it is employs a large proportion of the electorate and contributes
substantially to tax revenue. For the purpose of this analysis, the share of
carbon-intensive industries – manufacturing, mining and utilities – in each
country’s Gross Domestic Product was used as a rough proxy.
4. State administrative capacity. Once political leaders have announced a
course of policy action, the stated intention may or may not be translated into
state policy. This will depend, at least in part, on the administrative capacity
of the bureaucracy to draft regulations and laws, and submit them for
legislative and executive approval. This factor is only implicitly addressed in
the political economy literature, but might be important, particularly with
regard to the complex regulatory, legal and economic challenges associated
with climate policy. Countries with strong democracies, free media and weak
carbon-intensive industry lobbies might nevertheless have weak climate
change policies because of insufficient capabilities to design and implement
such policies – much less enforce them, an issue not dealt with in this paper.

14
The simple average of the World Bank’s “Government Effectiveness” and
“Regulatory Quality” Governance Indicators for 2007 was used to measure
state administrative capacity.
5. Per capita and total CO2 emissions. There are two possible ways that per
capita or total CO2 emissions might affect climate change policy adoption. On
the one hand, the countries with the highest per capita CO2 emissions tend to
be the highest income countries which have historically generated the most
atmospheric carbon and upon which most of international emission
reductions obligations (e.g. Kyoto Protocol, Cancun Agreement, Doha Climate
Gateway) are being placed. On the other hand, in countries with higher per
capita CO2 emissions, it is likely that introducing aggressive carbon emission-
reduction targets will be resisted more fiercely by both individuals and firms.
Countries that have lower total emissions may be more reluctant to cut
emissions because their contribution to climate change is small and hence
any decrease in emissions will only have a negligible effect on global
emissions. We therefore test empirically what kind of impacts per capita and
total CO2 emissions have on the adoption of climate change policy.
6. International commitments. In all countries the nature of internationally
negotiated carbon emission-reduction targets will play a role in domestic
leaders’ and polities’ cost/benefit deliberations on climate change policy
innovation. We therefore control for ratification of the Kyoto Protocol as well
as the size of the emission-reduction target to which Annex I countries
committed themselves. In addition, the most binding international
commitments are entailed by membership in the EU, which we control for
using a dummy variable in the regressions. We also use a dummy variable to
test whether being a post-communist transition country has a significant
effect on the adoption of climate change policy, controlling for other
variables.
4.1 The determinants of public opinion on climate change
Before turning to the empirical testing of the impact of the hypothesised political
economy drivers on climate policy adoption, it is necessary first to ‘unpack’ the
relationship between the first hypothesised driver – public knowledge of the
threat represented by climate change – and the adoption of climate policy. As
mentioned above, the observed correlation between public knowledge of the
threat posed by climate change and better climate policy could reflect causal
effects in both directions: better knowledge of the causes of climate change could
simultaneously influence, and be influenced by, climate change policies.
To see whether public information affects climate change policies, it is therefore
important to focus on cross-country differences in public knowledge that are
driven by factors unlikely to be influenced by climate policies, and that do not
influence policies independently. Three possible factors are considered in this
context:

15
1. Levels of tertiary education. Higher levels of tertiary education produce a
more sophisticated population, which is likely to be better informed about
the scientific evidence on climate change. We use the latest data available
from the World Bank’s World Development Indicators.
2. Freedom of the media. Independent and critical media play a crucial role in
assessing and disseminating scientific findings, particularly in such vital areas
as climate change. A free media is a key factor in shaping public
understanding of climate change. We use Freedom House’s Freedom of the
Media index for 2007 for this issue.
3. Vulnerability. If a country is vulnerable to climate change, the population is
more likely to be aware of climate change in general and its causes in
particular. For this variable, we use the Climate Change Vulnerability Index
2011 compiled by Maplecroft, a risk analysis and mapping firm.11
Table 1 reports the results of regressing various aspects of public opinion on
climate change, as found in the 2009 Gallup poll, on the three independent
variables listed above. The coefficients indicate whether countries with,
respectively, a higher degree of education, more media freedom, and a greater
vulnerability to climate change were more likely (positive coefficient) or less
likely (negative coefficient) to agree with the statements described in the column
headings.
11 We are very grateful to Maplecroft for sharing the aggregated results of their Climate Change
Vulnerability Index 2011 with us for this analysis.

16
Table 1: Determinants of knowledge of anthropogenic climate change
Dependent variable → Climate change a
threat Some knowledge
of climate change
Much
knowledge of
climate change
Global warming
caused by humans Global warming
has natural causes
Model →
A B C D E F G H I J
Education
0.019
0.051
.208***
.184***
.345***
.338***
.146***
.180***
-.340***
-.394***
Media freedom
0.029
0.153
-.149***
-.210***
-.190**
-.291**
-0.068
0.028
.319***
-0.046
Vulnerability
-.386***
-.330***
-0.011
-0.032
0.411
-0.447
-.441***
-.416***
.711**
.763***
EU
0.08
-0.031
-.215*
0.064
-.596***
Transition economy
-.250***
.143**
0.061
-.202***
.363***
Number of observations
71 71 83 83 83 83 81 81 81 81
R2
0.23
0.33
0.6
0.62
0.54
0.55
0.27
0.37
0.32
0.43
As model A in this table illustrates, when controlling for the average level of
education and for media freedom, the perception of climate change as a threat is
driven almost entirely by a country’s actual vulnerability to climate change.12
Model B demonstrated that this relationship holds when we control for whether
respondents live in the EU – with its aggressive climate change policies and
widespread public debate – or in the post-communist transition countries,
whose economies are the among the most energy intensive in the world and
where there is limited public debate on climate change-related concerns.
Models C-F show that people in countries with more widespread tertiary
education and greater media freedom are more likely to state that they have
knowledge of climate change. Countries’ actual vulnerability to climate change
has no significant effect here. In contrast, models G-H suggest that that
awareness that global warming is caused by humans depends on education and
country vulnerability, while media freedom makes no difference in this context.
For similar levels of education and country vulnerability, this awareness is
significantly weaker in the transition countries than in the rest of the world
(model H).
The same pattern is visible in the inverse question (models I-J): people in more
vulnerable countries and countries with more tertiary education are less likely to
believe that global warming is a natural phenomenon. Controlling for levels of
education and vulnerability, this belief tends to be less prevalent in EU countries
and more prevalent in the post-communist transition economies.
4.2 Political factors in climate change policy adoption
We are now in a position to test what variables affect the adoption of climate
change policy. We employ a two-stage least squares regression approach to
estimate the instrumental variable specification. This enables us to partially
12 The Maplecroft Climate Change Vulnerability Index is scored on a 1-10 scale, with 1 representing
extreme vulnerability and 10 representing no vulnerability.

17
address the problem of reverse causation outlined in the preceding section and
therefore make stronger statements about the impact of public knowledge on
climate change policy.
In order to justify the instrumental variable approach, our model needs to satisfy
two requirements:
1. Relevance: the instrumented variable needs to be correlated with the
instruments. In the first stage, we run a regression used in models G and H in
Table 1 (pooling the respondents who believe that global warming is caused
by human activity). This is used to construct predicted values of knowledge of
climate change across countries. Our three instruments explain about 30 per
cent of the variation in public understanding of climate change. The relevance
requirement is thus satisfied.
2. Exogeneity: the causal impact of the instrument on the dependent variable
must only be via the instrumented variable (conditional on the other
independent variables). The predicted level of climate change knowledge in
the first stage cannot be influenced by climate change policies. Hence, we
must assume that tertiary education, vulnerability and free media can only
affect climate change mitigation policy via public knowledge of climate
change. This seems like a reasonable assumption. Governments act under
pressure from the concerned electorate and our instruments explain how the
electorate obtains its information.
In the second stage, the predicted level of climate change knowledge as well as
the remaining potential determinants discussed at the beginning of the section
are then used to investigate the causes of cross-country variations in climate
change policy. The results of this second stage regression, using CLIMI as the
outcome variable, are presented in Table 2.

18
Table 2: Determinants of climate change mitigation policy - IV specifications
The OLS specification does not reject the hypothesis that greater knowledge of
climate change is associated with more extensive climate change policies.
However, all IV specifications show that, ceteris paribus, higher levels of popular
knowledge of climate change lead to the adoption of more extensive climate
change mitigation policies and measures. This result suggests that reverse
causation was indeed a problem and is entirely in line with our political economy
hypothesis. This is illustrated graphically in Figure 3 which shows that countries
where a larger proportion of the population believe that climate change is
anthropogenic tend also to have more ambitious climate policies – and hence to
score better on CLIMI.
Dependent variable →
Climate Laws, Institutions and Measures Index
Model →
OLS
A
B
C
D
E
F
Knowledge of climate
change
.833
3.012*** 2.213* 2.254*** 2.082** 2.087** 2.248**
Democracy
.173
-
0.218
-0.230
-0.133
-0.0441
-0.156
Carbon-intensive
industry size
-.581*
- - -0.687** -0.730** -0.942*** -0.871***
State administrative
capacity
.325
- - 1.002** 0.682 0.562
Kyoto Protocol target
-
-2.319**
-2.119**
-
-
-2.806***
-2.708**
CO
2
per capita
.262*
0.237*
0.196*
-
0.0990
0.223**
0.139
Total CO
2
emissions
- -0.0313 0.0168 0.0695 0.0501 0.0414 0.0453
EU
.478***
0.161
0.630**
0.389*
0.390**
0.430
0.393
Transition economy
.055
0.315
0.294
0.494**
0.371*
0.148
0.307
Other
Vulnerability
Income per
capita - - - - - -
Number of observations
73
75
71
77
71
71
71
R2
0.590
0.326
0.411
0.440
0.459
0.434
Instrumented
-
Knowledge of climate change
Instruments
-
Media Freedom, Level of Education, Vulnerability

19
Figure 3: Correlation between knowledge of anthropogenic climate change and CLIMI
On the other hand, specifications C-F show that the strength of the carbon-
intensive industry lobby is a factor holding back climate change policies (see in
particular models E and F). This is illustrated in Figure 4, which plots countries’
carbon emissions per tonne of CO2 against their scores on CLIMI.
Figure 4: Correlation between carbon intensity and CLIMI

20
Table 2 also shows that, once knowledge of climate change is taken into account,
and controlling for international commitments and CO2 emissions, democracy
and state administrative capacity are not significant predictors of good climate
change policies. State administrative capacity is a significant predictor of active
climate change policies only when Kyoto commitments and per capita CO2
emissions are excluded from the regressions. It is perhaps surprising that the
level of democracy does not drive the adoption of climate change policies and
measures, once we control for the other factors that influence the climate change
policy-making process, including popular awareness of climate change. As we
argued above, democratic political systems are intended to transmit popular
concerns and priorities into the policy-making process. As we argued above, in
the area of climate change policy-making concerns and priorities of the
electorate may be cutting both ways.
Similarly, models C and D show that EU members are significantly more likely to
adopt climate change policies than non-EU members – until Kyoto Protocol
commitments are controlled for.13 Thus, Kyoto targets are of overriding
significance for predicting cross-country variation in climate change policies,
followed by EU membership and state administrative capacity.14
Model F summarises the main robust results from the analysis. Controlling for all
other policy-influencing factors, including countries’ CO2 emission-reduction
targets under Kyoto, reveals several findings:
• Popular knowledge of climate change is a powerful driver of climate change
policy adoption. This is a robust result that holds across all IV specifications
we reported above and in Appendix 6.8. This means that even controlling for
democratic institutions, the public’s concerns about climate change are
reflected in climate policy. For every one per cent increase in public
knowledge of the anthropogenic causes of climate change, there is a 2.25 per
cent increase in countries’ score on CLIMI. Thus, for example, if the level of
public knowledge of climate change in Ukraine increased to the level seen in
Italy, Ukraine’s score on CLIMI would increase by 52 per cent – to be on a par
with New Zealand.
• The relative size of the carbon-intensive industry is significantly and
negatively associated with climate change policy adoption.
• There is no clear evidence that state administrative capacity matters: states
with low administrative capacity are just as likely to adopt climate change
policies as states with high administrative capacity.
13 The empirical finding that democracy is not a significant determinant of climate change policy
adoption is consistent with the theoretical argument by Aumann, Kurz and Neyman (1983) that voting
is irrelevant for pure (non-exclusive) public goods when resources are privately owned.
14 While democracy and state administrative capacity are not significant, we leave them in as control
variables to be sure that we are accurately capturing the effects of knowledge, the carbon-intensive
industry lobby, per capita emissions and EU membership on climate change mitigation policy.

21
• EU member countries tend to adopt more assertive climate policies than non-
EU members, although this effect is less robust across specifications than
countries’ adoption of emission-reduction targets under the Kyoto Protocol.
This is not surprising. EU countries share many EU-wide climate policies and
targets. In addition, EU bargains as a whole in international negotiations.
• After taking account of these factors, climate change policies in post-
communist transition countries do not appear to be different from those in
the rest of the world.
While we have identified several drivers of climate change policy, we have not
considered how governments could affect them. This is an interesting direction
for further policy research. Factors such as level of education, vulnerability to
climate change, and media freedom tend to evolve only very slowly over time.
Press freedom can change more quickly – for instance, after coups or popular
uprisings – but such events are relatively rare.
5 Conclusions and future work
This paper develops a new ranking of climate change mitigation policies and uses
a political economy approach to explain why some countries adopt extensive
climate change policies and measures while others do not. Our analysis leads to a
series of important conclusions.
We found that, ceteris paribus, the level of democracy is not a major driver of
climate change policy adoption. This is important as it means that there is no
reason to assume that countries with non-democratic regimes are unable to
make significant contributions to the global challenge of reducing carbon
emissions. Expectations of contribution to global climate stabilisation by a given
country need not, therefore, be limited by the nature of its political regime.
We also found that public knowledge of climate change is a powerful
determinant of climate change policy adoption: countries in which the public is
aware of the causes of climate change are significantly more likely to adopt
climate change mitigation policies than countries in which public knowledge is
low.
Public knowledge of climate change, in turn, is shaped by a number of key
factors, including the threat posed by climate change in a particular country, the
national level of education and the existence of free media. Democracy and free
media tend to go hand in hand: there are few if any countries with free media but
no democracy. Thus, the conclusion that democracy per se does not determine
climate change policy does not mean that certain key aspects of democracy, such
as free media, are not important drivers of policy adoption.
Our analysis found that the relative strength of the carbon-intensive industry is a
major deterrent to the adoption of climate change mitigation policies and
measures, regardless of the level of democracy or the administrative capacity of

22
the state. In many resource-rich economies, these industries are the largest
export earners, the largest employers and the largest contributors to the national
tax base. It comes as no surprise, therefore, that these carbon-intensive
industries influence governments’ approaches to climate change policy.
Moreover, carbon-intensive industries are unlikely to be replaced by low-carbon
industry in a short enough timescale to make a difference to mitigating global
climate change.
There are several avenues for further work. First, CLIMI could benefit from
several potential improvements:
• Developing a more granular scale for some measures. For example, emissions
reduction targets could be measured as a proportion of the most ambitious
target and carbon tax could be measured relative to the average household
energy bill.
• Including an implementation quality weight. Since CLIMI only considers
adopted policies and measures, it does not take into account how well they
are implemented on the ground. Measuring implementation quality would
require extensive consultations with country experts.
• Adding government research and development spending. Many governments,
such as the United States, commit to high levels of R&D spending rather than
to policies that address market failures. This alternative strategy also reflects
commitment. However, such data are not currently available for countries
outside the OECD region.
• Inclusion of comparative mitigation policies in the waste sector.
It would be important to assess how climate change policies are affecting CO2
emissions. Policies includes in CLIMI will take time to have a substantial effect on
emissions and future work can determine which were most effective. Many
seemingly robust climate change policies in developed countries reduce
domestic emissions at the expense of imports produced in carbon-intensive
economies. Hence, coordination of climate change mitigation policies across
different countries (e.g., within the EU) must be taken into account when
measuring their effectiveness.
It would also be fruitful to understand what factors influence the change in
climate change policies over time. The analysis presented in this paper is only a
snapshot encompassing many years of institutional change. In order to tackle
climate change, countries need to develop policies and build institutions that
commit them to emission reductions over several generations.

23
6 Appendix
6.1 CLIMI results
Rank
Country
CLIMI
Rank
Country
CLIMI
1
United Kingdom
0.801
49
Canada
0.316
2
Finland
0.787
50
Bolivia
0.296
3
France
0.783
51
FYR Macedonia
0.293
4
Switzerland
0.77
52
Croatia
0.29
5
Spain
0.758
53
Mongolia
0.288
6
Norway
0.749
54
Egypt
0.267
7
Denmark
0.722
55
Australia
0.265
8
Sweden
0.701
56
Belarus
0.262
9
Slovenia
0.698
56
Uzbekistan
0.262
10
Netherlands
0.691
58
Moldova
0.247
11
Ireland
0.667
59
Georgia
0.238
12
Germany
0.665
60
Fiji
0.233
13
Belgium
0.66
61
Kazakhstan
0.226
14
Czech Republic
0.653
62
Kyrgyz Republic
0.214
15
Austria
0.641
63
Armenia
0.201
15
Italy
0.641
64
Albania
0.199
17
Japan
0.636
65
Malta
0.183
18
South Korea
0.629
66
Rwanda
0.182
19
Lithuania
0.615
67
United Arab Emirates
0.159
20
Greece
0.608
68
Jordan
0.156
21
New Zealand
0.602
69
Sao Tome and Principe
0.143
22
Iceland
0.561
70
Samoa
0.142
23
Costa Rica
0.517
71
Serbia
0.139
24
Romania
0.497
72
Russia
0.134
25
Poland
0.496
72
Tajikistan
0.134
26
Mexico
0.486
74
Montenegro
0.133
27
China
0.485
75
Turkmenistan
0.115
28
Hungary
0.483
76
Azerbaijan
0.108
29
Singapore
0.468
77
DR Congo
0.091
29
Portugal
0.468
78
Venezuela
0.09
31
Brazil
0.464
79
Senegal
0.088
32
Bulgaria
0.457
80
Guinea Bissau
0.087
33
South Africa
0.456
81
Bahrain
0.086
34
Peru
0.437
82
Cameroon
0.084
35
Latvia
0.433
83
Bosnia and Herzegovina
0.081
36
Slovak Republic
0.422
84
Mauritania
0.071
37
Indonesia
0.402
85
Cote d'Ivoire
0.064
38
Argentina
0.401
86
Congo
0.049
39
Ukraine
0.398
87
Burundi
0.037
40
Estonia
0.383
88
Madagascar
0.029
41
Turkey
0.381
89
Niger
0.025
42
Uruguay
0.369
90
Mozambique
0.023
43
India
0.358
90
Saudi Arabia
0.023
44
Vietnam
0.345
90
Algeria
0.023
45
Colombia
0.34
93
Suriname
0.016
45
United States
0.34
93
Sierra Leone
0.016
47
Morocco
0.339
95
Tonga
0.011
48
Dominican Republic
0.319

24
6.2 Countries included in CLIMI
Annex I countries
Non-Annex I countries
with NCs after 2005
Non-Annex I countries
with NCs before 2005
Australia
Albania
Azerbaijan
Austria
Algeria
China
Belarus15
Argentina
India
Belgium
Armenia
Korea
Bulgaria
Bahrain
South Africa
Canada
Bolivia
Turkey16
Croatia
Bosnia and Herzegovina
Czech Republic
Brazil
Denmark
Burundi
Estonia
Cameroon
Finland
Colombia
France
Congo Brazzaville
Germany
Congo Kinshasa
Greece
Costa Rica
Hungary
Cote d'Ivoire
Iceland
Dominican Republic
Ireland
Egypt
Italy
Fiji
Japan
FYR Macedonia
Latvia
Georgia
Lithuania
Guinea Bissau
Netherlands
Indonesia
New Zealand
Israel
Norway
Jordan
Poland
Kazakhstan17
Portugal
Kyrgyzstan
Romania
Madagascar
Russia
Malta
Slovak Republic
Mauritania
Slovenia
Mexico
Spain
Moldova
Sweden
Mongolia
Switzerland
Montenegro
Ukraine
Morocco
United Kingdom
Mozambique
United States
Niger
Peru
Rwanda
Samoa
Sao Tome and Principe
Saudi Arabia
Senegal
Serbia
Sierra Leone
Singapore
Suriname
Syria
Tajikistan
Tonga
Turkmenistan
United Arab Emirates
Uruguay
Uzbekistan
Venezuela
Vietnam
15 Although listed in the Convention’s Annex I, Belarus is not included in the Protocol’s Annex B as it
was not a Party to the Convention when the Protocol was adopted (UNFCCC)
16 Although listed in the Convention’s Annex I, Turkey is not included in the Protocol’s Annex B as it
was not a Party to the Convention when the Protocol was adopted (UNFCCC)
17 Upon entry into force, Kazakhstan, which has declared that it wishes to be bound by the
commitments of Annex I Parties under the Convention, will become an Annex I Party under the
Protocol. As it had not made this declaration when the Protocol was adopted, Kazakhstan does not have
an emissions target listed for it in Annex B. (UNFCCC)

25
6.3 Structure of CLIMI
Policy or
institutional
variable name
Scoring range
Explanation, comments, examples and counter-examples
References
Kyoto ratification
Linear from 0
(not ratified) to 1
(earliest
ratification: Fiji)
Countries, which ratified Kyoto earlier, are more committed to
international cooperation in climate change.
Similar approach is
taken in Bättig et
al. (2008) and
Bättig and
Bernauer (2009)
JI or CDM domestic
projects existence
None (0),
existence (1)
(even if no CERs
have been issued
yet)
Countries, which have approved and developed JI or CDM projects
domestically and have submitted information about them to the
UNFCCC, have developed an adequate institutional framework to
implement flexible mechanisms. Countries, which only use their
funds to finance projects abroad, do not necessarily exhibit this
institutional framework.
This is a part of the
ISE in EBRD
(2008)
Existence of national
climate change
policy or law
None (0), Policy
(0.5), Law (1)
A clearly formulated, extensive, cross-sectoral policy adopted by the
government shows understanding and commitment to domestic
mitigation. A law passed by the legislative branch, which creates a
legally binding framework for mitigation is the strongest expression
of such commitment. Appropriate amendments to existing sectoral
laws score on par with separate legislation.
This is a part of the
ISE in EBRD
(2008)
National, ambitious
target for the post-
Kyoto period
None (0), NAMAs
(0.5), a national
target (including,
QEWET) (1)
Any ambitious medium term, post-Kyoto emissions reduction target
in domestic policies or in communications to the UNFCCC by Annex I
countries gets 1. EU countries, which are allowed an emissions
increase under EU burden sharing, but have not set their own
reduction targets get 0. Annex I countries, which are expected to
reach the targets they had set themselves due to the collapse of their
economies during the transition period get 0. Non-annex I countries,
which mention explicit targets from BAU in their NAMAs get 0.5.
Targets are
included in
AccountAbility
(2010)

26
Dedicated climate
change institution
None (0),
government
committee (0.5),
autonomous
agency (1)
Any sufficiently representative (ought to include scientists and/or
professional and/or civil society) inter-ministerial commission,
working group or committee dedicated to reviewing and drafting
climate change policy gets 0.5. A small department on climate change
in another ministry gets 0.5. A ministry with a core mandate for
climate change or a dedicated, professionally staffed, independent
agency gets 1. NGOs and ad-hoc working groups get no merit.
This is a part of the
Index of
Sustainable Energy
in EBRD (2008)
Energy
supply/renewable
energy
sources/energy
efficiency
No policy (0), a
set of policies
promoting
renewable energy
or energy
efficiency (0.5), a
comprehensive
law or policy with
targets in
renewable source
in electricity
production or
energy efficiency
and effective
implementation
and enforcement
instruments (1)
A comprehensive set of fiscal/regulatory policies such as feed-in
tariffs, green certificates, minimum RES requirements, tax breaks
etc., or a clearly-defined energy efficiency strategy gets 0.5; a law or
policy with medium-term targets for renewable energy in electricity
(or overall consumption) or energy intensity reduction targets and a
comprehensive set of secondary regulations gets 1.
See Bättig et al.
(2008) and Bättig
and Bernauer
(2009). For
weights see EBRD
(2008)
Transport
None (0), support
for mass/public
transport and
renewable energy
in transport (0.5),
emission
regulation (1)
A comprehensive set of policies which address transport emissions
(simply promoting public transport is necessary but not sufficient),
such as tax break for low emission vehicles or high fuel economy
standard or a commitment to biofuels gets 0.5; emissions standards
for new cars or emissions targets for the fleet with effective
implementation and enforcement instruments get 1. (Emission
standards for new cars do not automatically give countries, which do
not produce cars, 1)
For past analysis,
see An and Sauer
(2004) and for a
review of
instruments see
Santos et al.
(2010a) and
Santos et a.

27
(2010b). See also
Burck et al. (2012).
For weights see
IPCC (2007).
Buildings
None (0), energy
efficiency in
buildings or
residential
renewable energy
use (0.5),
building
insulation/renov
ation targets (1)
A comprehensive policy that addresses energy loss of buildings gets
0.5. Effective implementation and enforcement instruments and
targets for refurbishing the current housing stock in order to
improve energy efficiency, or tight regulation of energy consumption
requirements in new or large buildings, or targets for near-zero
energy buildings get 1.
See also Burck et
al. (2012). For
weights see IPCC
(2007).
Agriculture
None (0), sectoral
fiscal or
regulatory
policies aimed at
carbon
dioxide/methane
/
NOx reduction or
methane capture
(0.5), targets for
efficiency and
organic farming
(1)
Any policy that regulates emissions, in particular methane, from
livestock and land-use management, gets 0.5. Targets for farm
conversion to encourage farming with reduced emissions with
effective implementation and enforcement instruments get 1.
See also Burck et
al. (2012). For
weights see IPCC
(2007).
Forestry
None (0),
reforestation/aff
orestation fiscal
mechanisms get
0.5, REDD or
Strict and enforced regulations on logging, such as deforestation bans
or only cutting down a proportion of annual surplus or REDD or
very
large reforestation programmes get 1. Tax incentives, reforestation
programmes or extension of forest management coverage or
tradable certificates get 0.5. General forest conservation policy gets
For weights see
IPCC (2007).

28
national
reforestation/aff
orestation targets
0.
Industry
None (0),
comprehensive/s
ectoral GHG
regulatory policy
(with targets and
effective
implementation
and enforcement
instruments)
(0.5), carbon
emission
regulation across
industry, such as
EU ETS (1)
EU ETS also covers the energy sector and will soon include aviation,
so all the countries which are in EU ETS get 1. No credit for
regulating gases, which are already regulated outside the UNFCCC. In
order to score 0.5, need to show a regulatory framework for the
reduction of main GHG gases, such as carbon dioxide or methane,
even if only particular high-emitting sectors (such as cement or steel)
are covered.
For weights see
IPCC (2007). Also
see Chapter 14 in
Stern (2007) for
emissions trading.
Additional cross-
sectoral measures
None (0),
regional cross-
sectoral policy
(0.5), national
existence (1)
Any additional, extensive cross-sectoral emission reducing policy
beyond the sectoral policies already noted, such as a carbon tax or a
white certificates system, gets merit. Carbon tax is a tax on emissions
from fuels. This cross-sectoral tax must be above and beyond existing
sectoral taxes, such as petrol duties. The tax must create incentives
for almost all energy consumers (such as households) to reduce the
consumption of fossil fuels (e.g. by improving insulation in
dwellings). White certificates system allows energy consumers to
receive tradable certificates for example, for improvements in energy
efficiency of buildings, get 1. Extensive cross-sectoral regional policy,
which is common in federations, can get up to 0.5.
For economic
arguments, see
Chapter 14 in Stern
(2007).

29
6.4 CLIMI weights
Policy area
Policy
area
weight
Variable Score Sub
weight
Internationa
l cooperation
0.10
Kyoto ratification
0 to 1
0.50
JI or CDM
0/1
0.50
Domestic
institutions
and national
climate
change
mitigation
framework
0.40
Cross sectoral climate
change legislation
0/0.5/1
0.33
Carbon emissions
target
0/0.5/1
0.33
Dedicated climate
change institution
0/0.5/1
0.33
Significant
sectoral
fiscal or
regulatory
measures or
targets
0.40
Energy supply and
renewable energy
0/0.5/1
0.30
Transport
0/0.5/1
0.13
Buildings
0/0.5/1
0.07
Agriculture
0/0.5/1
0.13
Forestry
0/0.5/1
0.17
Industry
0/0.5/1
0.2
Additional
cross-
sectoral
fiscal or
regulatory
measures
0.10 Cross-
sectoral policy
measures 0/0.5/1 1.0

30
6.5 Sensitivity analysis
This section explains that although sectoral and policy weights were chosen
somewhat arbitrarily, they do not fundamentally affect the country ranking
according to the CLIMI.
We repeated the following Monte Carlo simulations (see Schwab, 2010, p. 12)
1000 times:
1) Generate 12 pseudo-random weights, which add to 1.
2) Apply these weights to the country scores.
3) Calculate CLIMI
4) Rank the countries
We then calculated the average rank and standard deviation of the rank of the
countries. The results are reported in Table 3. The correlation between the mean
CLIMI rank is 0.98 and is significant at over 0.01% level of significance.
We also checked whether CLIMI is sensitive to:
a) Using country-level sectoral weights: we weighted each sector according
to its emission contribution using UNFCCC data.
b) Excluding the Kyoto measure
c) Excluding the JI/CDM measure
d) Combinations of a), b) and c).
Empirical results were unaffected across all sensitivity specifications.
Table 3: CLIMI sensitivity analysis
Country
CLIMI
Mean
Simulated
Rank
Standard
deviation
of
simulated
rank
CLIMI
rank
Rank of mean
simulated rank
France
0.7831666
12.636
12.2027
3
1
Finland
0.7871667
14.796
13.0093
2
2
Switzerland
0.7701667
14.88
12.4647
4
3
Spain
0.7578334
16.265
14.3596
5
4
Slovenia
0.6975
17.765
16.0028
9
5
Norway
0.7491667
17.855
15.9188
6
6
Lithuania
0.6153333
18.685
16.6408
19
7
Netherlands
0.6911666
19.188
12.5136
10
8
Germany
0.6651667
20.604
16.6217
12
9
Ireland
0.6665
21.481
19.864
11
10
United Kingdom
0.8005
21.744
19.8786
1
11
Czech Republic
0.6531667
23.09
16.1949
14
12
Sweden
0.7011667
23.448
19.162
8
13
Austria
0.6411667
23.819
16.5801
15
14
Italy
0.6405
23.955
19.1053
16
15
Denmark
0.7218333
24.479
19.6389
7
16
Korea
0.6291667
25.024
19.4164
18
17
New Zealand
0.602
26.572
21.1068
21
18
Japan
0.6358333
27.069
21.177
17
19
Belgium
0.6598333
27.082
19.4259
13
20
China
0.4848333
29.112
21.0354
27
21
Romania
0.4968333
29.63
16.8775
24
22
Poland
0.4953333
29.921
20.4234
25
23
Greece
0.6078333
31.905
20.0826
20
24
Hungary
0.4828333
32.76
19.8418
28
25
Mexico
0.4855
33.064
16.9546
26
26
Singapore
0.468
34.308
20.7226
29
27
Bulgaria
0.4568333
34.839
19.0977
32
28
Portugal
0.4678333
34.918
19.2355
30
29

31
Estonia
0.383
35.548
21.6994
40
30
Slovak Republic
0.4218333
37.258
20.5341
36
31
Brazil
0.4635
37.406
17.5884
31
32
South Africa
0.456
37.645
20.517
33
33
Iceland
0.5611666
38.609
21.0014
22
34
Latvia
0.4333333
39.238
21.2036
35
35
Costa Rica
0.5168333
39.275
23.2902
23
36
Argentina
0.4008333
40.815
19.8696
38
37
Turkey
0.3803333
41.102
20.8611
41
38
Peru
0.437
41.564
22.074
34
39
Ukraine
0.3978333
42.855
22.2923
39
40
Indonesia
0.402
43.142
18.6819
37
41
India
0.3575
43.426
17.7806
43
42
Colombia
0.3398333
43.824
18.4792
46
43
Dominican
Republic
0.3188333
44.133
18.5374
48
44
Uruguay
0.3693333
44.439
20.9724
42
45
Vietnam
0.3443333
45.442
20.7096
44
46
Uzbekistan
0.2621667
46.01
20.0004
57
47
Canada
0.3153333
46.951
15.0496
49
48
Bolivia
0.2963333
47.256
22.8105
50
49
Morocco
0.3393333
47.797
23.1387
47
50
United States
0.34
48.655
19.0301
45
51
FYR Macedonia
0.2933333
48.837
18.6778
51
52
Moldova
0.2476667
49.065
18.6902
58
53
Georgia
0.2376667
49.342
20.8565
59
54
Mongolia
0.2883333
49.866
21.681
53
55
Croatia
0.2901667
50.189
17.9843
52
56
Belarus
0.2623333
52.846
15.9657
56
57
Egypt
0.2668333
54.49
19.3636
54
58
Jordan
0.156
56.038
20.7747
68
59
Kyrgyzstan
0.2138333
56.117
20.894
62
60
Fiji
0.2333333
56.992
25.4624
60
61
Rwanda
0.1821667
57.026
20.381
66
62
United Arab
Emirates
0.1595
57.436
20.0786
67
63
Albania
0.1991667
57.506
18.8045
64
64
Armenia
0.201
57.798
24.0438
63
65
Malta
0.1831667
60.845
21.5859
65
66
Kazakhstan
0.2256667
60.923
19.5841
61
67
Australia
0.2643333
61.544
22.3323
55
68
Russia
0.134
63.226
20.7136
72
69
Samoa
0.1416667
63.353
19.0167
70
70
Senegal
0.088
67.055
22.1888
79
71
Cameroon
0.0835
67.192
22.1537
82
72
Turkmenistan
0.1151667
67.224
21.9503
75
73
Azerbaijan
0.1081667
67.65
20.7293
76
74
Congo Kinshasa
0.0905
68.522
20.9979
77
75
Mauritania
0.0715
68.581
20.5439
84
76
Sao Tome and
Principe
0.1433333
68.819
15.7264
69
77
Tajikistan
0.1336667
69.083
15.6555
73
78
Serbia
0.1386667
69.35
15.2061
71
79
Cote d'Ivoire
0.064
70.886
19.96
85
80
Montenegro
0.1335
71.053
19.8794
74
81
Venezuela
0.0896667
72.461
16.3276
78
82
Guinea Bissau
0.0866667
72.851
15.6482
80
83
Congo
Brazzaville
0.0485
73.599
14.8122
86
84
Bahrain
0.0856667
73.872
15.1022
81
85
Bosnia and
Herzegovina
0.0806667
74.547
14.9357
83
86
Burundi
0.037
77.617
17.8718
87
87
Madagascar
0.029
79.461
16.2195
88
88
Niger
0.025
80.48
15.1648
89
89
Saudi Arabia
0.0235
80.602
14.3013
91
90
Mozambique
0.0235
81.091
14.1113
90
91
Algeria
0.023
81.574
13.5308
92
92
Suriname
0.0165
82.723
12.2713
93
93
Sierra Leone
0.0155
82.871
12.3851
94
94
Tonga
0.011
84.113
11.0805
95
95

32
6.6 Acronyms
The following acronyms are used in this paper:
CCPI Climate Change Policy Index (GermanWatch)
CDM Clean Development Mechanism
EBRD European Bank for Reconstruction and Development
GHG Greenhouse gases
IDR In-Depth Review
IEA International Energy Agency
ISE Index of Sustainable Energy
JI Joint Implementation
NAMA Nationally Appropriate Mitigation Actions
QEWET Quantified economy-wide emissions targets for 2020
REDD Reducing Emissions from Deforestation and Degradation
RES Renewable energy sources
UNFCCC United National Framework Convention on Climate Change

33
6.7 Additional sources for CLIMI
• Austrian Energy Agency: Energy in Eastern and Central Europe
(http://www.enercee.net/)
• Bundesministerium für Umwelt, Naturschutz und Reaktorsicherheit: Legal
sources on renewable energy (http://www.res-legal.eu/)
• Climatico Policy Monitor Baseline Report 2010 (edited by Paige Andrews and
Marie Karaisl)
• Deutsche Bank Climate Change Advisors Global Climate Change Policy
Tracker (March 2010)
• IEA Climate Change Database (http://www.iea.org/textbase/pm/index.html)
• National Communications to the UNFCCC
• National legislation, passed laws and official policies
• Official EU documents, such as EU Directives
• Official UNFCCC publications, such as IDRs
• Other national communications to the UN, such as the note verbale on the
Copenhagen Accord
• Renewable Energy and Energy Efficiency Partnership: Policy and Regulation
Review (http://www.reeep.org/9353/policy-database.htm)
• UNFCCC Climate Action Tracker (http://climateactiontracker.org/)

34
6.8 Alternative regression specifications
Here we present some alternative regression (OLS and IV) specifications to
convince the reader of the robustness of our empirical results and conclusions.
Specification 1
Include:
• CO2/capita
• GNI/capita
• Vulnerability
Dependent variable→ Climate Laws, Institutions and Measures Index
Specification →
A
B
C
D
E
CO2 per capita
-.091
-.076
.055
.202
.262*
Income per capita
.616*** .546*** .392**
.133
-.018
Vulnerability
-.353
-.644* -.557*
-.512
-.334
Transition economy
.130
.000
-.042
.055
EU
.575*** .483*** .447*** .478***
Dirty industry size
-.592**
-.454
-.581*
Democracy
.353
.173
State administrative capacity
.188
.325
Knowledge of climate change
.833
Number of observations
81
81
81
77
73
R
2
.37
.44
.49
.56
.59
Dependent variable→ Climate Laws, Institutions and Measures Index
Specification →
1
2
Model →
A
B
C
D
E
F
Knowledge of climate change
2.42** 2.40*** 2.57** 2.43*** 2.24*** 2.33***
CO2 per capita
.112
.311
.323
.112
.293
.311
Income per capita
.181
-.056
-.358
.181
-.007
-.286
Vulnerability
-.000
.158
.187
Transition economy
.397
.158
.293
.397
.167
.279
EU
.565**
.386
.374
.565** .411** .415*
Dirty industry size
-.861*** -.803**
-.830*** -.761**
Democracy
-.137
-.096
State administrative capacity
1.03
.919
Number of observations
74
74
70
74
74
70
R
2
.18
.29
.43
.18
.32
.47
Instrumented
Knowledge of climate change
Knowledge of climate change
Instruments
Media Freedom, Level of
Education
Media Freedom, Level of
Education, Vulnerability

35
Specification 2
Exclude at most one of:
• CO2/capita
• GNI/capita
• Vulnerability
Dependent variable→ Climate Laws, Institutions and Measures Index
Specification →
1
2
3
4
Knowledge of climate change
.833** .826*** .929*** 1.01***
Transition economy
.055
.060
.014
.413**
Dirty industry size
-.581* -.578** -.617*
-.225
State administrative capacity
.325
.308
.401
.257
Democracy
.173
.171
.168
.040
EU
.478*** .478*** .406*** .441***
Income per capita
-.018
-.108
.420***
Vulnerability
-.334
-.345
-.312
CO2 per capita
.262* .253** .275*
Number of observations
73
74
73
79
R
2
.59
.60
.58
.61
Dependent variable→ Climate Laws, Institutions and Measures Index
Specification →
1
2
3
4
Knowledge of climate change
2.57** 2.17**
1.95
4.45***
Transition economy
.292
.337
.208
.875***
Dirty industry size
-.803** -.724** -.716** -.790**
State administrative capacity
1.03
.643
.799
1.56**
Democracy
-.137
-.132
-.013
-.633
EU
.374
.380
.416
.160
Income per capita
.358
.250
-.142
Vulnerability
.187
-.077
.892
CO2 per capita
.322
.168
.304*
Number of observations
70
71
70
76
R
2
.43
.48
.58
.15
Instrumented
Knowledge of climate change
Instruments
Media Freedom, Level of Education

36
Specification 3
Include:
• GNI/capita
• Vulnerability
Dependent variable
→
Climate Laws, Institutions and Measures Index
Model →
A
B
C
D
E
F
G
Income per capita
.593*** .610*** .507***
.216
.144
.133
-.018
Vulnerability
-.235
-.325
-.393
-.330
-.301
.512
-.334
Transition economy
.208
.049
.131
.019
-.042
.055
Dirty industry size
-.702**
.488
-.493*
-.454
-.581*
CO2 per capita
.009
.050
.177
.202
.262*
State administrative
capacity
.914**
.429
.188
.325
Democracy
.383
.353
.173
EU
.447*** .478***
Knowledge of climate
change
.833**
Number of observations
94
94
81
81
77
77
73
R
2
.44
.44
.45
.48
.52
.56
.59
Dependent variable→ Climate Laws, Institutions and Measures Index
Model →
A
B
C
D
E
F
Knowledge of climate change
5.67*** 3.19*** 2.54*** 2.90*** 2.77** 2.57**
Vulnerability
1.70*
.552
.324
.504
.407
.187
Income per capita
-.043
.299**
.011
-.468
-.382
.358
Transition economy
.551**
.181
.373
.356
.292
Dirty industry size
-.984*** -.646* -.878** -.803**
CO2 per capita
.290
3.28
.299
.322
State administrative capacity
1.60** 1.37**
1.03
Democracy
-.172
-.137
EU
.374
Number of observations
80
80
74
74
70
70
R2
.
.19
.23
.22
.37
.43
Instrumented
Knowledge of climate change
Instruments
Media Freedom, Level of Education

37
Specification 4
Include:
• Vulnerability
• CO2/capita
Exclude:
• GNI/capita
Dependent variable→ Climate Laws, Institutions and Measures Index
Model →
A
B
C
D
E
F
G
Vulnerability
1.14*** 1.14***
-.043
-.222
-.224
-.445
-.345
Transition economy
.003
-.267*
.080
-.026
-.085
.060
Dirty industry size
-1.01*** -.533* -.506* -.463* -.578**
CO2 per capita
.347***
.143
.248** .268** .253**
State administrative capacity
1.18***
.560
.307
.308
Democracy
.420*
.382
.171
EU
.454*** .478***
Knowledge of climate change
.826***
Number of observations
95
95
82
82
78
78
74
R
2
.15
.15
.38
.47
.52
.55
.60
Dependent variable→ Climate Laws, Institutions and Measures Index
Model →
A
B
C
D
E
F
Knowledge of climate change
5.22*** 4.60*** 2.59*** 2.35*** 2.35** 2.17**
Vulnerability
1.60*** 1.41***
.340
.163
.129
-.077
Transition economy
.605**
.176
.423** .407*
.337
Dirty industry size
-.959*** -.560* -.798** -.724**
CO2 per capita
.305***
.123
.132
.168
State administrative capacity
1.07**
.958*
.643
Democracy
-.164
-.132
EU
.380
Number of observations
81
81
75
75
71
71
R2
.
.
.22
.33
.43
.48
Instrumented
Knowledge of climate change
Instruments
Media Freedom, Level of Education

38
Specification 5
Exclude at most one of:
• CO2/capita
• Vulnerability
Dependent variable→
Climate Laws, Institutions and Measures Index
Model →
A
B
Transition economy
.014
.413**
Dirty industry size
-.617*
-.225
Vulnerability
-.312
CO2 per capita
.275*
State administrative capacity
.401
.257
Democracy
.168
.040
EU
.406*** .441***
Knowledge of climate change
.929*** 1.01***
Income per capita
-.108
.420***
Number of observations
73
79
R
2
.58
.61
Dependent variable→
Climate Laws, Institutions and Measures Index
Model →
A
B
Knowledge of climate change
1.95
4.45***
Transition economy
.208
.875***
Vulnerability
.892
Dirty industry size
-.716**
-.790**
CO2 per capita
.304*
State administrative capacity
.799
1.56**
Democracy
-.013
-.633
EU
.416
.160
Income per capita
.250
-.142
Number of observations
70
76
R2
.58
.15
Instrumented
Knowledge of climate change
Instruments
Media Freedom, Level of Education

39
References
1. Accountability (2010), The Climate Competitiveness Index 2010: National
Progress in the low-carbon economy. Technical Report.
2. An, F. and A. Sauer (2004), Comparison of Passenger Vehicle Fuel Economy and
GHG Emission Standards Around the World, Pew Center on Global Climate
Change.
3. Andonova, L. B. (2008), “The climate regime and domestic politics: the case of
Russia”, Cambridge Review of International Affairs, Vol. 21(4), pp. 483-504.
4. Aumann, R. J., Kurz, M. and A. Neyman (1983), “Voting for public goods”,
Review of Economic Studies, 50, pp. 677-693.
5. Bagnoli, M. and B. L. Lipman (1989), “Provision of public goods: fully
implementing the core through private contributions”, Review of Economic
Studies, Vol. 56(4), pp. 583-601.
6. Barr, R., Fankhauser, S., and K. Hamilton (February, 2010), “The allocation of
adaptation funding”, Policy Paper, Centre for Climate Change Economics and
Policy and Grantham Research Institute on Climate Change and the
Environment, London.
7. Bättig, M. B. and T. Bernauer (2009), “National Institutions and Global Public
Goods: Are Democracies More Cooperative in Climate Change Policy?”
International Organisation, Vol. 63(2), pp. 281-308.
8. Bättig, M. B., Brander, S., and D. M. Imboden (2008), “Measuring countries’
cooperation within the international climate change regime”, Environmental
Science and Policy, Vol. 11(6), pp. 478-489.
9. Bättig, M. B., Wild, M., and D. M. Imboden (2007), “A climate change index:
Where climate change may be most prominent in the 21st
century”, Geophysical Research Letters, Vol. 34 (L01705),
doi:10.1029/2006GL028159.
10. Becker, G. (1983), “A theory of competition among pressure groups for
political influence”, Quarterly Journal of Economics, Vol. 98(3), pp. 371-400.
11. Bergstrom, T. and L. Blume, and H. Varian (1986), “On the private provision
of public goods”, Journal of Public Economics, Vol. 29(1), pp. 25-49.
12. Botcheva, L. (1996), “Focus and effectiveness of environmental activism in
Eastern Europe: a comparative study of environmental movements in
Bulgaria, Hungary, Slovakia, and Romania”, Journal of Environment and
Development, Vol. 5(3), pp. 292-308.
13. Burck, J., Hermwille, L., and L. Krings (2012) The Climate Change Performance
Index 2013, Germanwatch and CAN Europe, available via
http://germanwatch.org/en/CCPI
14. Charap S. and G. Safonov (2010), “Climate change and role of energy
efficiency”, in Russia after the Global Economic Crisis (eds. A. Åslund, S.
Guriev and A. Kuchins) pp. 125-150, Peterson Institute for International
Economics, Washington, D.C.
15. Dolsak, N. (2009), “Climate change policy implementation: a cross-sectional
analysis”, Review of Policy Research, Vol. 26(5), pp. 551-570.
16. EBRD (2011), “Political Economy of Climate Change Policy in the Transition
Region” (2011) in Chirmiciu A. and S. Fankhauser (eds.) The Low Carbon
Transition, Chapter 4, EBRD: London.

40
17. EBRD (April, 2008), Securing sustainable energy in transition economies,
Brochure 7277, European Bank for Reconstruction and Development,
London.
18. Emerson, J., Esty, D. C., Kim, C., Srebotnjak, T., Levy, M. A., Mara, V., de
Sherbinin, A., and M. Jaiteh, (2010), Environmental Performance Index 2010,
Yale Center for Environmental Law & Policy, Yale University and Center for
International Earth Science Information Network, Columbia University.
19. Giddens, A. (2009), Politics of Climate Change, Polity, London.
20. Grossman, G. N. and E. Helpman (1994), “Protection for sale”, American
Economic Review, Vol. 84(4), pp. 833-850.
21. Guardian (2009), “How do I know China wrecked the Copenhagen deal? I was
in the room”, 22 December 2009, accessible via
http://www.guardian.co.uk/environment/2009/dec/22/copenhagen-
climate-change-mark-lynas
22. Hansen J., Sato, M., Glascoe, J., and R. Ruedy (1998), “A common-sense climate
index: Is climate changing noticeably?” Proceedings of the National Academy
of Sciences (Geophysics), Vol. 95, pp. 4113–4120
23. Helm, D. (2008), “Climate-change policy: why has so little been achieved?”,
Oxford Review of Economic Policy, Vol. 24(2), pp. 211-238.
24. Helm, D. and C. Hepburn (2009), The Economics and Politics of Climate
Change, Oxford University Press, Oxford.
25. Holland, S. P., Hughes, J. E., Knittel, C. R. and N. C. Parker (2011), “Some
Inconvenient Truths About Climate Change Policy: The Distributional
Impacts of Transportation Policies”, Working Paper No. 17386, NBER.
26. Hovi, J., Sprinz, D. F. and A. Underdal (2009), “Implementing long-term
climate policy: time inconsistency, domestic politics, international anarchy”,
Global Environmental Politics, Vol. 9(3), pp. 20-39.
27. Kahn, M. E. and J. G. Matsuaka (1995), “Demand for Environmental Goods:
Evidence from Voting Patterns on California Initiatives”, Journal of Law and
Economics, Vol. 40, pp. 137-173.
28. IGBP (2009), IGBP Climate-Change Index, International Geosphere-Biosphere
Programme, Royal Swedish Academy of Sciences.
29. Inglehart, R. (1977), The Silent Revolution: Changing Values and Political
Styles among Western Publics, Princeton University Press, New Jersey.
30. IPCC (2007), Climate Change 2007: Synthesis Report, Contribution of
Working Groups I, II and III to the Fourth Assessment Report of the
Intergovernmental Panel on Climate Change, Pachauri, R.K and A. Reisinger,
A. (eds.). Intergovernmental Panel on Climate Change, Geneva.
31. Kroll, S. and J. F. Shogren (2008), “Domestic politics and climate change:
international public goods in two-level games,” Cambridge Review of
International Affairs, Vol. 21(4), pp. 563-583.
32. New York Times (2010), “A Scientist, His Work and a Climate Reckoning”, 22
December 2010, accessible via
http://www.nytimes.com/2010/12/22/science/earth/22carbon.html?page
wanted=all
33. O'Donoghue, T. and M. Rabin (1999), “Doing it now or later”, American
Economic Review, Vol. 89(1), pp. 103-124.
34. Olson, M. (1965), The Logic of Collective Action: Public Goods and the Theory of
Groups, Harvard University Press, Cambridge, MA.

41
35. On Its Eighth Session, Held At New Delhi From 23 October To 1 November 2002,
United Nations, FCCC/CP/2002/7/Add.2.
36. Putnam, R. D. (1988), “Diplomacy and domestic politics: the logic of two-level
games”, International Organization, Vol. 42(3), pp. 427-460.
37. Santos, G., Behrendt, H. and A. Teytelboym (2010) “Part II: Policy instruments
for sustainable road transport”, Research in Transportation Economics, Vol.
28(1), pp. 46-91.
38. Santos, G., Behrendt, H., Maconi, L., Shirvani, T. and A. Teytelboym (2010)
“Part I: Externalities and economic policies in road transport”, Research in
Transportation Economics, Vol. 28(1), pp. 2-45.
39. Schwab, K. (2010) The Global Competitiveness Report 2010-2011, World
Economic Forum, Geneva, Switzerland.
40. Snyder, J. and K. Ballentine (1996), “Nationalism and the marketplace of
ideas”, International Security, Vol. 21(2), pp. 5-40.
41. Stern, N. (2007), The economics of climate change: the Stern review, HM
Treasury, London.
42. Stern, N. (2009), Blueprint for a safer planet: How to Manage Climate Change
and Create a New Era of Progress and Prosperity, Bodley Head.
43. Tsebelis, G. (2002), Veto Players: How Political Institutions Work, Princeton
University Press, Princeton, NJ.
44. UNFCCC (1992), United Nations Framework Convention On Climate Change,
United Nations, FCCC/INFORMAL/84.
45. UNFCCC (1998), Kyoto Protocol To The United Nations Framework Convention
On Climate Change, United Nations.
46. UNFCCC (2000), Report Of The Conference Of The Parties
47. UNFCCC (2003), Review Of The Implementation Of Commitments And Of Other
Provisions Of The Convention, United Nations, FCCC/CP/1999/7.
48. World Bank (June, 2009) Adapting to Climate Change in Europe and Central
Asia, World Bank Report, available via
http://go.worldbank.org/7OOC1E7AU0.