the clean development mechanism and sustainable development: a panel data analysis

8/14/2019 The Clean Development Mechanism and Sustainable Development: A Panel Data Analysis.

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The Clean Development Mechanism and Sustainable

Development: A Panel Data Analysis.

Yongfu Huang and Terry Barker

February 2009

Tyndall Centre for Climate Change Research Working Paper 130


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The Clean Development Mechanism and Sustainable Development:A Panel Data Analysis

Yongfu Huang and Terry Barker

Tyndall Working Paper 130, February 2009

Please note that Tyndall working papers are "work in progress". Whilst they are

commented on by Tyndall researchers, they have not been subject to a full peer review.

The accuracy of this work and the conclusions reached are the responsibility of the

author(s) alone and not the Tyndall Centre.


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The Clean Development Mechanism

and Sustainable Development:

A Panel Data Analysis

Yongfu Huanga Terry Barkera

a4CMR, Department of Land Economy, University of Cambridge19 Silver Street, Cambridge CB3 9EP

February 17, 2009

Abstract

The Clean Development Mechanism (CDM) of the Kyoto Proto-col is designed to allow the industrialised countries to earn creditsby investing in greenhouse gas (GHG) emission reduction projects indeveloping countries, which contribute to sustainable development inthe host countries. This research empirically investigates the long-runimpacts of CDM projects on CO2 emissions for 34 CDM host coun-tries over 1990-2007. By allowing for considerable heterogeneity acrosscountries, this research provides strong evidence in support of a signif-icant effect of CDM projects on CO2 emission reductions in the hostcountries. It offers ample recommendation for improving CDM devel-opment and serves to encourage the developing countries to strengthentheir national capacity to effectively access the CDM for their sustain-able development objectives.

Keywords: Clean Development Mechanism; CO2 Emissions; Het-erogeneous Dynamic Panels

JEL Classification: O19; Q54; Q56

We thank Cathy Goss, Lynn Dicks, Esteve Corbera and seminar participants at the2008 international sustainable development conference at Strasbourg (France) for valuableinformation and helpful suggestions. The usual disclaimer

Corresponding author: Email: [email protected]; Tel: 0044-1223 764873; Fax: 0044-1223 337130; URL: http://www.landecon.cam.ac.uk/yh279/huangyf.htm.

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1 Introduction

Over the past 20 years, how to tackle climate change and achieve sustainable

development has become one of the most important challenges facing the in-

ternational community. As part of the Kyoto response towards mitigation

of global warming, the Clean Development Mechanism (CDM) was designed

to create opportunities for synergies between cost-effective climate change

mitigation and sustainable development. However, the question on whether

the CDM is doing what it promises to do has given rise to much controversy.

The research reported below empirically examines whether CDM projects

contribute to sustainable development in developing countries, based on dy-

namic heterogeneous panels for 34 CDM host countries over 1990-2007.

As a global effort to respond to climate change and protect the environ-

ment, the Kyoto Procotol was introduced in 1997, coming into force on 16

February 2005. The Protocol calls for legally-binding limits on greenhousegas (GHG) emissions by the developed countries (or the Annex I countries)

of 5.2% below their 1990 levels over the first commitment period (i.e. 2008-

2012). The CDM is an innovative cooperative mechanism under the Kyoto

Protocol. As part of the emerging global carbon market, the CDM aims to

achieve the dual aims of assisting developing countries in achieving sustain-

able development and assisting developed countries in achieving compliance

with their GHGs emission reduction commitments.1 The number of projects

proposed as candidate CDM projects has been steadily rising. By the end

1 At the global level, the CDM projects do not explicitly lead to a net decline in carbonemissions; instead, the emission reductions the CDM projects promise to bring about areessentially a zero-sum game in the sense that these reductions merely take place in adifferent place, not in the Annex I countries but in the non-Annex I countries. Therefore,whether or not the CDM contributes to the mitigation of global warming relies on theextent to which it results in reduced emissions in developing countries.

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of November 2008, there were 4252 CDM projects in the pipeline.2

The CDM is the only Kyoto mechanism that involves developing coun-

tries in the climate change negotiations. The CDM is expected to stimulate

foreign direct investment and speed up the transfer and deployment of low

and zero carbon technologies from developed countries to developing coun-

tries. It is also anticipated to arouse business interest and engagement from

the private sector into the issue of climate change mitigation via environ-mentally friendly investment, and ultimately help direct the host countries

onto a lower carbon trajectory.

However, there has been much controversy as to the impacts of the CDM

on sustainable development in developing countries. Examples are Banuri

and Gupta (2000), Kolshus et al. (2001), Brown et al. (2004), Kim (2004),

Cosbey et al. (2005), Sutter and Parreo (2007) and Boyd et al. (2007) to

mention a few.3 The existing research in this field is made up of one group

of research supporting positive impacts, another group of research indicat-

ing negative impacts, and some having mixed views. Some forward-looking

research (for example Banuri and Gupta, 2000) suggests that CDM projects

could cause the widespread adoption of less GHGs-intensive technologies in

non-Annex I countries, which would have positive implications for emission

reductions in the non-Annex I countries. However, recent studies, at either

the aggregated levels or the project level, suggest that, left to market forces,

the CDM does not significantly contribute to sustainable development be-cause the trade-off between the two benefits of the CDM falls in favor of

cost-effective reduction benefits, and neglects the sustainable development

benefits, which are not monetised in the carbon market (Sutter and Parreo,

2 Data from the UNEP Risoe Centre (2008).3 See Olsen (2007) for a recent review of literature on the sustainable development

contributions of the CDM.

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2007; Kolshus et al. 2001). Since it is crucial to examine whether the CDM

is fulfilling its sustainable development objective, this research carries out a

panel data analysis into this issue.

One difficulty facing this research is that the actual definitions of sustain-

able development vary across countries. As decided by the Kyoto Protocol

that it is the prerogative of host country to determine whether a CDM

project contributes to its sustainable development objective, diff

erent CDMhost countries define different sustainable development criteria according to

their development priorities. Olsen (2007) shows that the sustainable devel-

opment contributions of CDM projects can be evaluated at least in economic,

social and environmental dimensions, and there is no single, authoritative

and universally accepted approach or methodology applicable to any CDM

project regardless of project type or location.4 Given that the primary

objective of the CDM is to combat global warming, this research focuses on

the environmental dimension of sustainable development in terms of CO2

emission reductions.

More specifically, we empirically evaluate whether CDM projects lead to

a decline in CO2 emissions, at aggregated level, for 34 CDM host countries

over 1990-2007. Within an Environmental Kuznets Curve framework, this

research investigates the long-run and short-run dynamics of CDM project

development, while controlling for country specific effects. This research

employs the pooled mean group procedure to identify a common long-runeffect for CDM projects, while allowing for short-run dynamics to differ

across countries. This research provides strong evidence in support of a

decline in CO2 emissions associated with CDM projects. The finding of this

4 Some approaches have been proposed for sustainability assessment of CDM projects,but they are qualitative in nature (Olsen and Fenhann, 2008; Cosbey et al., 2005; Anag-nostopoulos et al., 2004).

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research adds to the growing debate on this topic, and serves to encourage

the developing countries to strenghen their national capacity to effectively

access the CDM.

The remainder of the paper proceeds as follows. Section 2 describes the

data and shows some preliminary evidence. Section 3 presents the econo-

metric methods. The empirical results are reported in Section 4. Section 5

concludes.

2 Data and preliminary evidence

This section outlines the measures and data for CO2 emissions, CDM, and

GDP.

The dependent variable is the logarithm of CO2 emissions per capita,

denoted by CO2. This analysis mainly makes use of the CO2 emissions

from fuel combustion (by sectoral approach), in total as well as the emissions

from energy sector, manufacturing industries and households, tertiary and

agriculture sectors, respectively. Data on CO2 emissions and population

from 1990 up to 2007 are from the Global Energy Market Data (2008) of

Enerdata. To check for robustness, it also considers the CO2 emissions per

capita from fuel combustion (by reference approach), which are taken from

Enerdata as well.

The independent variable is an indicator (dummy) variable for the Clean

Development Mechanism, simply denoted by CDM. It takes the value of

one in the year when a country has a CDM project in the pipeline and in

all years afterwards, and zero otherwise. The CDM projects in the pipeline

include not only those called confirmed projects that have been at the

registration stage, either registered or requested registration, but also those

called probable projects that are at the validation stage, waiting to be

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registered and implemented over the next 3 years. Data on CDM projects

in the pipeline are from the UNEP Risoe Centre (2008).

To reflect the so-called Environmental Kuznets Curve, which suggests

an inverse U-shaped pattern between carbon emissions and economic de-

velopment, this analysis includes GDP per capita in log and its squared

term in the regression, denoted by GDP andGDP2, respectively. Data on

GDP in US dollars at constant price and exchange rate (2005) per capitaover 1990-2007 are taken from the Global Energy Market Data (2008) of

Enerdata.

The whole sample includes 34 CDM host countries as listed in the Ap-

pendix Table 1. We exclude the CDM host countries which have their first

CDM projects in the pipeline after year 2006.

Since renewable energy, biomass/biogas, energy efficiency are among the

most popular project types to date, we present in Figure 1 some simple evi-

dence on the CO2 emissions from energy sector for 16 CDM host countries.5

The upper chart of Figure 1 displays the cross-country median CO2 emis-

sions per capita 4 years before and after having their first CDM projects in

the pipeline. The lower chart of Figure 1 plots the coefficients on the fixed ef-

fect estimates of 8 time dummies before and after the year when they started

to have their first CDM projects in the pipeline to reflect the dynamic effect

of the CDM development. The regression is estimated by OLS in which the

unobserved country specific effects, time effects and control variables suchas GDP per capita in log and GDP per capita in log squared are included.

The two figures show that CO2 emissions in the sample countries in general

move upwards sharply prior to having CDM projects in the pipeline. After

having CDM projects in the pipeline, CO2 emissions have been shown to

5 To facilitate a before-and-after event study, 16 CDM host countries are selected whichhad their first CDM projects in the pipeline before 2005.

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.7

.6

.5

.4

CO2emissionspercapita(inlog)

2000 2002 2004 2006 2008year

Crosscountry median carbon emissions per capita

.05

.1

.15

.2

.25

CO2emissio

nspercapita(inlog)

2000 2002 2004 2006 2008year

Fixed effect estimates of CO2 emissions per capita

Note: 16 CDM host countries having their first CDM projects in the pipeline by the endof 2004. Variables and data sources are described in the text. Upper figure shows thecrosscountry median CO2 emissions per capita in energy sector for these countrieswhile the lower figure plots the coefficients of fixed effect estimates of 8 time dummies

around the year when their first CDM projects were in the pipeline. The regression isestimated by OLS in which the country effects, time effects, GDP per capita in log andGDP per capita in log squared are included.

Figure 1: CO2 emissions in energy sector before and after having CDM projects


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move up slowerly in the upper chart and immediately experience a drop

in the lower chart. The charts vividly portray the main features of CO2

emissions before and after CDM projects are made available. The effect of

CDM projects on CO2 emission reductions, at least in the short run, has

been observed. However, this, alone, is not very convincing evidence. A

more detailed econometric analysis of the relationship between CDM and

CO2 emissions will be conducted in what follows, based on panel data of 34CDM host countries over 1990-2007.

3 Econometric methods

This analysis studies the impacts of CDM projects on CO2 emissions in

34 host countries over the period from 1990 to 2007. Since we are dealing

with a very dynamic process in which the geographic distribution of CDM

projects has been observed as uneven, and the CO2 emissions differ across

countries, we need a unique method by which these features can be better

captured. This section sets out a methodology that accounts for heteroge-

neous dynamic panels.

We assume the interactions between CDM projects and CO2 emissions

are represented by the unrestricted autoregressive distributed lag ARDL(p,

q, q, q) systems:

CO2it =pX

j=1

ijCO2i,tj +qX

j=0

ijCDMi,tj +

qXj=0

ijGDPi,tj +

qXj=0

ijGDP2i,tj + it + i + vit

i = 1, 2, ...,34 and t = 1,..., 18 (1)

where CO2it is the dependent variable and CDMit, GDPit and GDP2it

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are the explanatory variables, as described in section 2. t is a time trend.

i are the unobservable country specific effects. vit are errors assumed to

be serially uncorrelated and independently distributed across countries. We

allow for richer dynamics in the representations to control for business cycle

influences.

Following Perman and Stern (2003), Mller-Frstenberger and Wagner

(2007) and Wagner and Mller-Frstenberger (2008), we assume that the se-ries ofCO2it, CDMit, GDPit andGDP

2it are integrated, and cointegrated

for any individual countries, therefore vit is a stationary process for all i. As

shown by Engle and Granger (1987), there must be a vector error correction

representation governing the co-movements of these series over time. The

corresponding error correction equation to Equation (1) is as follows:

4CO2it =

0

ijCO2i,t1 +

0

ij

0ijCDMit +

0

ij

0ijGDPit +

0

ij

0ijGDP2

it!

p1Xj=1

"(

1Xm=2

im)4CO2i,t1

#

q1Xj=0

(

1Xm=j+1

im)4CDMi,t1

q1Xj=0

(

1Xm=j+1

im)4GDPi,t1

q1X

j=0

(

1Xm=j+1

im)4GDP2i,t1

+i + vit

i = 1, 2, ...,34 and t = 1, ...,18 (2)

where

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0

ij =

1

pXj=1

ij

0

ij =

qXj=0

ij

0

ij =

q

Xj=0

ij

0

ij =

qXj=0

ij

where 0

ij is the coefficient for the speed of adjustment.0

ij

0

ij

,0

ij

0

ij

, and0

ij

0

ij

are the long-run coefficients for CDMit, GDPit and GDP2it, respectively,

whileqP

m=j+1

im,qP

m=j+1

im, andqP

m=j+1

im are the short-run coefficients for

CDMit, GDPit and GDP2it, respectively.

To analyze a set of panel data with large time and large cross-sectionaldimensions, a number of methods have been proposed in the literature, for

example the within groups (WG) estimator, mean group (MG) estimator

due to Pesaran and Smith (1995) and pooled mean group (PMG) estimator

due to Pesaran et al. (1999).

The WG estimator is consistent for the dynamic homogeneous model

when time series dimemsion T is large, as cross-sectional dimention N

(Nickell, 1981). However, the WG estimator is based on rather restrictive

assumptions in terms of the homogeneity of all slope coefficients and error

variances, which are often not consistent with the reality for this context.

Here the divergent patterns of CO2 emissions, the development of CDM

projects, and the level of income are observed across countries.

The MG approach instead allows all slope coefficients and error vari-

ances to differ across countries, having considerable heterogeneity. The

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MG approach applies an OLS method to estimate a separate regression for

each country to obtain individual slope coefficients, and then averages the

country-specific coefficients to derive a long-run parameter for the panel6.

For large T and N, the MG estimator is consistent. With sufficiently high

lag order, the MG estimates of long-run parameters are super-consistent

even if the regressors are nonstationary (Pesaran et al., 1999). However, for

small samples or short time series dimensions, the MG estimator is likelyto be inefficient (Hsiao et al., 1999). For small T, the MG estimates of the

coefficients for the speeds of adjustment are subject to a lagged dependent

variable bias (Pesaran and Zhao, 1999).

Unlike the MG approach, which imposes no restriction on slope coeffi-

cients, the PMG approach imposes cross-sectional homogeneity restrictions

only on the long-run coefficients, but allows short-run coefficients, the speeds

of adjustment and the error variances to vary across countries. The restric-

tion of long-run homogeneity can be tested via a likelihood ratio test.7 Under

the null hypothesis of long-run homogeneity, the PMG estimators are consis-

tent and more efficient than the MG estimators. Since the PMG estimator

as well as the WG estimator are restricted versions of the set of individ-

ual group equations, the likelihood ratio test tends to reject the null at the

conventional significance levels. Moreover, Pesaran et al. (1999) show that

the PMG estimators are consistent and asymptotically normal irrespective

of whether the underlying regressors are I(1) or I(0).The PMG approach requires that the long-run coefficients for CDMit,

6 More specifically, the MG estimator and its standard errors are calculated as MG =

=

Ni=1

iN

and se(MG) =(i)N

=

Ni1

(i )2N1

N

, respectively.

7 The restriction of long-run homogeneity can also be tested via a Hausman test, whichis asymptotically distributed as a 2(p), where p is the number of parameters.

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GDPit and GDP2it are common across countries, that is,

0

ij =

1

pXj=1

j

0

ij =

qXj=0

j

0

ij =

qXj=0

j

0

ij =

qXj=0

j

4 Empirical evidence

In this section, the WG approach, MG approach and PMG approach are

applied and compared to determine whether CDM project development leads

to a decline in CO2 emissions for the host countries.

The number of lags is constrained by the number of observations. As

shown by Pesaran et al. (1999), the PMG estimator seems quite robust to

outliers and the choice of ARDL order, especially when T is large. We adopt

an autoregressive distributed lag ARDL(1, 1, 1, 1) system for this analysis

with the corresponding error correction equation as follows.8

8 The parameters reported in Tables 1, 2 and 3 for speeds of adjustment, long-run

coefficients and short-run coefficients correspond to model parameters

0

i1,

0

i1

0i1 ,

0

i1

0i1 ,

0

i1

0i1 ,

i1, i1, i1 of equation (3), respectively.

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4CO2it = 0

i1

CO2i,t1 +

0

i1

0

i1

CDMit +

0

i1

0

i1

GDPit +0

i1

0

i1

GDP2it

!

i1 4CDMi,t1 i14GDPi,t1 i14GDP2i,t1 + i + vit

i = 1, 2, ...,34 and t = 1, ...,18 (3)

where

0

i1 = (1 i1)

0

i1 = i0 + i1

0

i1 = i0 + i1

0

i1 = i0 + i1

Table 1 examines whether CDM projects result in reduced CO2 emissions

in the host countries, with the dependent variable being the CO2 emissions

(by sectoral approach) per capita in log. It reports three alternative pooled

estimates of WG, PMG and MG with and without a time trend. We expect

the long-run effects of CDM projects, level of GDP and squared GDP on

CO2 emissions to be homogenous across countries, although the short-run

adjustments are more likely to differ across countries. This analysis centers

on the PMG estimates.

The coefficients corresponding to the speeds of adjustment in Table 1

are significantly different from zero for two specifications, suggesting that

Granger causality going from CDM projects to CO2 emissions exists in the

cointegrated system.

Moving from the WG to PMG estimates, we find the PMG estimates

suggest much faster adjustment in two specifications than their WG coun-

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terparts. Imposing homogeneity on all slope coefficients except for the in-

tercept, the WG estimates in two specifications suggest no evidence for the

negative long-run effects of CDM projects on CO2 emissions. However, the

WG estimates show that an Environmental Kuznets Curve can be observed

in these countries. When heterogeneity is sought, the PMG estimates, which

impose homogeneity only on the long-run coefficients, provide strong evi-

dence in support of a negative eff

ect of CDM projects on CO2 emissions.This tends to underscore the importance of allowing for heterogeneity across

countries in this context. Moreover, the PMG estimates find evidence for

an Environmental Kuznets Curve in these countries in the sense that pollu-

tion goes up when the level of income increases; however, when the income

reaches a certain level, a decline in CO2 emissions can be expected.

Moving from the MG to PMG in Table 1 changes the results significantly

as well. In particular, imposing long-run homogeneity reduces the standard

errors and the speeds of adjustment. As it is clear, the MG estimator imposes

no restriction on all slope coefficients, and is potentially inefficient for small

sample size. The MG approach confirms the finding by the PMG approach

on a significant impact of CDM on CO2 emission reductions; but it finds

no evidence in support of a significant long-run effect of income on CO2

emissions. When the MG and PMG estimates are compared, the likelihood

ratio tests strongly reject the null of equality of all of long-run coefficients

at conventional levels; therefore it doesnt appear that we are imposing toostrong a constraint on data.

Table 2 looks at the impact of CDM development on CO2 emissions

per capita from manufacturing industries, energy sector, and households,

tertiary and agriculture sectors, respectively. The PMG estimates suggest

CDM projects reduce CO2 emissions at 1% significance level from either the

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manufacturing industries or energy sector while 10% level from households,

tertiary and agriculture. The significant impact of income on CO2 emissions

is also confirmed by the PMG estimates.

As a robustness test, Table 3 makes use of the CO2 emissons (by reference

approach) per capita over 1990-2007. The MG estimates suggest a positive

effect of CDM projects on CO2 emissions when a time trend is allowed while

a negative eff

ect when a time trend is absent. The PMG estimates confi

rmthat the CDM projects are associated with CO2 emission reductions and the

Environmental Kuznets Curve can be observed.

In sum, after allowing for heterogeneity across countries, this analysis

on annual data clearly shows a significant effect of CDM projects on CO2

emission reductions. The findings in general suggest that the development of

CDM projects could cause a decline in CO2 emissions and has the potential

to help developing countries achieve their sustainable development objective.

On the impacts of income on CO2 emissions, the WG and PMG estimates

support an EKC hypothesis while the MG estimates do not support it. This

finding is in line with Halkos (2003) among others who suggest that the

EKC hypothesis is hard to be tested due to enormous heterogeneity across

countries.

5 Concluding remarks

Under the Kyoto Procotol, the CDM is designed to allow the Annex I coun-

tries to invest in GHGs emission reduction projects in non-Annex I coun-

tries, while providing the non-Annex I countries with access to the flows of

technology and capital that could contribute to their sustainable develop-

ment objectives. The CDM projects in a country should act as a substantial

stimulus to the development of low-carbon technologies, which, in turn, pro-

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mote reduced CO2 emissions, and should also be conducive to increased en-

ergy efficiency and conservation, increased investment flows and technology

transfers, private and public capacity development as well as health, rural

development and poverty reduction. Substantial research has been carried

out to examine whether CDM projects contribute to sustainable develop-

ment, suggesting contradictory findings. Due to a lack of data, panel data

analysis or time series analysis on this issue at aggregated level has beenhitherto lacking.

To investigate the impacts of CDM projects on CO2 emissions, we con-

ducted a dymanic panel data study allowing for considerable heterogeneity

across countries for 34 CDM host countries over 1990-2007. It mainly fo-

cuses on the pooled mean group procedure which allows for heterogeneous

dynamic adjustments towards a common long-run equilibrium. This re-

search in general provides strong evidence in support of a significant impact

of CDM projects on CO2 emission reductions, indicating a decline in CO2

emissions can be expected in the CDM host countries in the long run.

FindingS show that the CDM can play an important role in reducing

CO2 emissions and achieving sustainable development in developing coun-

tries. It provides ample recommendation for improving CDM development

and serves to encourage developing countries to strengthen their national

capacity to effectively access the CDM for national sustainable development

objectives. Governments of developing countries should improve its institu-tional quality and formulate favorable policies to stimulate productivity of

CDM projects, especially at their early stage of development. Governments

can strengthen their capacity through international exchanges of experience

or international networking to acquire beneficial information on other coun-

tries CDM programs.

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Table 1. Does CDM contribute to emission reductions (sectoral approach)? 1990-2007

Dependent Variable: Without Time Trend Wit

Within Pooled Mean Within

Groups Mean Group Group Groups

Speed of adjustment -0.216 -0.296 -0.517 -0.212[0.000]*** [0.000]*** [0.000]*** [0.000]***

Long-run coefficients

-0.038 -0.170 -0.279 -0.031

[0.374] [0.000]*** [0.000]*** [0.451]

1.038 1.665 0.063 1.084

[0.000]*** [0.000]*** [0.575] [0.000]***

-0.097 -0.203 0.039 -0.094

[0.079]* [0.000]*** [0.695] [0.096]*

Short-run coefficients0.001 -0.779 0.416 0.023

[0.947] [0.017]** [0.000]*** [0.946]

0.301 2.177 -1.290 0.310

[0.133] [0.247] [0.175] [0.108]

0.027 0.027 -0.002 0.001

[0.639] [0.103] [0.835] [0.685]

Trend 0.000

[0.731]

Observations 578 578 578 578

Number of Countries 34 34 34 34

Log Likelihood 713.46 1039.64 1166.60 713.56

Note: The dependent variable is CO2 emissions (sectoral approach) per capita in log. Variables and data sources are described in the text. This table pres

the Pesaran, Shin and Smith (1999)'s Pooled Mean Group estimates (PMG) and the Pesaran and Smith (1995)'s Mean Group estimates (MG), without and

The PMG approach uses the MG estimates of long-run coefficients as initial values, and the Newton-Raphson algoithm. For the case of within group esti

corrected for possible heteroscedasticity in the cross-sectional error variances. All equations included a constant country-speific term. Log Likelihood is

of equality of all of the long-run coefficients. P-values are reported in the brackets. *, **, *** significant at 10%, 5%, 1%, respectively.

itCDM

itGDP

itGDP2

1,

tiCDM

1,

tiGDP

1,2

tiGDP

itCO

2

2CO


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Table 2. Does CDM contribute to emission reductions (by sector)? 1990-2007

Dependent Variable: Manufacturing Industries Energy Sector H

Within Pooled Mean Within Pooled Mean

Groups Mean Group Group Groups Mean Group Group

Speed of adjustment -0.294 -0.649 -0.870 -0.261 -0.627 -0.835[0.000]*** [0.000]*** [0.000]*** [0.000]*** [0.000]*** [0.000]***


-0.001 -0.065 -0.128 0.103 -0.092 0.420

[0.989] [0.000]*** [0.000]*** [0.490] [0.000]*** [0.000]***

1.223 1.626 0.016 0.762 0.892 -0.005

[0.007]*** [0.000]*** [0.821] [0.034]** [0.000]*** [0.941]

-0.346 -0.387 -0.118 0.108 0.206 0.014

[0.018]** [0.000]*** [0.469] [0.624] [0.001]*** [0.917]

Short-run coefficients

-0.027 3.703 0.056 0.354 13.678 0.004

[0.557] [0.667] [0.754] [0.841] [0.521] [0.931]

0.528 -14.937 -0.029 -0.008 -2.936 -0.140

[0.096]* [0.413] [0.550] [0.849] [0.092]* [0.000]***

-0.064 0.008 0.095 -0.039 0.032 -0.005

[0.269] [0.170] [0.002]*** [0.435] [0.313] [0.931]

Trend 0.000 -0.007 0.009 0.000 0.009 0.026

[0.973] [0.334] [0.110] [0.972] [0.246] [0.000]***

Observations 578 578 578 578 578 578

Number of Countries 34 34 34 34 34 34

Log Likelihood 189.25 670.16 797.62 -26.58 637.77 766.92

Note: The dependent variable is CO2 emissions per capita in log from manufacturing industries, energy sector, and households, tertiary, and agriculture

described in the text. See Table 1 for more notes.

itCDM

itGDP

itGDP2

1,

tiCDM

1,

tiGDP

1,2

tiGDP

itCO

2

2CO


25/34

Table 3. Does CDM contribute to emission reductions (reference approach)? 1990-2007

Dependent Variable: Without Time Trend With

Within Pooled Mean Within

Groups Mean Group Group Groups

Speed of adjustment -0.233 -0.315 -0.564 -0.232[0.000]*** [0.000]*** [0.000]*** [0.000]***


-0.035 -0.136 -0.049 -0.034

[0.394] [0.000]*** [0.290] [0.420]

1.045 1.609 0.114 1.049

[0.000]*** [0.000]*** [0.405] [0.001]***

-0.083 -0.172 0.069 -0.083

[0.102] [0.000]*** [0.577] [0.100]

Short-run coefficients -0.005 -0.500 -0.431 0.047

[0.703] [0.379] [0.044]** [0.703]

0.264 0.017 0.659 0.264

[0.203] [0.079]* [0.040]** [0.179]

0.048 2.118 -0.017 -0.005

[0.426] [0.196] [0.119] [0.426]

Trend 0.000

[0.976]

Observations 578 578 578 578

Number of Countries 34 34 34 34

Log Likelihood 679.58 1011.05 1136.66 679.58

Note: The dependent variable is CO2 emissions per capita from fuel combustion (by reference approach) in log over 1990-2007. Variables and data sourc

for more notes.

itCDM

itGDP

itGDP2

1,2

tiGDP

1,

tiGDP

1,

tiCDM

itCO

2

2CO


26/34

Appendix Table 1: The List of Countries in the Full Sample

No. Country Name Country Code First CDM Year

1 Argentina ARG 2004

2 Armenia ARM 2005

3 Bangladesh BGD 20054 Bolivia BOL 2004

5 Brazil BRA 2003

6 Chile CHL 2003

7 China CHN 2004

8 Cote d'Ivoire CIV 2005

9 Colombia COL 2005

10 Costa Rica CRI 2004

11 Ecuador ECU 2004

12 Guatemala GTM 2003

13 Honduras HND 2004

14 Indonesia IDN 200415 India IND 2003

16 Iran IRN 2005

17 Israel ISR 2005

18 Jamaica JAM 200519 Cambodia KHM 2005

20 South Korea KOR 2003

21 Sri Lanka LKA 2005

22 Morocco MAR 2004

23 Mexico MEX 2004

24 Malaysia MYS 2004

25 Nigeria NGA 2005

26 Nicaragua NIC 200527 Nepal NPL 2005

28 Panama PAN 2005

29 Peru PER 2005

30 Philippines PHL 2005

31 El Salvador SLV 2005

32 Thailand THA 2005

33 Uruguay URY 2005

34 South Africa ZAF 2004

Note: This table lists the country codes and country names for 34 CDM host countries considered in this analysis.

The First CDM Year is the year when a country had its first CDM project in the pipeline. Data are from the UNEPRisoe Centre CDM/JI Pipeline Analysis and Database (2008).


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Tyndall Working Papers 2000 - 2009

Tyndall Working Paper series

2000 - 2009

The Tyndall Centre working paper series presents results from research which are mature enough to

be submitted to a refereed journal, to a sponsor, to a major conference or to the editor of a book.

The intention is to enhance the early public availability of research undertaken by the Tyndall familyof researchers, students and visitors. They can be downloaded from the Tyndall Website at:

http://www.tyndall.ac.uk/publications/working_papers/working_papers.shtml

The accuracy of working papers and the conclusions reached are the responsibility of the author(s)

alone and not the Tyndall Centre.

Papers available in this series are:

Dawson R., Hall J, Barr S, Batty M.,Bristow A, Carney S, Dagoumas, A., EvansS., Ford A, Harwatt H., Kohler J., Tight M,(2009) A blueprint for the integratedassessment of climate change incities: Tyndall Working Paper 129

Carney S, Whitmarsh L, Nicholson-ColeS, Shackley S., (2009) A Dynamic

Typology of Stakeholder Engagementwithin Climate Change Research:Tyndall Working paper 128;

Goulden M, Conway D, Persechino A.,(2008) Adaptation to climate change ininternational river basins in Africa: areview: Tyndall Working paper 127;

Bows A., Anderson K., (2008)A bottom-up analysis of includingaviation within the EUs EmissionsTrading Scheme: Tyndall Working Paper126;

Al-Saleh Y., Upham P., Malik K., (2008)Renewable Energy Scenarios for theKingdom of Saudi Arabia: TyndallWorking Paper 125

Scrieciu S., Barker T., Smith V., (2008)World economic dynamics andtechnological change: projectinginteractions between economic output

and CO2 emissions :Tyndall WorkingPaper 124

Bulkeley H, Schroeder H., (2008)Governing Climate Change Post-2012:The Role of Global Cities - London:Tyndall Working Paper 123

Schroeder H., Bulkeley H, (2008)Governing Climate Change Post-2012:

The Role of Global Cities, Case-Study:Los Angeles: Tyndall Working Paper 122

Wang T., Watson J, (2008) CarbonEmissions Scenarios for China to2100: Tyndall Working Paper 121

Bergman, N., Whitmarsh L, Kohler J.,(2008) Transition to sustainabledevelopment in the UK housing

sector: from case study to modelimplementation: Tyndall Working Paper120

Conway D, Persechino A., Ardoin-BardinS., Hamandawana H., Dickson M, DieulinC, Mahe G, (2008) RAINFALL ANDWATER RESOURCES VARIABILITY INSUB-SAHARAN AFRICA DURING THE20TH CENTURY: Tyndall Centre WorkingPaper 119


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Starkey R., (2008) Allocatingemissions rights: Are equal shares,fair shares? : Tyndall Working Paper 118

Barker T., (2008) The Economics ofAvoiding Dangerous Climate Change:Tyndall Centre Working Paper 117

Estrada M, Corbera E., Brown K, (2008)How do regulated and voluntarycarbon-offset schemes compare?:Tyndall Centre Working Paper 116

Estrada Porrua M, Corbera E., Brown K,(2007) REDUCING GREENHOUSE GASEMISSIONS FROM DEFORESTATIONIN DEVELOPING COUNTRIES:

REVISITING THE ASSUMPTIONS:Tyndall Centre Working Paper 115

Boyd E., Hultman N E., Roberts T.,Corbera E., Ebeling J., Liverman D, BrownK, Tippmann R., Cole J., Mann P, KaiserM., Robbins M, (2007) The CleanDevelopment Mechanism: Anassessment of current practice andfuture approaches for policy: Tyndall

Centre Working Paper 114

Hanson, S., Nicholls, R., Balson, P.,Brown, I., French, J.R., Spencer, T.,Sutherland, W.J. (2007) Capturingcoastal morphologicalchange within regional integratedassessment: an outcome-driven fuzzylogic approach: Tyndall Working PaperNo. 113

Okereke, C., Bulkeley, H. (2007)Conceptualizing climate changegovernance beyond the internationalregime: A review of four theoreticalapproaches: Tyndall Working Paper No.112

Doulton, H., Brown, K. (2007) Tenyears to prevent catastrophe?Discourses of climate change andinternational development in the UKpress: Tyndall Working Paper No. 111

Dawson, R.J., et al (2007) Integratedanalysis of risks of coastal floodingand cliff erosion under scenarios oflong term change: Tyndall WorkingPaper No. 110

Okereke, C., (2007) A review of UKFTSE 100 climate strategy and aframework for more in-depth analysisin the context of a post-2012 climateregime: Tyndall Centre Working Paper109

Gardiner S., Hanson S., Nicholls R.,Zhang Z., Jude S., Jones A.P., et al (2007)The Habitats Directive, CoastalHabitats and Climate Change Case

Studies from the South Coast of theUK: Tyndall Centre Working Paper 108

Schipper E. Lisa, (2007) ClimateChange Adaptation and Development:Exploring the Linkages: Tyndall CentreWorking Paper 107

Okereke C., Mann P, Osbahr H, (2007)Assessment of key negotiating issuesat Nairobi climate COP/MOP and what

it means for the future of the climateregime: Tyndall Centre Working PaperNo. 106

Walkden M, Dickson M, (2006) Theresponse of soft rock shore profiles toincreased sea-level rise. : TyndallCentre Working Paper 105

Dawson R., Hall J, Barr S, Batty M.,Bristow A, Carney S, Evans E.P., Kohler J.,Tight M, Walsh C, Ford A, (2007) Ablueprint for the integratedassessment of climate change incities. : Tyndall Centre Working Paper104

Dickson M., Walkden M., Hall J., (2007)Modelling the impacts of climatechange on an eroding coast over the21st Century: Tyndall Centre Working

Paper 103

Klein R.J.T, Erickson S.E.H, Nss L.O,


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Hammill A., Tanner T.M., Robledo, C.,OBrien K.L.,(2007) Portfolio screeningto support the mainstreaming ofadaptation to climatic change intodevelopment assistance: Tyndall CentreWorking Paper 102

Agnolucci P., (2007) Is it going tohappen? Regulatory Change andRenewable Electricity: Tyndall CentreWorking Paper 101

Kirk K., (2007) Potential for storageof carbon dioxide in the rocks beneaththe East Irish Sea: Tyndall CentreWorking Paper 100

Arnell N.W., (2006) Global impacts ofabrupt climate change: an initialassessment: Tyndall Centre WorkingPaper 99

Lowe T.,(2006) Is this climate porn?How does climate changecommunication affect our perceptionsand behaviour?, Tyndall Centre WorkingPaper 98

Walkden M, Stansby P,(2006) Theeffect of dredging off Great Yarmouthon the wave conditions and erosion ofthe North Norfolk coast. Tyndall CentreWorking Paper 97

Anthoff, D., Nicholls R., Tol R S J,Vafeidis, A., (2006) Global and regionalexposure to large rises in sea-level: asensitivity analysis. This work wasprepared for the Stern Review on theEconomics of Climate Change:Tyndall Centre Working Paper 96

Few R., Brown K, Tompkins E. L,(2006) Public participation and climatechange adaptation, Tyndall CentreWorking Paper 95

Corbera E., Kosoy N, Martinez Tuna M,(2006) Marketing ecosystem services

through protected areas and ruralcommunities in Meso-America:Implications for economic efficiency,

equity and political legitimacy, TyndallCentre Working Paper 94

Schipper E. Lisa, (2006) ClimateRisk, Perceptions and Development inEl Salvador, Tyndall Centre WorkingPaper 93

Tompkins E. L, Amundsen H, (2005)Perceptions of the effectiveness of theUnited Nations Framework Conventionon Climate Change in promptingbehavioural change, Tyndall CentreWorking Paper 92

Warren R., Hope C, Mastrandrea M,Tol R S J, Adger W. N., Lorenzoni I.,

(2006) Spotlighting the impactsfunctions in integrated assessments.Research Report Prepared for theStern Review on the Economics ofClimate Change, Tyndall Centre WorkingPaper 91

Warren R., Arnell A, Nicholls R., LevyP E, Price J, (2006) Understanding theregional impacts of climate change:

Research Report Prepared for theStern Review on the Economics ofClimate Change, Tyndall Centre WorkingPaper 90

Barker T., Qureshi M, Kohler J.,(2006) The Costs of Greenhouse GasMitigation with Induced TechnologicalChange: A Meta-Analysis of Estimatesin the Literature, Tyndall Centre WorkingPaper 89

Kuang C, Stansby P, (2006)Sandbanks for coastal protection:implications of sea-level rise. Part 3:wave modelling, Tyndall Centre WorkingPaper 88

Kuang C, Stansby P, (2006)Sandbanks for coastal protection:implications of sea-level rise. Part 2:current and morphological modelling,Tyndall Centre Working Paper 87


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Stansby P, Kuang C, Laurence D,Launder B, (2006) Sandbanks forcoastal protection: implications ofsea-level rise. Part 1: application toEast Anglia, Tyndall Centre WorkingPaper 86

Bentham M, (2006) An assessmentof carbon sequestration potential inthe UK Southern North Sea casestudy: Tyndall Centre Working Paper 85

Anderson K., Bows A., Upham P.,(2006) Growth scenarios for EU & UKaviation: contradictions with climatepolicy, Tyndall Centre Working Paper 84

Williamson M., Lenton T., ShepherdJ., Edwards N, (2006) An efficientnumerical terrestrial scheme (ENTS)for fast earth system modelling,Tyndall Centre Working Paper 83

Bows, A., and Anderson, K. (2005)An analysis of a post-Kyoto climatepolicy model, Tyndall Centre WorkingPaper 82

Sorrell, S., (2005) The economics ofenergy service contracts, TyndallCentre Working Paper 81

Wittneben, B., Haxeltine, A., Kjellen,B., Khler, J., Turnpenny, J., and Warren,R., (2005) A framework for assessingthe political economy of post-2012global climate regime, Tyndall CentreWorking Paper 80

Ingham, I., Ma, J., and Ulph, A. M.(2005) Can adaptation and mitigationbe complements?, Tyndall CentreWorking Paper 79

Agnolucci,. P (2005) Opportunismand competition in the non-fossil fuelobligation market, Tyndall CentreWorking Paper 78

Barker, T., Pan, H., Khler, J.,Warren., R and Winne, S. (2005)

Avoiding dangerous climate change byinducing technological progress:scenarios using a large-scaleeconometric model, Tyndall CentreWorking Paper 77

Agnolucci,. P (2005) The role of

political uncertainty in the Danishrenewable energy market, TyndallCentre Working Paper 76

Fu, G., Hall, J. W. and Lawry, J.(2005) Beyond probability: newmethods for representing uncertaintyin projections of future climate,Tyndall Centre Working Paper 75

Ingham, I., Ma, J., and Ulph, A. M.(2005) How do the costs of adaptationaffect optimal mitigation when thereis uncertainty, irreversibility andlearning?, Tyndall Centre Working Paper74

Walkden, M. (2005) Coastalprocess simulator scoping study,Tyndall Centre Working Paper 73

Lowe, T., Brown, K., Suraje Dessai,S., Doria, M., Haynes, K. and Vincent., K(2005) Does tomorrow ever come?Disaster narrative and publicperceptions of climate change, TyndallCentre Working Paper 72

Boyd, E. Gutierrez, M. and Chang,M. (2005) Adapting small-scale CDMsinks projects to low-incomecommunities, Tyndall Centre WorkingPaper 71

Abu-Sharkh, S., Li, R., Markvart, T.,Ross, N., Wilson, P., Yao, R., Steemers,K., Kohler, J. and Arnold, R. (2005) CanMigrogrids Make a Major Contributionto UK Energy Supply?, Tyndall CentreWorking Paper 70

Tompkins, E. L. and Hurlston, L. A.(2005) Natural hazards and climatechange: what knowledge is


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transferable?, Tyndall Centre WorkingPaper 69

Bleda, M. and Shackley, S. (2005)The formation of belief in climatechange in business organisations: adynamic simulation model, Tyndall


Turnpenny, J., Haxeltine, A. andORiordan, T., (2005) Developingregional and local scenarios forclimate change mitigation andadaptation: Part 2: Scenario creation,Tyndall Centre Working Paper 67

Turnpenny, J., Haxeltine, A.,

Lorenzoni, I., ORiordan, T., and Jones, M.,(2005) Mapping actors involved inclimate change policy networks in theUK, Tyndall Centre Working Paper 66

Adger, W. N., Brown, K. andTompkins, E. L. (2004) Why doresource managers make links tostakeholders at other scales?, TyndallCentre Working Paper 65

Peters, M.D. and Powell, J.C. (2004)Fuel Cells for a Sustainable Future II,Tyndall Centre Working Paper 64

Few, R., Ahern, M., Matthies, F. andKovats, S. (2004) Floods, health andclimate change: a strategic review,Tyndall Centre Working Paper 63

Barker, T. (2004) Economic theoryand the transition to sustainability: acomparison ofapproaches, Tyndall Centre WorkingPaper 62

Brooks, N. (2004) Drought in theAfrican Sahel: long term perspectivesand future prospects, Tyndall CentreWorking Paper 61

Few, R., Brown, K. and Tompkins,E.L. (2004) Scaling adaptation: climatechange response and coastal

management in the UK, Tyndall CentreWorking Paper 60

Anderson, D and Winne, S. (2004)Modelling Innovation and ThresholdEffectsIn Climate Change Mitigation, Tyndall


Bray, D and Shackley, S.(2004) The Social Simulation of ThePublic Perceptions of Weather Eventsand their Effect upon theDevelopment of Belief inAnthropogenic Climate Change, TyndallCentre Working Paper 58

Shackley, S., Reiche, A. andMander, S (2004) The PublicPerceptions of Underground CoalGasification (UCG): A Pilot Study,Tyndall Centre Working Paper 57

Vincent, K. (2004) Creating anindex of social vulnerability to climatechange for Africa, Tyndall CentreWorking Paper 56

Mitchell, T.D. Carter, T.R., Jones,.P.D, Hulme, M. and New, M. (2004) Acomprehensive set of high-resolutiongrids of monthly climate for Europeand the globe: the observed record(1901-2000) and 16 scenarios (2001-2100), Tyndall Centre Working Paper 55

Turnpenny, J., Carney, S.,Haxeltine, A., and ORiordan, T. (2004)Developing regional and localscenarios for climate changemitigation and adaptation Part 1: Aframing of the East of England TyndallCentre Working Paper 54

Agnolucci, P. and Ekins, P. (2004)The Announcement Effect AndEnvironmental Taxation Tyndall CentreWorking Paper 53


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Agnolucci, P. (2004) Ex PostEvaluations of CO2 Based Taxes: ASurvey Tyndall Centre Working Paper 52

Agnolucci, P., Barker, T. and Ekins,P. (2004) Hysteresis and EnergyDemand: the Announcement Effects

and the effects of the UK ClimateChange Levy Tyndall Centre WorkingPaper 51

Powell, J.C., Peters, M.D., Ruddell,A. and Halliday, J. (2004) Fuel Cells for aSustainable Future? Tyndall CentreWorking Paper 50

Awerbuch, S. (2004) Restructuring

our electricity networks to promotedecarbonisation, Tyndall Centre WorkingPaper 49

Pan, H. (2004) The evolution ofeconomic structure undertechnological development, TyndallCentre Working Paper 48

Berkhout, F., Hertin, J. and Gann,

D. M., (2004) Learning to adapt:Organisational adaptation to climatechange impacts, Tyndall Centre WorkingPaper 47

Watson, J., Tetteh, A., Dutton, G.,Bristow, A., Kelly, C., Page, M. andPridmore, A., (2004) UK HydrogenFutures to 2050, Tyndall Centre WorkingPaper 46

Purdy, R and Macrory, R. (2004)Geological carbon sequestration:critical legal issues, Tyndall CentreWorking Paper 45

Shackley, S., McLachlan, C. andGough, C. (2004) The PublicPerceptions of Carbon Capture andStorage, Tyndall Centre Working Paper 44

Anderson, D. and Winne, S. (2003)Innovation and Threshold Effects in

Technology Responses to ClimateChange, Tyndall Centre Working Paper 43

Kim, J. (2003) SustainableDevelopment and the CDM: A SouthAfrican Case Study, Tyndall CentreWorking Paper 42

Watson, J. (2003), UK ElectricityScenarios for 2050, Tyndall CentreWorking Paper 41

Klein, R.J.T., Lisa Schipper, E. andDessai, S. (2003), Integratingmitigation and adaptation into climateand development policy: threeresearch questions, Tyndall Centre

Working Paper 40

Tompkins, E. and Adger, W.N.(2003). Defining response capacity toenhance climate change policy, TyndallCentre Working Paper 39

Brooks, N. (2003). Vulnerability,risk and adaptation: a conceptualframework, Tyndall Centre Working

Paper 38

Ingham, A. and Ulph, A. (2003)Uncertainty, Irreversibility,Precaution and the Social Cost ofCarbon, Tyndall Centre Working Paper 37

Krger, K. Fergusson, M. andSkinner, I. (2003). Critical Issues inDecarbonising Transport: The Role ofTechnologies, Tyndall Centre WorkingPaper 36

Tompkins E. L and Hurlston, L.(2003). Report to the Cayman IslandsGovernment. Adaptation lessonslearned from responding to tropicalcyclones by the Cayman IslandsGovernment, 1988 2002, TyndallCentre Working Paper 35

Dessai, S., Hulme, M (2003). Doesclimate policy need probabilities?,Tyndall Centre Working Paper 34


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Pridmore, A., Bristow, A.L., May, A.D. and Tight, M.R. (2003). ClimateChange, Impacts, Future Scenariosand the Role of Transport, TyndallCentre Working Paper 33

Xueguang Wu, Jenkins, N. andStrbac, G. (2003). IntegratingRenewables and CHP into the UKElectricity System: Investigation ofthe impact of network faults on thestability of large offshore wind farms,Tyndall Centre Working Paper 32

Turnpenny, J., Haxeltine A. and

ORiordan, T. (2003). A scoping study ofUK user needs for managing climatefutures. Part 1 of the pilot-phaseinteractive integrated assessmentprocess (Aurion Project), TyndallCentre Working Paper 31

Hulme, M. (2003). Abrupt climatechange: can society cope?, TyndallCentre Working Paper 30

Brown, K. and Corbera, E. (2003). AMulti-Criteria Assessment Frameworkfor Carbon-Mitigation Projects:Putting development in the centreof decision-making, Tyndall CentreWorking Paper 29

Dessai, S., Adger, W.N., Hulme, M.,Khler, J.H., Turnpenny, J. and Warren, R.(2003). Defining and experiencingdangerous climate change, TyndallCentre Working Paper 28

Tompkins, E.L. and Adger, W.N.(2003). Building resilience to climatechange through adaptivemanagement of natural resources,Tyndall Centre Working Paper 27

Brooks, N. and Adger W.N. (2003).Country level risk measures ofclimate-related natural disasters and

implications for adaptation to climatechange, Tyndall Centre Working Paper 26

Xueguang Wu, Mutale, J., Jenkins,N. and Strbac, G. (2003). Aninvestigation of Network Splitting forFault Level Reduction, Tyndall Centre

Working Paper 25

Xueguang Wu, Jenkins, N. andStrbac, G. (2002). Impact ofIntegrating Renewables and CHP intothe UK Transmission Network, TyndallCentre Working Paper 24

Paavola, J. and Adger, W.N. (2002).Justice and adaptation to climate

change, Tyndall Centre Working Paper 23

Watson, W.J., Hertin, J., Randall, T.,Gough, C. (2002). Renewable Energyand Combined Heat and PowerResources in the UK, Tyndall CentreWorking Paper 22

Watson, W. J. (2002). Renewablesand CHP Deployment in the UK to

2020, Tyndall Centre Working Paper 21

Turnpenny, J. (2002). Reviewingorganisational use of scenarios: Casestudy - evaluating UK energy policyoptions, Tyndall Centre Working Paper 20

Pridmore, A. and Bristow, A.,(2002). The role of hydrogen inpowering road transport, TyndallCentre Working Paper 19

Watson, J. (2002). Thedevelopment of large technicalsystems: implications for hydrogen,Tyndall Centre Working Paper 18

Dutton, G., (2002). HydrogenEnergy Technology, Tyndall CentreWorking Paper 17

Adger, W.N., Huq, S., Brown, K.,Conway, D. and Hulme, M. (2002).Adaptation to climate change: Setting


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the Agenda for Development Policyand Research, Tyndall Centre WorkingPaper 16

Khler, J.H., (2002). Long runtechnical change in an energy-environment-economy (E3) model for

an IA system: A model of Kondratievwaves, Tyndall Centre Working Paper 15

Shackley, S. and Gough, C., (2002).The Use of Integrated Assessment: AnInstitutional Analysis Perspective,Tyndall Centre Working Paper 14

Dewick, P., Green K., Miozzo, M.,(2002). Technological Change,

Industry Structure and theEnvironment, Tyndall Centre WorkingPaper 13

Dessai, S., (2001). The climateregime from The Hague to Marrakech:Saving or sinking the Kyoto Protocol?,Tyndall Centre Working Paper 12

Barker, T. (2001). Representing

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Barnett, J. and Adger, W. N. (2001).Climate Dangers and Atoll Countries,Tyndall Centre Working Paper 9

Adger, W. N. (2001). Social Capitaland Climate Change, Tyndall CentreWorking Paper 8 Barnett, J. (2001). Security andClimate Change, Tyndall Centre WorkingPaper 7

Goodess, C.M., Hulme, M. andOsborn, T. (2001). The identificationand evaluation of suitable scenariodevelopment methods for theestimation of future probabilities ofextreme weather events, TyndallCentre Working Paper 6

Barnett, J. (2001). The issue of'Adverse Effects and the Impacts of

Response Measures' in the UNFCCC,Tyndall Centre Working Paper 5

Barker, T. and Ekins, P. (2001).How High are the Costs of Kyoto forthe US Economy?, Tyndall CentreWorking Paper 4

Berkhout, F, Hertin, J. and Jordan,A. J. (2001). Socio-economic futures in

climate change impact assessment:using scenarios as 'learningmachines', Tyndall Centre Working Paper3

Hulme, M. (2001). IntegratedAssessment Models, Tyndall CentreWorking Paper 2

Mitchell, T. and Hulme, M. (2000). ACountry-by-Country Analysis of Pastand Future Warming Rates, TyndallCentre Working Paper 1

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