The Clean Development Mechanism And Sustainable Development: A Panel Data Analysis.

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

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.

The Clean Development Mechanism and Sustainable Development: A Panel Data Analysis∗ Yongfu Huanga† Terry Barkera a 4CMR, Department of Land Economy, University of Cambridge 19 Silver Street, Cambridge CB3 9EP February 17, 2009

Abstract The Clean Development Mechanism (CDM) of the Kyoto Protocol is designed to allow the industrialised countries to earn credits by investing in greenhouse gas (GHG) emission reduction projects in developing countries, which contribute to sustainable development in the host countries. This research empirically investigates the long-run impacts of CDM projects on CO2 emissions for 34 CDM host countries over 1990-2007. By allowing for considerable heterogeneity across countries, this research provides strong evidence in support of a significant effect of CDM projects on CO2 emission reductions in the host countries. It offers ample recommendation for improving CDM development and serves to encourage the developing countries to strengthen their national capacity to effectively access the CDM for their sustainable development objectives. Keywords: Clean Development Mechanism; CO2 Emissions; Heterogeneous Dynamic Panels JEL Classification: O19; Q54; Q56 ∗

We thank Cathy Goss, Lynn Dicks, Esteve Corbera and seminar participants at the 2008 international sustainable development conference at Strasbourg (France) for valuable information and helpful suggestions. The usual disclaimer † Corresponding author: Email: [email protected]; Tel: 0044-1223 764873; Fax: 00441223 337130; URL: http://www.landecon.cam.ac.uk/yh279/huangyf.htm.

1

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 international 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 dynamic heterogeneous panels for 34 CDM host countries over 1990-2007. As a global effort to respond to climate change and protect the environment, the Kyoto Procotol was introduced in 1997, coming into force on 16 February 2005. The Protocol calls for legally-binding limits on greenhouse gas (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. 20082012). 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 sustainable 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 carbon emissions; instead, the emission reductions the CDM projects promise to bring about are essentially a “zero-sum” game in the sense that these reductions merely take place in a different 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 the extent to which it results in reduced emissions in developing countries.

2

of November 2008, there were 4252 CDM projects in the pipeline.2 The CDM is the only Kyoto mechanism that involves developing countries 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 countries. It is also anticipated to arouse business interest and engagement from the private sector into the issue of climate change mitigation via environmentally 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 Parreño (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 indicating 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 because 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 Parreño, 2

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

3

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 sustainable 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, different CDM host countries define different sustainable development criteria according to their development priorities. Olsen (2007) shows that the sustainable development 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-run effect 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; Anagnostopoulos et al., 2004).

4

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 econometric 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 5

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 development, this analysis includes GDP per capita in log and its squared term in the regression, denoted by GDP and GDP2 , respectively. Data on GDP in US dollars at constant price and exchange rate (2005) per capita over 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 Appendix 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 evidence 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 emissions 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 effect 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 such as 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 which had their first CDM projects in the pipeline before 2005.

6

Figure 1: CO2 emissions in energy sector before and after having CDM projects CO2 emissions per capita (in log) −.7 −.6 −.5 −.4

Cross−country median carbon emissions per capita

2000

2002

2004 year

2006

2008

.05

CO2 emissions per capita (in log) .1 .15 .2 .25

Fixed effect estimates of CO2 emissions per capita

2000

2002

2004 year

2006

2008

Note: 16 CDM host countries having their first CDM projects in the pipeline by the end of 2004. Variables and data sources are described in the text. Upper figure shows the cross−country median CO2 emissions per capita in energy sector for these countries while 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 is estimated by OLS in which the country effects, time effects, GDP per capita in log and GDP per capita in log squared are included.

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 34 CDM 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 heterogeneous 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

p q X X = αij CO2i,t−j + β ij CDMi,t−j + j=1 q X

j=0 q X

δ ij GDP2i,t−j + θi t + μi + vit

γ ij GDPi,t−j +

j=0

j=0

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

(1)

where CO2it is the dependent variable and CDMit , GDPit and GDP2it 8

are the explanatory variables, as described in section 2. t is a time trend. μi 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), Müller-Fürstenberger and Wagner (2007) and Wagner and Müller-Fürstenberger (2008), we assume that the series of CO2it , CDMit , GDPit and GDP2it 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:

0

4CO2it = αij

Ã

0

CO2i,t−1 +

β ij 0

αij

CDMit +

" 1 # p−1 X X αim ) 4 CO2i,t−1 − ( j=1

0

αij

GDPit +

δ ij 0

αij

GDP2it

!

m=2

⎤ q−1 1 X X ⎣( − β im ) 4 CDMi,t−1 ⎦ j=0

⎡

0

0

γ ij

m=j+1

⎤ q−1 1 X X ⎣( − γ im ) 4 GDPi,t−1 ⎦ j=0

⎡

m=j+1

⎤ q−1 1 X X ⎣( − δ im ) 4 GDP2i,t−1 ⎦ j=0

⎡

m=j+1

+μi + vit

i = 1, 2, ..., 34 and t = 1, ..., 18 where

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(2)

0

αij 0

⎞ p X = − ⎝1 − αij ⎠

β ij = 0

γ ij = 0

δ ij =

⎛

j=1

q X

β ij

j=0 q X

γ ij

j=0 q X

δ ij

j=0

0

where αij is the coefficient for the speed of adjustment.

0

β ij 0

,

0

γ ij 0

, and

0

δij 0

αij αij αij GDP2it , respectively,

are the long-run coefficients for CDMit , GDPit and q q q P P P while β im , γ im , and δ im are the short-run coefficients for m=j+1

m=j+1

m=j+1

CDMit , GDPit and GDP2it , respectively.

To analyze a set of panel data with large time and large cross-sectional dimensions, 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 variances to differ across countries, having considerable heterogeneity. The 10

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 likely to 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 coefficients, 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 restriction 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 consistent and more efficient than the MG estimators. Since the PMG estimator as well as the WG estimator are restricted versions of the set of individual 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

−

θ= 7

More specifically, the MG estimator and its standard errors are calculated as e θM G = y SN

e i=1 θ i N

and se(e θMG ) =

σ(e θ ) √ i N

=

x − xS e − θ )2 w N (θ i i−1

√

N −1

N

, respectively.

The restriction of long-run homogeneity can also be tested via a Hausman test, which is asymptotically distributed as a χ2 (p), where p is the number of parameters.

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GDPit and GDP2it are common across countries, that is,

0

αij 0

⎛

⎞ p X = − ⎝1 − αj ⎠

β ij = 0

γ ij = 0

δ ij =

j=1

q X

βj

j=0 q X

γj

j=0 q X

δj

j=0

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 0

coefficients and short-run coefficients correspond to model parameters αi1 , −β i1 , −γ i1 , −δ i1 of equation (3), respectively.

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0

β i1 0

αi1

,

0

γ i1 0

αi1

,

0

δi1 0

αi1

,

0

4CO2it = αi1

Ã

0

0

0

β γ i1 δ i1 2 CO2i,t−1 + i1 0 CDMit + 0 GDPit + 0 GDPit αi1 αi1 αi1

!

−β i1 4 CDMi,t−1 − γ i1 4 GDPi,t−1 − δ i1 4 GDP2i,t−1 + μi + vit i = 1, 2, ..., 34 and t = 1, ..., 18 where

0

αi1 = − (1 − αi1 ) 0

β i1 = β i0 + β i1 0

γ i1 = γ i0 + γ i1 0

δ i1 = δ i0 + δ 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 coun13

(3)

terparts. Imposing homogeneity on all slope coefficients except for the intercept, 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 evidence in support of a negative effect 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 pollution 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 doesn’t appear that we are imposing too strong 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 effect when a time trend is absent. The PMG estimates confirm that 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 countries to invest in GHGs emission reduction projects in non-Annex I countries, while providing the non-Annex I countries with access to the flows of technology and capital that could contribute to their sustainable development objectives. The CDM projects in a country should act as a substantial stimulus to the development of low-carbon technologies, which, in turn, pro15

mote reduced CO2 emissions, and should also be conducive to increased energy 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 development, suggesting contradictory findings. Due to a lack of data, panel data analysis or time series analysis on this issue at aggregated level has been hitherto lacking. To investigate the impacts of CDM projects on CO2 emissions, we conducted a dymanic panel data study allowing for considerable heterogeneity across countries for 34 CDM host countries over 1990-2007. It mainly focuses on the pooled mean group procedure which allows for heterogeneous dynamic adjustments towards a common long-run equilibrium. This research 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 countries. 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 institutional 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 countries’ CDM programs.

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References [1] Anagnostopoulos, Kostantinos, Alexandros Flamos, Argyris G. Kagiannas and John Psarras, 2004.“The impact of clean development mechanism in achieving sustainable development.” International Journal of Environment and Pollution, 21(1): 1-23. [2] Banuri, Tariq and Sujata Gupta, 2000. “The Clean Development Mechanism and sustainable development: An economic analysis.” In Ghosh P (ed) In Implementation of the Kyoto Protocol, Asian Development Bank. [3] Boyd, Emily, Nathan E. Hultman, Timmons Roberts, Esteve Corbera, Johannes Ebeling, Diana M. Liverman, Kate Brown, Robert Tippman, John Cole, Phil Mann, Marius Kaiser, and Mike Robbins, 2007. “The Clean Development Mechanism: An assessment of current practice and future approaches for policy”. Tyndall Centre Working Paper No. 114. [4] Brown, Katrina, W. Neil Adger, Emily Boyd, Esteve Corbera-Elizalde and Simon Shackley, 2004. “How do CDM projects contribute to sustainable development?” Tyndall Centre Technical Report No. 16. [5] Cosbey, Aaron, Jo-Ellen Parry,Jodi Browne, Yuvaraj Dinesh Babu, Preety Bhandari, John Drexhage, Deborah Murphy, 2005. “Realizing the development dividend: making the CDM work for developing countries.” Phase 1 Report–Pre-publication Version, International Institute for Sustainable Development. [6] Engle, Robert F and Clive W J Granger, 1987. “Co-integration and error correction: Representation, estimation, and testing.” Econometrica, 55: 251-76. 17

[7] Global Energy Market Data (2008), the Enerdata. www.enerdata.fr. [8] Halkos, George E., 2003. “Environmental Kuznets Curve for Sulfur: Evidence using GMM estimation and random coefficient panel data models.” Environment and Dvelopment Economics, 8: 581-601. [9] Hsiao, Cheng, M Hashem Pesaran and A. Kamil Tahmiscioglu, 1999. “Bayes estimation of short-run coefficients in dynamic panel data models”. in C. Hsiao, K. Lahiri, L-F Lee and M.H. Pesaran (eds), Analysis of Panels and Limited Dependent Variables: A Volume in Honour of G S Maddala, Cambridge University Press, chapter 11, pp.268-296. [10] Kim, Joy A., 2004. “Sustainable development and the Clean Development Mechanism: A South African case study.” The Journal of Environment & Development, 13(3): 201-219. [11] Kolshus, Hans H., Jonas Vevatne, Asbjørn Torvanger and Kristin Aunan, 2001. “Can the Clean Development Mechanism attain both costeffectiveness and sustainable development objectives?” CICERO Working Paper 2001: 8. Oslo, Norway. [12] Müller-Fürstenberger, Georg and Martin Wagner, 2007. “Exploring the Environmental Kuznets hypothesis: Theoretical and econometric problems.” Ecological Economics, 62: 648-660. [13] Nickel , Stephen J, 1981. “Biases in dynamic models with fixed effects”. Econometrica, 49: 1418-1426. [14] Olsen, Karen Holm, 2007. “The Clean Development Mechanism’s contribution to sustainable development: A review of the literature.” Climatic Change, 84 (1): 59-73. 18

[15] Olsen, Karen Holm and Jørgen Fenhann, 2008. “Sustainable development benefits of clean development mechanism projects: A new methodology for sustainability assessment based on text analysis of the project design documents submitted for validation.” Energy Policy, 36(8): 2819-2830. [16] Perman, Roger and David I. Stern, 2003. “Evidence from panel unit root and cointegration tests that the Environmental Kuznets Curve does not exist”. The Australian Journal of Agricultural and Resource Economics, 47 (3): 325-347. [17] Pesaran, M Hashem and Zhongyun Zhao, 1999. “Bias reduction in estimating long-run relationships from dynamic heterogeneous panels”. in C. Hsiao, K. Lahiri, L-F Lee and M.H. Pesaran (eds), Analysis of Panels and Limited Dependent Variables: A Volume in Honour of G S Maddala, Cambridge University Press, chapter 12, pp.297-321. [18] Pesaran, M Hashem and Ron Smith. 1995. “Estimating long-run relationships from dynamic heterogeneous panels.” Journal of Econometrics, 68: 79-113. [19] Pesaran, M Hashem, Yongcheol Shin and Ron Smith. 1999. “Pooled mean group estimation of dynamic heterogeneous panels.” Journal of American Statistical Association 94: 621-34. [20] Sutter, Christoph and Juan Carlos Parreño, 2007. “Does the current Clean Development Mechanism (CDM) deliver its sustainable development claim? An analysis of officially registered CDM projects.” Climatic Change, 84 (1): 74-90. [21] UNEP Risoe Centre, CDM/JI Pipeline Analysis and Database (2008) 19

[22] Wagner, Martin and Georg Müller-Fürstenberger, 2008. “The carbon Kuznets Curve: A cloudy picture emitted by bad econometrics?” Forthcoming in Resource and Energy Economics.

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Table 1. Does CDM contribute to CO 2 emission reductions (sectoral approach)? 1990-2007 Dependent Variable: Within Groups

CO 2 it Speed of adjustment

Without Time Trend Pooled Mean Group

Mean Group

Within Groups

With Time Trend Pooled Mean Group

Mean Group

-0.216 [0.000]***

-0.296 [0.000]***

-0.517 [0.000]***

-0.212 [0.000]***

-0.490 [0.000]***

-0.816 [0.000]***

-0.038 [0.374] 1.038 [0.000]*** -0.097 [0.079]*

-0.170 [0.000]*** 1.665 [0.000]*** -0.203 [0.000]***

-0.279 [0.000]*** 0.063 [0.575] 0.039 [0.695]

-0.031 [0.451] 1.084 [0.000]*** -0.094 [0.096]*

-0.115 [0.000]*** 2.171 [0.000]*** -0.138 [0.002]***

-0.098 [0.033]** 0.007 [0.901] -0.217 [0.248]

0.001 [0.947] 0.301 [0.133] 0.027 [0.639]

-0.779 [0.017]** 2.177 [0.247] 0.027 [0.103]

0.416 [0.000]*** -1.290 [0.175] -0.002 [0.835]

0.023 [0.946] 0.310 [0.108] 0.001 [0.685] 0.000 [0.731]

0.029 [0.006]*** -2.361 [0.024]** 2.983 [0.194] -0.004 [0.294]

-0.010 [0.305] 0.061 [0.067]* -0.589 [0.835] 0.007 [0.000]***

578 34 713.46

578 34 1039.64

578 34 1166.60

578 34 713.56

578 34 1107.81

578 34 1278.02

Long-run coefficients

CDM

it

GDPit GDP

2

it

Short-run coefficients

Δ CDM

i , t −1

Δ GDP i ,t −1

Δ GDP 2 i ,t −1 Trend Observations Number of Countries Log Likelihood

Note: The dependent variable is CO2 emissions (sectoral approach) per capita in log. Variables and data sources are described in the text. This table presents the within group estimates, 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 with a time trend, respectively. 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 estimates, the standard errors are corrected for possible heteroscedasticity in the cross-sectional error variances. All equations included a constant country-speific term. Log Likelihood is to examine the null hypothesis of equality of all of the long-run coefficients. P-values are reported in the brackets. *, **, *** significant at 10%, 5%, 1%, respectively.

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

CO 2 it Speed of adjustment

Manufacturing Industries Within Pooled Mean Groups Mean Group Group -0.294 -0.649 -0.870 [0.000]*** [0.000]*** [0.000]***

Energy Sector Within Pooled Groups Mean Group -0.261 -0.627 [0.000]*** [0.000]***

Mean Group -0.835 [0.000]***

Households, Tertiary and Agriculture Within Pooled Mean Groups Mean Group Group -0.220 -0.584 -0.885 [0.000]*** [0.000]*** [0.000]***

Long-run coefficients

CDM

it

GDPit GDP

2

it

-0.001 [0.989] 1.223 [0.007]*** -0.346 [0.018]**

-0.065 -0.128 [0.000]*** [0.000]*** 1.626 0.016 [0.000]*** [0.821] -0.387 -0.118 [0.000]*** [0.469]

0.103 [0.490] 0.762 [0.034]** 0.108 [0.624]

-0.092 [0.000]*** 0.892 [0.000]*** 0.206 [0.001]***

0.420 [0.000]*** -0.005 [0.941] 0.014 [0.917]

-0.030 [0.718] 0.946 [0.108] -0.311 [0.001]***

-0.027 [0.557] 0.528 [0.096]* -0.064 [0.269] 0.000 [0.973] 578 34 189.25

3.703 0.056 [0.667] [0.754] -14.937 -0.029 [0.413] [0.550] 0.008 0.095 [0.170] [0.002]*** -0.007 0.009 [0.334] [0.110] 578 578 34 34 670.16 797.62

0.354 [0.841] -0.008 [0.849] -0.039 [0.435] 0.000 [0.972] 578 34 -26.58

13.678 [0.521] -2.936 [0.092]* 0.032 [0.313] 0.009 [0.246] 578 34 637.77

0.004 [0.931] -0.140 [0.000]*** -0.005 [0.931] 0.026 [0.000]*** 578 34 766.92

0.012 [0.485] 0.433 [0.440] -0.067 [0.015]** 0.000 [0.964] 578 34 367.89

-0.033 -0.291 [0.069]* [0.000]*** 0.577 0.036 [0.000]*** [0.727] 0.130 0.074 [0.009]*** [0.638]

Short-run coefficients

Δ CDM

i , t −1

Δ GDP i ,t −1

Δ GDP 2 i ,t −1 Trend Observations Number of Countries Log Likelihood

6.913 [0.302] 0.022 [0.612] -1.924 [0.410] -0.005 [0.463] 578 34 804.59

-0.168 [0.431] -0.113 [0.462] 0.040 [0.019]** 0.001 [0.739] 578 34 952.14

Note: The dependent variable is CO2 emissions per capita in log from manufacturing industries, energy sector, and households, tertiary, and agriculture, respectively. Variables and data sources are described in the text. See Table 1 for more notes.

Table 3. Does CDM contribute to CO 2 emission reductions (reference approach)? 1990-2007 Dependent Variable:

CO 2 it Speed of adjustment

Within Groups -0.233 [0.000]***

Without Time Trend Pooled Mean Group -0.315 [0.000]***

Mean Group -0.564 [0.000]***

Within Groups -0.232 [0.000]***

With Time Trend Pooled Mean Group -0.548 [0.000]***

Mean Group -0.874 [0.000]***

Long-run coefficients

CDM

it

GDPit GDP

2

it

-0.035 [0.394] 1.045 [0.000]*** -0.083 [0.102]

-0.136 [0.000]*** 1.609 [0.000]*** -0.172 [0.000]***

-0.049 [0.290] 0.114 [0.405] 0.069 [0.577]

-0.034 [0.420] 1.049 [0.001]*** -0.083 [0.100]

-0.108 [0.000]*** 2.104 [0.000]*** -0.094 [0.016]**

0.179 [0.000]*** 0.665 [0.003]*** -0.234 [0.433]

-0.005 [0.703] 0.264 [0.203] 0.048 [0.426]

-0.500 [0.379] 0.017 [0.079]* 2.118 [0.196]

-0.431 [0.044]** 0.659 [0.040]** -0.017 [0.119]

578 34 679.58

578 34 1011.05

578 34 1136.66

0.047 [0.703] 0.264 [0.179] -0.005 [0.426] 0.000 [0.976] 578 34 679.58

3.876 [0.118] 0.032 [0.005]*** -2.579 [0.009]*** -0.004 [0.442] 578 34 1089.20

1.292 [0.000]*** -0.010 [0.320] -2.036 [0.000]*** 0.008 [0.000]*** 578 34 1247.93

Short-run coefficients

Δ CDM

i , t −1

Δ GDP i ,t −1

Δ GDP 2 i ,t −1 Trend Observations Number of Countries Log Likelihood

Note: The dependent variable is CO2 emissions per capita from fuel combustion (by reference approach) in log over 1990-2007. Variables and data sources are described in the text. See Table 1 for more notes.

Appendix Table 1: The List of Countries in the Full Sample No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

23 24 25 26 27 28 29 30 31 32 33 34

Country Name Argentina Armenia Bangladesh Bolivia Brazil Chile China Cote d'Ivoire Colombia Costa Rica Ecuador Guatemala Honduras Indonesia India Iran Israel Jamaica Cambodia South Korea Sri Lanka Morocco Mexico Malaysia Nigeria Nicaragua Nepal Panama Peru Philippines El Salvador Thailand Uruguay South Africa

Country Code ARG ARM BGD BOL BRA CHL CHN CIV COL CRI ECU GTM HND IDN IND IRN ISR JAM KHM KOR LKA MAR MEX MYS NGA NIC NPL PAN PER PHL SLV THA URY ZAF

First CDM Year 2004 2005 2005 2004 2003 2003 2004 2005 2005 2004 2004 2003 2004 2004 2003 2005 2005 2005 2005 2003 2005 2004 2004 2004 2005 2005 2005 2005 2005 2005 2005 2005 2005 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 UNEP Risoe Centre CDM/JI Pipeline Analysis and Database (2008).

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 family of 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., Evans S., Ford A, Harwatt H., Kohler J., Tight M, (2009) A blueprint for the integrated assessment of climate change in cities: Tyndall Working Paper 129 • Carney S, Whitmarsh L, Nicholson-Cole S, Shackley S., (2009) A Dynamic Typology of Stakeholder Engagement within Climate Change Research: Tyndall Working paper 128;

and CO2 emissions :Tyndall Working Paper 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) Carbon • Goulden M, Conway D, Persechino A., Emissions Scenarios for China to (2008) Adaptation to climate change in 2100: Tyndall Working Paper 121 international river basins in Africa: a review: Tyndall Working paper 127; • Bergman, N., Whitmarsh L, Kohler J., (2008) Transition to sustainable • Bows A., Anderson K., (2008) development in the UK housing A bottom-up analysis of including sector: from case study to model aviation within the EU’s Emissions implementation: Tyndall Working Paper Trading Scheme: Tyndall Working Paper 120 126; • Conway D, Persechino A., Ardoin-Bardin • Al-Saleh Y., Upham P., Malik K., (2008) S., Hamandawana H., Dickson M, Dieulin Renewable Energy Scenarios for the C, Mahe G, (2008) RAINFALL AND Kingdom of Saudi Arabia: Tyndall WATER RESOURCES VARIABILITY IN Working Paper 125 SUB-SAHARAN AFRICA DURING THE 20TH CENTURY: Tyndall Centre Working • Scrieciu S., Barker T., Smith V., (2008) Paper 119 World economic dynamics and technological change: projecting interactions between economic output Tyndall Working Papers

2000 - 2009

• Starkey R., (2008) Allocating • Dawson, R.J., et al (2007) Integrated emissions rights: Are equal shares, analysis of risks of coastal flooding fair shares? : Tyndall Working Paper 118 and cliff erosion under scenarios of long term change: Tyndall Working • Barker T., (2008) The Economics of Paper No. 110 Avoiding Dangerous Climate Change: • Okereke, C., (2007) A review of UK Tyndall Centre Working Paper 117 FTSE 100 climate strategy and a • Estrada M, Corbera E., Brown K, (2008) framework for more in-depth analysis How do regulated and voluntary in the context of a post-2012 climate carbon-offset schemes compare?: regime: Tyndall Centre Working Paper Tyndall Centre Working Paper 116 109 • Estrada Porrua M, Corbera E., Brown K, (2007) REDUCING GREENHOUSE GAS EMISSIONS FROM DEFORESTATION IN DEVELOPING COUNTRIES: REVISITING THE ASSUMPTIONS: Tyndall Centre Working Paper 115

• Gardiner S., Hanson S., Nicholls R., Zhang Z., Jude S., Jones A.P., et al (2007) The Habitats Directive, Coastal Habitats and Climate Change – Case Studies from the South Coast of the UK: Tyndall Centre Working Paper 108

• Boyd E., Hultman N E., Roberts T., Corbera E., Ebeling J., Liverman D, Brown K, Tippmann R., Cole J., Mann P, Kaiser M., Robbins M, (2007) The Clean Development Mechanism: An assessment of current practice and future approaches for policy: Tyndall Centre Working Paper 114

• Schipper E. Lisa, (2007) Climate Change Adaptation and Development: Exploring the Linkages: Tyndall Centre Working Paper 107

• Hanson, S., Nicholls, R., Balson, P., Brown, I., French, J.R., Spencer, T., Sutherland, W.J. (2007) Capturing coastal morphological change within regional integrated assessment: an outcome-driven fuzzy logic approach: Tyndall Working Paper No. 113 • Okereke, C., Bulkeley, H. (2007) Conceptualizing climate change governance beyond the international regime: A review of four theoretical approaches: Tyndall Working Paper No. 112 • Doulton, H., Brown, K. (2007) ‘Ten years to prevent catastrophe’? Discourses of climate change and international development in the UK press: Tyndall Working Paper No. 111 Tyndall Working Papers

• Okereke C., Mann P, Osbahr H, (2007) Assessment of key negotiating issues at Nairobi climate COP/MOP and what it means for the future of the climate regime: Tyndall Centre Working Paper No. 106 • Walkden M, Dickson M, (2006) The response of soft rock shore profiles to increased sea-level rise. : Tyndall Centre 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) A blueprint for the integrated assessment of climate change in cities. : Tyndall Centre Working Paper 104 • Dickson M., Walkden M., Hall J., (2007) Modelling the impacts of climate change on an eroding coast over the 21st Century: Tyndall Centre Working Paper 103 •

Klein R.J.T, Erickson S.E.H, Næss L.O, 2000 - 2008

Hammill A., Tanner T.M., Robledo, C., O’Brien K.L.,(2007) Portfolio screening to support the mainstreaming of adaptation to climatic change into development assistance: Tyndall Centre Working Paper 102 • Agnolucci P., (2007) Is it going to happen? Regulatory Change and Renewable Electricity: Tyndall Centre Working Paper 101

equity and political legitimacy, Tyndall Centre Working Paper 94 • Schipper E. Lisa, (2006) Climate Risk, Perceptions and Development in El Salvador, Tyndall Centre Working Paper 93

• Tompkins E. L, Amundsen H, (2005) Perceptions of the effectiveness of the United Nations Framework Convention on Climate Change in prompting • Kirk K., (2007) Potential for storage behavioural change, Tyndall Centre of carbon dioxide in the rocks beneath Working Paper 92 the East Irish Sea: Tyndall Centre Working Paper 100 • Warren R., Hope C, Mastrandrea M, Tol R S J, Adger W. N., Lorenzoni I., • Arnell N.W., (2006) Global impacts of (2006) Spotlighting the impacts abrupt climate change: an initial functions in integrated assessments. assessment: Tyndall Centre Working Research Report Prepared for the Paper 99 Stern Review on the Economics of • Lowe T.,(2006) Is this climate porn? Climate Change, Tyndall Centre Working Paper 91 How does climate change

communication affect our perceptions Warren R., Arnell A, Nicholls R., Levy and behaviour?, Tyndall Centre Working • P E, Price J, (2006) Understanding the Paper 98 regional impacts of climate change: Research Report Prepared for the • Walkden M, Stansby P,(2006) The effect of dredging off Great Yarmouth Stern Review on the Economics of on the wave conditions and erosion of Climate Change, Tyndall Centre Working the North Norfolk coast. Tyndall Centre Paper 90 Working Paper 97 • Barker T., Qureshi M, Kohler J., • Anthoff, D., Nicholls R., Tol R S J, (2006) The Costs of Greenhouse Gas Vafeidis, A., (2006) Global and regional Mitigation with Induced Technological exposure to large rises in sea-level: a Change: A Meta-Analysis of Estimates sensitivity analysis. This work was in the Literature, Tyndall Centre Working prepared for the Stern Review on the Paper 89 Economics of Climate Change: Tyndall Centre Working Paper 96 • Kuang C, Stansby P, (2006) Sandbanks for coastal protection: • Few R., Brown K, Tompkins E. L, implications of sea-level rise. Part 3: (2006) Public participation and climate wave modelling, Tyndall Centre Working change adaptation, Tyndall Centre Paper 88 Working Paper 95 • Corbera E., Kosoy N, Martinez Tuna M, (2006) Marketing ecosystem services through protected areas and rural communities in Meso-America: Implications for economic efficiency, Tyndall Working Papers

• 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 2000 - 2008

• Stansby P, Kuang C, Laurence D, Launder B, (2006) Sandbanks for coastal protection: implications of sea-level rise. Part 1: application to East Anglia, Tyndall Centre Working Paper 86

Avoiding dangerous climate change by inducing technological progress: scenarios using a large-scale econometric model, Tyndall Centre Working Paper 77

• Agnolucci,. P (2005) The role of • Bentham M, (2006) An assessment political uncertainty in the Danish of carbon sequestration potential in renewable energy market, Tyndall the UK – Southern North Sea case Centre Working Paper 76 study: Tyndall Centre Working Paper 85 • Fu, G., Hall, J. W. and Lawry, J. • Anderson K., Bows A., Upham P., (2005) Beyond probability: new (2006) Growth scenarios for EU & UK methods for representing uncertainty aviation: contradictions with climate in projections of future climate, policy, Tyndall Centre Working Paper 84 Tyndall Centre Working Paper 75 • Williamson M., Lenton T., Shepherd J., Edwards N, (2006) An efficient numerical terrestrial scheme (ENTS) for fast earth system modelling, Tyndall Centre Working Paper 83

• Ingham, I., Ma, J., and Ulph, A. M. (2005) How do the costs of adaptation affect optimal mitigation when there is uncertainty, irreversibility and learning?, Tyndall Centre Working Paper 74

• Bows, A., and Anderson, K. (2005) An analysis of a post-Kyoto climate • Walkden, M. (2005) Coastal policy model, Tyndall Centre Working process simulator scoping study, Paper 82 Tyndall Centre Working Paper 73 • Sorrell, S., (2005) The economics of • Lowe, T., Brown, K., Suraje Dessai, energy service contracts, Tyndall S., Doria, M., Haynes, K. and Vincent., K Centre Working Paper 81 (2005) Does tomorrow ever come? Disaster narrative and public • Wittneben, B., Haxeltine, A., Kjellen, perceptions of climate change, Tyndall B., Köhler, J., Turnpenny, J., and Warren, Centre Working Paper 72 R., (2005) A framework for assessing the political economy of post-2012 • Boyd, E. Gutierrez, M. and Chang, global climate regime, Tyndall Centre M. (2005) Adapting small-scale CDM Working Paper 80 sinks projects to low-income communities, Tyndall Centre Working • Ingham, I., Ma, J., and Ulph, A. M. Paper 71 (2005) Can adaptation and mitigation be complements?, Tyndall Centre • Abu-Sharkh, S., Li, R., Markvart, T., Working Paper 79 Ross, N., Wilson, P., Yao, R., Steemers, K., Kohler, J. and Arnold, R. (2005) Can • Agnolucci,. P (2005) Opportunism Migrogrids Make a Major Contribution and competition in the non-fossil fuel to UK Energy Supply?, Tyndall Centre obligation market, Tyndall Centre Working Paper 70 Working Paper 78 • Tompkins, E. L. and Hurlston, L. A. • Barker, T., Pan, H., Köhler, J., (2005) Natural hazards and climate Warren., R and Winne, S. (2005) change: what knowledge is Tyndall Working Papers

2000 - 2008

transferable?, Tyndall Centre Working management in the UK, Tyndall Centre Paper 69 Working Paper 60 • Bleda, M. and Shackley, S. (2005) The formation of belief in climate change in business organisations: a dynamic simulation model, Tyndall Centre Working Paper 68

• Anderson, D and Winne, S. (2004) Modelling Innovation and Threshold Effects In Climate Change Mitigation, Tyndall Centre Working Paper 59

Turnpenny, J., Haxeltine, A. and O’Riordan, T., (2005) Developing regional and local scenarios for climate change mitigation and adaptation: Part 2: Scenario creation, Tyndall Centre Working Paper 67

• Bray, D and Shackley, S. (2004) The Social Simulation of The Public Perceptions of Weather Events and their Effect upon the Development of Belief in Anthropogenic Climate Change, Tyndall Centre Working Paper 58

•

• Turnpenny, J., Haxeltine, A., Lorenzoni, I., O’Riordan, T., and Jones, M., (2005) Mapping actors involved in climate change policy networks in the UK, Tyndall Centre Working Paper 66 • Adger, W. N., Brown, K. and Tompkins, E. L. (2004) Why do resource managers make links to stakeholders at other scales?, Tyndall Centre Working Paper 65

• Shackley, S., Reiche, A. and Mander, S (2004) The Public Perceptions of Underground Coal Gasification (UCG): A Pilot Study, Tyndall Centre Working Paper 57 • Vincent, K. (2004) Creating an index of social vulnerability to climate change for Africa, Tyndall Centre Working Paper 56

• Peters, M.D. and Powell, J.C. (2004) Fuel Cells for a Sustainable Future II, • Mitchell, T.D. Carter, T.R., Jones, Tyndall Centre Working Paper 64 .P.D, Hulme, M. and New, M. (2004) A comprehensive set of high-resolution • Few, R., Ahern, M., Matthies, F. and grids of monthly climate for Europe Kovats, S. (2004) Floods, health and and the globe: the observed record climate change: a strategic review, (1901-2000) and 16 scenarios (2001Tyndall Centre Working Paper 63 2100), Tyndall Centre Working Paper 55 • Barker, T. (2004) Economic theory and the transition to sustainability: a comparison of approaches, Tyndall Centre Working Paper 62

• Turnpenny, J., Carney, S., Haxeltine, A., and O’Riordan, T. (2004) Developing regional and local scenarios for climate change mitigation and adaptation Part 1: A framing of the East of England Tyndall • Brooks, N. (2004) Drought in the Centre Working Paper 54 African Sahel: long term perspectives and future prospects, Tyndall Centre • Agnolucci, P. and Ekins, P. (2004) Working Paper 61 The Announcement Effect And Environmental Taxation Tyndall Centre • Few, R., Brown, K. and Tompkins, Working Paper 53 E.L. (2004) Scaling adaptation: climate change response and coastal Tyndall Working Papers

2000 - 2008

• Agnolucci, P. (2004) Ex Post Technology Responses to Climate Evaluations of CO2 –Based Taxes: A Change, Tyndall Centre Working Paper 43 Survey Tyndall Centre Working Paper 52 • Kim, J. (2003) Sustainable • Agnolucci, P., Barker, T. and Ekins, Development and the CDM: A South P. (2004) Hysteresis and Energy African Case Study, Tyndall Centre Demand: the Announcement Effects Working Paper 42 and the effects of the UK Climate Change Levy Tyndall Centre Working • Watson, J. (2003), UK Electricity Paper 51 Scenarios for 2050, Tyndall Centre Working Paper 41 • Powell, J.C., Peters, M.D., Ruddell, A. and Halliday, J. (2004) Fuel Cells for a • Klein, R.J.T., Lisa Schipper, E. and Sustainable Future? Tyndall Centre Dessai, S. (2003), Integrating Working Paper 50 mitigation and adaptation into climate and development policy: three • Awerbuch, S. (2004) Restructuring research questions, Tyndall Centre our electricity networks to promote Working Paper 40 decarbonisation, Tyndall Centre Working Paper 49 • Tompkins, E. and Adger, W.N. (2003). Defining response capacity to • Pan, H. (2004) The evolution of enhance climate change policy, Tyndall economic structure under Centre Working Paper 39 technological development, Tyndall Centre Working Paper 48 • Brooks, N. (2003). Vulnerability, risk and adaptation: a conceptual • Berkhout, F., Hertin, J. and Gann, framework, Tyndall Centre Working D. M., (2004) Learning to adapt: Paper 38 Organisational adaptation to climate change impacts, Tyndall Centre Working • Ingham, A. and Ulph, A. (2003) Paper 47 Uncertainty, Irreversibility, Precaution and the Social Cost of • Watson, J., Tetteh, A., Dutton, G., Carbon, Tyndall Centre Working Paper 37 Bristow, A., Kelly, C., Page, M. and Pridmore, A., (2004) UK Hydrogen • Kröger, K. Fergusson, M. and Futures to 2050, Tyndall Centre Working Skinner, I. (2003). Critical Issues in Paper 46 Decarbonising Transport: The Role of Technologies, Tyndall Centre Working • Purdy, R and Macrory, R. (2004) Paper 36 Geological carbon sequestration: critical legal issues, Tyndall Centre • Tompkins E. L and Hurlston, L. Working Paper 45 (2003). Report to the Cayman Islands’ Government. Adaptation lessons learned from responding to tropical • Shackley, S., McLachlan, C. and cyclones by the Cayman Islands’ Gough, C. (2004) The Public Government, 1988 – 2002, Tyndall Perceptions of Carbon Capture and Centre Working Paper 35 Storage, Tyndall Centre Working Paper 44 • Dessai, S., Hulme, M (2003). Does • Anderson, D. and Winne, S. (2003) climate policy need probabilities?, Innovation and Threshold Effects in Tyndall Centre Working Paper 34 Tyndall Working Papers

2000 - 2008

implications for adaptation to climate • Pridmore, A., Bristow, A.L., May, A. change, Tyndall Centre Working Paper 26 D. and Tight, M.R. (2003). Climate Change, Impacts, Future Scenarios • Xueguang Wu, Mutale, J., Jenkins, and the Role of Transport, Tyndall N. and Strbac, G. (2003). An Centre Working Paper 33 investigation of Network Splitting for Fault Level Reduction, Tyndall Centre Working Paper 25 • Xueguang Wu, Jenkins, N. and Strbac, G. (2003). Integrating • Xueguang Wu, Jenkins, N. and Renewables and CHP into the UK Strbac, G. (2002). Impact of Electricity System: Investigation of Integrating Renewables and CHP into the impact of network faults on the the UK Transmission Network, Tyndall stability of large offshore wind farms, Centre Working Paper 24 Tyndall Centre Working Paper 32 • Paavola, J. and Adger, W.N. (2002). • Turnpenny, J., Haxeltine A. and Justice and adaptation to climate O’Riordan, T. (2003). A scoping study of change, Tyndall Centre Working Paper 23 UK user needs for managing climate futures. Part 1 of the pilot-phase • Watson, W.J., Hertin, J., Randall, T., interactive integrated assessment Gough, C. (2002). Renewable Energy process (Aurion Project), Tyndall and Combined Heat and Power Centre Working Paper 31 Resources in the UK, Tyndall Centre Working Paper 22 • Hulme, M. (2003). Abrupt climate change: can society cope?, Tyndall • Watson, W. J. (2002). Renewables Centre Working Paper 30 and CHP Deployment in the UK to 2020, Tyndall Centre Working Paper 21 • Brown, K. and Corbera, E. (2003). A Multi-Criteria Assessment Framework • Turnpenny, J. (2002). Reviewing for Carbon-Mitigation Projects: organisational use of scenarios: Case Putting “development” in the centre study - evaluating UK energy policy of decision-making, Tyndall Centre options, Tyndall Centre Working Paper 20 Working Paper 29 • Pridmore, A. and Bristow, A., • Dessai, S., Adger, W.N., Hulme, M., (2002). The role of hydrogen in Köhler, J.H., Turnpenny, J. and Warren, R. powering road transport, Tyndall (2003). Defining and experiencing Centre Working Paper 19 dangerous climate change, Tyndall Centre Working Paper 28 • Watson, J. (2002). The development of large technical • Tompkins, E.L. and Adger, W.N. systems: implications for hydrogen, (2003). Building resilience to climate Tyndall Centre Working Paper 18 change through adaptive management of natural resources, • Dutton, G., (2002). Hydrogen Tyndall Centre Working Paper 27 Energy Technology, Tyndall Centre Working Paper 17 • Brooks, N. and Adger W.N. (2003). Country level risk measures of • Adger, W.N., Huq, S., Brown, K., climate-related natural disasters and Conway, D. and Hulme, M. (2002). Adaptation to climate change: Setting Tyndall Working Papers

2000 - 2008

the Agenda for Development Policy • Adger, W. N. (2001). Social Capital and Research, Tyndall Centre Working and Climate Change, Tyndall Centre Paper 16 Working Paper 8 • Barnett, J. (2001). Security and • Köhler, J.H., (2002). Long run Climate Change, Tyndall Centre Working technical change in an energy- Paper 7 environment-economy (E3) model for an IA system: A model of Kondratiev • Goodess, C.M., Hulme, M. and waves, Tyndall Centre Working Paper 15 Osborn, T. (2001). The identification and evaluation of suitable scenario • Shackley, S. and Gough, C., (2002). development methods for the The Use of Integrated Assessment: An estimation of future probabilities of Institutional Analysis Perspective, extreme weather events, Tyndall Tyndall Centre Working Paper 14 Centre Working Paper 6 • Dewick, P., Green K., Miozzo, M., (2002). Technological Change, Industry Structure and the Environment, Tyndall Centre Working Paper 13

• 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). • Dessai, S., (2001). The climate How High are the Costs of Kyoto for regime from The Hague to Marrakech: the US Economy?, Tyndall Centre Saving or sinking the Kyoto Protocol?, Working Paper 4 Tyndall Centre Working Paper 12 • Berkhout, F, Hertin, J. and Jordan, • Barker, T. (2001). Representing A. J. (2001). Socio-economic futures in the Integrated Assessment of Climate climate change impact assessment: Change, Adaptation and Mitigation, using scenarios as 'learning Tyndall Centre Working Paper 11 machines', Tyndall Centre Working Paper 3 • Gough, C., Taylor, I. and Shackley, S. (2001). Burying Carbon under the • Hulme, M. (2001). Integrated Sea: An Initial Exploration of Public Assessment Models, Tyndall Centre Opinions, Tyndall Centre Working Paper Working Paper 2 10 • Mitchell, T. and Hulme, M. (2000). A • Barnett, J. and Adger, W. N. (2001). Country-by-Country Analysis of Past Climate Dangers and Atoll Countries, and Future Warming Rates, Tyndall Tyndall Centre Working Paper 9 Centre Working Paper 1

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Tyndall Working Papers

2000 - 2008