environmental kuznets revisited · 2015. 7. 2. · called environmental kuznets curve (ekc)....

Environmental Kuznets Curves for CO2:

Heterogeneity versus Homogeneity

Elbert Dijkgraaf

Erasmus University Rotterdam, Rotterdam

Betrand Meelenberg Department of Econometrics and CentER, Tilburg University

Herman R.J. Vollebergh

Erasmus University Rotterdam, Rotterdam

January 31th, 2004

Paper Submitted for the SURED-conference 2004 Ascona – Switzerland

June 7-10, 2004

Abstract We explore the emission-income relationship for CO2 in OECD countries using various modelling strategies. Even for this relatively homogeneous sample we find that the inverted U-shaped curve is quite sensitive to the degree of heterogeneity included in the panel estimations. This finding is robust, not only across different model specifications but also across estimation techniques, including the more flexible non-parametric approach. Differences in restrictions applied in panel estimations are therefore responsible for the widely divergent findings for an inverted U-shape for CO2. Our findings suggest that enough heterogeneity is essential to prevent spurious correlation from reduced form panel estimations. Moreover, this inverted U is likely to exist for some, but not for all countries. Keywords: Environmental Kuznets Curves, Semiparametric Estimation, Heterogeneity. JEL Code: C 33; O 50; Q 40

Correspondence: Herman R.J. Vollebergh, Department of Economics, Erasmus University Rotterdam PO Box 1738, 3000DR, Rotterdam, The Netherlands. Phone +31)104081498; Email: [email protected]

1. Introduction

Recently, attention has grown for the robustness of reduced form estimations of the typical

inverted U-shaped relationship between economic growth and the environment, the so-

called Environmental Kuznets Curve (EKC). Earlier findings reported in the literature have

been shown to be highly sensitive to data sets, model specifications, and differences in

employed estimation techniques. For instance, Harbaugh, Levinson and Wilson (2002)

show how the specification of functional forms in the parametric approach as well as the

inclusion of additional covariates and the use of different data sets causes much less

empirical support for the existence of an inverted U-shaped relationship for ambient air

pollution emissions. Millimet, List and Stengos (2003) go one step further and report that

modeling strategies are also important and they provide statistical evidence in favor of the

more flexible semi-parametric models. However, they still find support for so called EKC

Turning Points, that is points after which per capita emissions tend to decline with per

capita income levels, in the case of US NOx and SO2 emissions.

Estimations of inverted U-shaped patterns for CO2 emissions show an even more

scattered picture leaving an outsider confused behind. This literature started with the much

cited papers by Shafik (1994) and Holtz-Eakin and Selden (1995) who both employ the

traditional parametric approach. Both papers report a Turning Point though far out of

sample ($ 7 million per capita). In an interesting contribution to this literature Schmalensee

et al. (1998) (SSJ), using the much more flexible spline-based estimation technique, even

report clear evidence of a within sample Turning Point with negative income elasticities for

the highest income segment. However, with the even more flexible non-parametric

approach Azomahou and Van Phu (2000) have shown that the inverted U-shape would no

longer hold in the CO2 case. In their sample the richest countries do not reach a phase of

declining per capita emissions, which challenges the idea that countries become

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automatically cleaner if they reach higher income levels. They also claim their estimation

technique would be preferred to the traditional parametric approach, and more recent

papers even seem to question the panel approach as such (Egli, 2002). So whether an

“inverted U” exist for CO2 still remains a puzzle, which is rather discomforting in view of

the important international environmental challenge of climate change.

In this paper we take up this challenge and ask whether it is likely that an EKC

pattern exist for CO2. To that end we compare the different estimation techniques

examined in the literature so far using a panel for CO2-emissions in OECD countries

between 1950-2000. Moreover, we extend the semiparametric partially linear regression

(PLR) model followed by Millimet et.al. (2003) to capture potential criticisms that their

approach does not allow for heterogeneity in the time dummy. Accordingly we also

include estimations that allow for as much heterogeneity as possible but still benefit from

panel estimations techniques.

Our empirical results provide interesting new insights. First, we confirm earlier

findings in the literature showing that EKC findings for countries depend on the type of

estimation technique employed. The more flexible trend corrected non-parametric

estimation further adds to the widely divergent picture of EKC patterns for countries.

Second, our results tend to support the non-existence of an inverted U pattern. We find

support for the existence of the EKC pattern only in 1 country (Sweden) regardless of

estimation technique. In contrast 11 countries do not show this pattern (including rich

countries like Australia, Canada, France, Italy), whereas different estimation techniques

yield different windows for the other 12 (including a rejection of the EKC-hypothesis for

Japan and the US based on both non-parametric estimations). Our findings are robust for

different data sets. Moreover, we explore sensitivity of this outcome for covariates

reflecting (homogeneous) exogenous factors not yet explored in this context, and for

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different levels of aggregation. We conclude that an EKC for CO2 is rather unlikely for the

whole sample, but might still be likely for a subsample of countries. Our finding also

matches basic economic intuition, which suggest that “automatic” internalization of

climate change, with its large cross-country spillovers, is not very likely if countries grow

richer.

The paper is organized as follows. Section 2 describes our data set and shortly

discusses the econometric model specifications. Section 3 shows that applying commonly

used estimation techniques to our data set reproduces the mixed picture for the existence of

an EKC found in the literature. Section 4 explores in detail the role of (des)aggregation,

heterogeneity and exogeneity using both parametric and more flexible estimation

techniques. Section 5 concludes.

2. Empirical Strategy

2.1 Econometric approach

To maintain as much consistency as possible with previous studies on an inverted U for

CO2, we not only focus on polynomial specifications of country-level emissions as a

function of country per capita income allowing for both country and time (fixed) effects,

but we also include the spline function approach applied by SSJ, as well as non-parametric

models. In its most general form, we consider the specification

0],,[,),,( =+= tryEtryhc εε (1)

with c = C/N, y = Y/N, and where r stands for country/region r, and t stands for year t. To

start with, the function h is left unspecified. However, without further restrictions this

function is not identified, since for each (r,t)-combination only one (c,y)-observation is

available. In the traditional, homogeneous approach one imposes a structure like the

following:

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tt

trr

r dtdrygtryh ∑∑ ++= λαβ ),(),,( (2)

with β = (β0, β1, β2, β3)′ such that

( ) 33

2210, yyyyg βββββ +++= (3)

and with drr a dummy for country/region r, and dtt a dummy for year t. This model can be

estimated using standard panel data techniques (after imposing appropriate distributional

assumptions). This traditional approach is quite restrictive because it is typically assumed

that every cross-sectional unit reacts similar to shifts in the income parameters, even if the

units are allowed to differ in their intercepts. This homogeneity assumption also

characterizes more flexible estimation techniques applied in the literature, like the spline

method used by Schmalensee et.al. (1989) and the semiparametric methods used by

Galeotti and Lanza (1999) and Millimet et.al. (2003). These semiparametric alternatives

estimate a version of

tt

trr

r dtdrygtryh ∑∑ ++= λα)(),,( (4)

with g(.) left unspecified. This model can be estimated using Robinson (1988), as, for

example, in the analysis of Millimet et al. (2003) who apply the semiparametric partially

linear regression (PLR) model.

The common procedure behind both the traditional and non-parametric approach is

based on the general premise is that a single cross-sectional unit r undergoes the inverted-

U relationship over time. Usually, only country specific heterogeneity intercepts are

allowed and not heterogeneous slope parameters, i.e., δh(y,r,t)/δy is postulated not to

depend on r. The typical exception is List and Gallett (1999) and also Millimet et.al.

(2003) point at the potential relevance of heterogeneity. Extending List and Gallett’s

(1999) approach to more flexible estimation techniques, we show that estimations for the

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CO2-case are strongly dependent on the amount of heterogeneity allowed in either the

income and time parameters or both.

For both the parametric and spline approach we explicitly tested for the country

specific homogeneity assumption that all countries follow an isomorphic pattern for CO2-

emissions in relation to GDP. More precisely, for example, in case of equation (2) we

considered as a generalization

tt

trr

rrr dtdrygtryh ∑∑ ++= λαβα ),(),,( (5)

and we tested the null hypothesis βr = β. We also allowed for different degrees of

heterogeneity in the control variables (including country fixed effects as well as country

specific trends) and for both the polynomial and spline specifications.1

Note, however, that rejection of the null hypothesis might also indicate model

misspecification, and, thus, rejection of the hypothesis βr = β does not necessarily imply

non-homogeneity.2 So, to proceed it makes sense to consider

tt

t dtryftryh ∑+= λ),(),,( (6)

with f(.,.) left unspecified, and to test f(y,r) = g(y). Specification (6) fits in the Robinson

(1988)-framework, but there is one difficulty. In order to apply Robinson (1988) one has to

estimate in a first round

0],[ =rydtE t (7)

But for a given country/region r the dummy variable dtt is always zero, except for one

observation, namely year t, implying that there is not enough variation in the data to

estimate (7), making an application of Robinson (1988) to (6) impossible. As a

consequence, nonparametric testing for homogeneity does not seem to be possible if the

starting point is specification (4) as employed by, for instance, Millimet et al. (2003). To

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proceed, one has to impose more structure on the time component, i.e., instead of (6) one

could consider

)(),(),,( tGryftryh += (8)

with G(t) less flexible than G(t) = tt

t dt∑λ . For instance, by taking G(t) = ( )Htdrdrt rtt

t∑λ

[NOTE: To be included: further explanation of the PLR estimate with time heterogeneity] 3

2.2 Data

Our results are based on national-level data for 24 OECD countries (excluding ‘new’

members like the Czech Republic, Hungary, Korea, Mexico and Poland) between 1950 and

2000. We thus concentrate exclusively on the subsample of traditional OECD countries,

which alone is responsible for 50% of overall world carbon dioxide emissions in 1996. The

data included are the following:

C = CO2 emissions from energy consumption, millions of metric tons of C [??]

Y = GDP, millions of 1990 U.S. dollars

N = population

E = energy consumption, million Tons of Oil Equivalent (TOE)

Our overall data set contains 984 observations on these variables, for each country we have

41 observations.

Data on C are calculated from E using OECD (2002) and IEA (1991). To calculate

CO2 emissions, we use data for Total Primary Energy Supply (TPES) per fuel, corrected

for non-energy use of fuels such as chemical feedstocks. Fuels incorporated in the

calculations are coal, other solid fuels (wood for example), crude oil, petroleum products

and natural gas. Total energy use per country as well as emissions are corrected for exports

and imports of fuels, as well as for stock changes and international marine bunkers.4

6

Data on Y and N were taken from the OECD Energy Balances. All figures are

expressed in 1990 dollars, using purchasing power parities. Time coverage of these data is

considerably more recent compared to the widely used Penn World Table, which has

figures only until 1992. The data on Germany require some additional attention due to the

country’s unification in 1991. The OECD reconstructed data on Y for Germany as a whole

(including the former GDR) for the years between 1970 and 1989. We further extrapolated

GDP figures backward until 1960 using adjusted GDP levels for Western Germany with

the number of inhabitants of Eastern Germany. See Table 1 for descriptive statistics for all

variables and Figure 1 for a scatter plot of our data. [INSERT TABLE 1] [INSERT

FIGURE 1]

Note that our findings are based on a subset of the countries that are usually

analyzed in the case of CO2-emissions. Our panel, however, is particularly useful for a

study of the homogeneity assumption at the country level because there is a wide overlap

of observations of different countries at similar income levels. Moreover, the range of

observations is long enough to test for each country whether their slope coefficients are

sufficiently close to allow for panel based estimations of an EKC for CO2. If a problem

arises for this (high-income) subsample of OECD countries, one might expect the

homogeneity assumption to be even more problematic for samples including both OECD

and non-OECD countries. Note, in addition, that this panel also includes data on CO2-

emissions covering the ‘90s and is by far the most reliable source of information on these

emissions compared to non-OECD sources as well as non-energy related CO2-emissions.

3. Empirical results based on the homogeneity assumption

This section reproduces earlier estimations in the literature of the inverted-U pattern based

on the homogeneity assumption for the case of CO2-emissions as our benchmark. Figure 1

7

summarizes our main findings for (pooled) parametric cubic specification, the (linear)

spline method and the standard non-parametric estimation based on Robinson (1988).

[INSERT FIGURE 2]

The response coefficients for income in the cubic specification with both time and

country fixed effects are significantly different from zero at the p <0.01 levels

Interestingly, our results present a much gloomier picture of an EKC pattern for CO2-

emissions compared to earlier results based on polynomial specifications reported by

Shafik (1994) and Holtz-Eakin and Selden (1995). We find a within sample Turning Point

(TP) at $ 14.365 which is at 43% of the maximum panel observation. Vertical lines added

at the predicted peak of this parametric EKC and its upper and lower limit of the 95%

confidence interval for the estimated parametric peak indicate the robustness of this result.5

Further evidence for an EKC pattern is produced with the much more flexible

piecewise (linear) spline framework first applied in this context by SSJ (1998). Like SSJ,

we first started with a model featuring 20- and 24-segment splines and time-fixed effects,

where each segment contains the same number of data points. In our case, we reject

simplifications to 12 and 10 splines that preserve this symmetry, but the differences are

small. The same holds for simplifications from 16 to 8 splines. Our findings indicate a TP

at a much higher income level than the standard parametric estimation, though still within

sample, i.e. at 64 % maximum value and significant at p <0.01%.6

These results seem to provide overwhelming evidence for the existence of an

inverted U for CO2. Applying the semiparametric estimation technique, however, yields a

completely different picture. Although the fitted line more or less closely follows the EKC

pattern produced by the (parametric) cubic specification for income levels up to $20.000 or

59% of the maximum income level, we observe an emission-income relationship that casts

doubt on the existence of an inverted U for the observations at the upper end of the

8

sample.7 Where both the traditional parametric as well as the spline method generate a

negative income-elasticity, the semiparametric results are much less decisive and even

suggest an overall positive elasticity (see figure 1). This finding seems to confirm

Azomahou and Van Phu (2000) who – using the same specification – conclude that the

overall pattern more or less follows a monotonic increasing pattern of CO2 emissions per

capita with rising (per capita) income levels. Therefore they claim that the existence of an

EKC pattern for CO2-emissions should be rejected. However, the number of observations

at the upper end is small as is reflected in the much wider confidence bound.

Thus we more or less reproduce the existing findings in the literature. Accordingly,

the different findings for the three basic models applied in the literature on the EKC for

CO2-emissions confirm the main finding reported by Millimet et.al. (2003) that modeling

strategies matter. For our data, however, not only the location of the TPs is different, but

also the answer to the question whether an inverted U exist or not. Moreover, applying the

same specification test using the semi-parametric PLR method as the alternative (see

Zheng, 1996 and Li and Wang, 1998), we also reject the parametric but not the spline

based specification. This is a bit surprising and may be due to the fact that the PLR method

is highly inconclusive at the upper tail of the income distribution. The last spline, however,

closely follows the parametric estimation that is rejected. Therefore we belief there is little

reason to belief the spline method is more accurate given the upward shift at the end of the

distribution.[NOTE: results of the specification tests]

Closer inspection of the data shows that only one or two countries dominate the

data in the upper tail of the income distribution, in particular Luxemburg and the USA (see

also Figure 1). Re-estimating the models specified before without the data for Luxemburg

does not alter the parametric or spline-based result, but has a considerable effect on the

standard PRL model. An inverted emission-income pattern without the data for Luxemburg

9

is highly unlikely in this case. Therefore we conclude that not only parametric but also

semiparametric results may be dependent on relatively few observations in the upper tail of

the (income) distribution. Indeed, the weight of the data for Luxemburg – with only

400.000 inhabitants –is entirely similar to those of countries like the USA, Japan or

Germany. Moreover, one major event – the closing down of a large steel firm in the ‘80s in

Luxemburg8 – may affect our ultimate judgment on whether or not an EKC for CO2 exist.

It goes without saying that this is undesirable.

This issue points at an issue hardly explored in the literature so far, which is the

role of international specialization and divergent patterns in technological change across

countries (over time), which may not be adequately captured by country and time fixed

effects in a modeling environment based on the homogeneity assumption. Even without an

effective regulatory policy on CO2-emissions, OECD countries show considerable

improvements in their energy-efficiency although with remarkable differences across

countries at the same time. Because of the gradual change in the dependence on fossil fuels

of several, but not all OECD countries one might expect significant differences across

countries in terms of their CO2-emission-income relationship. Moreover, international

specialization of industries over time also affects differences in country specific energy-

intensity and its closely related CO2-emission intensity.

4. The role of heterogeneity

4.1 Homogeneity and model specification

Essentially reduced form estimations of the EKC hypothesis focus on the role of the

income parameters β, while preserving as much homogeneity between different cross-

sections r as possible, i.e. βr = β. This typically has the advantage to yield predictions: one

expects a country at the lower end of the income observations to follow the same emission-

10

income pattern as the other cross-sections even if its emission level is different (controlled

for by country fixed effects). Therefore, maintaining homogeneity is a desirable

characteristic of panel based estimations.

Unfortunately, explicit testing of the null hypothesis of homogeneous country-

specific slopes (i.e., whether βr = β in (5)) of the models presented in the previous section

yields a clear rejection of this core assumption at the p <0.01 levels [INSERT TABLE 2]

The magnitude of the Wald-test for the model with country-fixed effects only (not

included), and the model with both time- and country-fixed effects, is Wald(69 888) =

5,694 and Wald(69 851) = 4,830 at the p <0.01 level, respectively. This result does not

change if one allows for more flexibility in the time parameter by including country-

specific trends (see second column of Table 2). Even though this more general model

performs considerably better than the commonly estimated parametric models, the

homogeneity assumption on the GDP coefficients is still rejected (the Wald-statistic is

Wald(69 864) = 1,328.9

Further testing of homogeneity in the case of the spline (piecewise linear) function

yields similar results. As far as the homogeneity assumption is concerned, we also find

clear indications that even spline models with country-specific trends do not allow for

enough heterogeneity. With the same income levels for the different segments applied to

the country level, we find a rejection of this crucial assumption for the preferred models in

all cases.10 As we explained in section 2 these results may indicate that reduced form

parametric or spline based estimations assuming homogeneity in either the income or the

time parameter or both might be misleading.11

Direct testing of the homogeneity assumption in the semi-parametric Robinson

framework is more complicated. More heterogeneity in the income parameter is only

possible with enough (parametric) structure on the time component. We find that a third

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order time polynomial is the preferred (semi-parametric) model, which, in turn, could be

tested against its homogeneous specification. Again this yields a clear indication that the

homogeneity restriction on the income parameter is too strong.

The importance of heterogeneity is further illustrated by including country-specific

GDP variables for one country at a time in the panel model. Using a LR test, we have to

reject the hypothesis of homogeneity for 14 of the 24 countries at a 99% level of

significance (using the preferred model with country-specific trends).12 Furthermore, by

systematically testing the homogeneity of all possible sub-panels (in total, nearly 380,000

combinations are checked), we also found that sub-panels for which homogeneity is not

rejected are rare, and never exceed a group of five countries. Moreover, even for very

small sub-panels, homogeneity is rejected in nearly all cases. Thus, even for an apparently

homogeneous group of OECD countries, panel-based estimates for commonly used

polynomial estimators do not seem to allow for enough heterogeneity, and might yield

biased and inconsistent parameter estimates for CO2 emissions.

4.2 An EKC for CO2?

To test the implications of our result for the EKC hypothesis, we finally compare country-

specific income parameter estimates for the heterogeneous polynomial model including a

(country specific) trend with a PLR estimate for each country separately. We allow for

more heterogeneity in the time component of the PLR estimates by applying (9) which is

based on pairwise combinations of countries that have developed more or less closely over

time, like Belgium and the Netherlands (see Appendix for further details).

The result is remarkable (see Figure 3). [INSERT FIGURE 3] Now only 14 of the

24 countries have a within-sample TP, and of those 14 even 5 are (far) out of their own

country income range. Furthermore, of the 13 OECD countries mentioned explicitly by

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SSJ as having a within-sample turning point (all of them dated in the ‘70s), only four

countries confirm this picture based on our estimates (Germany, Luxembourg,

Switzerland, and the US). Also, the data for three of the seven highest income countries do

not indicate a turning point according to our estimates.

Again the semi-parametric estimates present a strikingly different pattern. For

several countries the polynomial based parametric estimates suggest a clear TP where the

PLR estimate points at a different development over time. [TO BE TESTED]

4.3 Robustness checks

One obvious objection to our findings is that our results are sensitive to the data sets used.

To check this sensitivity, we also tested whether the homogeneity hypothesis is rejected for

the data sets used by Holtz-Eakins and Selden (1995) and SSJ. We first tested for a sample

period excluding data between 1990-1997. We also used income data taken from the Penn

World Table until 1992 for the same (OECD) sample (this also accounts for potential

problems with data on Y for Germany, as these data are restricted to West Germany only).

Finally, we used emission figures for the same panel taken from the Carbon Dioxide

Information Analysis Center of the Oak Ridge National Laboratory. In all of these cases

our basic findings are similar.1

5. Conclusion

Our findings suggest that panel-based estimations of the inverted-U hypothesis for CO2

should be treated with care. Although non-parametric estimations of a rather restrictive

specification for the entire panel suggest that no such pattern exist, and specification tests

suggest this technique to be preferable, allowing for country specific estimations shows

that such inverted U-shaped patterns do exist for several countries. Thus the existence of

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an overall inverted-U for CO2 emissions ultimately depends on the balance between the

high-income countries with an (expected) inverted-U, and those high-income countries

with a still-growing amount of (per capita) emissions. An overall inverted-U seems

doubtful if so many counterexamples exist at the country level. Lack of homogeneity with

respect to CO2, however, should not come as a surprise, given the trends in international

specialisation, and other differences in local circumstances, as well as the absence of (co-

ordinated) policies against CO2 emissions in the past.

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References

Azomahou, Theophile, and Nguyen van Phu (2000), “Economic Growth and CO2

Emissions: a Nonparametric Approach”, Strasbourg, France.

Bruyn, Sander M. de (2000), Economic Growth and the Environment, Dordrecht: Kluwer

Academic Publishers.

Grossman, Gene M. and Allen B. Krueger (1995), “Economic Growth and the

Environment,” Quarterly Journal of Economics 110(2), 353-377.

Harbaugh, William, Arik Levinson and David Wilson (2002), “Re-examining the

Empirical Evidence for an Environmental Kuznets Curve”, Review of Economics and

Statistics, 84(3) 541-551.

Holtz-Eakin, Douglas and Thomas M. Selden, “Stoking the Fires? CO2 Emissions and

Economic Growth,” Journal of Public Economics 57 (1995), 85-101.

IEA/OECD, Greenhouse Gas Emissions: The Energy Dimension (Paris: OECD, 1991).

Lanne, M. and M. Liski (2003), “Trends and Breaks in per-capita Carbon Dioxide

Emissions, 1870-2028, MIT-CEEPR Working Papers, 2003-002, Boston.

List, John A. and Craig A. Gallet, “The Environmental Kuznets Curve: Does One Size Fit

All?,” Ecological Economics 31 (1999), 409-423.

Millimet, Daniel L., John A. List and Thanasis Stengos (2003) “The Environmental

Kuznets Curve: Real Progress or Misspecified Models?”, The Review of Economics

and Statistics, 85, 1038-1047.

OECD (2000), Energy Balances, Statistical Compendium, Paris.

Schmalensee, Richard, Thomas M. Stoker, and Ruth A. Judson, “World Carbon Dioxide

Emissions: 1950-2050,” The Review of Economics and Statistics, 80(1) (1998), 15-27.

Shafik, Nemat, “Economic Development and Environmental Quality: An Econometric

Analysis,” Oxford Economic Papers, 46(0) (1994), 757-773.

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Figure 1 Data plot of emission-income relationship

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Figure 2 Estimation results based on the homogeneity assumption

OECD: homogenous GDP coefficients

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Figure 3 CO2 –emissions: OECD countries

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Australia

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3000

4000

5000

6000

2500 7500 12500 17500 22500

Per capita income

Per c

apita

CO

2


Sweden

0

1000

2000

3000

4000

5000

6000

2500 7500 12500 17500 22500

Per capita income

Per c

apita

CO

2


Switserland

0

1000

2000

3000

4000

5000

6000

2500 7500 12500 17500 22500

Per capita income

Per c

apita

CO

2


Turkey

0

1000

2000

3000

4000

5000

6000

2500 7500 12500 17500 22500

Per capita income

Per c

apita

CO

2


United Kingdom

0

1000

2000

3000

4000

5000

6000

2500 7500 12500 17500 22500

Per capita income

Per c

apita

CO

2


United States

0

1000

2000

3000

4000

5000

6000

2500 7500 12500 17500 22500 27500 32500

Per capita income

Per c

apita

CO

2


Table 1: Descriptive statisticsa,b Variable Mean (SD) Minimum Maximum

Per Capita Carbon 2,601 (1,801) 167 12,333

Per Capita Income (1990$) 13,172 (4,992) 2,771 33,635

Population (mln) 33 (50) 0.2 275

a) Descriptive statistics are for the 24 OECD countries for the period 1960-2000 (n = 984). b) Emission levels are measured in tons.

Table 2: Main test resultsa)

[NOTE: to be completed]

Parametric Parametric Spline?? Semiparam??.

Independent variables GDP

-31.12*** (7.58)

-30.88*** (6.12)

xxx***

(7.48) xx*** (6.12)

GDP2

4.22*** (0.83)

3.80*** (0.67)

Xxx***

(0.82) xx*** (0.67)

GDP3

-0.18*** (0.03)

-0.15*** (0.02)

Xxxx*** (0.03)

xx*** (0.02)

Fixed-effects countries

Yesd Yesd Yesd Yesd

Fixed-effects years

Yesd

General trend

Yesd

Country-specific trend

Yesd Yesd

Specification tests

xx**

yy***e z*** P***e

Homogeneity tests Wald (GDP variables)

4,830*** g

1,328*** g

xx***g

xx*** g

Wald (country-specific trends)

1,867***h xx***h

Wald (all variables)

11,635***i xx***i

a) Dependent variable is CO2 emissions per capita; standard errors in parentheses. b) Wald test with H0: a1i=a1i+1 and a2i=a2i+1 and a3i=a3i+1 . c) Wald test with H0: βi= βi+1 . d) Wald test with H0: a1i=a1i+1 and a2i=a2i+1 and a3i=a3i+1 and βi= βi+1 . (*** Significant at 99% confidence interval).

18

Notes

1 This approach is slightly different from List and Gallett (1999) who showed the importance of

slope heterogeneity by including both a cross-section specific time trend and income estimators

based on a SUR estimation for individual states in the US. However, List and Gallett (1999) do not

test for the spline specification nor for the semi-parametric framework.

2 This can easily be illustrated by considering the case of two countries whose y-values do not

overlap (like Luxembourg and Turkey). In case of rejection of βLuxembourg = βTurkey homogeneity

might still be present.

3 Panel estimations are preferable due to more efficient estimators. Indications of potential of a unit

root in the series (see e.g. Egli, 2002) are of some concern. However, a recent paper on structural

breaks in carbon emissions per capita by Lanne and Liski (2003) based on much longer time series

(1878-1994) presents evidence that (endogenously determined) structural breaks are mainly located

at the beginning of the 20th century. This suggests that the oil price shock cannot be seen as such

an event. Moreover, using a deterministic trend is one way to overcome a potential unit root in the

data. Furthermore, our concern is restricted to within sample behaviour and has no aim to predict.

4 Our procedure to calculate CO2-emission from OECD energy consumption data is similar to the

approach followed by the Oak Ridge National Laboratory (ORNL) whose data are usually included

in empirical research on CO2-emisisons (see in particular Holtz-Eakin and Selden, 1995 and

Schmalensee et.al. 1998).

5 We present results only for the cubic model because the quadratic models were all clearly

rejected vis-à-vis the cubic specifications. Furthermore, both the quadratic and cubic models

without any fixed effects were also rejected. Response coefficients for the quadratic model, as well

as for models with country-fixed effects and time-fixed effects, are available upon request.

6 For the 24-spline estimation only the first two and the last splines are significant. This finding is

robust for the 20-, 16- and 12-spline specification.

7 [INCLUDE NOTE ON CONFIDENCE BOUND DUE TO FEWER OBSERVATIONS AT THE

UPPER END OF OUR OBSERVATIONS]

19

8 Steel production has been responsible for over 50% of industrial production in 1980 but has been

reduced to 3% in 2000.

9 We generate our Wald-statistics by comparing the sum of squared residuals of the general model

with and without heterogeneous coefficients for either only the GDP variables (‘traditional

models’) and/or the time-specific trend variable (general model). Because in the last case all

coefficients are country-specific, we estimated this model with time-series analysis. Although using

the Seemingly Unrelated Regressions (SUR) model potentially increases the efficiency of

estimation, the sum of squared residuals for our data is larger under SUR (3.43 versus 2.44),

indicating that time-series estimates are preferable. Also, 51% of the individual residuals do not

improve with SUR. These results are consistent with our finding that testing of the general model is

not possible, due to a near singular matrix.

10 For instance, the Wald-test on heterogeneous coefficients of the income variables for the 8-spline

model is F(140 716) = 11.67. We found similar results for 12-, 10- and the (non-preferred) 6-

spline models. Results are available upon request.

11 We also tested whether common exogenous covariates, like differences in temperature,

geological structure (mountainous landscape) or availability of (fossil fuel) resources may affect

our findings for the income variables. Interestingly, we succeeded in producing similar explanatory

power as the (parametric) model including fixed country effects without having much effect on the

income parameters. This suggest that fixed effects capture these exogenous factors rather well.

Results available on request.

12 Repeating this procedure by excluding countries with the largest LR statistics does not result in a

panel for which homogeneity cannot be rejected. Not surprisingly, we also find the same results for

models with a general trend or time-fixed effects. These results are available upon request.

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