INVESTIGATING THE ENVIRONMENTAL KUZNETS CURVE: PANEL DATA EVIDENCE1

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15

PANEL DATA EVIDENCE

¹

Maxensius Tri Sambodo^aand Esta Lestari^b

Economic Research Center, Indonesian Institute of Sciences, Email: smaxensius@yahoo.com

Economic Research Center, Indonesian Institute of Sciences and Research Institution, Mulawarman University

E-mail: el_nova@yahoo.com

ABSTRACT

This paper analyses the shape of the environmental Kuznets curve for three groups of countries: the world, the OECD, and the developing countries. We compare three different estimation techniques using pooled ordinary least squares, fi xed effect, random effect, and dynamic panel data. There are four major fi ndings. First, a state- ment of assumptions, data span, and model specifi cations have important implications for sign and parameter estimates. We argue that those conditions contribute to uncertainty in parameter estimation. Second, although the static models share common fi ndings, such as the inverted U-curve, in explaining the relation between per capita CO₂ emissions and per capita GDP, in the case of dynamic analysis, only in OEDC countries the turning point have been reached, whereas in developing countries, it will take more years. As a result, the dynamic analysis sug- gests that for the world, CO₂ emissions per capita still show an upward trend. Third, we fi nd that the economic transition from agriculture to services sector has a positive effect in reducing CO₂ emissions, especially in developing countries, but more effort is needed to reduce CO₂ emissions in the manufacturing sector. Fourth, in the long run, an increase in per capita GDP leads to much higher CO₂ emissions per capita than in the short run. We suggest those fi ndings because of uncertainty about the turning point and because most people are risk-averse, they would prefer anticipatory policies rather than reactive policies for global warming. A climate-friendly path needs to be a basis for economic development planning.

Keywords: environmental Kuznets curve (EKC), least squares, fi xed effect, random effect, dynamic panel data.

JEL: C33, Q56, Q27.

1 This paper was presented at the 13th International Convention of the East Asian Economic Association (EAEA), Singapore, 19–20 October 2012.

I. INTRODUCTION

Between 1973 and 2009, CO₂ emissions worldwide have increased almost twice as many, from 15,624 million tonnes to 28,999 million tonnes (IEA,

2012). This rise has been caused mainly by a rapid increase in coal use (from 35 to 43 per cent) as the main source of energy in developing countries.

Coal has displaced oil, which usage has

1 This paper was presented at the 13th In- ternational Convention of the East Asian

Economic Association (EAEA), Singa- pore, 19–20 October 2012.

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decreased by 14 per cent. OECD countries are still the largest contributors to the carbon emissions, 41.5 per cent of the world’s total emissions, but the OECD’s contribution has decreased by 20 per cent over the same period.

In contrast, other regions have been increasing their share of emissions, especially China, which have increased fourfold for the same period (Stern and Jotzo, 2010).

Escalation of CO₂ emissions ex- acerbates fears of the negative effects of climate change. Current estimates show that CO₂ emissions are around 60 per cent greater than necessary to keep the global temperature rising below 2^oC by 2035 (IEA, 2008). Ac- cording to the International Energy Agency (IEA, 2008), global fossil fuel use has increased and it continues to drive up energy-related CO₂ emissions, from 28 Gt in 2006 to 41 Gt by 2030, or an increase of about 45 per cent. The IEA (2008) also stated that

around 75 per cent of the projected increase in energy-related CO₂ emissions comes from China, India, and the Middle East, and 97 per cent of the global increase is mainly produced by countries other than the OECD.

As seen in Figure 1, CO₂ emissions in high-income countries with an average GDP per capita of about USD 16,581 reached a peak of about 12.84 Mt per capita in 1979. On the other hand, CO₂ emissions per capita in developing countries with an average GDP per capita of about USD 2397 in 2008 have continued to increase. This rapid increase of emissions was because of the inclusion of developing countries from Eastern Europe and Central Asia;

both regions have higher emissions per capita than any other region.

The increase in CO₂ emissions from developing countries is also caused by emissions ‘leakage’.

International trade and investment create more incentives for developed

Figure 1. CO₂ emissions per capita and GDP per capita between developed and developing countries

Source: Calculated from World Development Indicators, 2011.

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countries to relocate their production to countries that do not require stringent emissions control, which gives developing countries a comparative advantage in carbon- intensive goods and services (Aldy and Stavins, 2009). This is a disincentive for developing counties to engage in joint carbon mitigation.

The environmental Kuznets curve (EKC) has been under debate for a long time. Various studies have been conducted to test whether there is evidence in the world for the hypothesised inverted-U curve between GDP per capita and emissions per capita. At the early stages, developed countries were the major subject of these studies, which were to seek ways for them to reduce their emissions and when the turning point would be reached (e.g. Agras and Chapman, 1999; Carson, 2010; Dasgupta, 2002;

De Bruyn, 1997; Grossman and Krueger, 1991). This study attempts to fi nd whether developing countries would follow the same patterns and trends.

This paper investigates the environmental Kuznets curve (EKC) between economic growth and CO₂ emissions in the world, the OECD, and the developing countries. It is different from previous studies in two ways; fi rst, our main contribution to this literature is in combining several techniques of econometric estimation (static and linear modelling and dynamic linear modelling) with a large sample of panel data analysis and comparisons between groups of countries. Second,

instead of investigating bi-variable relations (CO₂ emissions and GDP), we add more empirical regressors into the model. This paper has six sections: an introduction; section 2 reviewing recent literature; section 3 discussing methods;

section 4 presenting the results and an analysis; section 5 discussing policy implications; and section 6 providing concluding remarks.

II. LITERATURE REVIEW

The environmental Kuznets curve was introduced by Grossman and Krueger (1991) to describe a hypothesised relation between environmental degradation and GDP per capita. Adapting the inverted-U curve by Kuznets, EKC shows that in the early stages of economic growth, pollution increases along with the increase in GDP per capita but at some later stage pollution and environmental degradation decrease along with higher per capita income.

Grossman and Krueger (1991) emphasised the concept of the EKC as the consequence of increasing free trade, especially as the effect of NAFTA’s implementation, rather than the environmental consequences of economic development. The EKC is caused by three economic activities that are a result of free trade: changes in scale of current production, changes in composition of current production, and shifts in production techniques.

The fi rst would lead to more pollution, the second would yield debate about the possibility of pollution havens

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and a ‘race to the bottom’, but the last factor leads to lower pollution (Carson, 2011).

Since it was introduced, the concept has become very popular and appears in many textbooks. Many studies attempt to prove the pattern using various data and methods (Goldman, 2012). Goldman identifi ed 120 different studies of EKC. Based on those studies, there are several issues raised.

First, the debate on the turning point at which GDP increases would bring about lower pollution. More developed countries are examined to see whether the same path is followed by developing countries. Second, the econometric issue is to fi nd out what models would best explain the data as evidence of an EKC presence, whereas criticism centres on econometric issues, such as panel models, unit root problems, cointegration, etc. The last concern is on the policy implication on how to cause a fl atter EKC, especially for developing countries.

Henriques and Kander (2010) argued that the idea behind the inverted-U curve of an EKC is related to structural change. Furthermore, Stern (2003) mentioned four factors that explain the economic and environment relation: scale of production; output mix changes; input mix; the state of technology, which covers production efficiency and emissions-specific changes in production. A more recent study by Goldman (2012), using 912 observations from 120 different studies, applies meta-analysis to explore variables that signifi cantly affect the

presence of EKC. She found that there are seven variables that infl uence the EKC; time, environmental quality, emissions, development, fitness anthropogenic-related gasses, carbon dioxide, and sulphur dioxide.

According to Goldman (2012), studies of the EKC have produced different fi ndings. Similarly Copeland and Taylor (2004), cited in Stern (2004), say that ‘Our review of both the theoretical and the empirical work on the EKC leads us to be sceptical about the existence of a simple and predictable relation between pollution and per capita income’. Even such factors as number of observations, panel data, global aspects, GDP measures, chemically active gasses, nitrogen, and air pollution could not presume to increase or decrease the probability of fi nding an EKC (Goldman, 2012).

As the result of the broad range of models, data, and observations, studies will fi nd different values for turning points. Stern and Common (2001) found that by employing panel data for fi xed and random effects, the turning points can vary hugely, from USD9181 to USD908,178 (real 1990 PPP).

OECD countries tend to have lower turning points (USD9181 to USD9239) but non-OECD can have very high income per capita (USD344,689 to USD908,178) before their pollution rates decline. Yandle et al. (2004) iden- tifi ed the extent to which the turning point depends on the countries, methods, and pollutants. For carbon dioxide, the turning points are relatively higher than for other pollutants (for example,

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nitrogen dioxide or smoke), around USD35,100 to USD54,500 (real 2000 PPP). Therefore, the variety of turning points might be confl icting.

In an econometric approach, Stern (2004) argued that econometric criticisms of the EKC fall into four main categories: heteroscedasticity, simultaneity, omitted variables bias, and cointegration issues. Following Wagner (2008) and Vollebergh et al.

(2009), cited by Stern (2010), it has been argued that the traditional method to estimate EKC has some problems.

Wagner said that the traditional method does not take into account the presence of powers of unit root variables and cross-sectional dependency. Moreover, Vollebergh et al. (2009) argued that the time effects are not uniquely identifi ed in the EKC model (Stern, 2010).

Therefore, Stern (2010) modifi ed the data by applying an in-between estimator for carbon and sulphur emissions in the OECD countries. The in-between estimator is a consistent estimator for the long-run relation between the variables when the time series are stationary or stochastically trending and is super-consistent for cointegrating panel data (Stern, 2010). Stern (2010) also argued that the in-between estimator provides consistent estimates for stationary and for non-stationary data, even if there is a possibility of mis- specifi ed dynamic and heterogeneous regression coeffi cients. However, he found that the model could not support the inverted–U pattern. Narayan and Narayan (2010) argue the EKC model suffers from the collinearity

or multicollinearity problem when we expand the income to income-squared and income-cubed.

Applying a reduced form, as it has been suggested, is a better way to explain an EKC. Grossman and Krueger (1995) argue that a reduced- form approach has two advantages.

First, the reduced-form estimates give us the net effect of a nation’s income on pollution. Second, the reduced- form approach does not require data on pollution regulation and the state of technology, which might not be readily available with unquestioned validity.

However, a reduced-form model is inadequate to explain the relation between income and pollution (Gross- man and Krueger, 1995; Carson, 2010).

Another issue is how to cause a fl atter EKC. Various results of EKC studies show that the pattern is not likely to be fi xed (Dasgupta et al., 2002) and can be conflicting. The pattern could be an inverted-U (for example, Leitão, 2010), monotonic, or even N-shaped as found by Poudel (2009).

Therefore, developing countries might have this relation from low income per capita and less environmental degradation or have a flatter EKC.

Studies (for example, by Dasgupta et al., 2002; Grossman and Krueger, 1995; Stern, 2003) show that there are many factors that might contribute to a better environmental status along with increasing per capita income. Those factors include adopting cleaner technology, regulation, institutional reform, economic liberalisation, pressures that infl uence production and the markets

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(such as those from society and mar- keting agents), and distribution and dis- semination of information to increase people’s awareness.

Grossman and Krueger (1995) pointed to fi ve ways how the environment can improve with economic growth. First, environmental quality can be improved if countries substitute cleaner technologies for dirtier ones or if there is a very pronounced effect on pollution from the typical patterns of structural transformation. Second, many countries have stringent environmental standards and strong law enforcement. Third, there is a possibility of relocating the pollution-intensive production of goods to other regions with less restrictive environment pro- tection laws. Stern (2004) argued that innovations and structural factors (input and output) can reduce emissions.

In terms of declining energy intensity, (Henriques and Kander (2010) found that technological changes have greater effect than the transition to a service economy. Dasgupta et al. (2002) add more variables, such as the role of environmental regulation, economic liberalisation, pervasive informal regulation, pressure from market agents and better information. They emphasise that all the variables play in different ways to affect many parties, including citizens, businesses, policy makers, regulators, NGOs, and other market participants, in their reaction to economic growth and its side effects.

Stern (2003) hypothesised that structural change in an economy to- wards information-intensive industries

and services might result in a decline in environmental degradation. Meanwhile, institutions are also seen to play a signifi cant role. Apart from its relevance to policy making and regulating, the way institutions implement clean gov- ernance tends to result in a higher turning point (Lopez and Siddharta, 2000).

Meanwhile, Lipford and Yandle (2010) attempt to connect the relevance of NAFTA and the EKC for the case of Mexico, the fi rst country studied. They found an EKC presence in Mexico in the post-NAFTA period, however, the country has neither suffered as much as pessimists feared nor improved as hoped by optimists. Environmental degradation improves along with increased income per capita, however, the level of income is inadequate to achieve the turning points for many pollutants.

III. RESEARCH METHODS 3.1 Static linear model

We analysed panel data for three groups; the world, OECD countries, and developing countries. Regarding world data, there were 179 countries with several countries excluded from the group because of incomplete variables data. The OECD comprised 23 countries and there were 46 developing countries. Even though the data were incomplete, a strong balanced panel could still be constructed.

We divided the reduced form model estimation into three strategies:

pooled ordinary least squares (OLS);

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fixed effect model; random effect model. The general form of the pooled OLS model is represented as follows

(Verbeek, 2008): In the dynamic approach, we

allowed for a weak exogeneity assumption or predetermined instruments assumption or sequential exogeneity assumption. Suppose if E{x_it_is}0 for s < t, but E{x_it_is}0 for all s

≥ t, the variable was said to be prede- termined. With this assumption, we could argue that the error term at time t had some feedback on the subsequent realisations of x_it , where x_it was a predetermined variable. Alternatively, we could expect that E{x_it_is}0 for s ≤ t but E{x_it_is}0 for all s >

t. Thus, in this case, we allowed x_it to be correlated with _it at time t or we call x_it as endogenous variables. We applied dynamic panel data analysis for the weak exogeneity assumption. We allowed _it to be correlated with current and past values of the regressor.

Thus, we needed to fi nd an instrument (moment conditions) (Pesaran and Smith, 1995). In this case, the lag of CO₂ emissions per capita became an instrumental variable.

3.3 Model specifi cation

yit was a measure of CO₂ emissions per capita (metric tons per capita) and

'

x was GDP per capita (constant, it

2000 USD)/(GDP/P). Other variables were electricity consumption (kWh) or (EC), agricultural value added (constant, 2000 USD) or (AG), manufacturing value added (constant, 2000 USD) or (MN), services value added (constant, 2000 USD) or (SR), We could allow E{x_it_i}0_or

the unobserved heterogeneity in _i which was correlated with one or more of the explanatory variables. The fi xed effects _i capture all unobservable time-invariant differences across countries and, in this approach, consistent estimation does not require that _i and xit are uncorrelated (Verbeek, 2008).

Alternatively, we apply a random effect (RE) model that allows covariance:

) 0 )

((x_itv_i  , but hold E⁽it ^|xitvi⁾^⁰

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where the subscript i stands for a country index (i = 1,…,N), and t is a time index (t = 1,…,T). Following the OLS assumptions that E{_it}0_and

0 } {x_it _it 

E  , the OLS estimate is consistent for ₀ and  under weak regularity conditions (Verbeek, 2008).

With this assumption, we can apply a pooled OLS model and the OLS will come with unbiased, consistent, or effi cient estimators. Next, we estimate specifi cally a fi xed model as follows (note that_it _i _it ) (Verbeek, 2008):

3.2 Dynamic linear model

We estimated the dynamic panel data (DPD) model to obtain the long-run elasticity. The DPD was presented as follows:

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2 Some studies included additional explanatory variables such as political freedom (for example, Torras and Boyce, [1998], cited by Stern [2004]), output structure (for example, Pa- nayotou [1997], cited by Stern [2004]), trade openness (for example, Jayanthakumaran et al. [2012]), energy consumption (for example, Jayanthakumaran et al. [2012]), and urbanisa-

tion (for example, Hossain [2011]). According to Stern (2004), the proximate factors of explanatory variables aim at capturing some effects such as scale effect, changes in economic structure or product mix, changes in technology, and changes in input mix as well as under- lying causes such as environmental regulation, awareness and education.

A turning point is estimated as follows (De Bruyn, 1997):

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2 /(

exp( α₁ α₂ κ = −

A dynamic specifi cation was as follows:

And a long-run elasticity is obtained as follows (Stern, 2004):

 

  ¹1 and a turning point was similar to equation (5).

IV. RESULTS AND DISCUSSION As seen in Figure 2, because China is included in all countries studied and has shown remarkable economic growth for the past 20 years, we also evaluated China’s performance. In contrast to the pattern of world economic growth, the percentage of China’s population has tended to decline and is currently about 19.6 per cent of the world’s population. However, in terms of GDP, the share of China increased rapidly from below 1 per cent in the early 1970s to about 7.8 per cent in 2010; in terms of PPP GDP, it reached 13.5 per cent, that is, over half of the GDP of the developing countries is generated by China. However, CO₂ (4)

energy use (kt oil equivalent) or (EU), trade as percentage of GDP or (TR), and telephone lines (TL).² It was in our belief that no one yet has to emphasise the importance of sectoral changing, electricity consumption, and telephone lines for the panel of developing countries. Thus in this paper, we inves- tigated those variables.

We argued that sectoral value added helps to provide more information on structural shifts from the agricultural to the services sector. Elec- tricity consumption captured the share electricity contributes to the emissions along with any rapid increase of coal power plant utilisation. Energy use was able to capture a broader perspective on the role of the energy sector. Trade as a percentage of GDP was an indica- tor of the value of export and import of goods and services to GDP. Finally, telephone lines were fi xed telephone lines that connect a subscriber’s termi- nal equipment to the public switched telephone network and had a port on a telephone exchange. The fi nal pool OLS model was represented as follows:

2 ^y^it   ln (^GDP/^P)^it 

ln ₀ ₁

it EC

P

GDP/ ))  ln ( )

(ln ( ² ₃ ₄

SR AG) ln (MN) ln ( )

ln ( 5 6 7

TL it

TR

EU)  ln ( )

ln ( ₈ ₉

P it

 GDP

 ₁ln ( / )

it

it y

y  ln _

ln ₀ ₁

it EC AG

P

GDP  

  

 ₂(ln ( / ))² ₃ln ( ) ₄ln ( )

EU SR

MN  

  

 ₅ln ( ) ₆ln ( ) ₇ln ( ) TL it

TR  

  

 ₈ ₉ln ( )

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emissions from China also increased rapidly from 5.7 per cent of the world in the 1970s to about 22 per cent in 2008. Thus, we can conclude that China dominates the GDP and CO₂ emission indicators.

Table 1 summarises the descrip- tive statistic of the three groups of countries by the variables estimated.

For the world, during early years of the period in the 1980s, CO₂ emission rates were relatively low. Namibia in 1989 was the country with the lowest CO₂ emissions, Germany is the country producing the greatest amount of emissions in 2011. Germany is also the richest country with the highest per capita income, which peaked in 2007.

It is not surprising that the eurozone showed rapid growth before the recent crisis. In term of electricity consumption, it seems that consumption of electric power is not always related to income and emissions, yet electric

power seems to be correlated with energy use. The USA, along with Japan and China, had the highest rate of electric power consumption in 2008. Less developed countries, especially sub- Saharan countries in contrast, such as Djibouti, consumed the least electricity.

It is also true that the USA and China, since the turn of this century, have the highest rates of energy use (Jaunky, 2011).

Among the developed countries in the OECD, Germany and Japan are predominant in wealth along with their power consumption. Germany had the highest per capita income in 2008, whereas Chile did the lowest in 1984.

Chile also contributed to the lowest carbon emissions in 1986. These facts indicates that, for OECD countries, a lower per capita income tends to cor- respond to lower emissions, especially in the 1980s. Japan, which is mainly using nuclear technologies to produce

Figure 2. Share of China to the world’s level

Source: Calculated from the World Development Indicators, 2011.

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energy, is the country with the highest rates of energy use (which peaked in 2004). In contrast, Germany, which is an emissions-producing country, tended to have a very small rate of energy use in the early 1990s (WDI, 2011). Therefore, we can conclude that carbon emissions in OECD countries increased rapidly after 2000.

Among the developing countries, the emerging economies dominate in wealth along with emissions and energy use. It is not surprising because the energy needs for increasing industri- alisation tend to be met by fossil fuels.

This is true for China. As the fastest growing economy, China’s consump-

Table 1. Descriptive statistics

WORLD OECD countries Developing countries

Mean Std De-

viati on Min Max Mean Std De-

viati on Min Max Mean Std Devia-

ti on Min Max

CO2/Capita (kg/

capita) 146.1215 1713.91 0.0051837 26080.52 1026.786 4556.609 1.776071 26080.52 1.802387 1.924849 0.0281281 10.35715 GDP/Capita

(constant 2000 USD)

3.03E+09 4.01E+10 54.50519 5.91E+11 2.29E+10 1.08E+11 2156.144 5.91E+11 1993.965 1867.194 82.67167 11601.63

Electric power consumpti on (million kWh)

1.18E+11 4.70E+11 1.60E+07 4.70E+12 1.40E+11 2.00E+11 2.89E+09 1.08E+12 5.59E+10 2.26E+11 6.90E+07 3.50E+12

Agriculture, value added (million, constant 2000 USD)

9.53E+09 3.61E+10 6041144 4.63E+11 2.76E+10 7.64E+10 6.54E+07 4.63E+11 1.06E+10 2.85E+10 9.61E+07 2.85E+11

Manufactur- ing, value added (million, constant 2000 USD)

3.97E+10 1.83E+11 764511.9 1.92E+12 1.59E+11 3.05E+11 9.37E+08 1.36E+12 1.93E+10 7.55E+10 5.38E+07 1.22E+12

Services etc., value added (million, constant 2000 USD)

1.53E+11 7.77E+11 -4.24E+10 9.04E+12 2.90E+11 6.10E+11 318528.9 3.57E+12 4.33E+10 1.09E+11 -4.24E+10 1.47E+12

Energy use (kt of

oil equivalent) 81235.19 294007.8 7.112655 2609574 82937.84 106693.7 43.81048 522464.6 56119.84 182138.5 775.951 2257101

Trade (% of GDP) 238633.1 3191251 0 5.48E+07 1722408 8430549 15.92399 5.48E+07 62.23332 33.31057 10.83072 220.4068 Telephone lines

(in thousands) 5106605 2.27E+07 468 3.68E+08 9023842 1.28E+07 84837 6.57E+07 4708040 2.65E+07 3500 3.68E+08

Source: Calculated from the World Development Indicators, 2011.

tion of electric power and its energy use has been the highest since 2000.

However, South Africa is the country that produced largest amount of emissions in the group of emerging nations.

(South Africa’s emissions rate peaked in 1985.)

A preliminary correlation between variables is mapped in Table 2. It is seen that carbon emissions are related to the GDP per capita for the world and for developing countries but not for OECD countries. Some explanatory variables are heavily correlated (over 80 per cent), that is, early indicators of multicollinearity. For the world, developing and OECD countries,

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energy use is highly correlated with particular economic sectors, such as agriculture, manufacturing, services and trade. Electric power is also correlated with energy use, meaning that out of the total energy used, electricity is the largest component. As for world and developing countries, agriculture correlates highly with other sectors, especially manufacturing sectors end services as well.

4.1 Static linear model

Tables 3, 4 and 5 show the estimation results for the world under the three groups of data. The estimation is un-

der robust standard error because the diagnostic test indicates the presence of heteroscedasticity. In general, the models show evidence of an inverted- U relation between CO₂ emissions and GDP per capita for all the country groups. The negative sign indicates that, beyond some point, emissions in all countries decrease along with higher income per capita. The turning points are different for each group of countries.

Based on pooled ordinary least squares (PLS) estimates, the results for each group are quite similar, in which

Table 2. Correlation among variables at each group of countries

Variables CO₂/ Capita

GDP/

Capita GDP/

Capita Electric

power cons.

Agricul- ture, value

added

Manufacturing, value added

Ser vices etc., value added

Energy use

Trade Telp.

lines

WORLD CO₂/Capita (kg/

capita)¹ 1

GDP/Capita (con-

stant 2000 USD)¹ 0.852 1

GDP/Capita (con-

stant 2000 USD)² 0.8314 0.995 1 Electric power

consumpti on (million kWh)

0.6235 0.529 0.5322 1 Agriculture, value

added (million, constant 2000 USD)

0.1054 0.1035 0.108 0.7511 1

Manufacturing, value added (million, constant 2000 USD)

0.5601 0.5941 0.5933 0.9153 0.8064 1

Services etc., value added (million, constant 2000 USD)

0.5722 0.6436 0.6473 0.9013 0.7829 0.9749 1

Energy use (kt of

oil equivalent) 0.5722 0.6436 0.6473 0.9013 0.7829 0.9749 1 1

Trade (% of GDP) 0.4027 0.3045 0.3178 0.9112 0.8671 0.8755 0.8726 0.8726 1

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GDP/

Capita GDP/

power cons.

added

Energy use

Trade Telp.

lines Telephone lines

(in thousands) 0.2742 0.2411 0.2403 -0.1272 -0.5142 -0.1615 -0.1632 -0.1632 -0.2681 1 OECD

CO₂/Capita (kg/

capita)¹ 1

GDP/Capita (con-

stant 2000 USD)¹ 0.6857 1

GDP/Capita (con-

-0.1388 -0.08 -0.093 0.8763 1

Manufacturing, value added (million, constant 2000 USD)

0.0697 0.1505 0.1391 0.9237 0.9277 1

0.1492 0.2575 0.2474 0.9191 0.894 0.9794 1

Energy use (kt of

oil equivalent) 0.1197 0.0117 -0.0011 0.9563 0.9318 0.9451 0.9291 1 Trade (% of GDP) 0.3048 0.1796 0.1908 -0.5211 -0.7064 -0.5276 -0.4766 -0.5282 1 Telephone lines

(thousands) 0.1051 0.0573 0.0412 0.9273 0.9211 0.9506 0.9333 0.9586 -0.5085 1 DEVELOPING

COUNTRIES CO₂/Capita (kg/

capita)¹ 1

GDP/Capita (con-

stant 2000 USD)¹ 0.8488 1

GDP/Capita (con-

0.2015 0.0156 0.0178 0.8078 1

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GDP/

Capita GDP/

power cons.

added

Energy use

Trade Telp.

lines Manufacturing,

value added (million, constant 2000 USD)

0.6033 0.4523 0.4452 0.9262 0.8523 1

0.6136 0.479 0.4786 0.9299 0.8482 0.98 1

Energy use (kt of

oil equivalent) 0.3535 0.0556 0.0624 0.9106 0.9099 0.8494 0.8576 1 Trade (% of GDP) 0.2539 0.2723 0.2602 -0.1413 -0.3884 -0.1778 -0.209 -0.3131 1

electricity consumption and energy use tend to affect emissions in the same direction except for developed countries of the OECD, which experienced lower emissions along with higher electricity consumption. The experience of developing countries shows that when electricity consumption increases by

1 per cent, the emissions increase by 0.3 per cent, holding other variables constant. A similar result is also found for the world case. This result is consistent with the positive relation between emissions, and energy use. Therefore, we can conclude that, in developing countries, their energy use might be

Table 3. Estimation results for the world

Variables PLS FE RE

Ln (GDP/P) 2.8738 (0.1354) 2.7442 (0.4740)* 2.8738 (0.1354)*

(Ln(GDP/P))² -0.1232 (0.0077)* -0.1251 (0.0215)* -0.1231 (0.0076)*

Ln (EC) 0.1803 (0.0417)* 0.1288 (0.1144)* 0.18028 (0.0417)*

Ln (AG) -0.1570 (0.0315) -0.1229 (0.0956) -0.1570 (0.0315)*

Ln (MN) 0.0039 (0.0562)* 0.0199 (0.1549) 0.0039 (0.0563)

Ln (SR) -0.6095 (0.0457)* -0.5059 (0.1746)* -0.6095 (0.0457)*

Ln (EU) 0.6405 (0.0557)* 0.6113 (0.1371)* 0.6404 (0.0557)*

TR -0.00049 (0.00035) -0.00033 (0.00074) -0.00049 (0.00035)

Ln (TL) -0.00814 (0.0152) -0.0091 (0.0409) -0.0081 (0.0152)

Constant -6.5221 (0.7369)* -7.4535 (3.0499) -6.5221 (0.7369)*

R-sq within 0.4578 0.4591 0.4578

R-sq between 0.9239 0.9162 0.9239

R-sq overall 0.9079 0.9023 0.9079

No. of observati ons 2,567 2,567 2,567

Turning point 95,752 57,960 116,665

Note: *Signifi cant at 5% critical level; **signifi cant at 10% critical level.

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predominantly for electricity production. However, developed countries illustrate inconsistency in which higher electricity consumption leads to lower emissions yet higher energy use causes greater emissions. This probably is the result of the use of renewable energy for their power supply.

Sectoral shifts in the economy to services are recommended for all countries. Inclusion of agricultural, manufacturing, and services value added to the model shows the expected sign ac- cords with theory. The results confi rm negative relations in the agricultural and services sectors. In the manufacturing sector, although it has a positive sign, it is statistically not signifi cant. We can argue that the economic transition from the agricultural sector to the services sector will contribute positively to

reduce emissions. If we compare developed and developing countries, it is shown that agriculture and services are more effective in reducing emissions in developing countries, but manufacturing has a greater effect in developed countries.

Estimates of PLS provide plau- sible arguments about the effect of energy and structural shifts in the economies. However, because PLS imposes too strict assumptions, the parameter estimated and expected signs that we obtain could be misleading. FE and RE models provide better estimations. By considering each group of countries, the estimations show quite different results. The result for FEM (fi nite element method) for developed countries consistently shows that energy use signifi cantly affects emissions

Table 4. Estimation results for OECD countries

Variables PLS FE RE

Ln (GDP/P) 2.2210 (0.2777)* 1.8108 (0.5529)* 2.2210 (0.2777)*

(Ln(GDP/P))² -0.0896 (0.0161)* -0.0880 (0.0355)* -0.0896 (0.0161)*

Ln (EC) -0.1807 (0.0545)* -0.3003 (0.0889)* -0.1807 (0.0544)*

Ln (AG) -0.0769 (0.0222)* -0.0217 (0.0297) -0.0769 (0.0222)*

Ln (MN) -0.1362 (0.0324)* -0.0132 (0.0779) -0.1362 (0.03244)*

Ln (SR) -0.3468 (0.0749)* -0.1510 (0.2228) -0.3468 (0.07490)*

Ln (EU) 0.9036 (0.0604)* 1.0565 (0.1204)* 0.9035 (0.0604)*

TR -0.00016 (0.00029) -0.00052 (0.00047) -0.00016 (0.00029)

Ln (TL) -0.0224 (0.0209) -0.0366 (0.0521) -0.0223 (0.0209)

Constant -1.8660 (1.9908) -5.7465 (5.5879) -1.8660 (1.9908)

R-sq within 0.8643 0.8738 0.8643

R-sq between 0.5195 0.0001 0.5195

R-sq overall 0.7107 0.0331 0.7107

No. of observati ons 453 453 453

Turning point 5,897,875 29,247 241,803

Note: *Signifi cant at 5% critical level; **signifi cant at 10% critical value

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positively yet electricity consumption affects them negatively. In developing countries, both variables, energy use, and electricity consumption have effects on emissions in the same direction. In contrast, structural shifts affect emissions in developing countries, but not developed ones. As for developing countries, a structural shift from agriculture to services significantly reduces emissions, but manufacturing has no signifi cant effect. This indicates that different stages of development and sectoral bases of the economy can have different effects on emissions and therefore implies different policies for sectors in economy.

REM (random effects model) shows a different picture from FEM, but quite similar to PLS estimates.

Electricity consumption and energy

use significantly affect emissions in all groups of countries. Among the world and developing countries, both variables affect positively, but with developed countries, electricity consumption affects emissions in the other direction. Interestingly, the mag- nitudes of those variables vary quite considerably. An increase in electricity consumption leads to higher emission rates of around 0.1–0.2 per cent within the world and developing countries, but brings about 0.2 per cent lower emissions in developed countries. Energy use in OECD countries contributes to much higher emissions than for other countries in the world. Generally, an increase in energy use by 1 per cent would only lead to higher emissions by 0.4 to 0.6 per cent, but in the case of the OECD, this would increase emis- Table 5. Estimation results for developing countries

Variables PLS FE RE

Ln (GDP/P) 4.2153 (0.1367)* 2.6463 (0.7278)* 3.0146 (0.5928)*

(Ln(GDP/P))² -0.2292 (0.0087)* -0.1103 (0.0439)* -0.1390 (0.0370)*

Ln (EC) 0.1425 (0.0323)* 0.2915 (0.1214)* 0.2729 (0.1008)*

Ln (AG) -0.3132 (0.0293)* -0.1783 (0.0917)** -0.1808 (0.0755)*

Ln (MN) -0.1754 (0.0387)* -0.0159 (0.1219) -0.0219 (0.0814) Ln (SR) -0.1643 (0.0505)* -0.3923 (0.2722) -0.3555 (0.1634)*

Ln (EU) 0.6478 (0.0305)* 0.4327 (0.1807)* 0.5303 (0.1371)*

TR 0.0013 (0.0003)* 0.0013 (0.0011) 0.0011 (0.00083)

Ln (TL) 0.0757 (0.0226)* -0.0700 (0.0559) -0.0852 (0.0508)**

Constant -7.1858 (0.6546* -2.8048 (4.2666) -4.8842 (2.1126)

R-sq within 0.9214 0.5522 0.5509

R-sq between 0.9021 0.9249

R-sq overall 0.8851 0.9066

No. of observations 987 987 987

Turning point 35,424 162,166 51,204

Note: *Signifi cant at 5% critical level; **signifi cant at 10% critical value

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sions by 0.9 per cent or almost in the same proportion. Therefore, we can conclude that in most countries and in developing countries, energy use is mainly for the production of electricity, but not in OECD countries. This argu- ment helps us to understand the reason why the OECD has become the main contributor of emissions in the world.

According to REM estimates, structural change is evident for all countries in which higher sectoral value added signifi cantly lowers emissions.

The economic shift to services leads to the largest reduction of emissions in the world. Higher manufactures’

value added leads to higher reduction in OECD countries and higher value added in agriculture leads to more reductions. Once again, differences in basic sectors between developing and developed countries affects to what extent emissions could be reduced.

Furthermore, based on REM, trade and telephone lines signifi cantly affect emissions in different directions. This strengthens the assumption that ap- plication of softer technology would reduce transport needs and, in turn, emissions.

The last debate is about what models best explain the data, especially the turning point. The three models provide a wide range of turning points. Previous studies, such as Agras and Chapman (1999), obtain the turning point for CO₂ emissions at about USD13,630. Holtz-Eakin and Selden (1995) fi nd the turning point at about USD35,424. Thus, the OLS turning point estimate is between the

previous estimates, but FE and RE give much higher estimates. Thus, it seems that it will take many years before the turning point is reached.

Because the turning point is different for each model, we can argue that model assumptions do matter.

We argued that FE has a consistent estimator (BLUE-best linear unbiased estimator) if _it IID(0,_²) and if

) , 0 ( _²

_it IID and _i IID(0,_²) , RE has consistent estimate. Further, the goodness-of-fi t measure shows that FE and RE have reasonable R²s in all dimensions. Moreover, the Hausman test indicates the FE estimators are consistent and effi cient.³

Based on three approaches; OLS, FE and RE, there are three main fi ndings. First, we obtained an inverted-U shape of CO₂ per capita and GDP per capita. This indicates that there must be a turning point when CO₂ per capita starts to fall. However, the approach comes with different estimates of the turning points depending on the stage of development of the economies.

Although FE and RE are more consistent and effi cient estimates than the pool OLS, the Hausman test shows that the FE estimator is consistent (see Table 4). However, Stern (2004) intuitively argues that more appropri- ate methods will lead to higher turning points, which is evident from this study. Second, economic transition in the economies of developing countries

3 We also calculate the turning point base on the new random sample and we obtain USD133,370 for FE model and USD51973 for RE model.

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was recommended to reduce emissions.

Third, energy use and electricity consumption generally increased emissions in most countries. However, in affect- ing emissions in OECD countries, energy use tends to provide higher parameter estimate values than electric power consumption. That electricity consumption itself reduces emissions in the OECD is an indication that cleaner energy standards have been applied.

4.2 Dynamic panel data analysis In the previous analysis we discussed static panel analysis. The Hausman test and the Breusch-Pagan LM test help us to decide which model we should use.

We then analysed the dynamic panel data (DPD). In the DPD we obtained a lag of a dependent variable in the right side of the equation. In this situation, we could not apply a PLS and RE, because we expected an upward bias of parameter estimates, but FE will produce a downward bias (Söderbom, 2011). There are two types of panel data (Cameron and Trivedi, 2010):

short panels where T fi xed and N →∞;

and long panels where N is small and T →∞.⁴ Random effect (RE) and fi xed effect (FE) usually apply for short and long panels. Furthermore, Baltagi and Levin (1992) estimate dynamic panel data (DPD) for 46 states in America between 1963 and 1992 or 30 years.

We applyied DPD for the world, the OECD and for developing countries.

4 Suppose for short panel we can have N = 300 and T = 10; and for the long panel T = 30 and N = 10.

The results of DPD estimations are summarised in Table 6.

4.2.1 World analysis

We apply the estimator to an AR(1) and AR(2) model with the following command:⁵

Generally speaking, there was not much difference between the two models, but we found that the second lag of the dependent variable was not signifi cant, thus we stay, with the AR(1) model. For a large value of T, the Arellano-Bond method generates many instruments, which potentially can lead to poor performance of asymptotic results (Cameron and Trivedi, 2010).

For example, in the AR(1) and AR(2) models we had 2300 and 2374 observation with 387 and 388 instruments, respectively. Thus, we needed to restrict the number of instruments using the maxldep(1) option. Therefore, the new command was as follows:

Now we had 37 instruments.

By restricting the number of instruments, we gained effi ciency because

5 We run under STATA software.

Table 6. Hausman test

World OECD Developing Countries Chi2(9) 799.77* 459.38* 1084.45*

Note: *Signifi cant at 5% critical level;

**Signifi cant at 10% critical level.

tabond [dependent variable] [independent variable], lags (1) vce (robust ) (W1) xtabond [dependent variable] [independent variable], lags (2) vce (robust) (W2)

xtabond [dependent variable] [independent variable], lags(1) vce(robust) maxldep(1) (W3)

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now electricity consumption and agricultural sector value added (constant, USD2000) became statistically signifi cant and services value added (constant, USD2000) becomes not signifi cant. Thus, we stick on (W3). Next, we applyied the two-step estimators to check the possibility of effi ciency gain compared to the one-step estimate.

The model we expressed is as follows:

We also needed to conduct a test for over-identifying restrictions. In model W3 and W4, we had 37 instruments to estimate 11 parameters, so there were 26 over-identifying restrictions. The estat sargan command implemented the test. This command could not work if the vce (robust) option was used because the test requires the errors to be independent and iden- tically distributed. Thus, we needed to run the model without the vce (robust) option. In the case of W3, the null hypothesis that the population moment conditions were correct was rejected because p = 0.0037. In the case of W4, the null hypothesis that the population moment conditions are correct was rejected because p = 0.0643. Thus the specification test suggested that we could use W3 and W4, but economics theory confi rms that model W4 was better than model W3.

4.2.2 OECD countries analysis Similar to the world analysis, we as- sumed the estimator follows an AR(1) and AR(2) model with the command:

xtabond [dependent variable] [independent variable], lags(1) vce(robust) maxldep(1) twostep (W4)

We found that now the one-step (W3) and two-step (W4) had similar estimated coeffi cients, and the standard errors. However, under M4, we have fi ve signifi cant variables at 5 per cent critical level, but for W3 we had only six signifi cant variables. Thus, there was little effi ciency gain in two-step estimations. However, model W4 confi rmed that although electricity consumption had a negative sign for GDP per capita, it was not signifi cant statistically.

Next, we conducted specifi cation tests for W3 and W4. The xtabond estimator required that error be serially uncorrelated or we expected to rejected at order 1 but not at higher orders. The test showed that M3 and M4 are indeed the case. For M3, we reject at order 1 because p = 0.0072 and at order 2 errors were serially un- correlated because p = 0.3001 > 0.05;

but in the case of W4 we rejected at order 1 because p = 0.0073 and at or- der 2 errors were serially uncorrelated because p = 0.3151 > 0.05. Thus, we argue that W3 and W4 are statistically good models.

xtabond [dependent variable] [independent variable], lags(1) vce(robust) (O1) xtabond [dependent variable] [independent variable], lags(2) vce(robust) (O2)

Because the second lag of dependent variable is not signifi cant, thus we stick on AR(1) model. In the AR(1) model we have 420 observation with 352 instruments. We try to restrict the number of instrument by using the maxldep() option. Thus, the new command is as follows:

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hypothesis that the population moment conditions are correct is not rejected because p = 0.999 > 0.05. Thus the specifi cation test suggests us to stick on O3.

4.2.3 Developing countries

Again, we applied the estimator to an AR(1) and AR(2) model with the command:

xtabond [dependent variable] [independent variable], lags(1) vce(robust) (D1) xtabond [dependent variable] [independent variable], lags(2) vce(robust) (D2)

and we found that the second lag of the dependent variable was not signifi cant, thus we sticked on AR(1) model. The AR(1) model had 1937 observation with 670 instruments. Thus, we attempted to restrict the number of instrument with the following command:

xtabond [dependent variable] [independent variable], lags(1) vce(robust) maxldep(2) (D3)

Now we had 76 instruments. By restricting the number of instruments, we obtained more signifi cant variables.

Thus we sticked on (D3). Next, we applyied the two-step estimators and the model we express as follows:

xtabond [dependent variable] [independent variable], lags(1) vce(robust)maxldep(2) twostep (D4)

We found that now the one-step (D3) and two-step (D4) had similar estimated coefficients, and standard errors. However, under D4 we had 7 significant variables at 5 per cent critical level, but on D3 we have only 5 signifi cant variables. Next, we conducted specifi cation tests for D3

xtabond [dependent variable] [independent variable], lags(1) vce(robust) maxldep(1) (O3)

Now we have 37 instruments. By restricting the number of instruments, we gain effi ciency because now the services value added (constant, USD2000) becomes statistically significant. We apply the two-step estimators to check the possibility of effi ciency gains compared to the one-step estimate. The model we express as follows:

xtabond [dependent variable] [independent variable], lags(1) vce(robust)maxldep(1) twostep (O4)

We found that now the one-step (O3) and two-step (O4) have different estimated coeffi cients and standard errors. Under O3 we have fi ve signifi cant variables at 5 per cent critical level, but on O4 we have only three variables signifi cant. Because standard errors on O4 are much higher than O3, there is considerable loss of effi ciency.

We conduct specifi cation tests for O3 and O4. The test shows that O3 is indeed the case. We reject at order 1 because p = 0.0315 and at order 2 errors are serially uncorrelated because p = 0.5149 > 0.05; but in the case of O4 we accept the null hypothesis at order 1 and 2. Thus we argue that O3 is statistically better than O4.

Next we conduct a test for over- identifying restrictions. In model O3 and O4, we have 37 instruments to estimate 11 parameters, so there are 26 over-identifying restrictions. In the case O3, the null hypothesis that the population moment conditions are correct is rejected because p = 0.0011. In the case O4, the null

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and D4. For D3, we rejected at order 1 because p = 0.0155 and at order 2 errors were serially uncorrelated because p = 0.5036 > 0.05; but in the case of D4 we rejected at order 1 because p = 0.0062 and at order 2 errors were serially uncorrelated because p = 0.5045 > 0.05. Thus, statistically we argued that D3 and D4 were good models. Next, we tested for over- identifying restrictions. In model D3 and D4, we had 76 instruments to estimate 11 parameters, so there were 65 over-identifying restrictions. In the case D3, the null hypothesis, that the population moment conditions were correct, was rejected because p = 0.000.

In the case D4, the null hypothesis, that the population moment conditions were correct, was not rejected because p = 0.387. Thus, the specifi cation test suggested that we could use D3.

4.3 Numerical results on DPD analysis

Table 7 shows the results from DPD analysis. The OECD and developing countries have the turning points at income levels USD71,885 and USD11,713, but for the world, the EKC shows the upward trend. Al- though the OECD countries have a higher turning point than developing countries, the mean income of OECD countries reached USD229 million, while developing countries reached USD1994. Thus, in the case of developing countries, it will take many years to reach the turning point. As a result, in the world, the EKC still shows an upward trend.

We can conclude that, in the short run, if GDP per capita increases by 1 per cent, CO₂ emissions per capita from the world, OECD and developing countries will increase by 0.89 per cent, 1.5 per cent and 1.2 per cent, respectively. Similarly, in the long run when GDP per capita increases by 1 per cent, CO₂ emissions per capita will increase by 3.4 per cent, 1.6 per cent and 3.1 per cent, respectively. This indicates that in the long run, the world and developing countries’ emissions growth will be higher than in the short run, but in OECD countries the growth is almost stable. In the case of OECD countries, we found that electricity consumption has a negative sign and it is statistically significant. This indicates that growth of electric power consumption has been supported by clean energy sources. However for all countries, energy use has a positive sign. Thus more effort is needed to improve energy effi ciency and conservation.

In the long run, CO₂ emissions from developing countries will increase much more than in the short run.

This indicates that most developing countries are trapped in a carbon

‘lock in’ situation. Thus there is a need to change the pattern of economic growth to green growth for example, and for the energy sector, fuel switch- ing to lower carbon intensity and im- provements in energy effi ciency must be a top priority for the energy sector (IEA, 2011).

Although growth of service sector value added has a negative sign, we obtained a positive sign for manufacturing

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value added in developing countries.

This means that technology coopera- tion on production processes needs to be improved between developed and developing countries. Finally, DPD results do not support emissions ‘leakage’ through trade.

If we compared the static and dynamic linear models, we can conclude two main points. First, we obtained an inverted-U EKC in a static model, but in the case of dynamic analysis, only the OECD countries have reached the turning point. Second, results came with different turning points, even by slightly changing the errors assumption, for example in the case of OECD countries.

Table 7. Arellano-Bond dynamic panel data estimation model

Variables World (W4) OECD (O3) Developing (D3)

Lag (Ln(Y_t)) 0.7368 (0.1104) * 0.0927 (0.0823) 0.6202 (0.1274) * Ln (GDP/P) 0.8945 (0.4529)* 1.5056 (0.50126)* 1.162 (0.4527)*

(Ln(GDP/P))² -0.0407 (0.0248) -0.0673 (0.0307)* -0.062 (0.0276)*

Ln (EC) -0.069 (0.0707) -0.3528 (0.11638)* -0.068 (0.0881) Ln (AG) -0.119 (0.0426) * -0.004 (0.03101) -0.0979 (0.0584)

Ln (MN) 0.0358 (0.0732) -0.0575 (0.1149) 0.1436 (0.0798) *

Ln (SR) -0.266 (0.109) * -0.3723 (0.1367)* -0.349 (0.11162) * Ln (EU) 0.7148 (0.1251)* 1.3214 (0.1576)* 0.7892 (0.1439)*

TR -0.0004 (0.00047) 0.00045 (0.00053) -0.0001 (0.00059)

Ln (TL) -0.0337 (0.026) -0.0033 (0.0568) -0.0549 (0.031)

Constant -0.945 (1.9886) -0.6727 (4.364) -3.08 (1.945)

N1 2,374 420 1,937

N2 112 19 78

N3 37 37 76

LRE 3.398 1.6596 3.0605

Turning point - 71,885 11,713

N1 = Number of observations; N2 = Number of groups; N3 = Number of instruments;

LRE = long run elasticity; *signifi cant at 5% critical level

V. CONCLUSION AND POLICY RECOMMENDATION

Although CO₂ emissions from OECD countries tend to decrease, the emissions from developing countries show an upward trend. This study shows that the inverted-U EKC is proved for OECD countries. We argue that although there are many approaches to measure the econometric critique of the EKC, our study shows that there is still uncertainty on the model estimate.

This is mainly because of three major issues such as model assumptions, data span and model specifi cation.

This result implies that developing countries need to promote development strategies for a pro-low carbon economy. Apart from a broad range of