Silviase/jgov_v1.4_full_ann huggingface dataset:
degradation of key ecological services. One of the most damaging criticisms of the EK curve that advocates caution in interpreting its causes and implications is based on the linkage between the EK curve and the international trading of industrial goods. Suri and Chapman (1998) and Rothman (1998), notably, argued that the EK curve might arise due to the relocation of “dirty” industries to developing countries as a country reaches higher levels of development. Cole (2004) stated that the EK curve may thus be no more than a “historical artifact.” Empirical studies of the relationship between economic growth and various indicators of environmental degradation are still ongoing, with mixed results.

### 2.2 Frontiers of EK-Curve Studies, Our Position

Most of the empirical studies so far have concentrated on validating the EK curve hypothesis and its requirements, using cross-sectional data. This cross-sectional approach adopted by most studies might, as Borghesi (1999) argued, be misleading, since environmental degradation is generally increasing in developing countries and decreasing in industrialized ones. Moreover, the EK curve within the cross-sectional framework might therefore reflect the mere juxtaposition of two opposite trends rather than describe the evolution of a single economy over time.

One of the frontiers of EK-curve studies, then, is to examine the EK curves of specific countries using time-series data, to compare them in terms of the height and timing of their peaks, their shapes, etc., and to investigate the causes of different EK-curve patterns. De Bruyn et al. (1998), noticing that conventional cross-section estimation techniques have generated spurious estimates of the EK curve, estimated time series models individually in four countries (the Netherlands, the UK, the US, and then-West Germany) for three types of emissions $ \left(\mathrm{CO}_{2}, \mathrm{NO}_{x} \text{ and } \mathrm{SO}_{2}\right) $ and showed that the time patterns of these emissions correlate positively with economic growth, and that emission reductions may have been achieved as a result of structural and technological changes in the economy. Irie et al. (2000) tested the empirical validity of the EK curves of individual countries for $ SO_{2} $ , using relevant time-series data from 30 developed countries (OECD countries and the former Soviet Union). The main findings were that 1) the EK curves were verified for $ SO_{2} $ emissions in 17 countries, 2) the EK curves varied in the shape of their trajectories and the height and timing of their peaks, and 3) the differences in the height can be explained by five factors: the technology available in the country, the scale of the economy, the quality of the fuel used, the leading industries, and the political system.

This time-series approach has been developed, as Dasgupta et al. (2002) argued, to
examine the hypothesis that developing societies, utilizing progressive environmental management and the technologies of more advanced countries, might be able to experience an EK curve that is lower and flatter than what the conventional wisdom would suggest; they might be able to develop their economies from low levels of per capita income with little degradation in environmental quality, and then at some point experience improvements in both income and environmental quality. Concerning environmental management, Panayotou (1997)—formulating a tentative equation for a sample of 30 developed and developing countries for 1982-1994—found that effective policies and institutions can significantly reduce environmental degradation at low income levels and speed up improvements at higher income levels, thereby lowering the EK curves, at least for ambient sulfur dioxide levels. Matsuoka et al. (2000) compared the EK curves of various Asian countries and explained the differences in their height by the dissemination of environmental monitoring systems in those countries. As for environmental technology, Martin and Wheeler (1992) argued that, because increased openness to trade tends to lower the price of cleaner imported technologies while increasing the competitive pressure to adopt them, firms in relatively open developing economies adopt cleaner technologies more quickly.

One counter-argument to this hypothesis of the latecomer’s advantage is the well-known “pollution heaven” hypothesis. For example, Dasgupta et al. (2002) argues that the relatively high environmental standards in high-income economies impose high costs on polluters, and shareholders pressure the firms to relocate to low-income countries, whose people are so eager to get jobs and income that their environmental regulations are weak or nonexistent. The scenario may not shift the latecomers’ EK curves downward; on the contrary, it may even lift them.

This article, which extends the time-series approach in the EK curve studies, tries to verify the existence of the latecomer’s advantage or its disadvantage in the area of environmental management and technology in developing countries in the world. The approach is to confirm the downward or upward shift of the EK curves of the sample latecomer economies on both sulfur emissions as local air pollutants and carbon emissions as global air pollutants.

### 3. Empirical Studies

We now turn to the empirical studies within the analytical framework of the environmental Kuznets curve. Our analysis consists of two steps. First, we will simply overview the relationships between per capita real GDP and environmental indices of sulfur and carbon emissions. We then move to a regression analysis using panel data to
examine the existence of the EK curve pattern and to see whether the latecomer’s advantage or its disadvantage dominates in the environmental management of developing countries.

### 3.1 Data

For sulfur emissions, we will use the data estimated by Lefohn et al. (1999). This database was developed for the purpose of estimating the global emissions of sulfur from 1850 to 1990, with a common methodology applied across all years and countries. In all cases, the emissions estimates for each country are based on the production, percent sulfur, and sulfur retention information associated with that country's activities. For carbon emissions, we will use the data estimated by Marland et al. (2008). The database, named "Global, Regional, and National Fossil Fuel $ CO_{2} $ Emissions," covers data from 1751 to 2005. The emissions estimates are based on a specific methodology using statistics on gas fuels, liquid fuels, solid fuels, gas flaring, cement manufacturing, and estimated parameters of carbon coefficients and oxidation rates. For the population and the real GDP per capita, we will use Version 6.2 of the Penn World Tables (PWT), estimated by Heston et al. (2006). As the per capita real GDP, we will use the time-series data of "REAL GDP PER CAPITA (CONSTANT PRICE: LASPEYRES)" in 2000 US dollar prices. The database covers data from 1950 to 2004. To conduct the following panel estimation, we constructed a table of the annual environmental and economic data from the 188 economies for 1950 to 2004 (See Appendix on sample countries and data availability). As sample countries, we have two cases for estimation on each emission: Group A and Group B. Group A collects countries at maximum extent; countries that have at least one available data in both the real GDP per capita and each of sulfur or carbon emissions for a certain year. Group B targets the countries with a long range of data availability, countries that have fully available data for a certain period (from 1960 to 1990 in sulfur emissions and from 1970 to 2003 in carbon emissions).

### 3.2 Overview of Cross-sectional and Time-series Relationships

Next we will summarize the relationships between per capita income and environmental indices of sulfur and carbon emissions. Figure 1 indicates the cross-sectional relationships between per capita real GDP and per capita sulfur emissions in 1970 and 1990, and between per capita real GDP and per capita carbon emissions in 1970 and 2003. We found that the case of sulfur emissions does not appear
to show a clear trend in the spot distribution, while the case of carbon emissions seems to reveal an increasing trend. As for the comparison of the spot locations between 1970 and a recent year, sulfur emissions indicate the downward shift of the average level of per capita emissions from 1970 to 1990, while carbon emissions show the upward shift from 1970 to 2003.

Figure 2 reports the time-series relationships between per capita real GDP and per capita emissions in main sample countries. Such developed countries as the United States, Japan, and Italy approximately create an inverted-U shape pattern in sulfur emissions, while none of countries do in carbon emissions. In addition, the downward shifts from the trajectories of developed countries to those of developing countries are clearer in sulfur emissions than in carbon emissions. China, which has the highest trajectory for both environmental indices, may be reflected in peculiar economic structure, which is characterized by a heavy dependence on coal as an energy source.

The income-emissions relationships described above may produce different implications in the shape and location of trajectories between sulfur and carbon emissions. This point will be statistically clarified through a panel estimation in the following section.

### 3.3 Regression Analysis Using Panel Data

We’ll now move to a regression analysis using panel data to examine the existence of the EK curve pattern and to see the latecomer’s advantage or its disadvantage dominates in the environmental management of developing countries.

#### 3.3.1 Methodology

We will first clarify some methodological points related to our analysis. To study the relationship between pollution and growth, there are two possible approaches to model construction. One is to estimate a reduced-form equation that relates the level of pollution to the level of income. The other is to model the structural equations relating environmental regulations, technology, and industrial composition to GDP, and then to link the level of pollution to the regulations, technology, and industrial composition. We here take the reduced-form approach for the following reasons. First, the reduced-form estimates give us the net effect of a nation's income on pollution. If the structural equations were to be estimated first, one would need to solve back to find the net effect, and confidence in the implied estimates would depend on the precision and potential biases of the estimates at every stage. Second, the reduced-form approach spares us
from having to collect data on pollution regulations and the state of the existent technology, which are not always available. Although a reduced-form relationship gives no indication of the direction of causality—namely, whether growth affects the environment or the other way around—we think that the reduced-form relationship between pollution and income is an important first step. $ ^{2} $

We then specify the reduced-form equation in accordance with our analytical interests. Our specific concern regarding the EK curves for the sample economies is whether the EK-curve trajectories have shifted downward or upward, depending on the dominance of either the latecomer's advantage or its disadvantage; in other words, whether the levels of environmental pollution have been affected not only by the levels of per capita income following the EK curve, but also by the speed of development. If a sample economy later reaches a certain level of development and then lowers its environmental pollutants, we speculate that the economy does not repeat the EK-curve trajectories already experienced by the developed economies; it should leapfrog over the environmental difficulties by absorbing their know-how, skills, and technology. On the contrary, if the later development is linked with higher pollution, the sample economies may suffer from the disadvantage caused by the “pollution heaven” scenario brought about by globalization. Therefore, we will include a term representing the speed of development of the latecomers into the ordinary regression model of the EK curve, and specify the modified model as follows.

$$ \left[SOP_{it},COP_{it},\right]=\alpha_{0}+\alpha_{1}YRP_{it}+\alpha_{2}YRP^{2}_{it}+\alpha_{3}YEAR_{it}+e_{it} $$

where i is the country index, t is the time index, and e is the error term. The dependent variables SOP and COP are measures of environmental pollutions: SOP is for the sulfur emissions per capita, and COP is the carbon emissions per capita. As for the independent variables, YRP is the real GDP per capita, YEAR is the year in which the data were collected and represents the speed of development of the latecomers.

To verify the inverted U-shapes of the EK curves, the signs and magnitudes of $ \alpha_{1} $ and $ \alpha_{2} $ should be examined. Environmental pollutants can be said to exhibit a meaningful EK curve with the real GDP per capita, if $ \alpha_{1}>0 $ and $ \alpha_{2}<0 $ , and if the turning point, $ -\alpha_{1}/2\alpha_{2} $ , is a reasonably low number. Of particular importance is the coefficient of YEAR, $ \alpha_{3} $ , which is useful for identifying the dominance of the latecomer's advantage or its disadvantage. The negative sign of $ \alpha_{3} $ , the downward shift of the trajectories, indicates that the latecomer's advantage surpasses its disadvantage, while the positive sign
of $ \alpha_{3} $ , the upward shift of the curve, reveals the dominance of the latecomer's disadvantage.

Apart from the real GDP per capita, the speed of development of the latecomers, there are also likely to be exogenous factors that affect emissions. For instance, climate, geography, and energy resources vary widely among countries and may well be correlated with emissions. Insofar as these factors cause the error term e to be correlated among countries for a given period, pooled cross-section estimates that ignore this correlation will be inefficient. To address this issue, we must specify an error-components model, in which:

$$ e_{\mathrm{i t}}=r_{\mathrm{i}}+u_{\mathrm{i t}} $$

where $ r_{i} $ is the country effect, and $ u_{it} $ is the remaining error term. In choosing between fixed-effects and random-effects estimation, an important issue is whether the country effect is correlated with the explanatory variables. In the absence of such a correlation, random-effects estimation is consistent and efficient. In contrast, if such a correlation exists, there may be omitted-variable bias, necessitating fixed-effects estimation. According to the statistics of the Wu-Hausman test (Hausman 1978), which is used to help choose between these two approaches, we use a fixed-effects estimation on sulfur emissions for the sample countries of Group A and on carbon emissions, and use a random-effects estimation on sulfur emissions for the sample countries of Group B (see Table 1).

#### 3.3.2 Estimation Results and Interpretations

Table 1 lists the results of the estimation of the sulfur and carbon emissions. First, we must verify the shape of the EK curve of each emission index. In the case of sulfur emissions, the estimates for the coefficients $ \alpha_{1} $ and $ \alpha_{2} $ have the signs of the inverted U-shapes, and are different from zero, as high levels of significance in both sample countries of Group A and B. The turning point in Group B case indicates a feasible number, US17,900. The case of Group A has the turning point of 82,000, which is beyond the observed range. It may be because the distribution of the panel data is biased in recent years. The indices for the sulfur emissions can, therefore, be said to reflect a meaningful, inverted U-shaped EK curve with the real GDP per capita. On the contrary, the carbon emission cases have those coefficients $ \alpha_{1} $ and $ \alpha_{2} $ , whose signs show U-shapes at high levels of significance in both cases of Group A and B. Since the turning points are negative, the observed ranges of trajectories on carbon emissions
reveal monotonous increasing trends with per capita income.

Second, we must see if the trajectories show a downward shift or an upward shift, and whether the latecomer's advantage or its disadvantage dominates in the environmental management of developing countries. In the case of sulfur emissions, the estimate for the coefficients of YEAR, $ \alpha_{3} $ , is negative and discernible, thereby representing the downward shift of the trajectories and the dominance of the latecomer's advantage. On the other hand, the carbon emission cases have the positive and discernible coefficient of $ \alpha_{3} $ , showing the upward shift of the trajectories, the dominance of the latecomer's disadvantage.

The contrasting result above on the shapes of the EK curves is consistent with the findings of the exiting empirical literature on local and global air pollutants. For example, Rothman (1998) and Stern (1998) argue that the EK curve pattern is usually found to hold for pollutants with local impacts such as sulfur emissions, which cannot easily be externalized, while carbon emissions—the impacts of which are relatively easy to externalize—show no tendency to decline with increasing per capita income. Another contrasting result on the latecomer’s effects is one that this study found for the first time in the literature on the EK curve.

These clear contrasts in estimation results between the case of sulfur emissions and that of carbon emissions seem to be interpreted as follows. Wealthy consumers in developed countries demand a cleaner environment and stringent environmental management, including regulations and technologies. This results in the environmental improvements of industrial production and/or the relocation of dirty industries abroad, thereby the decline in the levels of pollutants arising from production. This is nothing but the case of sulfur emissions showing the EK curve pattern, because sulfur emissions mainly come from manufacturing production activities. The verified latecomer’s advantage can be interpreted in such a way that the know-how, skills, and technology to abate sulfur emissions in advanced countries’ production activities are mature and feasible enough to be transferred and disseminated to developing countries $ ^{3} $ , and that the desulfurization effects coming from technological transfer overcome the “pollution heaven” impacts coming from the relocation of sulfur polluters in developing countries.

On the other hand, the case is different in carbon emissions. Carbon emissions arise from not only production but also from consumption such as automobile use and the burning of fossil fuels for the generation of electricity, thereby being easily externalized
even at the global level and thus not subject to regulation (Nahman and Antrobus 2005). That is why carbon emissions, which don’t follow the EK curve pattern, continue to rise with per capita income. The latecomer’s effect on carbon emissions may be explained in terms of the dominance of the “pollution heaven” impacts; the know-how and technology to mitigate carbon emissions coming from both production and consumption are too premature to be transferred and disseminated (Yaguchi et al. 2007). Thus, the effect of the relocation of pollution-intensive industries only remains in developing countries. This relocation effect may be so-called “carbon leakage”: the effect that there is an increase in carbon emissions in one country as a result of an emission reduction by a second country with a strict climate policy. For example, Babiker (2005), using a multi-regional computable general equilibrium model, estimated the carbon leakage rate in case of significant relocation of energy-intensive industries from countries with carbon controls to those without them.

### 4. Concluding Remarks

In this study, we set out to examine, using the analytical framework of the EK curve, whether developing countries in the world enjoy the latecomer’s advantage or suffer the latecomer’s disadvantage in the area of environmental management and technology. We focused on representative environmental indices: sulfur emissions as local air pollutants, and carbon emissions as global air pollutants. For this purpose, we carried out a regression analysis using panel data to examine the existence of the EK curve pattern and to see whether the latecomer’s advantage or its disadvantage dominates in the environmental management of developing countries.

Through this analysis, we found two contrasting results between sulfur emissions and carbon emissions on the EK curve shapes and the latecomer’s effects: 1) sulfur emissions follow the inverted U-shape pattern expected as the EK curves while carbon emissions show monotonous increasing trends with per capita income in the observed range, and 2) sulfur emissions represent the dominance of the latecomer’s advantage while carbon emissions reveal that of the latecomer’s disadvantage.

We speculate that the contraction in the shapes comes from the difference in the origin of emissions: sulfur emissions come mainly from production (emissions from production are easily regulated on the local level), and carbon emissions come from both production and consumption (emissions from consumption are easily externalized). We also presume that the contrast in the latecomer’s effects lies in the degree of maturity in the know-how and technology to abate emissions: prevailing desulfurization and unrestricted “carbon leakage”. The results imply the urgent necessity to facilitate
the internalization of external diseconomy in carbon emissions through such methods as emissions charge and greenhouse taxes.

This study is only an initial step in the analysis of the latecomer’s effects. Analytical issues still remain that need to be addressed. First, environmental degradation involves a wide variety of pollutants and ecosystems; empirical testing of emissions and factors other than sulfur and carbon is therefore needed. Second, methodological improvements for the estimation in panel data should be introduced by conducting panel unit root tests and co-integration tests. Further studies on the EK curve will provide significant information, enabling the improved planning and evaluation of global environmental policies.
Figure 1 Cross-sectional relations in sample countries
Figure 2 Time-series Relationships in Main Sample Countries
Table 1 Estimation Results for Sulfur and Carbon Emissions

Variables	SOP*106	COP*106
Group A	Group B	Group A	Group B
Const.	3.25*108***	4.45*108***	-2.78*109	-3.00*109*
(5.41)	(-9.26)	(-1.61)	(-1.66)
YRP	8.50*102***	3.44*103***	4.32*104***	4.23*104***
(5.02)	(14.9)	(7.33)	(6.87)
YRP²	-5.17*10-3**	-9.59*10-2***	4.92*10-1***	5.04*10-1***
(-2.11)	(-13.0)	(5.02)	(4.95)
YEAR	-1.59*105***	-2.26*105***	1.79*106**	1.91*106**
(-5.16)	(-9.13)	(2.02)	(2.06)
Adj R**2	0.86	0.08	0.86	0.86
Turning Point	8.22*104	1.79*104	-4.40*104	-4.19*104
<the>Wu-Hausman Test>
Chi-Sq. Statistic	32.14	0.49	324.76	303.68
Chi-Sq. d.f.	3	3	3	3
Prob.	0.00	0.92	0.00	0.00
Estimation Type	Fixed	Random	Fixed	Fixed

Note:

a) The T-value is shown in parentheses.

One, two, or three asterisks indicate that a coefficient estimate is significantly

different from zero at 10, 5, or 1% percent level, respectively.

b) Constant terms for fixed-effect model indicate the mean of the estimated country effects.

Sources: Lefohn A.S., J.D.Husar, and R.B.Husar (1999)

Marland, G., T.A. Boden, and R. J. Andres (2008)

Heston Alan, Robert Summers and Bettina Aten (2006)