Shining a new light on the environmental Kuznets curve for CO2 emissions

Journal Pre-proof Shining a new light on the environmental Kuznets curve for CO2 emissions

Andrzej Kacprzyk, Zbigniew Kuchta PII:

S0140-9883(20)30043-8

DOI:

https://doi.org/10.1016/j.eneco.2020.104704

Reference:

ENEECO 104704

To appear in:

Energy Economics

Received date:

24 October 2019

Revised date:

20 January 2020

Accepted date:

29 January 2020

Please cite this article as: A. Kacprzyk and Z. Kuchta, Shining a new light on the environmental Kuznets curve for CO2 emissions, Energy Economics(2020), https://doi.org/10.1016/j.eneco.2020.104704

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© 2020 Published by Elsevier.

Journal Pre-proof Shining a new light on the Environmental Kuznets Curve for CO2 emissions

Andrzej Kacprzyk*, Zbigniew Kuchta** Institute of Economics, University of Lodz, 41 Rewolucji 1905 St., 90-214 Lodz, Poland. *

Corresponding author. E-mail: [email protected] E-mail: [email protected]

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**

Economic development

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JEL classification: E01, O44, Q53, Q54

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Keywords: Environmental Kuznets Curve; CO2 emissions; DMSP-OLS nighttime lights;

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Journal Pre-proof Abstract This paper examines the existence of an inverted U-shaped relationship between income and CO2 emissions from fossil fuels for a panel of 161 countries over the period 1992–2012. Our contribution, empirical in nature, is threefold. (i) We estimate the Environmental Kuznets Curve for CO2 emissions using three conventional measures of GDP taken from the PWT and the WDI and show that the results of the estimates are unstable and that the turning points are dependent on the measure of GDP. (ii) We use a new dataset of GDP based on the Defense

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Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) nighttime

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light imagery as a proxy for the development level, and using these data, which are less prone

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to the measurement errors in the System of National Accounts statistics, we find evidence for

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the existence of an Environmental Kuznets Curve for CO2 emissions. (iii) We apply a U-test that confirms that the estimated extreme point is within our data range. The evaluated turning

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point, beyond which CO2 emissions start to fall as income rises, is considerably lower than

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earlier studies show, and thus, our results provide more optimistic prospects of the possible

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environmental benefits of economic growth.

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Journal Pre-proof 1. Introduction

The issue of whether the levels of environmental degradation and income per capita follow an inverted U-shaped pattern has been extensively analyzed in the literature since the early 1990s. A number of researchers have attempted to empirically test the validity of this relationship, known as the Environmental Kuznets Curve (EKC). This strand of research was started by Grossman and Krueger (1991), Shafik and Bandyopadhyay (1992), Panayotou

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(1993), Selden and Song (1994), and Holtz-Eakin and Selden (1995).

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These pioneering studies were followed by hundreds of researchers who tried to validate

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EKC for different indicators of pollution, using different samples and various methods.1 A

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detailed review of the literature on the EKC is beyond the scope of this paper and it can be

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found in a number of papers (see the survey by Dinda 2004; Stern 2004; Kijima et al. 2010; Kaika and Zervas 2013a, b; Youssef et al. 2016; Liobikienė and Butkus 2017; Hu et al. 2018;

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Marques et al. 2018). The evidence on the EKC which emerges from this literature is, at best,

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mixed. This inconclusiveness of the results has triggered critical studies on the EKC. The major line of attack points to econometric issues. Many authors criticize the reduced-form regression approach which is a common estimation method employed in the EKC empirical literature (Stern et al. 1996, Panayotou 1997; List and Gallet 1999; Copeland and Taylor 2004; Wagner 2008; Vollebergh et al. 2009; Youssef et al. 2016). They warn against inferring causality or drawing policy conclusions based on such estimates. For example, Vollebergh et al. (2009) show how imposing identifying assumptions regarding the flexibility of time trends affects the rejection or acceptance of the proposed reduced-form relationship and leads to subjectively-based instead of data-driven inferences. Several authors 1

A search made in Scopus and Web of Science in September 2019 using the term ‘environmental Kuznets curve’ revealed more than 1800 results in the former and more than 2200 results in the latter database from 1994 to 2019.

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Journal Pre-proof show the sensitivity of the EKC estimates to the econometric specifications and functional forms used (Harbaugh et al. 2002; Azomahou et al. 2006; Galeotti et al. 2006; Wagner 2008). The results are also sensitive to the inclusion of additional controls as well as changes in the sample size and years sampled (Kaufmann et al. 1998; Rothman 1998; Torras and Boyce 1998; Harbaugh et al. 2002; Itkonen 2012). Last but not least, it has also been shown that standard cointegration techniques that are commonly used in empirical EKC studies are very often inappropriate (Perman and Stern 2003; Stern 2004; Bradford et al. 2005; Müller-

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Fürstenberger and Wagner 2007; Wagner 2008; Chow and Li 2014; Wagner 2015). For

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example, Wagner (2008) discusses the consequences of ignoring the implications of panel

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estimations involving nonlinear transformations of integrated regressors and/or neglecting

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cross-sectional dependence in the data. The author presents an estimation and testing methodology appropriate for the analysis of cointegrating polynomial relationships. Stern

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(2010) proposes another solution to avoid the issues raised by both Wagner (2008) and

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Vollebergh et al. (2009). Following Hauk and Wacziarg (2009), Stern argues that panel between estimator (BE) is the best estimator of the long-run relationship among panel data

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estimators. Since BE averages data over time for each country, it does not require any specification of dynamics and is consistent for both stationary and nonstationary data. Due to averaging across time, BE is also likely to reduce the effects of non-systematic measurement errors and cross-sectional dependence. Using 40- and 50-year-long data from Vollebergh et al. (2009) and Wagner (2008), Stern finds the between estimator to be superior to other estimators, including OLS, first differences, fixed effects (FE), and random effects (RE). Other opponents question the sense of any empirical studies on EKC: ‘(…) EKCs are nothing more than conditional correlations, without meaningful interpretations other than that pollution does not necessarily increase with economic growth’ (Levinson and O'Brien

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Journal Pre-proof 2018). There are even ironic voices: ‘(…) the pollution-income path must be inverse U shaped. (What goes down must, after all, have first gone up)’ (Harbaugh et al. 2002). While most of the critical remarks on the EKC are convincing and well-grounded, it is hard to agree that the EKC concept is meaningless. What is the impact of economic growth on the environment? Are they friends or foes? The answers to these questions are of crucial importance given the problem of global warming that has become an urgent issue in recent

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years. Since a large part of the world energy supply comes from fossil fuels and, at the same time, the use of energy and CO2 emissions are directly related, the implications of the

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(non)existence of the EKC are not negligible, and special attention should be paid to the value

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of implied turning points. Turning points that are extremely high or even out of the sample

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imply that the vast majority of countries will have to experience rising CO2 emissions in the

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next decades and give rise to the call for an active environmental policy. Lower turning points, easier to achieve for many countries, suggest that tightening the environmental policy

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for the whole world may not be necessary for CO2 reductions.

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In this empirical paper, we add to the existing critical literature on EKC by examining what the implications are of using different GDP data for the estimates of EKC for CO2 emissions from fossil fuels. We hypothesize that the results may depend upon the source of the data. To verify this, we use data for a panel of 161 countries over the period 1992-2012 and re-estimate the standard reduced-form EKC specification using three measures of GDP from the World Development Indicators (WDI) and the Penn World Tables 9.0 (PWT) (Feenstra et al. 2015). Indeed, the results of the estimates are mixed and the implied turning points are unstable. To tackle the problem, we estimate the model using alternative data on GDP based on the Defense Meteorological Satellite Program’s Operational Linescan System

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Journal Pre-proof (DMSP–OLS) nighttime light imagery provided by Lessmann and Seidel 2017.2 To compare our results with those of the existing literature, we build on the seminal paper of Holtz-Eakin and Selden (1995) (henceforth HE-S), who were the first to estimate EKC for CO2 in a large panel covering 130 countries. HE-S examined the link between per capita carbon dioxide (CO2) emissions from fossil fuels and per capita income. Qualitatively, their estimates are consistent with the EKC hypothesis, but the implied turning point beyond which emissions switch from growing to declining is 35 thousand in 1986 USD and is out of sample.3 The

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authors’ forecasts based on these results suggest a rapid increase in aggregate CO2 emissions

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by the end of the 21st century. HE-S’s paper was recently replicated by Sheldon (2019), and

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the results obtained using updated and extended data on carbon emissions and GDP are

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similar to those of HE-S, with the main difference referring to the implied turning point. Sheldon (2019) finds slightly lower implied turning point: 30 thousand in 1986 USD

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(equivalent to 61 thousand in 2011 USD).4 Given that the estimated turning points are

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relatively high in both cases, there is not much prospect of rapidly achieving environmental benefits of growth in many countries.

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The vast majority of the existing literature on EKC uses the WDI or the PWT as GDP data sources. Besides the standard difficulties in measuring GDP, well known and extensively discussed since the official System of National Accounts emerged in 1953, using official measures of GDP poses additional problems in research covering large groups of countries. The first is the low quality (or even lack) of official national accounts data for numerous subSaharan African countries. Systematic measurement errors in official data on economic

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Our period of analysis (1992-2012) cannot be extended due to data limitations. Namely, Global DMSP-OLS Nighttime Lights Time Series from National Centers for Environmental Information used by Lessmann and Seidel (2017) to estimate GDP based on nighttime light imagery stop at 2013. 3 The lack of turning point in the sample may be seen as another argument against the EKC, since the true relation between CO2 emission and GDP may be non-linear but monotonic (see Lind and Mehlum 2010). 4 Following Sheldon (2019), we use the calculator available on the website of the U.S. Bureau of Labor Statistics (https://data.bls.gov/cgi-bin/cpicalc.pl) to compare the results from different years.

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Journal Pre-proof development provided by the statistical offices in African countries can seriously distort estimation results and the policy conclusions offered by the policymakers and researchers who use such data in their analyses (see, e.g., Devarajan 2013; Jerven 2013).5 The next issue faced by researchers analyzing economic growth in broad samples of countries over relatively long horizons is the problem of GDP data for countries such as former members of the Eastern Bloc, transitioning from centrally-planned to market

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economies at the beginning of the 1990s, and many other former socialist or communist states. GDP data reported prior to the beginning of the transition are dubious (sometimes even

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fictitious) and data in the early years of the transformation are biased by the rapid growth of

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the informal economy (see, e.g., Leamer and Taylor 1999; Shleifer and Treisman 2005). The

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IMF’s ‘Investment and Capital Stock Dataset’ (2017) is a recent example of possible

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problems with backward estimations of economic data for previously centrally planned economies. According to this dataset covering 1960–2015, 86.6 percent of all investment in

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Russia under communism was private (Murphy, O’Reilly 2018).

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Last but not least, another limitation of existing EKC research rests with the nature of the data from the WDI and PWT. It has been widely documented in the growth literature that the impact of the choice of one of those two most popular data sources on estimates is not negligible (Hanousek et al., 2008; Johnson et al., 2013; Ram and Ural, 2014; Pinkovskiy and Sala-i-Martin 2016a). Moreover, even different versions of the same dataset are likely to produce different estimates, and it has been shown in empirical studies that a newer vintage is

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A detailed discussion of the problem named by Devarajan (2013) “Africa’s statistical tragedy” can be found in special issues of three journals: Review of Income and Wealth Vol. 59, Issue Supplement S1 2013 (Measuring Income, Wealth, Inequality, and Poverty in Sub Saharan Africa: Challenges, Issues, and Findings), Canadian Journal of Development Studies Vol. 35(1) 2014 (Measuring African Development: Past and Present), and The Journal of Development Studies Vol. 51(2) 2015 (Statistical Tragedy in Africa? Evaluating the Database for African Economic Development).

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Journal Pre-proof not always a better one (Ciccone and Jarociński 2010; Ponomareva and Katayama 2010; Johnson et al. 2013; Pinkovskiy and Sala-i-Martin 2016b). Our study addresses these limitations by analyzing the EKC hypothesis for CO2 emissions using an alternative measure of GDP based on nightlight data. Using nighttime light as a proxy for various socioeconomic variables has a long tradition in economics (see Croft 1978; Sutton and Costanza 2002; Sutton et al. 2007; Ghosh et al. 2010, among others).

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In 2012, Henderson et al., in their prominent study, found a strong relationship between changes in nighttime light and economic growth at country level. Subsequent studies

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confirmed that luminosity is a good proxy for income at different levels of aggregation (see

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Michalopoulos and Papaioannou 2013, 2014; Hodler and Raschky 2014; Pinkovskiy and

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Sala-i-Martin 2016a; Alesina et al. 2016; Campante and Yanagizawa-Drott 2018; Henderson

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et al. 2018, among others). Recently, Lu and Liu (2014), Meng et al. (2014), Shi et al. (2016), Wang and Liu (2017) and Chen et al. (2018) used this data to estimate CO2 emissions at

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city/county/prefecture level in China; however, to the best of our knowledge, our study is the first attempt to empirically verify the validity of EKC for CO2 emissions using nightlight data

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as a proxy for the level of economic development. Of course, luminosity data do not measure income without error, but this error is independent of the measurement errors in official national accounts (Pinkovskiy and Sala-i-Martin 2016a). For this reason, our data are less prone to bias due to the above-mentioned problems. The results of our estimates show that including an alternative measure of GDP allows us to obtain a lower value of the extreme point, in comparison to HE-S and Sheldon (2019). The estimated turning point of our benchmark model suggests that per capita emissions reach a peak at an income level close to 44 thousand in 2011 USD. We also apply the U-test (Lind and Mehlum 2010), which confirms the existence of the extreme point in the sample. Finally,

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Journal Pre-proof we repeat our estimates for two sub-samples by excluding 1% and 5% of countries with the lowest total CO2 emissions and show that our estimates are robust to changes in sample size. The remainder of the paper is structured as follows. The empirical model, methodology, and data are described in the next section. The results are presented in section 3. The last section concludes the paper.

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2. Methodology and data

To ensure comparability with the results of H-ES and Sheldon (2019), our empirical

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model refers to their specification and takes the following form:

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2 𝑐𝑜2𝑖,𝑡 = 𝛼0 + 𝛼1 𝑔𝑑𝑝𝑖,𝑡 + 𝛼2 𝑔𝑑𝑝𝑖,𝑡 + 𝑓𝑖 + 𝛾𝑡 + 𝜀𝑖,𝑡 ,

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where:

(1)

per capita CO2 emissions from fossil fuels in country 𝑖 at time 𝑡;

𝑔𝑑𝑝𝑖,𝑡 :

real per capita GDP in country 𝑖 at time 𝑡;

𝑓𝑖 :

country fixed effects;

𝛾𝑡 :

year fixed effects;

𝜀𝑖,𝑡 :

stochastic error term.

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𝑐𝑜2𝑖,𝑡 :

The model can be estimated using several estimators, including fixed effects (FE), between effects (BE), and random effects (RE), among others. FE exploits within the dimension of the data, whereas BE uses only the variation between cross-sections. The RE estimator is a weighted average of the FE and BE; thus, it is the most efficient method of estimation since it combines the information from both dimensions. We use FE, and there are three reasons for this choice. The first reason is formal: to be in accordance with econometric theory, we 9

Journal Pre-proof perform a test of fixed vs. random effects.

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If the test does not reject the null of random

effects, the more efficient RE estimator could be used. The BE would also be consistent in this case, although less efficient than RE (Wooldridge 2010). A test of fixed vs. random effects rejects the random formulation in favor of the FE model in all our models and samples. The second reason refers to heterogeneity bias and measurement errors problem. As we mentioned in the introduction section, Stern (2010) found the BE estimates to be preferred

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when compared to an array of potential panel estimators. The author argues that the process of averaging across time within each cross-sectional unit is likely to reduce the negative effects

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of non-systematic measurement errors. However, the examples provided in the introduction

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suggest that measurement errors in official data on GDP are rather systematic since their

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magnitudes vary across countries. At the same time, the use of BE does not come at no cost,

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and the major drawback of BE is that it can be subject to omitted variable bias. Since our specification is very parsimonious, one may still worry that the results are driven by

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unobserved factors that are constant over time, such as climate, resource endowment, among

our case.

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others. Ignoring them would lead to biased estimates; thus, BE offers no advantage over FE in

A third reason for the use of FE is that we follow H-ES and Sheldon (2019) who also used it in their estimates. Even though we are not able to replicate their papers directly due to data limitations, namely, differences in the time spans of the data (see Note 2), we use the same method of estimation and follow the same specification in order to achieve the highest possible comparability.

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The standard Hausman test cannot be used to choose between the FE and RE models when estimates are with robust standard errors. Since a test of fixed vs. random effects can be seen as a test of overidentifying restrictions, we use xtoverid command from SSC that allows to perform this test with robust SEs (see Schaffer and Stillman 2006 for details).

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Journal Pre-proof Although for the purpose of comparability we limit our attention to CO2 emissions and GDP only, we conduct a number of available robustness checks. Both for standard and alternative measures of GDP, we test the sensitivity of regression results to profound change in sample size. Additionally, all models are estimated using FE and RE methods as those estimates tend to converge for a relatively large number of cross-sections and years.7 The estimates of 𝛼1 and 𝛼2 are commonly used to infer the relation between per capita

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GDP and per capita CO2 emissions and the turning point. However, the inference may be false when the true relationship is monotone over the analyzed dataset. To overcome this difficulty,

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Lind and Mehlum (2010) proposed an U-test which confirms the existence of an extreme

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point in the sample by investigating the significance of the evaluated slopes at the maximum

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and minimum observed values of the independent variable. If the slope is significantly

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positive for the minimum and statistically negative for the maximum, the turning point is contained in the sample. More formally, the test of U shape applies Sasabuchi’s (1980)

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Arcand et al. 2015):

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likelihood ratio and verifies the following null hypothesis (see Lind and Mehlum 2010;

𝐻0 : (𝛼1 + 2𝛼2 𝑔𝑑𝑝𝑚𝑖𝑛 ≤ 0) ∩ (𝛼1 + 2𝛼2 𝑔𝑑𝑝𝑚𝑎𝑥 ≥ 0),

(2)

against the alternative:

𝐻1 : (𝛼1 + 2𝛼2 𝑔𝑑𝑝𝑚𝑖𝑛 > 0) ∩ (𝛼1 + 2𝛼2 𝑔𝑑𝑝𝑚𝑎𝑥 < 0),

(3)

where: 𝑔𝑑𝑝𝑚𝑖𝑛 :

minimum value of GDP per capita in a given sample;

𝑔𝑑𝑝𝑚𝑎𝑥 :

maximum value of GDP per capita in a given sample.

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Since, the EKC estimates have been widely criticized in the literature for being very sensitive to the inclusion of additional controls, we examine the robustness of our results to adding control variables to the reduced-form specification. The results of robustness checks are presented in Appendix C.

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Journal Pre-proof Rejection of the null hypothesis confirms the existence of the turning point in the sample. Our analysis is conducted for 161 countries8 from 1992 to 2012.9 Our dataset consists of data on per capita CO2 emissions from fossil fuels net gas flaring and on real GDP per capita. The data on emissions are measured in thousands of metric tons and come from the Carbon Dioxide Information Analysis Center (CDIAC) (Boden et al. 2016). We express them in kilograms per person.10 The official data on real GDP per capita (in constant PPP 2011 USD)

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come from the PWT and WDI. The nighttime-based measure of GDP is provided by

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Lessmann and Seidel (2017) and is expressed in constant PPP 2011 USD.11 Nighttime light data are collected by satellites from the U.S. Meteorological Satellite

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Program. The satellites orbit the earth 14 times per day, recording the intensity of Earth-based

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lights for a pixel of approximately 0.86 square kilometers. Each satellite observes every

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location on the planet every night at some time point between 8:30 and 10:00 pm local time. Before publication, scientists from the National Geophysical Data Center remove

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observations for locations where natural sources of light occur (Henderson et al. 2012;

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Lessmann and Seidel 2017). Publicly available data contain integers restricted to the interval between 0 (no light) and 63. However, censoring data introduces several difficulties. First, measurement inaccuracy causes many zero-coded pixels, whereas there may be production observed in these areas. Second, introducing the upper bound causes that, in some regions, the intensity of nighttime light was equal and constant, whereas production may vary (see Lessmann and Seidel, 2017). Lessmann and Seidel (2017) overcome the censoring problem and construct a regional measure of GDP based on nighttime light using the following 8

Lessmann and Seidel (2017) provide data for 180 countries. In order to ensure comparability of estimates with the use of data from the PWT, the WDI and Lessmann and Seidel (2017), we limit the size of our main sample to 161 countries for which data exist in all datasets. The list of countries is given in Appendix A. 9 HE-S’s analysis was performed over the period 1951–1986. Sheldon (2019) extends it to 1950–2013. 10 Population data come from the WDI database provided by the World Bank. 11 Lessmann and Seidel’s data are expressed in constant PPP 2005 USD. To ensure comparability between official measures and nighttime light data we convert the GDP pc light series into 2011 USD (see Note 3).

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Journal Pre-proof approach. In the first step, they estimate a random effects model that links official regional GDP with the information about nighttime light. In the second step, they use the obtained estimates to predict in-sample values of GDP (see Lessmann and Seidel 2017, for details). Finally, they aggregate regional values to obtain a country-level measure of real income. To check the robustness of our results, we re-estimate the models with two subsamples.12 Although the choice of subsamples may always be a source of controversy, we

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focus on total CO2 emissions in 2012. In the first case, we limit our sample to countries accounting for 99% of the total CO2 emissions from fossil fuels. This leaves us with 81

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countries (subsample 1). In the second case, we include only the emitters responsible for 95%

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of total CO2 emissions from fossil fuels, and end up with 46 countries (subsample 2).

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Table 1 contains descriptive statistics of CO2 emission per capita (CO2 pc), the

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nighttime light measure of real GDP per capita (GDP pc light), the expenditure-side real GDP per capita (GDP pc PWT expenditure) and the output-side real GDP per capita (GDP pc PWT

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output) from the PWT database as well as the real GDP per capita from the WDI database

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(GDP pc WDI) for the full sample of 161 countries. Table 1. Descriptive statistics – the full sample of 161 countries. Statistic

CO2 pc

GDP pc light

GDP pc PWT GDP pc GDP pc WDI expenditures PWT output Mean 1,222.78 12,358.36 13,555.91 18,478.00 15,192.43 S.D. 1,750.86 13,937.00 16,198.87 105,276.23 18,072.79 C.V. 1.432 1.128 1.195 5.697 1.190 Max 19,179.76 88,644.63 159,825.72 2,893,629.75 132,514.5 Min 3.622 123.03 142.39 133.42 246.67 Skewness 3.944 1.807 2.444 20.91 2.364 Kurtosis 28.37 6.637 12.70 473.57 10.67 IQR 1,622.076 14,693.26 16,622.91 15,603.18 18,254.06 N 3,306 3,306 3,306 3,306 3,291 Notes: S.D. denotes standard deviation, C.V. denotes coefficient of variation, IQR denotes interquartile range.

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All panels are unbalanced. The lists of countries included in the full sample and the two subsamples are provided in the Appendix A.

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Journal Pre-proof All our series are highly differential, highly positively skewed, and heavy-tailed. The average value of CO2 emissions per capita equals 1.2 tons per capita. The average value of the nighttime light measure of GDP per capita equals 12.4 thousand USD per person, and it seems comparable with official measures. Moreover, the variability of the GDP series is also similar between them. The exception is GDP pc PWT output, which is strongly affected by extremely large values for Bermuda.

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Tables 2 and 3 present the descriptive statistics separately for subsample 1 and

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subsample 2.

Table 2. Descriptive statistics – subsample 1 (99% of total CO2 emissions). 18,243.05 15,085.75 0.827 88,644.63 1,310.25 1.351 5.19 21,694.38 1674

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Mean 1,941.94 S.D. 2,054.00 C.V. 1.058 Max 19,179.76 Min 41.519 Skewness 3.650 Kurtosis 22.23 IQR 1,809.67 N 1,674 Notes: As in Table 1.

GDP pc PWT expenditures 19,133.03 17,289.22 0.905 159,825.72 408.02 2.203 12.53 23,462.43 1,674

GDP pc PWT output 18,889.59 17,542.35 0.929 164,136.5 428.15 2.396 13.58 22,089.33 1,674

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GDP pc light

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CO2 pc

GDP pc WDI 21,323.57 18,693.70 0.877 132,514.50 1,344.60 2.089 10.18 25,095.25 1,668

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Statistic

Statistic

CO2 pc

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Table 3. Descriptive statistics – subsample 2 (95% of total CO2 emissions). Mean 2,150.26 S.D. 2,323.80 C.V. 1.081 Max 19,179.76 Min 77.62 Skewness 3.71 Kurtosis 21.93 IQR 1,895.56 N 964 Notes: As in Table 1.

GDP pc light 19,399.25 16,461.17 0.849 88,644.63 1,583.14 1.47 5.31 22,101.86 964

GDP pc PWT expenditures 19,885.53 19,022.23 0.957 159,825.72 408.02 2.57 13.85 23,304.48 964

GDP pc PWT output 19,582.31 19,128.28 0.977 164,136.45 428.15 2.75 15.16 22,177.19 964

GDP pc WDI 21,684.82 20,292.89 0.936 132,514.50 1,666.71 2.56 11.92 23,494.28 958

In subsample 1, which includes 81 countries, the average level of CO2 emissions per capita is 1.9 thousand tons per person while the minimum value is 41.5 kg per person. A further reduction in the number of countries increases the average and minimum value of CO2

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Journal Pre-proof emissions in subsample 2 to 2.2 tons per person and 77.6 kg per capita, respectively. The variability measured by the coefficient of variation is also lower in both subsamples than in the full sample of 161 countries. Similarly, reducing the sample size results in higher average and minimum values of all measures of GDP pc whereas their variabilities are comparable.

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3. Results

This section reports the results of estimating Eq. (1) using four different measures of

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GDP per capita. We start with the results obtained using three conventional measures of GDP

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and show that the estimates and the turning points are very sensitive to the choice of GDP

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measure. In the next step we present results of our baseline empirical model estimated using

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alternative data on GDP and show that the estimates are stable and robust. Following HE-S and Sheldon (2019), we use an FE estimator. Additionally, we also apply an RE estimator. To

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check the robustness of our results, we re-estimate Eq. (1) with two subsamples. Although a test of fixed vs. random effects rejects the random formulation in favor of the FE model in all

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models and samples, we present outcomes from both methods and comment on both since parameters estimates across models are relatively close. Table 4 contains the estimates for the expenditure-side GDP per capita from the PWT.

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Journal Pre-proof Table 4. Estimation results (using expenditure-side GDP per capita from the PWT). (1)

(2)

(3)

Full sample

(4)

(5)

Subsample 1

(6) Subsample 2

FE

RE

FE

RE

FE

RE

GDP pc PWT expenditures

0.0641***

0.0740***

0.0802**

0.0873***

0.0558

0.0825***

(0.019)

(0.015)

(0.034)

(0.028)

(0.038)

(0.026)

-0.0000*** (0.000)

-0.0000*** (0.000)

-0.0000*** (0.000)

-0.0000*** (0.000)

-0.0000*** (0.000)

-0.0000*** (0.000)

509.6589*

368.4054

644.4821

511.1325

1,341.9859

818.3277

(271.068) 56,691.21

(255.060) 60,658.67

(684.260) 59,449.55

(655.809) 62,100.13

(848.371) 51,907.58

(831.238) 62,437.41

p-value

0.0004

0.0000

0.0098

0.0008

0.0754

0.0008

Observations

3,306

3,306

1,674

1,674

964

964

161

161

81

81

46

46

Constant Turning point

No. of countries

ro

GDP pc PWT expenditures squared

of

VARIABLES

lP

re

-p

R-squared 0.216 0.265 0.343 Notes: Corresponding results of U-tests are in Table B1 of the Appendix B. FE and RE denotes fixed-effect estimator and random-effects estimator, respectively. Robust standard errors in parenthesis. All regressions include a full set of time dummies, not reported to conserve space. The estimated coefficients of time dummies and results of a test of fixed vs. random effects are available upon request from the authors. ***, **, * denote significance at the 1%, 5%, and 10%, respectively.

Columns 1 and 2 of Table 4 display the results for the full sample of 161 countries. The

na

estimated coefficients are positive for the linear term and negative for the quadratic term,

Jo ur

which suggests the existence of an inverse U-shaped relationship between CO2 emissions and GDP. The estimated turning point is 56.7 thousand USD under FE and 60.7 thousand USD under RE estimation. The results of the U-test confirm the existence of turning points in the sample.13 The coefficient signs and statistical significance remain unchanged in subsample 2 (columns 3 and 4), with slightly higher, statistically significant turning points. Finally, columns 5 and 6 of Table 1 show the results for subsample 2 which includes 46 countries. The FE estimator yields inconsistent results with regard to the existence of the EKC. The statistically insignificant linear term (column 5) indicates a monotonically decreasing relationship between the CO2 emissions per capita and expenditure-side GDP per capita;

13

Detailed results of all U-tests are provided in the Appendix B.

16

Journal Pre-proof however, the result of the U-test confirms the EKC hypothesis by indicating the statistically significant turning point in the sample.14 The implied turning point equals 51.9 thousand USD and is lower than in the full sample. This result seems to be somewhat counterintuitive, since the average and minimum value of all variables is higher in the subsample than in the full sample, whereas their variability decreases as the number of countries in the sample falls. On the other hand, the RE model (column 6) confirms the existence of EKC and the implied turning point is within the data range, according to the U-test. Its value is slightly higher than

of

in the full sample and subsample 1.

ro

Table 5 contains the results of estimating Eq. (1) using the output-side real GDP per

-p

capita. The estimates of linear and quadratic terms are statistically insignificant in the full

re

sample (columns 1 and 2), and so are turning points. Reducing the number of countries leads

lP

to statistically significant estimates in subsample 1. Results in columns 3 and 4 are consistent with the EKC hypothesis. The significance of this relationship is also confirmed by the U-test.

na

Turning points occur at 62.5 thousand USD under FE and 64.8 thousand USD under RE, and both are slightly higher than the turning points estimated using the expenditure-side GDP

Jo ur

data. Columns 5 and 6 of Table 5 present the results from subsample 2.

14

See column 5 of Table B1 in Appendix B.

17

Journal Pre-proof Table 5. Estimation results (using output-side GDP per capita from the PWT). (1)

(2)

(3)

RE

FE

Full sample VARIABLES

FE

(4)

(5)

RE

FE

Subsample 1

(6) Subsample 2 RE

GDP pc PWT output

-p

ro

of

-0.0004 0.0001 0.0856*** 0.0920*** 0.0602 0.0882*** (0.001) (0.001) (0.031) (0.026) (0.036) (0.023) GDP pc PWT 0.0000 -0.0000 -0.0000*** -0.0000*** -0.0000*** -0.0000*** output squared (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) 1,263.7664*** 1,256.4372*** 540.2211 421.5947 1,263.9151 709.3727 Constant (32.550) (139.852) (625.168) (594.835) (800.432) (750.197) Turning point 1,317,453 1,638,016 62,449.21 64,809.37 54,397.68 65,182.73 p-value 0.385 0.488 0.0036 0.0002 0.0503 0.0001 Observations 3,306 3,306 1,674 1,674 964 964 No. of countries 161 161 81 81 46 46 R-squared 0.012 0.295 0.385 Notes: Corresponding results of U-tests are in Table B2 of the Appendix B. FE and RE denotes fixed-effect estimator and random-effects estimator, respectively. Robust standard errors in parenthesis. All regressions include a full set of time dummies, not reported to conserve space. The estimated coefficients of time dummies and results of a test of fixed vs. random effects are available upon request from the authors. ***, **, * denote significance at the 1%, 5%, and 10%, respectively.

re

The results of the FE model are quantitatively identical to the corresponding results from

lP

column 5 of Table 4. We found a statistically significant turning point of 54.4 thousand USD, although the linear term is no longer statistically significantly different from zero. The CO2

na

emission peaked at a much lower level of income than in subsample 1. With RE, the EKC

Jo ur

hypothesis is confirmed both by the estimates of the parameters and by the U-test. The CO2 emissions reached a peak at 65.2 thousand USD. The results of the estimates using the third official measure of GDP taken from the WDI database are given in Table 6. The first two columns display the estimates for the whole sample of countries. Using FE, we obtained a positive statistically significant coefficient on the linear term and a negative but insignificant coefficient on the quadratic term. With RE, both the linear and quadratic terms have the expected signs and are statistically significant. However, according to the U-test, the implied turning points are statistically insignificant in both cases; therefore, there is no evidence supporting the EKC hypothesis in the full sample of 161 countries. The re-estimation of the model in subsample 1 leaves the relationship between CO2 and GDP pc unchanged (column 3 and 4). 18

Journal Pre-proof Table 6. Estimation results (using GDP per capita from the WDI). (1)

(2)

(3)

(4)

(5)

(6)

Full sample Subsample 1 Subsample 2 FE RE FE RE FE RE 0.0747** 0.0815*** 0.1421** 0.1286*** 0.1589*** 0.1469*** GDP pc WDI (0.031) (0.025) (0.060) (0.046) (0.057) (0.050) GDP pc WDI -0.0000 -0.0000* -0.0000* -0.0000** -0.0000** -0.0000** squared (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) 221.5487 111.6801 -783.4705 -545.3083 -951.7818 -754.6273 Constant (365.295) (254.729) (1,062.701) (709.521) (1,003.191) (751.409) Turning point 79,365.95 91,187.35 82,805.87 91,255.52 84,102.6 94,560.13 p-value 0.195 0.178 0.114 0.113 0.0973 0.0943 Observations 3,291 3,291 1,668 1,668 958 958 No. of countries 161 161 81 81 46 46 R-squared 0.124 0.190 0.226 Notes: Corresponding results of U-tests are in Table B3 of the Appendix B. FE and RE denotes fixed-effect estimator and random-effects estimator, respectively. Robust standard errors in parenthesis. All regressions include a full set of time dummies, not reported to conserve space. The estimated coefficients of time dummies and results of a test of fixed vs. random effects are available upon request from the authors. ***, **, * denote significance at the 1%, 5%, and 10%, respectively.

-p

ro

of

VARIABLES

re

Based on the U-test, the turning points are statistically insignificant although the coefficients

lP

on the linear and quadratic term are significant and have the expected signs. Finally, we find evidence of the existence of an EKC relationship in subsample 2. The results of the estimates

na

are statistically significant in both models. Also, implied turning points are significant; however, only at the 10% level. The EKC peaked at 84.1 thousand USD under FE and 94.5

Jo ur

thousand USD under RE. These values are considerably higher than the turning points evaluated using the PWT measures of GDP. Table 7 displays the estimates of Eq. (1) using the alternative measure of GDP based on the DMSP-OLS nighttime light imagery provided by Lessmann and Seidel (2017). Columns 1 and 2 contain estimates for the full sample of 161 countries. All coefficients are statistically significant and had the expected signs regarding the direction of the relationship between per capita GDP and per capita CO2 emissions. The estimated coefficients are positive for the linear term and negative for the quadratic term, which suggests the existence of an inverse Ushaped relationship between CO2 emissions and GDP. The estimated turning point of the

19

Journal Pre-proof model is 44.1 thousand USD and 47.6 thousand USD under the FE and RE estimations, respectively. Table 7. Estimation results (using GDP per capita, based on satellite nighttime light data). (1)

(2)

(3)

Full sample

(4) Subsample 1

(5)

(6) Subsample 2

FE RE FE RE FE RE 0.1239*** 0.1338*** 0.2035*** 0.1936*** 0.2249*** 0.2159*** GDP pc light (0.042) (0.039) (0.065) (0.059) (0.069) (0.070) GDP pc light -0.0000** -0.0000** -0.0000*** -0.0000*** -0.0000*** -0.0000*** squared (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) 44.7516 -89.3518 -1,008.9314 -873.7287 -1,234.0979 -1,158.2930 Constant (385.063) (267.837) (952.295) (697.159) (1,102.439) (837.828) 44,116.26 47,609.48 49,649.36 50,755.93 50,393.47 53,084.49 Turning point p-value 0.0523 0.0449 0.0185 0.0205 0.0171 0.0234 Observations 3,306 3,306 1,674 1,674 964 964 No. of countries 161 161 81 81 46 46 R-squared 0.158 0.232 0.322 Notes: Corresponding results of U-tests are in Table B4 of the Appendix B. FE and RE denotes fixed-effect estimator and random-effects estimator, respectively. Robust standard errors in parenthesis. All regressions include a full set of time dummies, not reported to conserve space. The estimated coefficients of time dummies and results of a test of fixed vs. random effects are available upon request from the authors. ***, **, * denote significance at the 1%, 5%, and 10%, respectively.

lP

re

-p

ro

of

VARIABLES

The turning points are lower than the 65 thousand USD obtained by Sheldon (2019) in her

na

largest sample of 175 countries, and 71 thousand USD obtained by HE-S in their 130-country

Jo ur

sample, both found using identical specifications. The next two columns of Table 7 display the corresponding estimates for subsample 1, composed of 81 countries which account for 99% of total CO2 emissions. The results are essentially unchanged, with a slightly higher implied turning point occurring at 49.7 thousand USD under FE and 50.8 thousand USD under RE. Finally, Columns 5 and 6 of Table 7 confirm the previous results in the smallest subsample of 46 countries responsible for 95% of total CO2 emissions. The estimated turning point of the model obtained using the FE estimator indicates that per capita CO2 emissions reached a peak at 50.4 thousand USD. In the case of RE, the level of income beyond which the per capita CO2 emissions began to decline is 53.9 thousand USD. Both turning points are reasonably close to the previous ones. Finally, we perform the U-test for the presence of a turning point in our data. We find statistically significant and positive estimates of the slope 20

Journal Pre-proof for the lowest value of income and statistically significant and negative estimates of the slope for the highest value of income in all the estimated models in Table 7. Consequently, the test statistics support the EKC hypothesis both in the full sample and in the two subsamples. As mentioned in the introduction, the results to date from numerous empirical EKC studies are mixed. For this reason, many authors have carried out analyses using various models and methods, trying to find out why the pollution-income relationship is not robust. However, to our knowledge, the sensitivity of results to the choice of the source of GDP data

of

has not received any attention so far. Our results fill this gap. As shown in Tables 4-6, the

ro

estimates are very sensitive to the choice of GDP measure. Moreover, they also vary greatly

-p

with the sample used and across estimation methods. Given the above findings, there appears

re

to have been surprisingly little discussion regarding the choice of GDP measure in empirical EKC studies. Furthermore, when it comes to turning points, those that are statistically

lP

significant according to the U-test, differ across samples, methods of estimation and across

na

GDP measure used. Their location ranges from 51.9 thousand USD to 94.6 thousand US in extreme cases. Moreover, it is not only the levels of turning points that are questionable but so

Jo ur

is their very existence. In the three models estimated using official data on GDP, the turning points turned out to be statistically insignificant according to the U-test, although the estimates of the coefficients do suggest their existence. On the other hand, in two cases, despite the statistically insignificant estimates of the coefficients on the level term, the turning point passed the test for the presence of an inverted U shape. These examples show how important the appropriate testing of an inverted U-shaped relationship is. The results of the estimates with the use of the alternative measure of GDP based on satellite nighttime light data presented in Table 7 give a different picture of the CO2-GDP relationship, both quantitatively and qualitatively. They provide strong support for the EKC hypothesis regardless of the sample size. They are also robust across the two alternative

21

Journal Pre-proof methods of estimation. The existence of turning points is confirmed both by the parameter estimates and the results of the U-test. All implied turning points are considerably lower than the corresponding ones obtained using standard GDP measures. They range from 44.1 thousand USD to 53.1 thousand USD in extreme cases. Coupling the results obtained using standard and alternative measures of GDP clearly shows the advantages of nighttime lightbased data in large panels of heterogeneous countries.

ro

of

4. Conclusion

-p

Since the early 1990s, an increasing number of researchers have attempted to test the

re

validity of the EKC hypothesis and determine whether environmental quality eventually improves with economic growth. This study used a new indicator of GDP, based on satellite

lP

nighttime light data from Lessmann and Seidel (2017), to reexamine the empirical evidence

na

documenting an inverse U-shaped relationship between income and CO2 emissions from fossil fuels. Given that measurement errors in nighttime lights are orthogonal to the

Jo ur

measurement errors in national accounts, the measure based on luminosity may serve as a very useful proxy for GDP in large and heterogeneous samples of countries. Using this new indicator, we re-ran the regression specification used by HE-S and Sheldon (2019) for a panel of 161 countries.

Our estimates confirm the EKC hypothesis for CO2 emissions. The implied turning point, beyond which CO2 emissions start to decrease as income increases, is at 44 thousand in 2011 USD in our baseline model and is much lower than the turning points estimated by HES and Sheldon (2019). To check the robustness of our estimates, we repeat them for two subsamples. The results hold well after a substantial reduction in sample size. Finally, we apply the inverse U-test, which confirms that the relationship of interest is really non-

22

Journal Pre-proof monotone within the data range for each of our three samples. It has to be noted that Eq. (1) is in reduced-form. For this reason we are far away from drawing strong policy conclusions. Nevertheless, based on our results, a more optimistic picture of the income-CO2 emissions nexus emerges. Since our implied turning point is lower than in the replicated papers, the environmental benefits of economic growth may thus be easier to achieve. Therefore our findings suggest that it is worthwhile studying mechanisms that underlie the observed

of

correlation between CO2 and GDP. We are also aware that our results should be interpreted with some caution since the

ro

time dimension of our dataset (T=21) is relatively short, compared with those in the main

-p

strand of the EKC literature. A longer time frame could give more reliable estimates.

re

Therefore, our findings serve as a pilot study and should be confirmed by future studies.

lP

Nevertheless, we believe that the advantages of nighttime light data outweigh their disadvantages, as they are available with the same quality for all countries, including those for

na

which official statistics either do not exist or are of poor quality. Moreover, these data may be applied at various levels of aggregation – country, state, sub-state, and municipal. These

Jo ur

features open a promising avenue for further research, as an increasing amount of luminosity data becomes available.

Acknowledgments Funding: This work was supported by the National Science Centre, Poland (decision DEC2016/21/B/HS4/01565) Declarations of interest: none

23

Journal Pre-proof References Alesina, A., Michalopoulos, S., Papaioannou, E., 2016. Ethnic inequality. Journal of Political Economy 124(2), 428–488. doi.org/10.1086/685300 Arcand, J.L., Berkes, E., Panizza, U., 2015. Too much finance? Journal of Economic Growth 20(2), 105–148. doi:10.1007/s10887-015-9115-2 Azomahou, T., Laisney, F., Van, P.N., 2006. Economic development and CO2 emissions: a nonparametric panel approach. Journal of Public Economics 90(6–7), 1347–1363. doi.org/10.1016/j.jpubeco.2005.09.005

ro

of

Boden, T.A., Marland, G., Andres, R.J., 2016. Global, Regional, and National Fossil-Fuel CO2 Emissions. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tenn., U.S.A. doi:10.3334/CDIAC/00001_V2016

-p

Bradford, D.F., Fender, R.A., Shore, S.H., Wagner, M., 2005. The environmental Kuznets curve: exploring a fresh specification. Contributions in Economic Analysis & Policy 4(1). https://doi.org/10.2202/1538-0645.1073

lP

re

Campante, F., Yanagizawa-Drott, D., 2018. Long-range growth: economic development in the global network of air links. The Quarterly Journal of Economics 133(3), 1395–1458. https://doi.org/10.1093/qje/qjx050

na

Chen, J., Xu, C., Li, K., Song, M., 2018. A gravity model and exploratory spatial data analysis of prefecture-scale pollutant and CO2 emissions in China. Ecological Indicators 90, 554–563. https://doi.org/10.1016/j.ecolind.2018.03.057

Jo ur

Chow, G.C., Li, J., 2014. Environmental Kuznets curve: conclusive econometric evidence for CO2. Pacific Economic Review 19(1), 1–7. https://doi.org/10.1111/1468-0106.12048 Ciccone, A., Jarociński, M., 2010. Determinants of economic growth: will data tell? American Economic Journal: Macroeconomics 2(4), 222–246. doi:10.1257/mac.2.4.222 Copeland, B.R., Taylor, M.S., 2004. Trade, growth, and the environment. Journal of Economic Literature 42(1), 7–71. doi:10.1257/002205104773558047 Croft, T.A., 1978. Night-time Images of the Earth from Space. Scientific American 239(1), 86–101. http://www.jstor.org/stable/24955779 Devarajan, S., 2013. Africa's statistical tragedy. Review of Income and Wealth 59(S1), S9– S15. doi:10.1111/roiw.12013 Dinda, S., 2004. Environmental Kuznets curve hypothesis: a survey. Ecological Economics 49(4), 43–455. https://doi.org/10.1016/j.ecolecon.2004.02.011 Feenstra, R.C., Inklaar, R., Timmer, M.P., 2015. The next generation of the Penn World Table. American Economic Review 105(10), 3150–3182, available for download at www.ggdc.net/pwt. doi:10.1257/aer.20130954

24

Journal Pre-proof Galeotti, M., Lanza, A., Pauli, F., 2006. Reassessing the environmental Kuznets curve for CO2 emissions: A robustness exercise. Ecological Economics 57(1), 152–163. https://doi.org/10.1016/j.ecolecon.2005.03.031 Ghosh, T., Powell, R.L., Elvidge, Ch.D., Baugh, K.E., Sutton, P.C., Anderson, S., 2010. Shedding Light on the Global Distribution of Economic Activity. The Open Geography Journal 3, 147–160. doi:10.2174/1874923201003010147 Grossman, G.M., Krueger, A.B., 1991. Environmental impacts of a North American free trade agreement (No. w3914). National Bureau of Economic Research. doi:10.3386/w3914

of

Hanousek, J., Hajkova, D., Filer, R.K., 2008. A rise by any other name? Sensitivity of growth regressions to data source. Journal of Macroeconomics 30(3), 1188–1206. doi.org/10.1016/j.jmacro.2007.08.015

ro

Harbaugh, W.T., Levinson, A., Wilson, D.M., 2002. Reexamining the empirical evidence for an environmental Kuznets curve. Review of Economics and Statistics 84(3), 541–551. https://doi.org/10.1162/003465302320259538

-p

Hauk, W.R., Wacziarg, R., 2009. A Monte Carlo study of growth regressions. Journal of Economic Growth 14(2), 103–147. https://doi.org/10.1007/s10887-009-9040-3

lP

re

Henderson, J.V., Squires, T., Storeygard, A., Weil, D., 2018. The global distribution of economic activity: Nature, history, and the role of trade. The Quarterly Journal of Economics 133(1), 357–406. https://doi.org/10.1093/qje/qjx030

na

Henderson, J.V., Storeygard, A., Weil, D.N., 2012. Measuring economic growth from outer space. The American Economic Review 102(2), 994–1028. doi:10.1257/aer.102.2.994

Jo ur

Hodler, R., Raschky, P.A., 2014. Regional favoritism. The Quarterly Journal of Economics 129(2), 995–1033. doi.org/10.1093/qje/qju004 Holtz-Eakin, D., Selden, T.M., 1995. Stoking the fires? CO2 emission and economic growth. Journal of Public Economics 57(1), 85–101. doi.org/10.1016/0047-2727(94)01449-X Hu, H., Xie, N., Fang, D., Zhang, X., 2018. The role of renewable energy consumption and commercial services trade in carbon dioxide reduction: Evidence from 25 developing countries. Applied Energy 211, 1229–1244. https://doi.org/10.1016/j.apenergy.2017.12.019 International Monetary Fund, 2017. Investment and Capital Stock Dataset. Itkonen, J.V.A., 2012. Problems estimating the carbon Kuznets curve. Energy 30, 274–280 doi.org/10.1016/j.energy.2012.01.018

Jerven, M., 2013. Comparability of GDP estimates in Sub‐Saharan Africa: The effect of Revisions in Sources and Methods Since Structural Adjustment. Review of Income and Wealth 59(S1), S16–S36. doi:10.1111/roiw.12006 Johnson, S., Larson, W., Papageorgiou, C., Subramanian, A., 2013. Is newer better? Penn World Table revisions and their impact on growth estimates. Journal of Monetary Economics 60(2), 255–274. https://doi.org/10.1016/j.jmoneco.2012.10.022 25

Journal Pre-proof Kaika, D., Zervas, E., 2013a. The Environmental Kuznets Curve (EKC) theory - Part A: Concept, causes and the CO2 emissions case. Energy Policy 62, 1392–1402. https://doi.org/10.1016/j.enpol.2013.07.131 Kaika, D., Zervas, E., 2013b. The environmental Kuznets curve (EKC) theory. Part B: Critical issues. Energy Policy 62, 1403–1411. doi.org/10.1016/j.enpol.2013.07.130 Kaufmann, R.K., Davidsdottir, B., Garnham, S., Pauly, P., 1998. The determinants of atmospheric SO2 concentrations: reconsidering the environmental Kuznets curve. Ecological Economics 25(2), 209–220. https://doi.org/10.1016/S0164-0704(99)80000-7

of

Kijima, M., Nishide, K., Ohyama, A., 2010. Economic models for the environmental Kuznets curve: A survey. Journal of Economic Dynamics and Control 34(7), 1187–1201. https://doi.org/10.1016/j.jedc.2010.03.010

ro

Leamer, E.E., Taylor, M.P., 1999. Estimating growth equations for previously centrally planned economies: Dealing with dubious data and disparate information. Journal of Macroeconomics 21(4), 639–672. https://doi.org/10.1016/S0164-0704(99)80000-7

re

-p

Lessmann, Ch., Seidel, A., 2017, Regional inequality, convergence and its determinants – A view from outer space. European Economic Review 92, 110–132. https://doi.org/10.1016/j.euroecorev.2016.11.009

lP

Levinson, A., O’Brien, J., 2018. Environmental Engel Curves: Indirect Emissions of Common Air Pollutants. Review of Economics and Statistics 101(1), 121–133. https://doi.org/10.1162/rest_a_00736

na

Lind, J.T., Mehlum, H., 2010. With or Without U? The Appropriate Test for a U-Shaped Relationship. Oxford Bulletin of Economics and Statistics 72(1), 109–118. doi:10.1111/j.1468-0084.2009.00569.x

Jo ur

Liobikienė, G., Butkus, M., 2017. Environmental Kuznets Curve of greenhouse gas emissions including technological progress and substitution effects. Energy 135, 237–248. https://doi.org/10.1016/j.energy.2017.06.120 List, J.A., Gallet, C.A., 1999. The environmental Kuznets curve: does one size fit all?. Ecological Economics 31(3), 409–423. https://doi.org/10.1016/S0921-8009(99)00064-6 Lu, H., Liu, G., 2014. Spatial effects of carbon dioxide emissions from residential energy consumption: A county-level study using enhanced nocturnal lighting. Applied Energy 131, 297–306. https://doi.org/10.1016/j.apenergy.2014.06.036 Marques, A.C., Fuinhas, J.A., Leal, P.A., 2018. The impact of economic growth on CO2 emissions in Australia: the environmental Kuznets curve and the decoupling index. Environmental Science and Pollution Research 25(27), 27283–27296. https://doi.org/10.1007/s11356-018-2768-6 Meng, L., Graus, W., Worrell, E., Huang, B., 2014. Estimating CO 2 (carbon dioxide) emissions at urban scales by DMSP/OLS (Defense Meteorological Satellite Program's Operational Linescan System) nighttime light imagery: Methodological challenges and a case study for China. Energy 71, 468–478. doi.org/10.1016/j.energy.2014.04.103 26

Journal Pre-proof Michalopoulos, S., Papaioannou, E., 2013. Pre-Colonial ethnic institutions and contemporary African development. Econometrica 81(1), 113–152. doi:10.3982/ECTA9613 Michalopoulos, S., Papaioannou, E., 2014. National institutions and subnational development in Africa. The Quarterly Journal of Economics 129(1), 151–213. doi.org/10.1093/qje/qjt029 Müller-Fürstenberger, G., Wagner, M., 2007. Exploring the environmental Kuznets hypothesis: Theoretical and econometric problems. Ecological Economics 62(3–4), 648–660. https://doi.org/10.1016/j.ecolecon.2006.08.005 Murphy, R.H., O’Reilly, C., 2018. And the IMF Said, Let There Be Data, and There Was Data: Private Capital Stocks in the Eastern Bloc. Econ Journal Watch 15(3), 290–300.

of

Panayotou, T., 1993. Empirical tests and policy analysis of environmental degradation at different stages of economic development. ILO Working Paper 992927783402676. International Labour Organization.

-p

ro

Panayotou, T., 1997. Demystifying the environmental Kuznets curve: turning a black box into a policy tool. Environment and Development Economics 2(4), 465–484. https://doi.org/10.1017/S1355770X97000259

lP

re

Perman, R., Stern, D.I., 2003. Evidence from panel unit root and cointegration tests that the environmental Kuznets curve does not exist. Australian Journal of Agricultural and Resource Economics 47(3), 325–347. https://doi.org/10.1111/1467-8489.00216

na

Pinkovskiy, M., Sala-i-Martin, X., 2016a. Lights, Camera… Income! Illuminating the national accounts-household surveys debate. The Quarterly Journal of Economics 131(2), 579–631. https://doi.org/10.1093/qje/qjw003

Jo ur

Pinkovskiy, M., Sala-i-Martin, X., 2016b. Newer need not be better: evaluating the Penn World Tables and the World Development Indicators using nighttime lights (No. w22216). National Bureau of Economic Research. doi:10.3386/w22216 Ponomareva, N., Katayama, H., 2010. Does the version of the Penn World Tables matter? An analysis of the relationship between growth and volatility. Canadian Journal of Economics 43(1), 152–179. doi:10.1111/j.1540-5982.2009.01567.x Ram, R., Ural, S., 2014. Comparison of GDP per capita data in Penn world table and world development indicators. Social Indicators Research 116(2), 639–646. https://doi.org/10.1007/s11205-013-0284-0 Rothman, D.S., 1998. Environmental Kuznets curves - real progress or passing the buck?: A case for consumption-based approaches. Ecological Economics 25(2), 177–194. https://doi.org/10.1016/S0921-8009(97)00179-1 Sasabuchi, S., 1980. A test of a multivariate normal mean with composite hypotheses determined by linear inequalities. Biometrika 67, 429–439. doi.org/10.1093/biomet/67.2.429 Schaffer, M.E., Stillman, S., 2006. "XTOVERID: Stata module to calculate tests of overidentifying restrictions after xtreg, xtivreg, xtivreg2, xthtaylor," Statistical Software Components S456779, Boston College Department of Economics, revised 05 Jan 2016. 27

Journal Pre-proof Selden, T.M., Song, D., 1994. Environmental quality and development: is there a Kuznets curve for air pollution emissions? Journal of Environmental Economics and Management 27(2), 147–162. https://doi.org/10.1006/jeem.1994.1031 Shafik, N., Bandyopadhyay, S., 1992. Economic growth and environmental quality: timeseries and cross-country evidence. Policy Research Working Papers no. 904. World Bank Publications. Sheldon, T.L., 2019. Carbon emissions and economic growth: A replication and extension. Energy Economics 82, 85–88. https://doi.org/10.1016/j.eneco.2017.03.016

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Shi, K., Chen, Y., Yu, B., Xu, T., Chen, Z., Liu, R., Li, L., Wu, J., 2016. Modeling spatiotemporal CO2 (carbon dioxide) emission dynamics in China from DMSP-OLS nighttime stable light data using panel data analysis. Applied Energy 168, 523–533. https://doi.org/10.1016/j.apenergy.2015.11.055

ro

Shleifer, A., Treisman, D., 2005. A normal country: Russia after communism. Journal of Economic Perspectives 19(1), 151–174. doi:10.1257/0895330053147949

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Stern, D.I., 2004. The rise and fall of the environmental Kuznets curve. World Development 32(8), 1419–1439. https://doi.org/10.1016/j.worlddev.2004.03.004

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Stern, D.I., 2010. Between estimates of the emissions-income elasticity. Ecological Economics 69(11), 2173–2182. https://doi.org/10.1016/j.ecolecon.2010.06.024

na

Stern, D.I., Common, M.S., Barbier, E.B., 1996. Economic growth and environmental degradation: the environmental Kuznets curve and sustainable development. World Development 24(7), 1151–1160. https://doi.org/10.1016/0305-750X(96)00032-0

Jo ur

Sutton, P.C., Costanza, R., 2002. Global Estimates of Market and Non-market Values Derived from Nighttime Satellite Imagery, Land Cover, and Ecosystem Service Valuation. Ecological Economics 41(3), 509–527. https://doi.org/10.1016/S0921-8009(02)00097-6 Sutton, P.C., Elvidge, Ch.D., Ghosh, T., 2007. Estimation of Gross Domestic Product at Subnational Scales Using Nighttime Satellite Imagery. International Journal of Ecological Economics and Statistics 8(S07), 5–21. Torras, M., Boyce, J.K., 1998. Income, inequality, and pollution: a reassessment of the environmental Kuznets curve. Ecological Economics 25(2), 147–160. doi:10.1016/S09218009(97)00177-8 Vollebergh, H.R., Melenberg, B., Dijkgraaf, E. 2009. Identifying reduced-form relations with panel data: The case of pollution and income. Journal of Environmental Economics and Management 58(1), 27–42. https://doi.org/10.1016/j.jeem.2008.12.005 Wagner, M., 2008. The carbon Kuznets curve: a cloudy picture emitted by bad econometrics?. Resource and Energy Economics 30(3), 388–408. doi.org/10.1016/j.reseneeco.2007.11.001 Wagner, M., 2015. The environmental Kuznets curve, cointegration and nonlinearity. Journal of Applied Econometrics 30(6), 948–967. https://doi.org/10.1002/jae.2421

28

Journal Pre-proof Wang, S., Liu, X., 2017. China’s city-level energy-related CO2 emissions: Spatiotemporal patterns and driving forces. Applied Energy 200, 204–214. https://doi.org/10.1016/j.apenergy.2017.05.085 Wooldridge, J.M. 2010. Econometric Analysis of Cross Section and Panel Data. The MIT Press World Development Indicators, 2017. The World Bank.

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na

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re

-p

ro

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Youssef, A.B., Hammoudeh, S., Omri, A., 2016. Simultaneity modeling analysis of the environmental Kuznets curve hypothesis. Energy Economics 60, 266–274. https://doi.org/10.1016/j.eneco.2016.10.005

29

Journal Pre-proof Appendix A. Lists of countries in the samples S1

S2

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re

-p

ro

of

FS

Jo ur

na

Table A1. Countries in samples COUNTRIES Albania Algeria Angola Antigua and Barbuda Argentina Armenia Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia and Herzegovina Botswana Brazil Brunei Bulgaria Burkina Faso Burundi Cabo Verde Cambodia Cameroon Canada Central African Republic Chad Chile China Colombia Comoros The Democratic Republic of Congo Congo Costa Rica Cote d'Ivoire Croatia Cyprus Czech Republic Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Estonia Ethiopia

30

ro -p re lP na Jo ur

Finland France Gabon Gambia Georgia Germany Ghana Greece Grenada Guatemala Guinea Guinea-Bissau Haiti Honduras Hong Kong Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Japan Jordan Kazakhstan Kenya Kyrgyzstan Laos Latvia Lesotho Liberia Lithuania Luxembourg Macedonia Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Montenegro Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua COUNTRIES Niger Nigeria Norway

of

Journal Pre-proof

FS

S1

S2

31

Journal Pre-proof

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na

lP

re

-p

ro

of

Oman Pakistan Panama Paraguay Peru Philippines Poland Portugal Qatar Romania Russia Rwanda Sao Tome and Principe Saudi Arabia Senegal Serbia Sierra Leone Slovakia Slovenia South Africa South Korea Spain Sri Lanka St. Kitts and Nevis St. Lucia St. Vincent and the Grenadines Suriname Swaziland Sweden Switzerland Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela Vietnam Yemen Zambia Zimbabwe Notes: FS, S1 and S2 denotes Full sample, Subsample 1 and Subsample 2, respectively.

32

Journal Pre-proof Appendix B. Results of U-tests Table B1. Results of the U-tests (using expenditure-side GDP per capita from the PWT). (1)

(2)

(3)

Full sample

(5)

Subsample 1

(6)

Subsample 2

FE

RE

FE

RE

FE

RE

56,691.21

60,658.67

59,449.55

62,100.13

51,907.58

62,437.41

0.080***

0.087***

0.056*

0.082***

-0.135***

-0.137***

-0.116***

-0.129***

1.46*

3.16***

Slope at GDP pc PWT 0.064*** 0.074*** expendituresmin Slope at GDP pc PWT -0.117*** -0.121*** expendituresmax Overall 3.41*** 4.87*** test t-value Notes: ***p<0.01, **p<0.05, *p<0.1.

of

Turning point

(4)

3.16***

-p

ro

2.38***

(2)

Full sample FE

RE

1,317,453

1,638,016

Jo ur

Slope at GDP pc PWT -0.0004 0.00005 outputmin Slope at GDP pc PWT 0.0004 -0.00004 outputmax Overall 0.29 0.03 test t-value Notes: ***p<0.01, **p<0.05, *p<0.1.

(4)

Subsample 1

(5)

(6)

Subsample 2

FE

RE

FE

RE

62,449.21

64,809.37

54397.68

65,182.73

0.085***

0.092***

0.060*

0.088***

-3.882***

-4.014***

-3.141***

-3.828***

2.75***

3.59***

1.68*

3.84***

na

Turning point

(3)

lP

(1)

re

Table B2. Results of the U-tests (using output-side GDP per capita from the PWT).

Table B3. Results of the U-tests (using GDP per capita from the WDI).

33

Journal Pre-proof (1)

(2)

(3)

Full sample

Turning point

(4)

Subsample 1

(5)

(6)

Subsample 2

FE

RE

FE

RE

FE

RE

79,365.95

91,187.35

82,805.87

91,255.52

84,102.6

94,560.13

0.142***

0.128***

0.159***

0.147***

-0.085

-0.058

-0.091*

-0.059*

1.22

1.21

1.32*

1.32*

of

Slope at GDP pc 0.075*** 0.081*** WDImin Slope at GDP pc -0.050 -0.037 WDImax Overall 0.86 0.92 test t-value Notes: ***p<0.01, **p<0.05, *p<0.1.

ro

Full sample

Turning point

(5)

RE

FE

RE

49,649.36

50,755.93

50,393.47

53,084.49

0.203***

0.193***

0.224***

0.215***

-0.160**

-0.145**

-0.171**

-0.145**

2.12**

2.05**

2.18**

1.99**

(2)

(3)

(4)

Subsample 1

FE

RE

44,116.26

47,609.48

FE

(6)

Subsample 2

Jo ur

na

lP

Slope at GDP pc 0.124*** 0.134*** lightmin Slope at GDP pc -0.125* -0.115** lightmax Overall 1.63* 1.70** test t-value Notes: ***p<0.01, **p<0.05, *p<0.1.

-p

(1)

re

Table B4. Results of the U-tests (using GDP per capita, based on satellite nighttime light data).

Appendix C. Sensitivity analysis (for online publication only) In the main text we present estimates of the parsimonious model of H-ES and Sheldon (2019), to ensure comparability with their results. However, the EKC estimates have been widely criticized in the literature for being very sensitive to the inclusion of additional controls. Therefore, this section analyzes the robustness of our results to adding control variables to the baseline specification. We only use controls that are not affected by traditional data on GDP in order to avoid possible biases described in the introduction section. Thus, we consider three variables in line with the existing EKC literature. The first is renewable energy consumption (renewable energy consumption) expressed as a share in total final energy consumption. The second control variable measures electricity production from oil, gas and 34

Journal Pre-proof coal sources (electricity production) as a percentage of total electricity production. The last one is electric power consumption measured in kWh per capita (electric power consumption). Additionally, we re-estimate the model from the main text using all four GDP measures and excluding Bermuda from the full sample. Descriptive statistics in Table 1 clearly show that Bermuda is an outlier when GDP per capita is measured from the output-side. Table C1 displays results for the full sample of countries without Bermuda. Indeed, the estimates of

of

linear and quadratic terms are now statistically significant for the output-side GDP per capita from the PWT. So are turning points occurring at 58.4 thousand USD under FE and 62.3

ro

thousand USD under RE. Both are reasonably close to the turning points evaluated using

-p

expenditure-side GDP per capita from the PWT. After dropping Bermuda regression results

re

and the turning points estimated using other GDP measures remains almost unchanged.

lP

The results of the re-estimation of all models from the main text with additional control variables are given in Tables C2-C5. Tables C2_FS, C2_S1 and C2_S2 show the results of the

na

re-estimation of models from Table 4. The results of the re-estimation of equations of Table 5

Jo ur

are presented in tables C3_FS, C3_S1 and C3_S2. Tables C4_FS, C4_S1 and C4_S2 rerun models from Table 6 and finally, models from Table 7 are replicated in Tables C5_FS, C5_S1 and C5_S2.

The results of the estimates with the use of standard GDP measures and control variables follow the same pattern as corresponding results in the main text. The existence of turning points and their levels vary greatly across samples and across GDP measures. Additionally, they are also very sensitive to the choice of covariates. At the same time, the results of the estimates with the use of the alternative measure of GDP based on satellite nighttime light data are fully robust to changes in the sample size and to the inclusion of additional controls.

35

Journal Pre-proof Table C1. Estimation results using all GDP measures for the sample of 160 countries (without Bermuda).

VARIABLES

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

FE

RE

FE

RE

FE

RE

FE

RE

0.0748* *

0.0819** *

(0.031)

(0.026)

0.1239** *

0.1339** *

(0.043) 0.0000**

(0.039) 0.0000**

GDP pc PWT expenditure

0.0645*** 0.0747***

GDP pc PWT expenditure squared

(0.019) (0.016) 0.0000*** 0.0000*** (0.000)

(0.000)

GDP pc PWT output

0.0659*** 0.0754***

GDP pc PWT output squared

(0.018) (0.015) 0.0000*** 0.0000*** (0.000)

of

(0.000)

ro

GDP pc WDI

-0.0000

-0.0000*

(0.000)

(0.000)

-p

GDP pc WDI squared

re

GDP pc light

Constant

lP

GDP pc light squared

505.6476*

362.0086

487.1474*

354.8146

(258.762)

(251.825)

(235.374)

3,285

3,285

3,285

3,285

R-squared

0.216

na

(275.982) Observations

0.235

(0.000) (0.000) 224.164 1 110.4430 46.4896 -87.8041 (363.693 ) (255.544) (384.797) (268.011) 3,270

3,270

0.124

3,285

3,285

0.158

160

160

160

160

160

160

160

160

Sample

FS

FS

FS

FS

FS

FS

FS

FS

56,891

60,962

58,436

62,302

79,668

91,903

44,119

47,615

ur

Number of id Turning point

Jo

Turning point p-value 0.000520 8.41e-07 0.000173 2.77e-07 0.199 0.182 0.0525 0.0451 Notes: FE and RE denotes fixed-effect estimator and random-effects estimator, respectively. FS stands for full sample. Robust standard errors in parenthesis. All regressions include a full set of time dummies, not reported to conserve space. The estimated coefficients of time dummies and results of a test of fixed vs. random effects are available upon request from the authors. ***, **, * denote significance at the 1%, 5%, and 10%, respectively.

Table C2_FS. Estimation results with control variables for the full sample (using expenditure-side GDP per capita from the PWT). (1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

VARIABLES

FE

RE

FE

RE

FE

RE

FE

RE

FE

RE

FE

RE

GDP pc PWT expenditure

0.0760 ***

0.0829 ***

0.0742 ***

0.0780 ***

GDP pc PWT expenditure squared

0.070 0.079 0.0648 0.0722 0.071 0.082 0.069 0.077 1*** 1*** *** *** 8*** 6*** 3*** 7*** (0.025 (0.020 (0.025 (0.019 (0.025 (0.021 (0.025) (0.019) (0.025) (0.019) ) ) (0.020) (0.016) ) ) ) ) 0.0000 0.0000 0.0000 0.0000 0.000 0.000 0.0000 0.0000 0.000 0.000 0.000 0.000 *** *** *** *** 0*** 0*** *** *** 0*** 0*** 0*** 0*** (0.000) (0.000) (0.000) (0.000) (0.000 (0.000 (0.000) (0.000) (0.000 (0.000 (0.000 (0.000

Journal Pre-proof ) renewable energy consumption

13.254 8***

electricity production

(3.188) (1.960) (3.345) (2.606) 4.5724 5.2372 ** ***

18.185 3***

10.533 4***

)

)

)

12.314 2***

(2.463) (2.709) 7.074 8.718 7.126 8.596 4*** 6*** 5*** 5*** (1.900 (1.876 (1.914 (1.893 (1.849) (1.697) ) ) ) ) 0.017 0.030 0.018 0.028 0.0271 0.0368 9 6 6 9 (0.028 (0.038 (0.029 (0.038 (0.026) (0.034) ) ) ) ) 567.12 449.76 861.67 796.06 54.77 247.7 855.25 804.56 99.72 165.4 486.0 303.1 59 80 46** 08** 20 761 23*** 23*** 25 296 989 021 (357.5 (362.9 (375.1 (364.4 (424.9 (313.5 (296.35 (308.42 (424.5 (340.4 (409.8 (349.8 21) 98) 84) 03) 82) 43) 0) 6) 05) 33) 95) 06)

Observations

2,595

R-squared

0.246

2,595

2,595

2,595

0.246

2,595 0.233

Number of id

127

127

127

127

Sample

FS

FS

FS

FS

127

2,595

of

Constant

16.988 2***

)

3,264

ro

electric power consumption

14.359 9***

)

3,264

0.237

127

161

2,595

2,595

0.230 161

127

2,595

2,595

0.218 127

127

127

lP

re

-p

FS FS FS FS FS FS FS FS 58,69 62,29 59,37 63,29 58,31 61,60 Turning point 60,822 63,768 60,080 62,398 5 5 57,426 61,020 3 3 8 6 Turning point p0.0016 4.56e- 0.0020 3.09e- 0.002 3.63e- 0.0005 2.59e- 0.002 6.71e- 0.003 8.25evalue 6 06 5 05 84 05 96 06 19 06 12 05 Notes: FE and RE denotes fixed-effect estimator and random-effects estimator, respectively. FS stands for full sample. Robust standard errors in parenthesis. All regressions include a full set of time dummies, not reported to conserve space. The estimated coefficients of time dummies and results of a test of fixed vs. random effects are available upon request from the authors. ***, **, * denote significance at the 1%, 5%, and 10%, respectively.

PWT). (2) RE

(3)

FE

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

RE

FE

RE

FE

RE

FE

RE

FE

RE

0.0883 0.0940 0.0611 0.0594 0.0567 0.0585* 0.0887 0.0929 0.0836 ** *** * ** * * ** *** ** (0.032 (0.034 (0.035) (0.027) (0.033) (0.029) ) (0.029) (0.035) (0.027) ) GDP pc PWT 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000* 0.0000 0.0000 0.0000 expenditure squared *** *** *** *** *** ** *** *** *** (0.000 (0.000 (0.000) (0.000) (0.000) (0.000) ) (0.000) (0.000) (0.000) ) renewable energy 22.303 22.386 25.942 26.804 30.070 30.847 consumption 1*** 6*** 4*** 7*** 0*** 8*** electricity production

electric power consumption

Jo

GDP pc PWT expenditure

FE

ur

(1) VARIABLES

na

Table C2_S1. Estimation results with control variables for the subsample 1 (using expenditure-side GDP per capita from the

0.0921 *** (0.027 ) 0.0000 *** (0.000 )

0.052 0.053 2 2* (0.031 (0.030 ) ) 0.000 0.000 0*** 0*** (0.000 (0.000 ) )

(7.409) (4.070) (6.449) (4.983) (6.881) (4.650) 7.8436 9.2076 11.246 15.1108 14.912 16.273 *** *** 7*** *** 3*** 9*** (2.512 (3.033 (2.636 (2.861) (2.330) ) (2.665) ) ) 0.2785 0.2427 0.2747 0.2511* 0.295 0.266 *** *** *** ** 5*** 2*** (0.073 (0.071 (0.073 (0.069) (0.071) ) (0.072) ) )

Journal Pre-proof

Constant

326.18 126.77 58.830 289.44 1,050. 42 29 4 61 8139 (594.4 (628.4 (684.4 (481.7 (782.6 78) 88) 62) 92) 33)

Observations

1,674

R-squared

0.306

1,674

1,674

1,674

0.368

1,674

1,231.7 632** (556.89 9) 1,674

0.357

989.74 926.15 447.54 704.07 292.9 160.1 75 24 20 15 890 868 (642.5 (640.2 (772.2 (659.1 (737.0 (541.3 55) 71) 51) 03) 60) 20) 1,674

1,674

0.301

1,674

1,674

0.293

1,674

1,674

0.342

Number of id

81

81

81

81

81

81

81

81

81

81

Sample

S1

S1

S1

S1

S1

S1

S1

S1

S1

S1

81

81

ro

of

S1 S1 47,98 49,44 Turning point 61,881 64,137 52,058 52,805 50,235 52,263 61,931 63,850 60,597 63,537 7 1 Turning point p0.0071 0.0002 0.0069 0.0003 0.0077 0.0003 0.050 0.036 value 2 47 0.0356 0.0210 0.0398 0.0222 3 12 8 36 8 5 Notes: FE and RE denotes fixed-effect estimator and random-effects estimator, respectively. S1 stands for subsample 1. Robust standard errors in parenthesis. All regressions include a full set of time dummies, not reported to conserve space. The estimated coefficients of time dummies and results of a test of fixed vs. random effects are available upon request from the authors. ***, **, * denote significance at the 1%, 5%, and 10%, respectively.

PWT). (2)

(3)

(4)

FE

RE

FE

RE

0.0831 ***

0.0291

Constant

Observations R-squared

(8)

(9)

(10)

(11)

(12)

FE

RE

FE

RE

FE

RE

0.0823 0.0197 0.0252 0.0206 0.0624 *** (0.047 (0.047) ) (0.046) (0.040) (0.025) 0.0000 0.0000 0.0000 0.0000 * * -0.0000* *** *** (0.000 (0.000) ) (0.000) (0.000) (0.000) 21.694 28.769 33.010 0*** 0*** 2***

(0.000) (0.000) 24.285 23.788 1*** 4**

(6.654) (9.358) (5.277) 11.508 11.748 2*** 0*** (3.110 (4.269) ) 0.2502 0.3742 0.2436 *** ** *** (0.089 (0.087) (0.187) ) 830.1 337.25 1,014. 544.89 75.322 398 54 3419* 63 4 (713.3 (712.9 (601.7 (378.2 (824.0 62) 45) 01) 46) 94) 964

(7)

lP

(0.024) (0.049) 0.0000 0.0000 *** *

(6)

RE

na

0.060 8 (0.040 ) GDP pc PWT 0.000 expenditure squared 0*** (0.000 ) renewable energy 19.43 consumption 60* (11.52 9) electricity 9.767 production 7** (4.481 ) electric power consumption

FE

Jo

GDP pc PWT expenditure

(5)

re

(1)

ur

VARIABLES

-p

Table C2_S2. Estimation results with control variables for the subsample 2 (using expenditure-side GDP per capita from the

964

0.377

964

964

0.420

964

0.0837 0.021 0.019 0.0568 *** 1 2 (0.038 (0.025 (0.04 (0.04 ) ) 6) 8) 0.0000 0.0000 0.000 0.000 *** *** 0* 0* (0.000 (0.000 (0.00 (0.00 ) ) 0) 0)

(9.158) (7.777) 17.8838* **

16.426 19.142 3*** 4*** (3.165 (4.635 ) )

(5.054)

0.270 0.386 1*** 7** (0.08 (0.17 6) 7)

0.3973**

(0.186) 1,193.30 1,640.3 1,309.4 142.28 583.38 721.8 183.7 47*** 131* 309 38 14 869 091 (441.310 (818.1 (870.3 (908.8 (627.2 (672. (439. ) 91) 46) 74) 71) 217) 279) 964

0.414

964

964

0.371

964

964

0.368

964

964

0.401

Number of id

46

46

46

46

46

46

46

46

46

46

46

46

Sample

S2

S2

S2

S2

S2

S2

S2

S2

S2

S2

S2

S2

54,11

62,953

35,398

30,121

54,670

Turning point

29,353 32,261

62,782 52,457 62,883 28,39 27,90

Journal Pre-proof 7

5

3

Turning point p0.069 0.0003 0.0004 0.0004 value 1 57 0.280 0.337 0.297 0.329 0.0654 32 0.0702 72 0.325 0.344 Notes: FE and RE denotes fixed-effect estimator and random-effects estimator, respectively. S2 stands for subsample 2. Robust standard errors in parenthesis. All regressions include a full set of time dummies, not reported to conserve space. The estimated coefficients of time dummies and results of a test of fixed vs. random effects are available upon request from the authors. ***, **, * denote significance at the 1%, 5%, and 10%, respectively.

Table C3_FS. Estimation results with control variables for the full sample (using output-side GDP per capita from the PWT). (1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

VARIABLES

FE

RE

FE

RE

FE

RE

FE

RE

FE

RE

FE

RE

GDP pc PWT output

0.0804 ***

0.0868 ***

0.0786 ***

0.0865 *** (0.019 ) 0.0000 *** (0.000 )

0.0741 *** (0.024 ) 0.0000 *** (0.000 )

0.0819 *** (0.020 ) 0.0000 *** (0.000 )

electric power consumption

0.0765 *** (0.024 (0.016) ) 0.0000 0.0000 *** *** (0.000 (0.000) ) 11.976 6***

of

0.0724 ***

ro

-p

re

electricity production

0.0835 0.0652 *** *** (0.019 ) (0.018) 0.0000 0.0000 *** *** (0.000 ) (0.000) 10.142 3***

(2.918) (1.971) (3.245) (2.604) (2.763) 4.4799 5.1943 7.0030 8.6030 ** *** *** *** (1.824 (1.806 (1.714) (1.598) ) )

(2.919) 7.0557 8.4993 *** *** (1.841 (1.826 ) )

lP

renewable energy consumption

na

GDP pc PWT output squared

0.0821 0.0750 *** *** (0.024 (0.024) (0.018) (0.024) (0.019) ) 0.0000 0.0000 0.0000 0.0000 0.0000 *** *** *** *** *** (0.000 (0.000) (0.000) (0.000) (0.000) ) 13.418 14.232 17.061 18.002 9*** 9*** 2*** 3***

0.0283 (0.035 ) 300.77 838.18 793.13 28.093 221.59 54 44*** 22*** 7 82 (299.6 (280.02 (284.62 (404.2 (320.6 64) 4) 4) 57) 47)

0.0172 0.0268 (0.028 (0.035 ) )

Observations

2,595

2,595

2,595

R-squared

0.270

Jo

ur

0.0347 0.0164 (0.027 (0.025) (0.032) ) 507.59 388.59 797.83 733.77 15.025 78 00 62** 91** 9 (347.1 (344.01 (363.35 (344.39 (409.2 03) 1) 0) 8) 95)

Constant

0.0260

2,595

2,595

2,595

0.270

2,595 0.256

3,264

3,264

0.254

2,595

2,595

0.254

415.33 246.95 10 42 (393.5 (331.1 54) 55) 2,595

0.242

Number of id

127

127

127

127

127

127

161

161

127

127

127

127

Sample

FS

FS

FS

FS

FS

FS

FS

FS

FS

FS

FS

FS

Turning point 63,167 65,889 62,446 64,609 61,355 64,689 58,558 62,143 61,902 65,502 60,853 63,838 Turning point p0.0006 1.11e- 0.0007 6.55e- 0.0010 6.76e- 0.0002 1.71e- 0.0008 1.59e- 0.0012 2.00evalue 07 06 40 06 4 06 78 06 41 06 1 05 Notes: FE and RE denotes fixed-effect estimator and random-effects estimator, respectively. FS stands for full sample. Robust standard errors in parenthesis. All regressions include a full set of time dummies, not reported to conserve space. The estimated coefficients of time dummies and results of a test of fixed vs. random effects are available upon request from the authors. ***, **, * denote significance at the 1%, 5%, and 10%, respectively.

Table C3_S1. Estimation results with control variables for the subsample 1 (using output-side GDP per capita from the PWT).

VARIABLES

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

FE

RE

FE

RE

FE

RE

FE

RE

FE

RE

FE

RE

Journal Pre-proof

GDP pc PWT output

0.0935 ***

GDP pc PWT output squared

(0.032) (0.025) (0.030) 0.0000 0.0000 0.0000 *** *** ***

renewable energy consumption

(0.000) (0.000) (0.000) 22.872 22.565 27.295 5*** 6*** 7***

0.0890 *** (0.031 ) 0.0000 *** (0.000 )

0.0968 0.061 0.062 *** 7** 2** (0.025 (0.029 (0.027 ) ) ) 0.0000 0.000 0.000 *** 0*** 0*** (0.000 (0.000 (0.000 ) ) )

15.142 16.426 5*** 4*** (3.100 (2.742 ) )

ro

of

(6.977) (4.262) (6.086) (5.054) (6.646) (4.883) 7.8816 9.3137 11.832 15.2391 *** *** 4*** *** (2.496 (2.888) (2.398) ) (2.676) 0.2596 0.2273 0.2542 0.2347* *** *** *** ** (0.070 (0.066) (0.064) ) (0.065) 233.19 32.285 207.24 1,174. 1,329.9 901.95 841.10 89 2 4.2475 09 3522 420** 98 03 (551.1 (567.1 (673.3 (465.4 (751.3 (534.30 (587.0 (577.9 80) 99) 76) 51) 44) 8) 18) 68)

-p

Constant

0.0682 0.0661 0.0673* 0.0938 0.0976 *** ** ** *** *** (0.029 (0.026) ) (0.026) (0.032) (0.025) 0.0000 0.0000 0.0000* 0.0000 0.0000 *** *** ** *** *** (0.000 (0.000) ) (0.000) (0.000) (0.000) 27.486 30.659 31.139 9*** 3*** 8***

Observations

1,674

R-squared

0.337

1,674

1,674 0.393

Number of id

81

81

81

Sample

S1

S1

S1

1,674

re

electric power consumption

0.0703 **

1,674

1,674

0.380

lP

electricity production

0.0987 ***

1,674

1,674

0.332

0.274 0.249 7*** 5*** (0.069 (0.068 ) ) 568.12 804.10 376.9 252.5 48 89 076 818 (704.7 (595.7 (718.2 (524.4 41) 68) 90) 17) 1,674

1,674

0.323

1,674

1,674

0.364

81

81

81

81

81

81

81

S1

S1

S1

S1

S1

S1

S1

81

81

Jo

ur

na

S1 S1 53,56 54,63 Turning point 64,733 66,819 57,010 57,536 55,523 57,067 64,747 66,526 63,567 66,229 2 2 Turning point p0.0023 3.48e0.0043 0.0023 4.73e- 0.0026 5.45e- 0.017 0.009 value 5 05 0.0105 5 0.0121 0.00482 0 05 7 05 2 91 Notes: FE and RE denotes fixed-effect estimator and random-effects estimator, respectively. S1 stands for subsample 1. Robust standard errors in parenthesis. All regressions include a full set of time dummies, not reported to conserve space. The estimated coefficients of time dummies and results of a test of fixed vs. random effects are available upon request from the authors. ***, **, * denote significance at the 1%, 5%, and 10%, respectively.

Table C3_S2. Estimation results with control variables for the subsample 2 (using output-side GDP per capita from the PWT).

VARIABLES GDP pc PWT output

GDP pc PWT output squared

renewable energy consumption

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

FE

RE

FE

RE

FE

RE

FE

RE

FE

RE

FE

RE

0.065 0.0891 7* *** 0.0354 0.0319 (0.038 ) (0.021) (0.046) (0.041) 0.000 0.0000 0.0000 0.0000 0*** *** ** ** (0.000 ) (0.000) (0.000) (0.000) 20.32 24.265 24.537 22.171 07* 3*** 8*** 3***

0.0310 0.0326 0.0673* (0.044 ) (0.041) (0.038) 0.0000 0.0000* ** 0.0000** ** (0.000 ) (0.000) (0.000) 29.3270 ***

(11.22 (6.599) (9.115) (5.245)

(9.105)

0.0884 0.0612 *** * (0.035 (0.022) ) 0.0000 0.0000 *** *** (0.000 (0.000) ) 32.906 9*** (7.579)

0.0894 0.026 *** 8 (0.022 (0.04 ) 3) 0.0000 0.000 *** 0** (0.000 (0.00 ) 0)

0.029 7 (0.04 3) 0.000 0** (0.00 0)

Journal Pre-proof 3) 9.411 11.333 6** 3*** (4.320 ) (4.184)

electric power consumption

Constant

Observations R-squared Number of id

780.3 539 (684.6 00)

11.933 1*** (2.982 ) 0.2366 0.3428 0.2306 *** ** *** (0.078 (0.075) (0.167) ) 235.35 975.64 473.98 130.59 62 89 49 63 (638.1 (604.2 (359.2 (804.6 43) 44) 85) 98)

964

964

964

0.419 46

964 46

0.257 1*** (0.07 5)

0.3657**

(0.167) 1,260.49 1,559.3 1,191.3 66.899 681.09 682.0 36*** 096** 771 4 08 666 (426.013 (774.05 (784.7 (862.5 (563.6 (666. ) 5) 07) 39) 30) 800) 964

964

0.451

46

16.369 18.988 0*** 9*** (3.133 (4.457 ) )

(4.937)

964

0.458 46

17.7977* **

964

0.414

46

46

46

964

46

46

964

0.359 4** (0.15 9) 119.4 117 (423. 259) 964

0.438 46

46

46

S2 S2 S2 S2 S2 S2 S2 S2 S2 S2 S2 S2 56,75 33,50 37,39 Turning point 2 65,751 40,166 40,166 37,019 40,543 57,299 65,604 54,966 65,625 2 3 Turning point p0.045 1.65e2.29e2.67evalue 1 05 0.225 0.220 0.242 0.213 0.0423 05 0.0459 05 0.270 0.245 Notes: FE and RE denotes fixed-effect estimator and random-effects estimator, respectively. S2 stands for subsample 2. Robust standard errors in parenthesis. All regressions include a full set of time dummies, not reported to conserve space. The estimated coefficients of time dummies and results of a test of fixed vs. random effects are available upon request from the authors. ***, **, * denote significance at the 1%, 5%, and 10%, respectively.

re

-p

ro

Sample

964 0.410

of

electricity production

(2)

(3)

FE

RE

FE

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

RE

FE

RE

FE

RE

FE

RE

FE

RE

na

VARIABLES

(1)

lP

Table C4_FS. Estimation results with control variables for the full sample (using GDP per capita from the WDI).

electricity production

(3.428) (2.197) (3.495) 5.1119* 5.7106* ** **

Jo

GDP pc WDI squared

ur

renewable energy consumption

0.1043* 0.0977* 0.1006* 0.0896* 0.0975 * ** * ** ** (0.045 (0.045) (0.032) (0.045) (0.033) ) 0.0000* 0.0000 0.0000* * 0.0000* 0.0000* * (0.000 (0.000) (0.000) (0.000) (0.000) ) 13.4119 11.8543 17.1426 15.8220 *** *** *** ***

GDP pc WDI

(1.877) electric power consumption

Constant

(1.669) 0.0187

0.0973 0.070 0.0736 0.0994 *** 5** *** ** (0.033 (0.03 (0.028 (0.044 ) 3) ) ) 0.0000 0.000 0.0000 ** 0 0.0000 * (0.000 (0.00 (0.000 (0.000 ) 0) ) ) 7.350 8.7204 6* ** (4.25 (3.653 (2.369) 5) ) 7.7028 8.6539 7.7370 *** *** *** (1.912 (1.736 (1.918 ) ) ) 0.0227

0.0090 (0.029 (0.028) (0.032) ) 325.530 417.915 534.37 64.8093 58.6775 9 3 49 (611.65 (459.02 (609.79 (405.93 (663.7 9) 2) 2) 1) 85)

0.1013 0.095 0.0974 *** 2** *** (0.032 (0.04 (0.034 ) 5) ) 0.0000 0.000 0.0000 ** 0 ** (0.000 (0.00 (0.000 ) 0) )

8.5731 *** (1.764 )

0.010 0.0152 1 0.0144 (0.034 (0.03 (0.035 ) 1) ) 637.97 527.7 502.25 528.33 626.37 45.96 116.13 62 703 49 47 82 11 47 (408.1 (446. (331.2 (663.9 (421.0 (643. (397.8 46) 153) 48) 73) 21) 023) 03)

Journal Pre-proof Observations

2,580

R-squared

0.160

2,580

2,580

2,580

0.156

2,580

2,580

3,249

0.143

3,249

2,580

0.135

Number of id

127

127

127

127

127

127

Sample

FS

FS

FS

FS

FS

FS

2,580

0.143

161

161

127

2,580

2,580

0.124 127

127

127

FS FS FS FS FS FS 80,63 80,30 80,162 90,472 0 96,482 80,474 89,620 4 89,550

Turning point 81,915 91,737 81,846 94,124 Turning point pvalue 0.134 0.137 0.143 0.169 0.144 0.124 0.222 0.242 0.141 0.115 0.152 0.131 Notes: FE and RE denotes fixed-effect estimator and random-effects estimator, respectively. FS stands for full sample. Robust standard errors in parenthesis. All regressions include a full set of time dummies, not reported to conserve space. The estimated coefficients of time dummies and results of a test of fixed vs. random effects are available upon request from the authors. ***, **, * denote significance at the 1%, 5%, and 10%, respectively.

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

FE

RE

FE

RE

FE

RE

FE

RE

FE

RE

FE

RE

0.1569 0.1312 0.1105 0.0775 ** *** * *

GDP pc WDI squared

(0.060) (0.045) (0.058) 0.0000 0.0000 ** ** 0.0000

re

(7.870) (4.096) (6.568) (5.258) (6.923) (5.041) 6.0071 10.051 12.154 16.2312 15.626 * 7*** 4*** *** 2***

16.9252 ***

(3.247) (2.872)

(3.161)

(2.648) (3.172) 0.2754 0.2148* *** **

(3.244)

0.075 8 (0.04 8) 0.000 0 (0.00 0)

0.235 4*** (0.06 5) 717.9 064 (735. 479)

Observations

1,668

1,668

R-squared

0.251

ur

0.2991 *** (0.082 (0.081) (0.060) (0.083) (0.061) ) 971.06 932.11 1,023. 167.78 2,184.3 1,800.8 480.05 54.405 1,929.7 1,781.7 1,336. 49 84 4153 01 960* 517** 66 0 120* 065** 6765 (887.5 (785.4 (1,049. (671.8 (1,114. (800.77 (1,003. (671.3 (1,088. (798.69 (1,109 17) 58) 136) 11) 258) 0) 725) 45) 116) 0) .095)

Constant

0.2734 0.2096 *** ***

Jo

electric power consumption

0.1320* ** 0.0921 (0.058 (0.047) (0.057) (0.046) (0.060) (0.045) (0.059) (0.044) ) 0.0000 0.0000 0.0000 0.0000* 0.0000 -0.0000 -0.0000 ** ** * * 0.0000 (0.000 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) ) 27.712 36.264 30.853 6*** 2*** 0***

na

electricity production

0.1457 **

lP

(0.000) (0.000) (0.000) renewable energy 30.245 21.500 31.759 consumption 1*** 9*** 2***

0.0989 0.1581 0.1299 * 0.0785* *** ***

-p

GDP pc WDI

ro

VARIABLES

of

Table C4_S1. Estimation results with control variables for the subsample 1 (using GDP per capita from the WDI).

1,668

1,668

1,668

0.314

1,668

1,668

0.290

1,668

1,668

0.248

1,668

1,668

0.224

1,668 0.270

Number of id

81

81

81

81

81

81

81

81

81

81

81

Sample

S1

S1

S1

S1

S1

S1

S1

S1

S1

S1

S1

81

S1 90,79 79,606 0

Turning point 84,572 93,538 82,397 97,108 80,963 95,882 84,538 93,434 83,662 92,585 Turning point pvalue 0.0908 0.111 0.172 0.289 0.196 0.273 0.0877 0.118 0.109 0.102 0.208 0.268 Notes: FE and RE denotes fixed-effect estimator and random-effects estimator, respectively. S1 stands for subsample 1. Robust standard errors in parenthesis. All regressions include a full set of time dummies, not reported to conserve space. The estimated coefficients of time dummies and results of a test of fixed vs. random effects are available upon request from the authors. ***, **, * denote significance at the 1%, 5%, and 10%, respectively.

Journal Pre-proof Table C4_S2. Estimation results with control variables for the subsample 2 (using GDP per capita from the WDI). (2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

FE

RE

FE

RE

FE

RE

FE

RE

FE

RE

FE

RE

0.1234 **

0.0622 *

(8.380) (6.113) (7.439) (4.919) 7.7347 12.733 13.753 * 2*** 2***

16.006 5***

GDP pc WDI squared

(0.056) (0.050) 0.0000 0.0000 ** **

(0.000) (0.000) renewable energy 27.491 16.077 consumption 0*** 9*** electricity production

0.1698 0.1397 0.1603* 0.1477* 0.1157 *** *** ** ** * (0.060 (0.058) (0.041) (0.058) (0.037) (0.055) (0.051) (0.056) (0.049) ) 0.0000 0.0000 0.0000* 0.0000* 0.0000 0.0000 -0.0000 -0.0000 ** ** * * 0.0000 (0.000 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) ) 31.813 18.917 34.839 26.069 2*** 1*** 5*** 3***

(3.971) (4.536)

Observations R-squared

(7.901) (5.351)

17.1850 17.8971 *** ***

(3.204) (3.885) 0.1795 0.2643 ** ***

(3.625)

(3.823)

0.2124 *** (0.073 (0.066) (0.060) (0.071) (0.058) ) 1,272. 1,354. 842.55 73.111 2,122.6 1,639.3 641.05 234.50 2,208.1 2,050.5 1,147. 0621 9273 20 3 648* 486* 66 01 320** 050** 4469 (913.4 (1,049. (1,064. (788.8 (1,146. (893.61 (946.8 (765.5 (1,047.0 (920.74 (1,128 97) 819) 142) 23) 446) 3) 56) 54) 14) 5) .895)

re

958

958

0.282

958

958

0.311

Number of id

46

46

Sample

S2

S2

lP

Constant

0.1894 0.2565 *** ***

958

na

electric power consumption

46

958

958

0.290

46

S2

S2 139,71 84,638 94,881 83,400 4

0.070 7 (0.04 4) 0.000 0 (0.00 0)

of

0.1681 0.1430 0.1303 *** *** ** 0.0573

ro

GDP pc WDI

-p

VARIABLES

(1)

958

0.278

958

958

0.261

958

0.263 5*** (0.06 6) 611.6 012 (766. 095) 958

0.268

46

46

46

46

46

46

46

S2

S2 133,29 0

S2

S2

S2

S2

S2

46

S2 112,3 82,297 04

Jo

ur

Turning point 82,731 84,740 95,896 84,168 93,917 Turning point pvalue 0.0810 0.108 0.146 eoi 0.159 eoi 0.0785 0.121 0.0938 0.0874 0.177 0.382 Notes: FE and RE denotes fixed-effect estimator and random-effects estimator, respectively. S2 stands for subsample 2. eoi denotes extremum outside interval. Robust standard errors in parenthesis. All regressions include a full set of time dummies, not reported to conserve space. The estimated coefficients of time dummies and results of a test of fixed vs. random effects are available upon request from the authors. ***, **, * denote significance at the 1%, 5%, and 10%, respectively.

Table C5_FS. Estimation results with control variables for the full sample (using GDP per capita, based on satellite nighttime light data). (1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

VARIABLES

FE

RE

FE

RE

FE

RE

FE

RE

FE

RE

FE

RE

GDP pc light

0.1488 ***

0.1447 ***

0.1462 ***

0.1366 ***

GDP pc light squared

(0.054) 0.0000 **

(0.047) 0.0000 **

(0.054) 0.0000 **

0.141 0.1439 0.120 0.1248 0.143 0.1492 0.140 0.145 4*** *** 6*** *** 9*** *** 9*** 2*** (0.053 (0.046 (0.045 (0.042 (0.054 (0.046 (0.053 (0.047 (0.047) ) ) ) ) ) ) ) ) 0.0000 0.000 0.0000 0.000 0.0000 0.000 0.0000 0.000 0.000 ** 0** ** 0** ** 0** ** 0** 0**

Journal Pre-proof (0.000 (0.000 (0.000 ) ) ) 8.165 3** (4.112 (3.627) (2.586) (3.719) (2.550) ) 4.5509 5.6593 6.859 8.4092 ** *** 7*** *** (2.057 (1.871 (2.024) (1.862) ) ) 0.016 0.0250 0.0288 6 0.0231 (0.026 (0.031 (0.025) (0.028) ) ) 159.78 236.59 147.01 201.23 612.5 802.85 349.1 85 58 96 93 696 25* 888 (592.07 (497.48 (596.62 (438.41 (613.3 (434.5 (465.7 4) 0) 2) 2) 54) 85) 88) (0.000) 11.203 0***

(0.000) 15.906 2***

(0.000) 15.144 7***

Observations

2,595

2,595

2,595

2,595

R-squared

0.186

electric power consumption

Constant

2,595

0.185

ro

electricity production

2,595

0.175

Number of id

127

127

127

127

Sample

FS

FS

FS

FS

3,264

3,264

0.171

127

127

-p

renewable energy consumption

(0.000 (0.000 (0.000 (0.000 (0.000 ) ) ) ) ) 9.2818 *** (3.449 ) 6.914 8.2821 1*** *** (2.063 (1.898 ) ) 0.017 0.022 4 1 (0.027 (0.031 ) ) 305.28 581.8 766.82 196.1 300.8 37 519 35* 858 055 (365.1 (608.2 (437.8 (605.4 (411.2 67) 89) 46) 11) 60)

of

(0.000) 12.327 2***

161

2,595

2,595

0.172 161

127

2,595

2,595

0.161 127

127

127

na

lP

re

FS FS FS FS FS FS FS FS 45,79 44,68 46,33 45,45 48,05 Turning point 47,514 50,049 46,883 49,156 4 48,855 6 48,507 6 49,440 7 2 Turning point p0.046 0.069 0.046 0.045 0.039 value 0.0505 0.0553 0.0503 0.0603 8 0.0424 9 0.0733 6 0.0412 0 7 Notes: FE and RE denotes fixed-effect estimator and random-effects estimator, respectively. FS stands for full sample. Robust standard errors in parenthesis. All regressions include a full set of time dummies, not reported to conserve space. The estimated coefficients of time dummies and results of a test of fixed vs. random effects are available upon request from the authors. ***, **, * denote significance at the 1%, 5%, and 10%, respectively.

Table C5_S1. Estimation results with control variables for the subsample 1 (using GDP per capita, based on satellite nighttime

GDP pc light

GDP pc light squared

renewable energy consumption

electricity production

electric power

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

FE

RE

FE

RE

FE

RE

FE

RE

FE

RE

FE

RE

Jo

VARIABLES

ur

light data).

0.2151 *** (0.066 ) 0.0000 *** (0.000 ) 24.830 2*** (7.475 ) 6.2864 * (3.332 )

0.1952 0.1801 *** *** (0.068 (0.059) ) 0.0000 0.0000 *** ** (0.000 (0.000) ) 18.346 26.978 2*** 8*** (7.525 (4.811) ) 10.482 0***

0.1481 0.1705 ** ** (0.063 ) (0.067) 0.0000 0.0000 ** ** (0.000 ) (0.000) 24.855 9*** (5.876 ) 11.036 9***

(3.130) 0.2283 0.1844

0.1503* 0.2166 * *** (0.066 (0.062) ) 0.0000* 0.0000 * *** (0.000 (0.000) ) 31.106 0*** (7.168 ) 15.5126 ***

(3.492)

(3.474)

0.2292

0.1887*

0.1937 0.2059 *** *** (0.060 ) (0.066) 0.0000 0.0000 *** *** (0.000 ) (0.000) 28.092 0*** (5.461 ) 14.098 4***

0.1962* **

0.165 0.148 4** 4** (0.068 (0.064 (0.059) ) ) 0.0000* 0.000 0.000 ** 0** 0** (0.000 (0.000 (0.000) ) )

(3.609)

(3.349)

16.2572 ***

0.249

0.207

Journal Pre-proof consumption

***

***

**

**

7***

9***

Constant

1,239. 4275 (843.8 20)

1,344. 3143* (814.4 64)

(0.085 (0.064 (0.088 (0.072 ) ) (0.089) (0.064) ) ) 1,305. 645.36 2,357.1 2,184.67 724.55 431.01 2,034.9 2,058.04 1,602. 1,149. 2375 27 728** 47*** 33 73 551** 25*** 6920 7778 (1,002 (710.6 (1,020. (836.29 (928.5 (686.6 (958.69 (792.51 (1,025 (736.4 .779) 32) 823) 3) 26) 46) 3) 2) .898) 80)

Observations

1,674

1,674

1,674

R-squared

0.272

1,674

0.314

1,674

1,674

1,674

0.301

1,674

0.269

1,674

1,674

0.256

1,674

1,674

0.286

Number of id

81

81

81

81

81

81

81

81

81

81

Sample

S1

S1

S1

S1

S1

S1

S1

S1

S1

S1

81

81

-p

ro

of

S1 S1 44,52 45,04 Turning point 51,662 52,544 47,056 47,101 45,675 46,841 51,707 52,332 50,474 51,959 6 1 Turning point p0.030 0.039 value 0.0228 0.0298 0.0335 0.0543 0.0329 0.0448 0.0219 0.0309 0.0218 0.0234 3 2 Notes: FE and RE denotes fixed-effect estimator and random-effects estimator, respectively. S1 stands for subsample 1. Robust standard errors in parenthesis. All regressions include a full set of time dummies, not reported to conserve space. The estimated coefficients of time dummies and results of a test of fixed vs. random effects are available upon request from the authors. ***, **, * denote significance at the 1%, 5%, and 10%, respectively.

Table C5_S2. Estimation results with control variables for the subsample 2 (using GDP per capita, based on satellite nighttime

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

FE

RE

FE

RE

FE

RE

FE

RE

FE

RE

FE

RE

0.2086 *** (0.072 ) 0.0000 ** (0.000 ) 11.474 5 (7.488 ) 14.659 7** (5.966 )

0.2098 *** (0.074 ) 0.0000 *** (0.000 ) 25.931 3** (9.700 )

0.1416 **

Jo

Constant

Observations R-squared Number of id

0.1260 ** (0.060 ) 1,465. 1,948. 1,190. 2105 7942 2005 (1,001 (1,272 (1,098 .132) .408) .239) 964

964

0.351 46

0.2027 0.1465* 0.2314 0.2055 *** * *** ***

0.2226 ***

0.2136* 0.2008 ** ** (0.075 (0.072) (0.073) (0.069) (0.068) (0.072) (0.069) (0.069) ) 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000* 0.0000 * *** 0.0000* *** ** *** ** *** (0.000 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) ) 17.272 28.181 22.893 6*** 4*** 5***

na

0.2289 *** (0.069 ) GDP pc light 0.0000 squared *** (0.000 ) renewable energy 21.940 consumption 3** (10.12 1) electricity production 6.5212 (5.190 ) electric power consumption GDP pc light

lP

(1)

ur

VARIABLES

re

light data).

964

(5.582)

0.1982 ***

0.362 46

46

(9.210) (5.958) 11.907 19.0896 9** ***

14.069 18.1015 8*** ***

(4.741) (6.001) 0.1210 0.2067* * **

(4.852)

(4.797)

0.1440 ** (0.067 (0.077) (0.064) (0.073) ) 666.78 2,275.8 2,424.1 946.97 674.44 2,215.0 2,437.1 1,485. 00 723* 309** 44 54 289* 692** 8123 (946.7 (1,173. (1,230.4 (1,066. (875.1 (1,108. (1,056.6 (1,140 40) 460) 25) 097) 21) 816) 75) .962) 964

964

964

0.352 46

0.1594 ** (0.072 ) 0.0000 ** (0.000 )

46

964

964

0.348 46

46

964

964

0.340 46

46

964

0.2012 ** (0.080 ) 1,174. 3755 (888.0 66) 964

0.340 46

46

46

Journal Pre-proof Sample

S2

S2

S2

S2

S2

S2

S2

S2

S2

S2

S2

S2

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Turning point 51,359 53,845 48,838 49,545 47,976 50,119 51,519 53,743 50,574 53,560 47,341 48,394 Turning point pvalue 0.0211 0.0345 0.0257 0.0819 0.0248 0.0658 0.0205 0.0376 0.0198 0.0260 0.0225 0.0495 Notes: FE and RE denotes fixed-effect estimator and random-effects estimator, respectively. S2 stands for subsample 2. Robust standard errors in parenthesis. All regressions include a full set of time dummies, not reported to conserve space. The estimated coefficients of time dummies and results of a test of fixed vs. random effects are available upon request from the authors. ***, **, * denote significance at the 1%, 5%, and 10%, respectively.

Journal Pre-proof Authors Statement Andrzej Kacprzyk: Conceptualization, Methodology, Software, Formal analysis, Writing - Original Draft, Writing - Review & Editing Zbigniew Kuchta: Conceptualization, Methodology, Software, Formal analysis, Writing - Original Draft,

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Writing - Review & Editing

Journal Pre-proof Highlights We found a new source of the fragility of the EKC estimates We estimated the EKC for CO2 emissions using three standard measures of GDP The estimates and the turning points are very sensitive to the choice of GDP measure We re-estimated the EKC using alternative data based on DMSP–OLS nighttime lights

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Our results are stable and the turning point is noticeably lower than in past studies