Revisiting environmental kuznets curve for carbon dioxide emissions: The role of trade

Structural Change and Economic Dynamics 50 (2019) 245–257

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Structural Change and Economic Dynamics journal homepage: www.elsevier.com/locate/sced

Revisiting environmental kuznets curve for carbon dioxide emissions: The role of trade Lei Jiang a,b , Shixiong He a , Zhangqi Zhong a,b,∗ , Haifeng Zhou c , Lingyun He d a

School of Economics, Zhejiang University of Finance & Economics, 18 Xueyuan Street, Jianggan District, Hangzhou, 310018, China Center for Regional Economy & Integrated Development, Zhejiang University of Finance & Economics, Hangzhou, 310018, China School of Environment, Beijing Normal University, Beijing 100875, China d School of Management, China University of Mining and Technology, Xuzhou, 221116, China b c

a r t i c l e

i n f o

Article history: Received 13 March 2019 Received in revised form 25 June 2019 Accepted 12 July 2019 Available online 15 July 2019 Keywords: EKC hypothesis CO2 emissions International trade Input-output analysis

a b s t r a c t In the three decades since the environmental Kuznets curve (EKC) hypothesis was proposed, there have been a massive amount of empirical studies on the relationship between per capita income and carbon dioxide (CO2 ) emissions based on the production accounting approach. International trade has played a critical role in effectively and efficiently allocating resources in the process of economic globalization. Through international trade, part of the responsibilities for CO2 emission mitigation which should have been taken by consumers have been transferred to the producers of traded goods and resources. The focus of this research is on the re-examination of the EKC hypothesis, taking the role of international trade into account. The findings are as follows. In the first stage, we calculated the CO2 emissions of 39 countries using the input-output analysis approach based on production accounting and consumption accounting. Currently, China is the largest CO2 emitter, surpassing the U.S. However, the per capita CO2 emissions of China lag behind those of the U.S. In the second stage, although both an inverted U-shaped curve and an N-shaped curve were obtained, the cubic functional form model is better fitted. In addition, the turning point occurs earlier if adopting the production-based accounting rather than adopting the consumption-based accounting, implying that international trade postpones the peak time of global CO2 emissions. From the above analysis, it follows that when testing for the validity of the EKC for global CO2 emissions, the role of trade cannot be overstated. © 2019 Elsevier B.V. All rights reserved.

1. Introduction Trade not only is of vital significance to the global economy but also strongly affects the global environment. It is estimated that more than 20% of global emissions are embodied in international trade (Mi et al., 2018). In recent years, the rapid development of the global economy has occurred at the expense of consuming a large amount of extensive fossil fuel energy, leading to a series of severe environmental pollution issues (Hertwich et al., 2015; Zhang et al., 2017). Therefore, for all countries, proactive and active initiatives have been taken to promote the regional ecological environment. Moreover, promoting environmental quality improvement has increasingly become the common significant mission regarding the achievement of green development and

∗ Corresponding author. E-mail addresses: lei [email protected] (L. Jiang), [email protected] (Z. Zhong). https://doi.org/10.1016/j.strueco.2019.07.004 0954-349X/© 2019 Elsevier B.V. All rights reserved.

high-quality economic growth for the international community. More importantly, however, with the strong push of pollution control measures across the globe, the rate of economic growth in various countries or regions has gradually entered into a new transition pattern from high-speed growth to medium- and low-speed growth (Huang, 2016). In this context, the global environmental pollution problems caused by greenhouse gas emissions, notably CO2 emissions, from fossil fuel combustion and anthropogenic activities increasingly present some new characteristics and trends. Specifically, the geographic separation of consumers and the pollution emitted during the production of consumable items are becoming more obvious as the global economic trading partnerships among countries grow closer (Steen-Olsen et al., 2016). Therefore, the total global greenhouse gas emissions continued to increase by approximately 4% each year from 1995 as international trade increased. This is because pollutant emissions produced within a country and triggered by a country’s consumption of pollution-intensive commodities throughout global supply chains are separated by international trade and environmental

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responsibilities in the context of a globalized world might be circumvented by the consumers (Jiang and Guan, 2016; Wang and Ang, 2018). Consequently, the ecological environmental quality in certain regions, particularly developing countries such as China and India, would thus be worsened and further aggravated by exported goods produced in developing countries through international trade (Wang et al., 2017a,b). In summary, trade not only is attributed to economic growth, notably for developing countries but also may be a contributor to worsening the environment of developing countries, and even the global environment. To mitigate global climate change, the international community has made an unequivocal commitment to limit global greenhouse gas emissions. In 2016, more than 195 countries worldwide signed a legally binding climate change agreement (i.e., the Paris Protocol) to reduce global pollutant emissions, and thus committed to mitigating pollutant emissions and ensuring that they peaked as soon as possible to avert an average temperature rise greater than 2 degrees, compared to preindustrial levels. Furthermore, most of these countries called for the temperature rise to be capped at 1.5 degrees, which was lower than the 2 degrees endorsed by major developing countries. However, since the Kyoto Protocol, which was signed by specific countries, the target of global climate change issues in terms of slowing the growth of greenhouse gases, especially CO2 , and of limiting the temperature rise has not yet been achieved. Therefore, the issue of environmental protection has emerged as one of the largest challenges for the international community in a long time. Within the context of closer trade links among countries worldwide, governments have actively taken a series of measures to promote the use of clean energy and carried out major ecological projects aimed at ecological environmental protection to reach a peak in global greenhouse gas emissions. In this context, this paper will focus on the following two issues. One is whether international trade has affected the peak of global greenhouse gases emissions, especially CO2 emissions. The second concerns whether win-win opportunities exist for various countries or regions in the world between economic growth and environmental quality. These are the major practical issues of concern to the relevant stakeholders and researchers. Thus, the main aim of this research is to reveal the role international trade has played in the relationship between environmental degradation and economic growth.

2. Literature review The relationship between environmental degradation and income has been fiercely debated over the past three decades. In examining the peak of global greenhouse gas emissions, we find evidence from developed countries which reveals that environmental pollution is gradually increasing, accompanied by the rise of national average income levels in the early stage of economic development. However, when these countries pass a certain stage of economic development, the environmental pollution begins to ease, and thus the environmental quality gradually improves (Omri et al., 2015). The ¨inverted U-shapedt¨ rajectory between economic development and environmental pollution was referred to as the environmental Kuznets curve (EKC) (Grossman and Krueger, 1991). Basically, when the economy reaches a certain phase, the residents’ demand for environmental quality becomes stronger, and thus regional environmental protection standards also tend to be strict (Ozturk and Al-Mulali, 2015). In that context, other significant determinants, such as industrial structure transition and advanced clean technological progress, would boost economic development and environmental quality to enter a win-win track. By reviewing the existing relevant literature on the peak of greenhouse gas emissions, a growing number of empirical stud-

ies have focused on the relationship between per capita GDP and per capita CO2 emissions. Inevitably, a quick literature review on this topic is needed. Thus, we list some recently published empirical studies on the EKC hypothesis for CO2 emissions, which are summarized in Table 1. As shown in the last column of Table 1, it can be found that there is no consensus among the researchers about the verification of the EKC hypothesis, although they employed various datasets and applied complex econometric methods to verify the hypothesis. Technically, one strand of the existing literature focused on one single country and used time-series econometric methods. For example, Sugiawan and Managi (2016) focused on Indonesia and considered the autoregressive distributed lag (ARDL) approach to examine the evidence of the EKC hypothesis. They found an inverted U-shaped curve. Besides, Shahbaz et al. (2016) applied the same approach to investigate the EKC hypothesis in 19 African countries for the period of 1971-2012. The EKC existed in 6 countries and the U-shaped curve was found in 2 countries. The second strand focused on panel data models to examine the validity of the inverted U-shaped relationship between income and per capita CO2 . For instance, Tamazian and Rao (2010) considered panel data of 24 transition countries from 1993 to 2004 and employed the panel data approach to control for unobserved heterogeneity and endogeneity to test for the EKC hypothesis. Their findings supported the existence of the EKC hypothesis. LópezMenéndez et al. (2014) used panel data to investigate the EKC hypothesis for 27 European Union countries during the period of 1996-2010. The existence of EKC was verified. Chiu (2017) applied the panel smooth transition regression model to explore the EKC hypothesis for 99 countries for the period of 1971–2010 and found an inverted U-shaped curve. In summary, they verified the existence of either the inverted U-shaped (N-shaped) curve or the U-shaped (N-shaped) curve. On the other hand, other researchers also found little evidence in support of the EKC hypothesis. For instance, Lin et al. (2016) examined the validity of the EKC hypothesis based on 5 African countries and found no evidence. Similarly, Haq et al. (2016) focused on another African country, Morocco and applied a cointegration technique to verify the EKC hypothesis. The results did not confirm the validity of the EKC hypothesis in the long run. A recent work by Alshehry and Belloumi (2017) studied the EKC hypothesis for Saudi Arabia over the period 1971-2011. They found that the inverted U-shaped curve did not exist. Once again, to conclude from the existing literature conflicting empirical conclusions have resulted regarding the EKC hypothesis. Although the existing studies have enriched the knowledge concerning the EKC, there are basically two shortcomings. One is that most empirical studies have employed CO2 emissions based on the production principle and ignored the role of international trade in testing for the EKC hypothesis for global CO2 emissions. In actuality, although international trade plays a critical role in national economic development by providing a mechanism to efficiently allocate resources, such as energy and capital in the process of economic globalization (Czarnitzki and Hottenrott, 2009), a side effect is the geographic separation of consumers and the pollution emitted during the production of consumable items. Furthermore, international trade also provides a mechanism to shift environmental impacts, such as CO2 emissions, to other countries through trade links (Peters and Hertwich, 2008a). Thus, part of the responsibilities for CO2 emissions reduction that would otherwise belong to the consumers are transferred to the producers of traded goods and resources (Schulz, 2010). Consumption-based accounting should be applied to reassign the responsibilities of mitigating CO2 emissions since large net carbon flows from developing countries such as China to developed countries such as the U.S. (Meng et al., 2018). For related stakeholders, international trade has not only changed

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Table 1 Some recent studies on the EKC hypothesis for CO2 emissions. Author(s)

Period

Country/Region

Methodology

Key findings

Wang and Ye, 2017 Wang et al., 2017a,b Özokcu and Özdemir, 2017 Fernández-Amador et al., 2017 Chiu, 2017 Sugiawan and Managi, 2016 Shahbaz et al., 2016

2012 2000-2013 1980-2010

China/City-level China/Province-level 78 countries

1997-2011

78 countries

Spatial econometric model Semi-parametric panel model Panel data model with Driscoll-Kraaay stardard errors Panel threshold model

1971-2010 1971-2010

99 countries Indonesia

Panel smooth transition regression Autoregressive distributed lag model

Monotonic increasing Inverted U-shaped curve N-shaped curve and inverted N-shaped curve no evidence for inverted U-shaped curve Inverted U-shaped curve Inverted U-shaped curve

1971-2012

African countries

Autoregressive distributed lag model

Rodríguez et al., 2016 Luo et al., 2017

1979-2001 1960-2010

15 OECD countries G20 countries

Fixed effects panel model Generalized method of moments

Lin et al., 2016 Li et al., 2016 Jebli et al., 2016 Haq et al., 2016 Congregado et al., 2016

1980-2011 1996-2012 1980-2010 1971-2011 1973-2015

5 African countries China/ 28 provinces 25 OECD countries Morocco USA

Balaguer and Cantavella, 2016 Alshehry and Belloumi, 2017 Ali et al., 2017 Yang et al., 2015

1874-2011

Spain

Fully modified ordinary least squares Dynamic panel model Fully modified ordinary least squares Johansen cointegration method Arai-Kurozumi-Kejriwal cointegration tests with structural breaks Autoregressive distributed lag model

1971-2011

Saudi Arabia

Autoregressive distributed lag model

No EKC

1971-2012 1971-2010

Malaysia 67 countries

Autoregressive distributed lag model Symbolic regression method

Ozturk and Al-Mulali, 2015 Bölük and Mert, 2015 Bernard et al., 2015 Baek, 2015 Onafowora and Owoye, 2014 López-Menéndez et al., 2014 Kivyiro and Arminen, 2014 Tiwari et al., 2013

1996-2012

Cambodia

Generalized method of moments

Inverted U-shaped curve Inverted N-shaped and M-shaped for developed countries U-shaped curve

1961-2010 1960-2007 1960-2010 1970-2010

Turkey 114 countries 7 Arctic countries 8 countries

Autoregressive distributed lag model Nonparametric method Autoregressive distributed lag model Autoregressive distributed lag model

1996-2010

27 EU countries

Fixed effects panel model

1971-2009 1966-2009

6 Sub-Saharan African countries India

Fosten et al., 2012

1830-2003

United Kingdom

Arouri et al., 2012

1981-2005

Tamazian and Rao, 2010 Iwata et al., 2010

1993-2004

12 Middle East and North African countries 24 transition economies France

Autoregressive distributed lag model and Granger causality test Autoregressive distributed lag model and Granger causality test Cointegration and error correction model Panel cointegration and error correction model Dynamic generalized method of moments Autoregressive distributed lag model and Granger causality test

1960-2003

the global economy, but has also strongly affected the global environment. Therefore, important global climate policy implications stemming from using consumption-based accounting instead of a more conventional production-based accounting have been discussed, while more attention has also been given to the impact of international trade on regional CO2 emissions and its reduction obligation assignments. Thus, when testing for the EKC hypothesis, international trade should be considered when re-examining the relationship between per capita income and per capita CO2 emissions based on consumption accounting. Otherwise, biased and incomplete results may be obtained. Moreover, the other shortcoming is that very few studies calculate the turning points after having verified the existence of the EKC hypothesis. Notably, when the inverted N or N-shaped curve is found, the economic significance of turning points needs to be examined since it may have no real roots. Hence, the contribution of this work may be twofold. In the first stage, we calculated the CO2 emissions of 39 countries from 1995 to 2011 based on production accounting and consumption accounting using a multi-regional input-output (MRIO) analysis, and then analyzed their temporal variations. In addition, we focused on the

Inverted U-shaped curves are found in 8 countries. A monotonic and positive relationship EKC in developed countries and no EKC in developing countries No evidence for the EKC in Africa Inverted U-shaped curve Inverted U-shaped curve No EKC The existence of the EKC Inverted U-shaped curve

U-shaped curve Inverted U-shaped curve No EKC Inverted U-shaped curve for Japan and South Korea N-shaped curve Inverted U-shaped curve Inverted U-shaped curve Inverted U-shaped curve Poor evidence in support of the EKC Inverted U-shaped curve Inverted U-shaped curve

two largest CO2 emitters worldwide, namely China and the U.S., and compared the trends of CO2 emissions for the two countries. In the second stage, we verified the EKC hypothesis by using panel data econometric models, then estimated the turning points of the two types of calculated CO2 emissions and last, discussed the differences between them and how trade affects the peak time of CO2 emissions, notably for two typical countries, such as China and the U.S. Thus, this research is of vital significance in revealing the important effects of international trade on the global environment. 3. Methods and data sources 3.1. CO2 emission accounting approaches The main aim of our paper is to quantitatively investigate and compare the CO2 emissions embodied in trade in two kinds of important economic activities, i.e., production and consumption, for 39 countries worldwide from 1995 to 2011. For this purpose, the input-output (IO) analysis that stems from Leontief’s contribution to economic activity analysis (Leontief, 1970) is employed in

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this research, which possesses an apparent advantage in capturing direct, indirect and induced economic effects within an economy through a detailed accounting of interdependencies between inputs and outputs across economic sectors, as well as analyzing the indirect environmental impacts caused by upstream production, which is especially suitable for the estimation of the pollution and resource use embodiments of traded commodities (Davis and Caldeira, 2010). Therefore, this macroeconomic modeling technique has been recognized by a large number of empirical studies as an effective framework to quantify the embodied CO2 emissions and resources used in one region for the production of goods and services exported to other regions (See among others, Su and Ang, 2013; Mi et al., 2016). In the previous literature, bilateral-region input-output (BRIO) analysis and multi-region input-output (MRIO) analysis were commonly used to estimate the CO2 emissions embodied in interregional trade in line with the IO principles (Wiedmann et al., 2007). Because MRIO analysis is based on the monetary flows between industrial sectors and regions, considering the total economic output of each sector in each region, each sector’s output produced in one region and consumed in another, and a matrix of intermediate consumption where columns reflect the input from sectors in each region required to produce one unit of output of each sector in each of the other regions (Davis and Caldeira, 2010). It is superior to the BRIO analysis for analyzing the inter-regional trade effects between regions and for encapsulating the relations between different industrial sectors because it accounts explicitly for heterogeneity (Zhong et al., 2017). Hence, the MRIO analysis is chosen as our analytical framework to estimate the 39 countries’ consumption-based emission and production-based CO2 emissions in global trade, respectively. More detailed description regarding this methodology can be referred to lots of reputable literatures (Peters and Hertwich, 2008b) and thus are not repeated here. Illustrated in the follows are given the imputation of responsibilities for anthropogenic CO2 emissions hinges upon the emission accounting approach, especially a consumption-based accounting versus a production-based accounting approach for 39 countries from 1995 to 2011 in this research. In prior studies, two accounting approaches (namely, production-based accounting and consumption-based accounting) to calculate CO2 emissions for different countries in regional climate policy had been widely used (Zhong et al., 2017). The former approach focuses on CO2 emissions occurring from economic production or activities within a region, in a way that is analogous to Gross Domestic Product accounting; hence emissions are imputed to the place of production. In contrast, the latter approach is concerned with CO2 emissions due to economic consumption and assigns emissions to regions based on their total consumption (including intermediate and final consumption). As discussed above, we define the production-based accounting as the total emissions occurring from economic production in region r, which can be expressed as follows: r,t ECO2

Prod

= ETr,t

(1)

Where ETr,t denotes the total carbon emissions occurring in region r. Moreover, according to the MRIO table, a basic MRIO model can be expressed as follows: Vr,t = (I − Ar−r,t )

−1

(Yr−r,t +



BE r−s,t )

(2)

s

Where V r,t is the gross output of country r in year t. I denotes a unit matrix. Ar−r,t represents the intermediate requirements of produced goods of country r demanded by the sectors in country r in year t. Yr−r,t indicates the commodities used (including produced and consumed) in country r in year t. BE r−s,t denotes the bilateral

exports in international trade from country r to country s in year t. Hence, according to the Eq. (2), considering the contribution of international and domestic trade to the emissions of the economy   in a country, the total carbon emissions occurring in region r ETr,t can be explicitly decomposed into local and traded components (both local and international) as follows: ETr,t = SF r,t (I − Ar−r,t )−1 (Yr−r,t +



BE r−s,t )

(3)

s

Where SF r,t with each element indicating the carbon emissions per unit sector output is a row vector. Also, the consumption-based accounting as the total emissions occurring from economic consumption within a region r is defined as follows: r,t ECO2

Cons

= ETr,t − EEE r,t + EEI r,t r,t

(4)

r,t

Where EEE and EEI denotes the carbon emissions embodied in exports from region r to all other regions and emissions embodied in imports from all other regions to region r, respectively. More detailed description can also be referred to Xu and Dietzenbacher (2014) and Zhong et al. (2018)’s studies. 3.2. Econometric methods The inverted U-shaped relationship between per capita GDP and per capita CO2 emissions, namely, the EKC hypothesis can be tested by using a logarithmic quadratic function. It is expressed as follows. LnPCCO2it = ˛ + 1 LnPCGDP it + 2 LnPCGDP2it + Zit ˇ + εit

(5)

where subscripts i and t denote country i and year t, respectively. The dependent variable, LnPCCO2 denotes per capita carbon dioxide emissions in logarithms. In addition, the main aim of this study is to test for the EKC for CO2 based on the production accounting and consumption accounting approach, respectively. Hence, A subscript is needed to distinguish them. Specifically, LnPCCO2 Prod and LnPCCO2 Cons denote per capita carbon dioxide emissions in logarithms based on production and consumption principles, respectively. Besides, LnPCGDP and LnPCGDP2 denote per capita GDP in logarithms and its squared term. Z denotes a set of explanatory variables, which will be introduced later. ˛ is the constant term and ε is the error term. 1 , 2 , and ˇ are unknown parameters to be estimated. Besides, 1 and 2 are employed to test the various forms of CO2 -income relationships. (a) 1 = 2 = 0, it reveals no relationship between per capita CO2 emissions and per capita GDP; (b) 1 > 0 and 2 = 0, it suggests a monotonically increasing linear relationship; (c) 1 < 0 and 2 = 0, it represents a monotonically decreasing linear relationship; (d) 1 > 0 and 2 < 0, it reveals an inverted U-shaped curve; (e) 1 < 0 and 2 > 0, it indicates a U-shaped curve. Eq. (3), the pooled least squares model, is referred as to the benchmark model. Since there are huge differences in these countries that cannot be ignored, we next consider the fixed effects model. It is given as follows. LnPCCO2it = ˛ + 1 LnPCGDP it + 2 LnPCGDP2it + Zit ˇ + i + t + εit

(6)

where i denotes country fixed effects. It refers to controlling for all unobservable country-specific and time-invariant variables that are not included in the model. Similarly, t represents timeperiod fixed effects. It refers to controlling for all time-specific and country-invariant variables that are not included in the model. On the other hand, i and t can also be treated as random variables that are independently and identically distributed with zero mean 2 and  2 , respectively. Moreover, the random variand variance   ables i , t and the error term εit are independent of each other.

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Since Eq. (4) ignores the possible extended EKC, namely, an inverted N-shaped or N-shaped curve, for robustness check we also take into account the cubic functional form to investigate if there is an inverted N-shaped curve. The model can be written as follows. LnPCCO2it = ˛ + 1 LnPCGDP it + 2 LnPCGDP2it + 3 LnPCGDP3it + Zit ˇ + i + t + εit

249

(shown in Fig. 2) spanning from 1995 to 2011. It can be freely available from the website at http://www.wiod.org/database. In addition, data for energy use and economic indicators included in this study are available from the World Bank database (http:// data.worldbank.org.cn). Besides, all economic variables are taken in 1995 constant prices (US dollars).

(7)

Where LnPCGDP3 denotes the cubic term of per capita GDP. It is used to find the evidence of the possible inverted N-shaped curve. Specifically, (a) 1 < 0, 2 > 0, and 3 < 0, it represents an inverted N-shaped curve. (b) 1 >0, 2 < 0, and 3 > 0, it shows an N-shaped curve. The analysis processes described in brief to show how to verify the EKC hypothesis for CO2 emissions are displayed in Fig. 1.

4. Empirical results We obtained CO2 emissions data based on production accounting and consumption accounting by means of Eqs. (1 and 2). For simplicity, CO2 emissions based on production accounting and consumption accounting have been shortened to CO2 Prod , and CO2 Cons , respectively. In this section, we first analyze the spatiotemporal characteristics of two types of CO2 emissions and then verify the EKC hypothesis and finally calculate the turning points.

3.3. Variables 4.1. Variations of CO2 emissions When testing for the EKC relationship between per capita CO2 emissions and per capita GDP, we also control for a set of exogenous variables in the regression models, including energy efficiency (EEF), intermediate inputs in local region (II), final requirements in local region (FR), the share of the secondary industry (ISSI), the share of the tertiary industry (ISTI), the share of the primary industry (ISPI), the ratio of clean energy to total energy use (ES), intermediate inputs in others regions (IIM), and final requirements in others regions (FRM). Energy efficiency is the major contributor to CO2 emissions since energy use is the main source of various pollutants, including CO2 emissions (Zhong et al., 2018). In this study, energy efficiency is defined as a unit of energy used to produce the amount of economic output. Generally, the higher the energy efficiency, the lower the CO2 emissions. Hence, it is hypothesized to have a negative effect on CO2 emissions in this study. Intermediate inputs play an important role in CO2 emissions since energy flows are the main source of intermediate inputs in international trade. This implies that more intermediate inputs mean higher CO2 emissions. Hence, we hypothesize that the expected sign is positive (Xu and Dietzenbacher, 2014). Regarding the final requirements, Xu and Dietzenbacher (2014) state that local final requirements affect CO2 emissions. For one country, if final products are imported from other countries as final requirements to satisfy the demand of the local country in international trade, this may reduce CO2 emissions. Hence, this is hypothesized to have a negative effect. CO2 emissions are largely attributed to industrialization because the secondary industry is the principal energy consumer and thus the main CO2 emitter. In short, the industrial structure is the key to affecting CO2 emissions. Generally, the share of the secondary industry has a positive effect on CO2 emissions while the increase in the share of the tertiary industry may contribute to reducing CO2 emissions. Another contributor to CO2 emissions is the energy use structure. If fossil fuel dominates the energy mix, then high CO2 emissions are generated. In short, an increase in the share of clean energy to total energy use may help to reduce CO2 emissions. Hence, this is expected to have a negative effect. In order to reduce the possible issue of heterogeneity, all variables are taken in logarithms. The descriptive statistics of the variables involved in the regression models (mean, standard deviation, minimum and maximum) are summarized in Table 2. 3.4. Data sources CO2 emissions based on the production accounting and consumption accounting approach are calculated from the world input-output database (WIOD), which contains 39 countries, namely, 27 European Union countries and 12 major countries

To display the temporal variations of CO2 Prod and CO2 Cons during the sample period, we select 10 major countries and create bat plots, which are shown in Fig. 3. As shown in Fig. 3, China and the U.S. were the largest CO2 emitters from 1996 to 2011, implying that the two countries should contribute to the reduction of global CO2 emissions. Moreover, it was found that both the CO2 Prod and CO2 Cons of the U.S. increased slightly each year. In 1996, China was the second largest CO2 emitter worldwide, just behind the U.S. In contrast, China became the largest CO2 emitter in 2006, surpassing the U.S. Until 2011, the CO2 emissions of China were approximately twice those of the U.S. The main reason was that the emissions embodied in China’s exports significantly increased (Mi et al., 2018). Moreover, the other 8 countries accounted for the minority of global CO2 emissions. Finally, it should be noted that, as shown in Fig. 3, for energy-exporting Russia, the differences between CO2 Prod and CO2 Cons widened each year throughout the sample period, implying that Russia exported a large amount of energy resources, e.g., coal, oil and natural gas. In addition, we intentionally selected China and the U.S. from these 39 countries and plotted the differences between the CO2 Prod and CO2 Cons (see Fig. 4), since they are the two largest CO2 emitters worldwide. As displayed in Fig. 4, the differences between the CO2 Prod and CO2 Cons of the U.S. from 1995 to 2011 present an inverted N-shaped curve, and the CO2 Cons is always greater than CO2 Prod throughout the sample period, implying that the U.S. is a CO2 -consuming-dominant country. In contrast, for exportdriven China, its CO2 Cons gradually approaches the CO2 Prod each year, implying that as the income levels of the Chinese have rapidly increased and large-scale urbanization has expanded in recent decades, China has consumed a large number of energyextensive products, e.g., vehicles, air-conditioners, cement and steel. Although the aggregated CO2 emissions of China are much higher than those of the U.S., China’s per capita CO2 emissions are still far behind those of the U.S. For example, in 2011, per capita CO2 emissions of the U.S. were approximately twice as much as those of China. Hence, as China’s economic income levels continue to rise at such a rapid rate, it is projected that China will become a high−CO2 -product consumer in the future, since its CO2 emissions have been continuously increasing in recent years. Thus, the success of the Paris Protocol may be dependent on curtailing the growth of CO2 emissions in industrializing countries, such as China (Mi et al., 2017). 4.2. Results of the EKC hypothesis Before estimating the EKC model, we first present two scatterplots of the two variables, per capita CO2 emissions based on

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Fig. 1. Flowchart of methodology for verifying the EKC for CO2 emissions. Table 2 Descriptive statistics for variables. Variable

Definition

Mean

Std. Dev.

Min

Max

LnPCCO2 Prod LnPCCO2 Cons LnPCGDP LnEEF LnII LnFR LnISSI LnISTI LnISPI LnES LnIIM LnFRM

Per capita CO2 emissions based on production accounting in logarithms Per capita CO2 emissions based on consumption accounting in logarithms Per capita GDP in logarithms Energy efficiency in logarithms Intermediate inputs in local region Final requirements in local region The share of the secondary sector The share of the tertiary sector The share of the primary sector The ratio of clean energy to total energy use Intermediate inputs in others regions Final requirements in others regions

4.1077 4.1612 8.9183 2.0398 11.9242 12.0109 −0.8668 −0.6639 −3.4015 1.6328 10.7546 10.2066

0.6641 0.6694 1.6890 0.3560 2.0343 1.9566 0.2304 0.2150 0.7302 1.9496 1.6702 1.7223

2.0151 1.9952 3.8434 1.0677 7.2942 8.0102 −1.9755 −1.4128 −5.8360 −9.2103 6.7502 6.3128

5.4383 5.4725 11.3595 2.7641 16.0601 16.2224 −0.3776 −0.1529 −1.4910 3.9266 13.9408 13.6685

Fig. 2. Map of our study area.

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Fig. 3. CO2 emissions based on production and consumption principles in 1996, 2001, 2006, 2011.

production and consumption principles and per capita GDP, respectively, to clearly discover their relationships. They are shown in Fig. 5. Fig. 5(a) exhibits the relationship between per capita CO2 emissions based on production principle and per capita GDP in logarithms. Intuitively, these scatters show an increasing trend. Specifically, as per capita GDP rises, per capita CO2 emissions also increase. However, we also find that for a couple of scatters with high per capita GDP in the upper right corner, their per capita CO2 emissions are not proportionally high, which may imply that there may be a turning point at a higher income level. In short, as per capita GDP rises, per capita CO2 emissions first increase, and then reach the peak point, and finally continue to decline. This may imply an inverted U-shaped curve between per capita GDP and per capita CO2 emissions. Fig. 5(b) discovers the relationship between per capita CO2 emissions based on the consumption principle and per capita GDP in logarithms. Compared with Fig. 5(a and b) also exhibits a possible inverted U-shaped curve. In short, for the case of CO2 emissions based on the consumption principle, there is also a turning point where per capita CO2 emissions continue to decline as per capita GDP rises. The largest differences between Fig. 5(a and b) are where the turning points occur. Traditionally, the existing studies estimated turning points based on the production principle. However, there

may be two shortcomings. One is that for export-driven countries, such as China, per capita CO2 emissions are overestimated. In contrast, for those countries characterized with a massive amount of imports, such as the U.S. and EU countries, per capita CO2 emissions are significantly underestimated. The other shortcoming is that turning points based on production principle are likely to be biased because of the unjustifiable calculation of CO2 emissions. Hence, this study attempts to re-estimate the turning points. Table 3 reports the estimation results of pooled least squares (PLS) models as benchmark models. As shown in Table 3, we take both the cubic form and the quadratic form into account when testing the EKC hypothesis. For the CO2 Prod , it can be found that in the quadratic form, LnPGDP2 is highly insignificant, indicating a linear relationship. However, in the cubic form, we find that LnPCGDP is significant and negative, LnPCGDP2 is significant and positive, and LnPCGDP3 is significant and negative, indicating that an inverted N-shaped curve exists. Similarly, for the CO2 Cons , there also exists an inverted Nshaped curve between per capita CO2 emissions and per capita GDP. Since the PLS models do not control for country fixed effects and time fixed effects, biased estimation results may be obtained. This is because, for the case of 39 countries, the observed heterogeneity is likely to be correlated with the explanatory variables, and the

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Fig. 4. Differences between CO2 production and CO2 consumption. Note that positives denote CO2 Prod larger than CO2 Cons while negatives denote CO2 Prod smaller CO2 Cons .

Fig. 5. Scatterplot of the relationship between CO2

differences between countries cannot be assumed to be random. Hence, we estimate the fixed effects models. The results of country fixed effects, time fixed effects, two-way fixed effects (namely, country and time fixed effects), and random effects quadratic and cubic models for CO2 Prod are summarized in Table 5 and in Table 6, respectively. As shown in Table 4, to select the best fitted fixed effects model, we first applied the likelihood ratio (LR) test to examine the null hypothesis that the country fixed effects are jointly insignificant.

Prod

and CO2

Cons ,

and GDPPC in logarithms.

The results showed that the null hypothesis was strongly rejected. Similarly, the null hypothesis that the time fixed effects are jointly insignificant can also be strongly rejected by means of the LR test. Hence, both LR test results justify controlling for country and time fixed effects. In addition, we performed a Hausman test to uncover if the two-way fixed effects model is ruled out. The results disclosed that the random effects model was rejected at a 1% significance level. In summary, the two-way fixed effects model is the best fitted model. As shown in the fourth column of Table 4, LnPCGDP is

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Table 3 Results of pooled least squares models. CO2 Prod Variable LnPCGDP LnPCGDP2 LnPCGDP3 LnEI LnII LnFR LnISSI LnISTI LnISPI LnES LnIIM LnFRM Constant R2 Adjusted R2 F-statistic P-value

Cubic −2.0084*** (0.5344) 0.3015*** (0.0703) −0.0126*** (0.0030) −0.5302*** (0.0492) 0.4078*** (0.0944) −0.3045*** (0.0805) 1.2941*** (0.1748) 2.1566*** (0.2320) 0.1408*** (0.0497) −0.0908*** (0.0096) −0.0974* (0.0557) 0.0469(0.0361) 10.6504*** (1.3482) 0.6648 0.6586 107.43 0.0000

CO2 Cons Quadratic 0.2182** (0.0873) 0.0057(0.0054) −0.5410*** (0.0498) 0.4839*** (0.0938) −0.3911*** (0.0788) 1.1585*** (0.1741) 2.1800*** (0.2350) 0.1758*** (0.0497) −0.0860*** (0.0097) −0.0740(0.0561) 0.0378(0.0365) 5.3198*** (0.4786) 0.6556 0.6498 112.67 0.0000

Cubic −1.9495*** (0.4854) 0.2859*** (0.0638) −0.0115*** (0.0027) −0.4653*** (0.0447) 0.4902*** (0.0857) −0.3645*** (0.0731) 1.0852*** (0.1588) 1.8920*** (0.2108) 0.0961** (0.0452) −0.0952*** (0.0088) −0.1346*** (0.0506) 0.0288(0.0328) 10.2851*** (1.2245) 0.7278 0.7278 144.86 0.0000

Quadratic 0.0806(0.0793) 0.0162*** (0.0049) −0.4751*** (0.0452) 0.5596*** (0.0852) −0.4434*** (0.0716) 0.9616*** (0.1581) 1.9133*** (0.2134) 0.1280*** (0.0451) −0.0908*** (0.0088) −0.1133** (0.0510) 0.0205(0.0332) 5.4249*** (0.4348) 0.7203 0.7156 152.43 0.0000

Note: standard errors in parenthesis. * p < 0.10. ** p < 0.05.

Table 4 Results of fixed and random effects quadratic models for CO2 Prod . Variable

Country fixed effects

Time fixed effects

Two-way fixed effects

Random effects

LnPCGDP LnPCGDP2 LnEI LnII LnFR LnISSI LnISTI LnISPI LnES LnIIM LnFRM Constant R2 Adjusted R2 F-statistic P-value

0.2271*** (0.0666) −0.0130*** (0.0046) −0.2218*** (0.0572) 0.1382*** (0.0458) −0.1682*** (0.0589) −0.3447*** (0.1293) −0.4282*** (0.1750) 0.0065(0.0398) −0.0086(0.0085) 0.2151*** (0.0380) −0.0373(0.0279) 1.5009*** (0.5778) 0.9703 0.9680 497.4168 0.0000

0.2610*** (0.0883) 0.0035(0.0054) −0.5860*** (0.0519) 0.4826*** (0.0953) −0.3741*** (0.0804) 1.1647*** (0.1740) 2.1638*** (0.2353) 0.1853*** (0.0500) −0.0907*** (0.0098) −0.1044* (0.0569) 0.0454(0.0368) 5.4420*** (0.4832) 0.6654 0.6512 46.7846 0.0000

0.2641*** (0.0708) −0.0161*** (0.0051) −0.2517*** (0.0620) 0.1656*** (0.0471) −0.2406*** (0.0616) −0.2838** (0.1328) −0.4173** (0.1743) −0.0139(0.0427) −0.0082(0.0084) 0.1791*** (0.0413) −0.0438(0.0282) 2.5850*** (0.6898) 0.9721 0.9690 517.4719 0.0000

0.1694*** (0.0667) −0.0066(0.0046) −0.2749*** (0.0560) 0.1208*** (0.0467) −0.1881*** (0.0511) −0.2895** (0.1283) −0.2512(0.1753) −0.0007(0.0385) −0.0122(0.0084) 0.2126*** (0.0385) −0.0304(0.0283) 2.1447*** (0.4695) 0.1981

Note: standard errors in parenthesis. * p < 0.10. ** p < 0.05. *** p < 0.01.

Table 5 Results of fixed and random effects cubic models for CO2 Prod. Variable

Country fixed effects

Time fixed effects

Two-way fixed effects

Random effects

LnPCGDP LnPCGDP2 LnPCGDP3 LnEI LnII LnFR LnISSI LnISTI LnISPI LnES LnIIM LnFRM Constant R2 Adjusted R2 F-statistic P-value

−1.1417 (0.2849) 0.1757*** (0.0385) −0.0082*** (0.0017) −0.2561*** (0.0565) 0.1368*** (0.0450) −0.1601*** (0.0578) −0.2360* (0.1288) −0.3653** (0.1722) −0.0383(0.0401) −0.0077(0.0084) 0.2098*** (0.0373) −0.0442* (0.0274) 4.6782*** (0.8579) 0.9715 0.9692 416.96 0.0000

−2.3225 (0.5486) 0.3476*** (0.0724) −0.0147*** (0.0031) −0.5889*** (0.0511) 0.3785*** (0.0962) −0.2565*** (0.0828) 1.3325*** (0.1747) 2.1310*** (0.2315) 0.1474*** (0.0498) −0.0979*** (0.0098) −0.1381** (0.0564) 0.0603* (0.0364) 11.3285*** (1.3850) 0.6771 0.6628 47.47 0.0000

−1.6130 (0.2922) 0.2462*** (0.0400) −0.0117*** (0.0018) −0.3493*** (0.0617) 0.1821*** (0.0456) −0.2706*** (0.0597) −0.0463(0.1332) −0.3212* (0.1690) −0.0554(0.0417) −0.0058(0.0081) 0.1331*** (0.0405) −0.0561** (0.0273) 7.7081(1.0263) 0.9740 0.9711 338.18 0.0000

−1.3252*** (0.2893) 0.1992*** (0.0391) −0.0090*** (0.0017) −0.3008*** (0.0551) 0.1191*** (0.0457) −0.1778*** (0.0502) −0.1753(0.1275) −0.2013(0.1719) −0.0474(0.0387) −0.0110(0.0082) 0.2057*** (0.0377) −0.0378(0.0278) 5.5974*** (0.8001) 0.2234

Note: standard errors in parenthesis. * p < 0.10. ** p < 0.05. *** p < 0.01.

***

***

***

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Table 6 Results of fixed and random effects quadratic models for CO2 Cons . Variable

Country fixed effects

Time fixed effects

Two-way fixed effects

Random effects

LnPCGDP LnPCGDP2 LnEI LnII LnFR LnISSI LnISTI LnISPI LnES LnIIM LnFRM Constant R2 Adjusted R2 F-statistic P-value

0.2059*** (0.0601) −0.0111*** (0.0042) −0.0986* (0.0516) 0.0751* (0.0413) −0.0167(0.0531) −0.2835*** (0.1166) −0.3374** (0.1578) −0.0147(0.0359) −0.0196*** (0.0077) 0.1695*** (0.0343) −0.0196(0.0252) 0.6390(0.5213) 0.9763 0.9744 514.2502 0.0000

0.1309* (0.0794) 0.0136*** (0.0049) −0.5259*** (0.0467) 0.5651*** (0.0857) −0.4295*** (0.0723) 0.9634*** (0.1564) 1.8974*** (0.2115) 0.1399*** (0.0449) −0.0962*** (0.0088) −0.1506*** (0.0511) 0.0297(0.0331) 5.5423*** (0.4343) 0.7341 0.7228 64.9175 0.0000

0.2229*** (0.0633) −0.0122*** (0.0046) −0.1185** (0.0554) 0.1157*** (0.0422) −0.0833(0.0551) −0.2637** (0.1188) −0.3266** (0.1560) −0.0325(0.0382) −0.0202*** (0.0075) 0.1291*** (0.0369) −0.0209(0.0252) 1.4277** (0.6173) 0.9780 0.9756 408.3090 0.0000

0.1313** (0.0609) −0.0032(0.0042) −0.1494*** (0.0511) 0.0578(0.0427) −0.0911** (0.0466) −0.2118* (0.1171) −0.1590(0.1600) −0.0385(0.0351) −0.0224*** (0.0076) 0.1748*** (0.0351) −0.0152(0.0259) 1.8580*** (0.4274) 0.2727

Note: standard errors in parenthesis. * p < 0.10. ** p < 0.05. *** p < 0.01. Table 7 Results of fixed and random effects cubic models for CO2 Cons . Variable

Country fixed effects

Time fixed effects

Two-way fixed effects

Random effects

LnPGDP LnPGDP2 LnPGDP3 LnEI LnII LnFR LnISSI LnISTI LnISPI LnES LnIIM LnFRM Constant R2 Adjusted R2 F-statistic P-value

−1.0970*** (0.2564) 0.1685*** (0.0346) −0.0078*** (0.0015) −0.1313*** (0.0509) 0.0737* (0.0405) −0.0090(0.0521) −0.1800(0.1159) −0.2775*(0.1550) −0.0574(0.0361) −0.0188** (0.0075) 0.1644*** (0.0336) −0.0262(0.0247) 3.6634*** (0.7721) 0.9773 0.9754 526.10 0.0000

−2.2322*** (0.4928) 0.3284*** (0.0650) −0.0135*** (0.0028) −0.5286*** (0.0459) 0.4699*** (0.0864) −0.3220*** (0.0744) 1.1169*** (0.1569) 1.8675*** (0.2079) 0.1052** (0.0447) −0.1027*** (0.0088) −0.1815*** (0.0506) 0.0434(0.0327) 10.8697*** (1.2440) 0.7436 0.7323 65.67 0.0000

−1.4505*** (0.2616) 0.2217*** (0.0358) −0.0104*** (0.0016) −0.2056*** (0.0552) 0.1304*** (0.0408) −0.1100** (0.0534) −0.0520(0.1192) −0.2409* (0.1513) −0.0695* (0.0373) −0.0181** (0.0073) 0.0881** (0.0362) −0.0318(0.0245) 6.0108*** (0.9186) 0.9795 0.9772 431.27 0.0000

−1.2925*** (0.2636) 0.1928*** (0.0356) −0.0086*** (0.0015) −0.1736*** (0.0501) 0.0560(0.0416) −0.0800* (0.0457) −0.1038(0.1161) −0.1125(0.1565) −0.0827** (0.0352) −0.0213*** (0.0075) 0.1681*** (0.0343) −0.0223(0.0253) 5.1334*** (0.7282)

Note: standard errors in parenthesis. * p < 0.10. ** p < 0.05. *** p < 0.01.

significant and positive, while LnPCGDP2 is significant and negative, indicating that there is an inverted U-shaped curve. On the other hand, Table 5 reports the results of fixed effects and random effects cubic models for the CO2 Prod . We also repeated the LR and Hausman tests to determine the best fitted models. A similar result is obtained where the two-way fixed effects model is the best candidate. From the fourth column in Table 5, the results showed an inverted N-shaped curve, since LnPCGDP, LnPCGDP2, and LnPCGDP3 are significantly negative, positive and negative, respectively. Similarly, for CO2 Cons , the estimation results of the fixed effects and the random effects quadratic and cubic models can be obtained in Table 6 and in Table 7, respectively. By repeating the LR and Hausman tests, we also find that the random effects model is strongly rejected. Hence, for CO2 Cons , the two-way fixed effects quadratic model is also the best fitted model. From the fourth column in Table 4, LnPCGDP is significant and positive while LnPCGDP2 is significant and negative, also indicating that an inverted U-shaped curve exists between per capita GDP and per capita CO2 emissions. For the cubic model CO2 Cons , the LR and Hausman tests are repeated to test for the fixed effects model versus the random effects model. The results of the LR and Hausman tests show that

the two-way fixed effects model is the best model among the four regression models. From the estimated coefficients, there also exists an inverted N-shaped curve. Next, we focus on the estimated coefficients of the explanatory variables. Since the results from Tables 4–7 are very similar, we opt for the results of the two-way fixed effects cubic model based on the consumption principle CO2 emissions in Table 7 which we will now discuss. This is because international trade plays an important role in affecting the turning points of the EKC for CO2 emissions. The coefficient of energy efficiency (LnEEF) is significant and negative, indicating that an increase in energy efficiency causes a decrease in per capita CO2 emissions. This is because energy efficiency improvement is the main technological factor lowering energy waste and CO2 emissions. The variable of LnII (intermediate inputs in local region) is found to be significant and positive, implying that the intermediate inputs are attributed to an increase in per capita CO2 emissions. Energy flows and foreign capital are the main sources of inputs in international trade, which lead to consuming high carbon embodied products. Regarding the variable of LnFR (final requirements), it has a significant and negative impact on per capita CO2 emissions. One reasonable explanation is that for each sector of a country, it needs to import final products

L. Jiang et al. / Structural Change and Economic Dynamics 50 (2019) 245–257 Table 8 Turning points of cubic and quadratic curves (Unit: US Dollars). Principle

Cubic

Turning point CO2 Prod CO2 Cons

1st turning point 181 166

Quadratic 2nd turning point 6989 8746

Turning point 3593 9330

and services from other regions and countries as the final requirements to meet the needs of the local region through international trade, thus reducing CO2 emissions. In addition, LnISTI is found to be significant and negative, indicating that the share of the tertiary industry has a negative impact on per capita CO2 emissions. It is widely recognized that the tertiary industry is characterized with high value-added goods and low pollution. Hence, increasing the share of the tertiary industry should be highly encouraged. Moreover, we find that the estimated coefficient of LnES is also significant and negative, indicating that an increase of the ratio of clean energy to total energy use contributes to lowering CO2 emissions. The energy use structure is of crucial importance in affecting CO2 emissions since clean energy, such as wind power, hydro power, solar power, and geothermal power, hardly produces CO2 . Hence, the increase in clean energy should be highly encouraged to mitigate ever-growing CO2 emissions. Finally, regarding the variable of LnIIM, it has a significant and positive impact on per capita CO2 emissions. One possible interpretation is that an increase in the intermediate inputs in other regions may stimulate the input of intermediate products, e.g., the energy of local regions to satisfy the demand from other regions, thus resulting in increased CO2 emissions. 4.3. Discussions of turning points From Tables 4–7, for CO2 Prod , both an inverted U-shaped curve and an inverted N-shaped curve are obtained. Similarly, for CO2 Cons , we also find that both an inverted U-shaped curve and an inverted N-shaped curve exist. To determine which EKC model is better, we calculated the turning points of quadratic and cubic curves. They are summarized in Table 8. For CO2 Prod , according to the estimated coefficients from Table 4, there is only one turning point at 3593 USD for the inverted U-shaped curve. In addition, from the estimated coefficients in Table 5, the inverted N-shaped curve has two turning points at 181 USD and 6989 USD, respectively. On the other hand, for CO2 Cons , the turning point calculated based on the estimated coefficients from Table 6 occurs at 9330 USD for the inverted U-shaped curve. Similarly, for the inverted N-shaped curve, we calculate and obtain two turning points at 166 USD and 8746 USD. Next, to determine which model is better fitted—the quadratic model or the cubic model—we discuss two aspects. On one hand, for the case of the quadratic form, there are large differences between the turning points of the inverted U-shaped curves from the two types of CO2 emissions calculated based on production and consumption principles. Specifically, the turning point of 9330 for CO2 Cons is approximately two and a half times as much as that of 3593 for CO2 Prod . Evidently, the quadratic functional form may not be a good candidate model. We prefer to regard the cubic functional form as the better candidate model, since this has rational turning points. On the other hand, from the statistical viewpoint, log-likelihood value of the cubic model is greater than that of the quadratic model, because the higher the log-likelihood value, the better fitted. Most importantly, we perform the LR test and the results reveal that the cubic term should be taken into account. To conclude, both justified the idea that the cubic model is better fitted.

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As shown in Table 8, we focus more on the second turning points for the two inverted N-shaped curves. It can be found that for CO2 Prod , the turning point of 6989 USD indicates that CO2 emissions will reach the summit earlier than the turning point of 8746 based on the consumption principle. In short, the turning point is underestimated if the production principle is adopted when verifying the EKC hypothesis. The inverted N-shaped indicates that as income levels increase, per capita CO2 emissions decline to the nadir, and then begin to increase after passing the first turning point as incomes continue to rise. Then, per capita CO2 emissions reach the summit, the second turning point, and then decline as incomes go up. We are more interested in concentrating on the two largest CO2 emitters, China and the US. According to the calculated turning points, we find that the U.S. has already passed them, while China has not yet. Furthermore, China will pass the turning point (6989 USD) much earlier if adopting the production principle rather than the consumption principle (8746 USD). As income levels of the Chinese continue to rise and their environmental awareness enhances, China will also pass the turning point and lower per capita CO2 emissions in the near future. 5. Conclusions and policy implications International trade has greatly influenced the global environment, which may, however, have been ignored when testing for the EKC hypothesis in the previous studies. Hence, the main aim of the research is to re-examine the validity of the EKC hypothesis for CO2 emissions by taking the role of international trade into account. Accordingly, this research, in the first stage, calculated the CO2 emissions of 39 countries worldwide from 1995 to 2011 based on the multi-regional input-output tables from the WIOD and in the second stage, applied panel data models to re-examine the evidence in support of the EKC. 5.1. Policy implications The policy implications based on the main findings of the research are as follows. First, for global carbon emission accounting in the global commodity value chains, through close trade links, developing countries have provided a large number of resources-intensive products for the economic growth of developed countries. Although transnational trade provides an engine to promote national economic development, more adverse environmental impacts and parts of emissions reduction commitments are likely to be undertaken by developing countries such as China than ever before. Moreover, for some developed countries, such as the U.S., through spatial shifts and structural changes, a large number of energy-intensive industries and highly polluted industries were moved to developing countries because of their lower environmental standards. Thus, their emission reduction responsibilities as consumers greatly surpassed the responsibilities on the production side. Consequently, economic development and environmental quality might both have received extensive development and improvement to some extent. Hence, for related stakeholders in climate policies, developed countries should improve advanced technologies and solutions for CO2 emissions reduction and lessen the demand for fossil fuel energy. In addition, these countries could accelerate energy technological progress and technology spillovers to improve other developing countries’ production technology levels in tackling global climate change, which might be more helpful in significantly curtailing greenhouse gas emissions, and thus reach the peak of CO2 emissions ahead of schedule. Second, from the perspective of per capita carbon emissions, compared to some countries, such as the U.S., although China’s per person

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CO2 emissions are relatively low, the 1.34 billion population and the increase in the liberalization of population policy would further lead to a large demand for inter- and extraregional products and resources, which should inevitably stimulate the country’s need for a large amount of fossil fuel energy. The importance of this truth cannot be overstated by policy makers. Currently, China is the largest CO2 emitter in the world, surpassing the U.S., and has taken on more responsibility to reduce CO2 emissions and tackle climate change. Thus, the Chinese government has made a significant effort to lower the share of fossil fuel energy in the energy mix by increasing the use of nuclear power, wind power, hydropower, etc. In observing the peak CO2 emissions, we notice that global trade should postpone the peak time of global CO2 emissions, which also suggests that trade has played a critical role in formulating global carbon-reduction policies. Furthermore, regarding the influencing factors affecting global greenhouse gas emissions, increasing energy efficiency and improving the energy use structure would contribute significantly to lowering global CO2 emissions and thus be conductive to making the global CO2 emissions peak ahead of schedule. Moreover, specific to the world’s two largest emitters, namely, the U.S. and China, compared to a productionbased accounting approach, China’s carbon emissions peak will be postponed under a consumption-based accounting approach. In summary, international trade plays an important role in carbon transfer, and inevitably affects global climate change. These results may show that on one hand, to cope with global climate change for China, more efforts should be made regarding economic structural adjustments and investments in energy technologies to effectively lessen the dependence on fossil fuels. On the other hand, if the stakeholders accounted for global emissions reductions from the perspective of a consumption-based approach, this would lead to reducing the pressure to cut CO2 emissions for China to some extent, but the peak time of China’s carbon dioxide emissions would be delayed accordingly. Therefore, this point deserves special attention within the context of global climate policies.

5.2. Conclusions In this subsection, we briefly review the main findings of the research, which basically contains two aspects. One is the temporal variations of CO2 emissions based on the production and consumption principles of 39 countries during the sample period. China and the U.S. are the two largest CO2 emitters in the world. Furthermore, in 2011, the total CO2 emissions of China were twice those of the U.S. However, per capita CO2 emissions of China are far behind those of the U.S. As the income level and consuming capacity of the Chinese rapidly increase, it is projected that China will consume more high-carbon products than before, leading to a large challenge for global climate change. The other is the re-examination of the existence of the EKC for CO2 emissions. We took both the quadratic and cubic functional forms into account for a robustness check. The estimated results verified the existence of both an inverted U-shaped and N-shaped curves. According to turning points calculated by the estimated coefficients, the cubic functional form was regarded as the better candidate model, which had two turning points. We also found that for CO2 Prod , the second turning point occurred earlier than it did for CO2 Cons , which justified the important role of international trade when testing for the EKC hypothesis. Otherwise, biased results may be obtained. On the other hand, the U.S. has already passed the turning point, while China has not yet. However, as China’s economic growth rapidly develops and income levels increase, it is predicted that China will pass its turning point in the coming years.

Acknowledgements The authors are grateful for the financial support provided by the National Natural Science Foundation of China (41801118,71742001), the Natural Science Foundation of Guangdong Province (2018B030312004), the Ministry of Education of Humanities and Social Science project of China (17YJC790061, 18YJC790237), and the National Social Science Foundation of China (18ZDA111), the Natural Science Foundation of Zhejiang Province (LY19G030013).

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