Linking international trade and foreign direct investment to CO2 emissions: Any differences between developed and developing countries?

Science of the Total Environment 712 (2020) 136437

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Linking international trade and foreign direct investment to CO2 emissions: Any differences between developed and developing countries? Obed Kwame Essandoh a,b, Moinul Islam a,c,⁎, Makoto Kakinaka a,d a

Graduate School for International Development and Cooperation, Hiroshima University, 1-5-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8529, Japan Ghana Grid Company LTD., P.O. Box CS7979, Tema, Ghana c Urban Institute, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan d Network for Education and Research on Peace and Sustainability (NERPS), Hiroshima University, 1-3-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8530, Japan b

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Linkages among CO2 emissions, international trade, and FDI inflows are investigated. • A sample of 52 developed and developing countries are analyzed for the period 1991–2014. • A positive long-run relationship exists between CO2 emissions and FDI inflow exclusively for low-income countries. • A negative long-run relationship exists between CO2 emissions and trade openness solely for high-income countries.

a r t i c l e

i n f o

Article history: Received 10 October 2019 Received in revised form 26 December 2019 Accepted 29 December 2019 Available online xxxx Editor: Pavlos Kassomenos Keywords: CO2 emissions International trade Trade openness Foreign direct investment Energy consumption PMG-ARDL model

⁎ Corresponding author. E-mail address: [email protected] (M. Islam).

https://doi.org/10.1016/j.scitotenv.2019.136437 0048-9697/© 2018 Elsevier B.V. All rights reserved.

a b s t r a c t International trade, together with foreign direct investment (FDI), promotes economic integration with complex global supply value chains, which is now recognized as a crucial factor in determining CO2 emissions. Production reallocation across countries, often associated with FDI, promotes cross-border trade of emission-embodied products. By applying panel pooled mean group-autoregressive distributive lag (PMG-ARDL) models, this study discusses the long-run relevance among CO2 emissions, international trade, and FDI inflows with the consideration of the short-run dynamics over 52 countries during the period from 1991 to 2014. Focusing on possible differences between developed and the developing countries, this study reveals that CO2 emissions have a negative long-run relationship with trade exclusively for developed countries, while they have a positive long-run relationship with FDI inflows solely for developing countries. The recent trend of increased trade and FDI would promote the transfer of high emission-intensive production units from developed countries to developing countries, causing developed countries to achieve emission reduction at the expense of developing countries. © 2018 Elsevier B.V. All rights reserved.

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1. Introduction International trade leads to more integrated and globalized markets which increase trade flows among developed and developing countries (de Benedictis and Tajoli, 2011). Trade has a positive impact on economic growth by ensuring comparative advantages and resource transfers (Antweiler et al., 2001). Economic growth enhances the welfare of human life (Kumari and Sharma, 2018). However, trade openness might affect the environment in a positive or negative direction in developed and developing countries through changes in trade patterns associated with the prevalence of global supply chains. Since the industrial revolution, carbon dioxide (CO2) emissions have shot-up significantly in the last 130 years (Bekun et al., 2018). Due to trade openness, developed countries reduce their dirty commodity production significantly (Mehra and Das, 2008). The cross-border transfer of industrial production processes induces the transfer of its associated emissions among countries. A country's transfer to its trading partners of the production units of goods that are imported back as intermediate inputs directly reduce its associated domestic emissions but directly increases them in its partner countries. This phenomenon is acknowledged as emissions embodied in international trade. The official carbon reporting applies production- and consumption-based accounting systems. The production-based accounting under the UN Framework Convention on Climate Change (UNFCCC) assigns emissions to each country generating them. In contrast, consumption-based accounting, based on emissions embodied in trade, assigns the responsibility of emissions to the country consuming the final products (Baumert et al., 2019). Imbalances in emissions embodied in trade between countries do not necessarily originate from only the outsourcing of production units, but they could also reflect the differences in the production technologies between countries (Kim et al., 2018). The effects of such a transfer on emissions would indirectly depend on the differences in the emission intensities of the production units. The linkage between international trade and environmental quality is an important issue in trade and environmental policy. Although many empirical studies have examined this linkage, their results have been rather mixed. Omri et al. (2015) find a positive linkage from trade openness to CO2 emissions in 12 Greater Middle East countries, and Balsalobre-Lorente et al. (2018) also show a positive effect of trade openness on CO2 emissions in 5 European countries. Some studies show the negative effect of trade on CO2 emissions. Dogan and Seker (2016) find that trade is negatively associated with CO2 emissions. Some studies discuss the effect of trade on CO2 emissions depend on the country and its trading partners. Kim et al. (2018) reveal that trade leads to a reduction in CO2 emissions for advanced countries. They also show that for developing countries, trade with developed countries worsens CO2 emissions but trade with developing countries mitigate CO2 emissions. Foreign direct investment (FDI) is an important determinant of economic growth, especially when domestic savings are not sufficient to deal with domestic investments (OECD, 2002). Proponents believe that FDI supports environmental sustainability with clean and improved technologies. Recent studies discuss the pollution-haven hypothesis, suggesting that a weak environmental regulation in a host economy will attract FDI which lead to environmental degradation in the country, and confirm a positive relationship between FDI and CO2 emissions in some countries or regions (Shahbaz et al., 2018 for France; Sun et al., 2017 for China; Sarkodie and Strezov, 2019, for Indonesia). However, the nexus between CO2 emissions and FDI is still inconclusive due to the pollution-halo hypothesis. The pollution-halo hypothesis recommends that universal standard environmental regulation transfer green technology of a country to its partners through FDI inflow (Pao and Tsai, 2011). Primary energy consumption is another fundamental characteristic of economic growth which is often criticized for its relevance to

environmental degradation. Numerous studies examine the relationships among CO2 emissions, energy consumption, and various macroeconomic conditions, including economic growth. CO2 emissions are intently guided by fossil fuel consumption, so that limiting energy consumption can be considered as a straight forward path to deal with the environmental emissions problem. However, the possible adverse effects on economic growth by reducing energy consumption is not a commonly accepted road for most countries. To reduce CO2 emissions and to lessen the adverse impact of climate change, both developing and developed countries need to discount their economic growth (Coondoo and Dinda, 2002). However, it is evident from China (Soytas and Sari, 2006a) and the G-7 countries (Soytas and Sari, 2006b) that countries may have different policy setups to reduce environmental emissions. Renewable energy has emerged as an alternative energy source to reduce fossil fuel consumption as well as to reduce CO2 emissions (Apergis and Payne, 2012). Renewable energy use could help improve environmental quality (Sims et al., 2007). This research focuses on how CO2 emissions are related to two crucial drivers of economic growth: international trade and FDI inflows in developing as well as developed countries. We provide important implications about emissions embodied in trade and emission transfers associated with international trade and FDI inflows and discuss possible differences between developed and developing countries. Previous studies examine the relationship between trade openness and CO2 emissions for developed and developing countries. Among them, Aller et al. (2015) examine the relationship between trade network and CO2 emissions, and Kim et al. (2018) study the relationship between trade and CO2 emissions. However, they do not consider the exclusive roles of FDI inflows. Other works also study the relationship between CO2 emissions and FDI for several individual countries and regions (Sarkodie and Strezov, 2019, for 5 developing countries; Sapkota and Bastola, 2017, for 14 Latin American countries; and Shahbaz et al., 2015, for 99 countries). However, they do not discuss either the possible effects of trade, which is closely related to FDI in a globalized economy. As the contribution to the literature, first, we evaluate both the longand short-run relationships among trade openness, FDI, and CO2 emissions within a single model to discuss the roles of trade openness and FDI in determining their environmental effects. To do so, we apply the panel pooled mean group-autoregressive distributive lag (PMG-ARDL) model. Second, we discuss the role of countries' development stages in determining the link of trade and FDI to CO2 emissions and evaluate their differences between developing and developing countries. Accounting for different causes and consequences of emission embodied trade between developing and developing countries, this study segregates countries into developed and developing in line with the World Bank's income classification. Third, this study identifies the causal direction of the relationships between CO2 emissions and other variables, such as trade and FDI inflows by employing causality analysis. Finally, we discuss the policy implications relevant to energy-intensive goods based on the estimated results. The rest of the paper is arranged as follows. Section 2 reviews previous literature. Section 3 presents the empirical analysis which consists of data and model specification, Section 4 presents the results and discussions. Finally, Section 5 concludes with policy suggestions.

2. Literature review Several studies focus on the causal relationship between international trade, FDI, energy consumption and CO2 emissions. For clarity, we decompose the literature review into three parts (see Table 1). In part A, we present the studies which investigate the relationship between CO2 emissions and international trade as well as trade openness in principal. In part B, we present the literature on the relationships between CO2 emissions and FDI. In part C, we present the literature on the nexus between CO2 emissions and energy consumption.

O.K. Essandoh et al. / Science of the Total Environment 712 (2020) 136437

Kim et al. (2018) study the trade and CO2 nexus by applying a panel data instrumental-variable (IV) quantile approach in the context of north and south trade. This study confirms a heterogeneous effect: the North trade contributes to increasing CO2 emissions, while the South trade mitigates CO2 emissions. Using the fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) approaches, Dogan and Seker (2016) empirically analyze how real income, renewable energy use, energy consumption, trade openness, and financial development influence CO2 emissions. The results indicate a negative relationship of CO2 emissions with renewable energy use, trade openness, and economic development and a positive relationship between CO2 emissions and conventional energy consumption. Omri et al. (2015) examine the relationship between CO2 emissions, trade, financial development, and economic growth by using the simultaneous-equation models (SEM) for panel data on 12 MENA countries for the period 1990–2011. The findings indicate bidirectional causality between CO2 emissions and economic growth and a unidirectional causality running from trade openness to CO2 emissions. Aller et al. (2015) study the role of the world trade network on environmental sustainability by identifying the most important countries in connecting trade using social network analysis. After employing a three-stage least square procedure to estimate environmental, trade, income, and network equations, the results indicate that while network effects adversely impact environmental quality in developed countries, they improve the environment of developing countries.

Balsalobre-Lorente et al. (2018) investigate the economic growth and CO2 emissions nexus for EU-5 countries (Italy, Spain, Germany, France, and the United Kingdom) during 1985–2016. The results show that an N-shaped relationship exists for economic growth and CO2 emissions in the countries investigated. It reveals that environmental quality is improved by renewable electricity use, natural resource, and innovation. In addition, trade openness, economic growth, and renewable electricity usage exert a positive impact on CO2 emissions. Bekun et al. (2018) investigate the causal interaction as well as the long-run relationships among CO2 emissions, renewable energy, nonrenewable energy, resource rent, and economic growth based on balanced annual panel data from 1996 to 2014 for 16 selected EU countries. Using panel pooled mean group-autoregressive distributive lag (PMG-ARDL) models, the research affirms that economic growth and consumption of nonrenewable energy intensify CO2 emissions. However, CO2 emissions are mitigated by renewable energy consumption. The panel causality analysis reveals bi-directional relationships between economic growth and nonrenewable energy consumption and between renewable energy and economic growth. The second group of studies reported in part B of Table 1 focuses on the relationship between CO2 emissions and FDI in principal. Shahbaz et al. (2018) study the role of economic development in influencing CO2 emissions in France considering the following variables: economic growth, GDP, energy use, FDI, and research innovations. FDI degrades the environment, supporting the pollution-haven hypothesis in

Table 1 Summary of literature review. Authors

Period

Study area

Variables

3

Method

Interpretations

A) Studies focusing on the relationship between CO2 emissions and international trade as well as trade openness in principal Trade, CO2, NOx Kim et al. (2018) 1960–2013 Developing and GMM North trade contributes to increasing carbon emissions. South trade advanced mitigates carbon emissions. countries FMOLS and Negative relation among carbon emissions and renewable energy use, Dogan and Seker 1985–2011 Top renewable CO2, renewable energy use, DOLS trade openness, and financial development. Positive relation for (2016) energy countries trade openness, financial environmental sustainability and conventional energy consumption. development Omri et al. (2015) 1990–2011 12 MENA CO2, trade, GDP, FDI SEM for panel Bidirectional causality between CO2 and economic growth. Similarly countries data bidirectional causality between trade openness and financial development. CO2, GDP, trade, FDI, Aller et al. (2015) 1996–2010 Developed 3SLS Network effects harm environmental quality of developed countries countries and centrality but improve the environment of developing countries. developing countries CO2, GDP, trade openness, Panel Least Renewable electricity use, natural resource and energy innovation Balsalobre-Lorente 1985–2016 Italy, Spain, Square (PLS) improve environment. Trade openness, interaction of renewable et al. (2018) Germany, France, renewable electricity use, electricity and economic growth deteriorate environmental quality UK energy innovation Bekun et al. (2018) 1996–2014 16 selected EU CO2, GDP, renewable and PMG-ARDL Non-renewable energy consumption and economic growth increase countries CO2. Renewable energy consumption declines CO2 emissions. nonrenewable energy consumption B) Studies focusing on the relationship between CO2 emissions and foreign direct investment (FDI) in principal bootstrapping FDI has positive and energy research innovations have negative impact Shahbaz et al. 1955–2016 France CO2, FDI, GDP, energy ARDL on French CO2 emissions. (2018) consumption, energy research innovation Bu et al. (2019) 2005–2007 China FDI, energy intensity OLS FDI firms have lower energy intensity than their local counterparts. Sun et al. (2017) 1980–2012 China CO2, GDP, energy use, FDI, ARDL CO2 emission increase by 0.058% with 1% increase in inward FDI. trade openness STIRPAT FDI contribute to CO2 emissions reduction in China Zhang and Zhou 1995–2010 China CO2, FDI (2016) Panel Strong bidirectional causality between CO2 emissions and FDI Pao and Tsai 1980–2007 BRIC CO2, FDI co-integration (2011) C) Studies focusing on the relationship between CO2 emissions and energy consumptions in principal Balaguar and 1874–2011 Spain CO2, energy price, GDP ARDL Manuel (2016) ARDL Jayanthakumaran 1971–2007 China, India CO2, energy, GDP et al. (2012) OLS with FE López-Menéndez 1996–2010 EU CO2, energy consumption and RE et al. (2014) ARIMA, ARDL Park and Hong 1991–2011 South Korea CO2, energy consumption (2013) FMOLS, DOLS Bilgili et al. (2016) 1977–2010 OECD CO2, renewable energy consumption

Energy price increase reduce CO2 emissions Energy contributes to CO2 in China and India Renewable energy consumptions mitigate CO2 emissions Energy consumption and CO2 emissions moving together Renewable energy consumptions mitigate CO2 emissions

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France. Moreover, economic growth and CO2 emissions nexus is reported as an inverted U-shaped, validating the environmental Kuznets curve (EKC). Bu et al. (2019) also report that in the chemical industry, FDI firms have lower energy intensity, but they find no difference between FDI and non-FDI in the textile industry. Using the ARDL model, Sun et al. (2017) examine the impact of FDI inflows, trade openness, energy use, and economic growth on CO2 emissions and show the positive relationship of FDI inflows with CO2 emissions, which validates the pollution haven hypothesis in China. They claim that an increase in CO2 emissions come from the manufacturing, mining, and electricity industries, which have been relocated into China from developed countries. Contrastingly, firm-level data used by Bu et al. (2019) to investigate the FDI and energy intensity nexus in China record a negative coefficient for FDI. Locally owned firms without foreign investments have relatively higher energy intensity than firms with foreign investments. The technology gap tends to narrow between foreign and local firms with FDI, as global technology is transferred effectively and efficiently. Zhang and Zhou (2016) show that FDI inflows contribute to the transfer of new technology and to the reduction of CO2 emissions in China. Pao and Tsai (2011) identify a strong bidirectional causal relationship between CO2 emissions and FDI. By using the panel co-integration technique, this research identifies this relationship for energy-dependent BRIC countries. This study suggests that FDI would attract both green foreign technologies and emitting industries. The third group of studies reported in part C of Table 1 focuses on the relationship between CO2 emissions and energy consumption. Balaguer and Cantavella (2016) use real oil price as an indicator related to energy consumption in Spain and identify that oil price affects CO2 emissions. Jayanthakumaran et al. (2012) investigate the relationship between energy consumption and CO2 emissions in China and India. In both countries, CO2 emissions are influenced by energy consumption. Park and Hong (2013) study the possible way to reduce CO2 emissions in South Korea to implement the Kyoto Protocol. They notice that there is a significant correlation between economic growth and fossil fuels, which emits CO2 in South Korea from 1991 to 2011. However, several studies mention that renewable energy consumption could mitigate CO2 emissions (Bilgili et al. (2016), for 17 OECD countries; López-Menéndez et al. (2014), for 27 European Union countries). 3. Empirical analysis 3.1. Data In this study, we employ panel data from 52 countries. Our sample covers the period 1991 to 2014. The full sample is divided into two groups: high- and low-income countries using the World Bank's income classification, since our interest is on the examination of the critical roles of countries' development stages. The low-income group comprises low and lower-middle-income countries, while the high-income group is made up of the high- and upper-middle-income group countries. Table 2 shows the list of sample countries for the various categories. This study uses six variables: the log of CO2 emissions (lnCO2), the log of real gross domestic product (GDP) per capita (lnRGDPPC), the log of primary energy consumption (lnEC), the share of renewable energy consumption to total energy consumption (RREC), the ratio of trade flows (exports and imports) to GDP (TRADE), and the ratio of the net inflows of FDI to GDP (FDI). CO2 emissions are measured by the amount of those stemming from the burning of fossil fuels and the manufacture of cement in terms of kilotons. Real GDP per capita is measured as expenditure-side real GDP at chained PPPs (millions of 2011 US dollars) per population. The ratio of trade flows to GDP captures trade openness. Our data are taken from the Penn World Table, International Energy Agency (IEA), and the World Development Indicators (WDI). Availability is principally responsible for the restricted period of data series as well as the relevant country selection for the study. Table 3 represents descriptive statistics of the variables for each of the two groups

Table 2 List of sample countries. Low-income countries Azerbaijan Bangladesh Bulgaria Belarus Brazil China Colombia

Ecuador Egypt Indonesia India Iran Kazakhstan Sri Lanka

High-income countries Argentina Germany Australia Denmark Austria Spain Belgium Finland Canada France Switzerland United Kingdom Chile Greece Czech Republic Ireland

Morocco Mexico Malaysia Pakistan Peru Philippines Romania

Russia Thailand Turkey Ukraine Venezuela Vietnam South Africa

Israel Italy Japan Korea Luxembourg Netherlands Norway New Zealand

Poland Portugal Saudi Arabia Singapore Sweden United States

(low- and high-income countries) during the period from 1991 to 2014. Table 4 shows the correlation matrix of the variables. 3.2. Model specification This study discusses how trade and FDI relate to CO2 emission at the country level by evaluating the long- and short-run relationships among CO2 emissions, trade, and FDI, with the consideration of total energy consumption and renewable energy penetration. Subsequent to the works of Bekun et al. (2018) and Bekun et al. (2019), we estimate the following empirical equation: ln CO2it ¼ β0 þ β1 ln RGDPPCit þ β2 ln ECit þ β3 RRECit þ β4 TRADEit þ β5 FDIit þ ϵit ;

ð1Þ

where CO2it is CO2 emissions, RGDPPCit is per capita real GDP, RRECit, is the ratio of renewable energy consumption to total energy consumption (renewable energy penetration), EC it is primary energy consumption, TRADEit is the ratio of trade flows to GDP (trade openness), and FDIit stands for the ratio of net FDI inflows to GDP in the respective country i at year t. ϵit is the error term, and β0-β5 are the coefficients. Our main interest in this study is on how trade openness (TRADE) and FDI inflows (FDI) are related to CO2 emissions. We also incorporate primary energy consumption (EC) and renewable energy penetration (RREC) into the model, since many studies such as Balsalobre-Lorente et al. (2018) and Shahbaz et al. (2015) argue that these two variables are crucial determinants of CO2 emissions. Moreover, we include real GDP per capita (RGDPPC) into the model

Table 3 Descriptive statistics. Variables

Mean

Std. Dev.

Min.

Max.

Low-income countries 528 ln CO2 lnRGDPPC 528 lnEC 528 RREC 528 TRADE 528 FDI 528

Obs.

4.7880 8.7519 3.8726 11.9793 0.6376 0.0254

1.3923 0.6518 1.3311 10.3705 0.3841 0.0271

1.4223 7.1747 0.7437 0.0375 0.1564 −0.0276

9.1277 10.0501 7.9975 39.2974 2.2041 0.3124

High-income countries ln CO2 720 lnRGDPPC 720 lnEC 720 RREC 720 TRADE 720 FDI 720

4.8214 10.3294 4.0505 13.6488 0.8096 0.0417

1.5521 0.4154 1.4724 18.0889 0.6076 0.1012

0.7537 8.8606 0.5175 0.0000 0.1375 −0.4346

8.6795 11.3215 7.7497 83.1687 4.4160 1.9807

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(Pesaran et al., 1999) and ARDL(p,q) models:

Table 4 Correlation matrix. ln CO2 Low-income countries ln CO2 1.0000 t-Statistic – Prob. – lnRGDPPC 0.2054 t-Statistic 4.8124 Prob. 0.0000 lnEC 0.9926 t-Statistic 186.8347 Prob. 0.0000 RREC −0.2996 t-Statistic −7.2015 Prob. 0.0000 TRADE −0.1003 t-Statistic −2.3115 Prob. 0.0212 FDI −0.0072 t-Statistic −0.1658 Prob. 0.8684 High-income countries ln CO2 1.0000 t-Statistic – Prob. – lnRGDPPC 0.0971 t-Statistic 2.6148 Prob. 0.0091 lnEC 0.9742 t-Statistic 115.6443 Prob. 0.0000 RREC −0.4918 t-Statistic −15.1357 Prob. 0.0000 TRADE −0.2508 t-Statistic −6.9413 Prob. 0.0000 FDI −0.1040 t-Statistic −2.8015 Prob. 0.0052

5

lnRGDPPC

lnEC

RREC

TRADE

FDI

lnCO2it ¼

p X

αij lnCO2it− j þ

j¼1

1.0000 – – 0.2243 5.2784 0.0000 0.0421 0.9671 0.3339 0.2918 6.9972 0.0000 0.1723 4.0116 0.0001

1.0000 – – −0.2162 −5.0776 0.0000 −0.1295 −2.9951 0.0029 −0.0047 −0.1088 0.9134

1.0000 – – −0.2775 −6.6237 0.0000 0.0404 0.9265 0.3546

1.0000 – – 0.3412 8.3249 0.0000

1.0000 – –

q X

Xit− j γij þ μ i þ ηit ;

where Xit is a vector of the independent variables (lnRGDPPCi, t, lnECi, t, RRECi, t, TRADEi, t, and FDIi, t) with equal lags across individual crosssectional units (countries); p is the lags of the dependent variable; q is the lags of the independent variables; μi is the cross-section (country) fixed effects; ηit is the error term; and αij and γij are the coefficients. Eq. (2) allows for different coefficients across countries. When the variables in Eq. (2) are integrated of order one (i.e., I(1)) and are cointegrated, the error term would follow the process that is integrated of order zero (i.e., I(0)) so that all countries have the long-run equilibrium relationship among the variables. A key feature is that the time paths of the cointegrated variables reflect the deviation from their long-run equilibrium. In an error correction form, the short-run adjustment of the variables can be explained by the deviation from the longrun equilibrium: ΔlnCO2it ¼ ϕi ECTit þ

p−1 X

ψij ΔlnCO2it− j þ

j¼1

1.0000 – – 0.1639 4.4509 0.0000 0.1771 4.8231 0.0000 0.2801 7.8180 0.0000 0.1315 3.5543 0.0004

q−1 X

ΔXit− j βij þ εit

1.0000 – – −0.1096 −2.9535 0.0032 −0.0420 −1.1274 0.2600

1.0000 – – 0.3273 9.2805 0.0000

1.0000 – –

to control for a country's income level. The well-known environmental Kuznets curve (EKC) argument claims significant differences in the income-emission relationship between developed and developing countries. The fragmented two-tier emission mitigation policy in the Kyoto Protocol suggests that countries' mitigation policies should be based on the common but differentiated responsibilities, depending on countries' development stages (Peters et al., 2011). These arguments emphasize that countries' development stages play an important role in relating to the income-emission relationship. To account for such an issue, this study classifies our sample countries into two country groups: low- and high-income countries. This division enables us to examine how development stages are associated with the relationship among the variables and to evaluate their possible differences between the two groups. If a long-run equilibrium relationship between the dependable and independent variables is assumed with their short-run adjustment processes, ordinary least squares (OLS) estimators are asymptotically biased. That is, the distribution depends on nuisance parameters (regressors) which are not part of the true data-generating process, introducing endogeneity and serial correlation that are undesirable. Alternatively, GMM and dynamic fixed effects approaches would suffer from inconsistent estimations (Arellano and Bover, 1995). Panel data models with individual effects applying standard autoregressive distributive lag (ARDL) models may suffer from biased estimators due to the possible correlation between the white noise term and the mean-difference independent variables. To overcome these concerns, this study follows Sarkodie and Strezov (2018) and Bekun et al. (2019), and applies the PMG-ARDL model, which is a combination of PMG estimators

ð3Þ

j¼0

ECTit ¼ ln CO2it−1 −Xit θ 1.0000 – – −0.3054 −8.5942 0.0000 −0.2919 −8.1787 0.0000 −0.1229 −3.3176 0.0010

ð2Þ

j¼0

ð4Þ

where Δ is the difference operator; the error correction term is ECTi, t; the error term is εi, t; and ϕi, ψij, βij, and θ are the coefficients. The parameters, ϕi and θ, represent the short-run adjustment and the long-run coefficients. Pesaran and Smith (1995) and Pesaran et al. (1999) show that the ARDL model can be reliably estimated by employing the mean group (MG) estimator, which estimates the parameters for each country and then averages for the group. They also suggest that the assumption of the homogeneous long-run coefficients across countries enables the PMG estimators to be more efficient and to allow the short-run coefficients to vary across countries but with the homogenous long-run coefficients. In addition, the panel ARDL approach can be applied even when the variables follow different orders of integration or a mixture of both (Pesaran et al., 1999). The final phase of the study is to evaluate the direction of the relationship between variables by employing the panel Granger causality analysis. The standard Granger causality test can be applied to analyze the panel data under the homogenous panel assumption with identical intercepts and slope coefficients (Granger, 1969). Although many studies use the standard causality tests, recent studies including Koçak and Şarkgüneşi (2017) have applied panel causality tests based on the heterogeneous assumption, initiated by Dumitrescu and Hurlin (2012), to discuss causality in various energy and environmentally related fields. Since countries are generally heterogeneous in terms of their energy use patterns as well as trade-investment structures, this study also applies the heterogeneous Dumitrescu-Hurlin panel causality test, which accounts for two-dimensional heterogeneities: the heterogeneity of the regression model for Granger causality test and the heterogeneity of the causality relationship. Specifically, Dumitrescu and Hurlin (2012) propose the model: K

ðkÞ

K

ðkÞ

yit ¼ αi þ ∑k¼1 γi yit−k þ ∑k¼1 βi xit−k þ εit ;

ð5Þ

where x and y are stationary variables in country i at year t; αi is the (2) (K) country fixed effect; βi = (β(1) i , βi , ⋯, βi ) are slope parameters; and (2) (K) γi = (γ(1) , γ , ⋯, γ ) are lag parameters. The model allows β(k) and i i i i (k) γi to vary across countries but are assumed to be fixed over time. The null hypothesis H0 of the Dumitrescu-Hurlin panel causality test is no causal relationship for any given cross-section of the panel, known as homogeneous noncausality hypothesis, i.e., H0: βi = 0 for any i = 1, 2, ⋯, N. The alternative hypothesis H1 is the existence of a causal

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relationship in at least one cross-section unit, i.e., H1: βi = 0 for any i = 1, ⋯, N1 and any i = N1 + 1, ⋯, N. The null hypothesis yields a homogeneous result, while the alternative hypothesis yields a heterogeneous result. Dumitrescu and Hurlin (2012) calculate the individual Wald statistics for each cross-section and then calculate the test statistic for the panel by taking the average of all individual Wald statistics: WHNC NT ¼

N 1X W ; N i¼1 iT

ð6Þ

where WiT represents the individual Wald statistic for country i in year T, WHNC NT is the average Wald test statics over all countries, and N is the number of countries. Given the test statistic WHNC NT , Dumitrescu and Hurlin (2012) show its limiting distribution and derive an alternative test statistic: ZHNC N;T ¼

rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  N  HNC WN;T −K →Nð0; 1Þ 2K

ð7Þ

where ZHNC N, T represents the alternative test statics and K is the number of lags. Dumitrescu and Hurlin (2012) recommend WHNC N, T if the time dimension is lower than the cross-section dimension and ZHNC N, T if the time dimension is higher than the cross-section dimension. Since all variables must be stationary to investigate the Granger causality, we use the first differences of variables (lnCO2it, lnRGDPPCit, lnECit, RRECit, TRADEit, and FDIit). Following past studies on time series analysis, our study first conducts the stationarity tests to confirm the stationarity of the variables at the level and at the first-difference. Then, the application of Pedroni (1999, 2004) and Kao (1999) panel cointegration tests are made to check whether long-run relationships exist among the variables. Once the long-run relationship is verified, the evaluation of the long-run estimates with short-run dynamics by PMG-ARDL estimation methods is conducted in line with Pesaran and Smith (1995) and Pesaran et al. (1999) recommendations. Moreover, this study discusses causality in Granger's sense by applying the method of Dumitrescu and Hurlin (2012).

4. Results and discussion 4.1. Panel unit root and cointegration tests To ascertain the stationarity of the variables, this study performs five different panel unit root tests: Levin-Lin-Chu (LLC) (Levin et al., 2002), Breitung (Breitung and Das, 2005), Im-Pesaran-Shin (IPS) (Im et al., 2003), PP-Fisher (Phillips and Perron, 1988) and Fisher-ADF (ADF) (Choi, 2001) tests. The LLC test proposed by Levin et al. (2002) assumes the process of a common unit root across countries. The IPS test proposed by Im et al. (2003) and the two Fisher-type tests are the extension of the augmented Dicky and Fuller (Choi, 2001) and Phillips and Perron tests (Phillips and Perron, 1988), which allow for individual unit root processes across countries. The null hypothesis of all tests is the existence of a unit root. Table 5 presents the results for the panel unit root tests for all variables at the level and first difference. The results show non-stationarity at the level and stationarity at the first difference for all variables except FDI. The FDI variable shows stationarity both at the level and at the first difference. The panel unit root tests confirm that all variables are integrated of order either zero or one, i.e., I(0) or I(1), which allows us to apply panel ARLD analysis. The next step is to perform panel cointegration tests. Once we confirm that the model has cointegrated variables, we can support the application of panel ARDL models to evaluate the long-run relationship among the variables with their short-run dynamics. This study applies the Kao residual cointegration (Kao, 1999) tests with the null hypothesis of no cointegration. Table 6 presents the results which support that there is a cointegration among the variables used in this study (lnCO2, lnRGDPPC, lnEC, RREC, TRADE, and FDI) for each group of low- and high-income countries. This suggests that long-run relationships exist among the variables. As an alternate approach, we also conduct the Pedroni panel cointegration tests (Pedroni, 1999, 2004). The Pedroni panel cointegration test derives several test statistics to test for the null hypothesis of no cointegration. The results in Table 7 present that five or six test statistics are statistically significant for each group of low- and high-income countries, although some test statistics show less significance. The Pedroni panel cointegration tests generally

Table 5 Panel unit root tests (1991–2014). Null: unit root ADF - fisher Low-income countries 59.3861⁎ lnCO2 ΔlnCO2 216.1440⁎⁎⁎ lnRGDPPC ΔLRGDPPC lnEC ΔlnEC RREC ΔRREC TRADE ΔTRADE FDI ΔFDI

24.6151 72.1488⁎⁎⁎ 42.5458 189.8890⁎⁎⁎ 54.4957 298.0180⁎⁎⁎ 48.0270 250.5220⁎⁎⁎ 69.7736⁎⁎⁎ 253.0090⁎⁎⁎

High-income countries lnCO2 20.5657 ΔlnCO2 331.4850⁎⁎⁎ lnRGDPPC 32.4684 ΔlnRGDPPC 164.4300⁎⁎⁎ lnEC ΔlnEC RREC ΔRREC TRADE ΔTRADE FDI ΔFDI

49.1136 414.0120⁎⁎⁎ 26.7774 320.8430⁎⁎⁎ 66.8052 367.4320⁎⁎⁎ 114.1870⁎⁎⁎ 542.1970⁎⁎⁎

[0.061] [0.000] [0.992] [0.005] [0.534] [0.000] [0.133] [0.000] [0.313] [0.000] [0.008] [0.000]

[1.000] [0.000] [0.999] [0.000] [0.841] [0.000] [0.999] [0.000] [0.255] [0.000] [0.000] [0.000]

Im, Pesaran and Shin

PP - fisher

−0.7259 −11.5254⁎⁎⁎ 2.4977 −2.6174⁎⁎⁎ 0.5120 −9.9065⁎⁎⁎

[0.234] [0.000] [0.994] [0.004] [0.696] [0.000] [0.735] [0.000] [0.377] [0.000] [0.008] [0.000]

63.2977⁎⁎ 746.7090⁎⁎⁎ 24.0035 119.5920⁎⁎⁎ 47.4034 583.0700⁎⁎⁎ 94.5138⁎⁎⁎ 990.7250⁎⁎⁎ 66.9785⁎⁎ 534.1200⁎⁎⁎ 107.8030⁎⁎⁎ 1352.0300⁎⁎⁎

[0.030] [0.000] [0.994] [0.000] [0.336] [0.000] [0.000] [0.000] [0.014] [0.000] [0.000] [0.000]

[1.000] [0.000] [1.000] [0.000] [1.000] [0.000] [1.000] [0.000] [0.189] [0.000] [0.001] [0.000]

47.1906 742.8450⁎⁎⁎ 37.2626 343.2820⁎⁎⁎ 87.6737⁎⁎ 938.9520⁎⁎⁎

[0.886] [0.000] [0.991] [0.000] [0.011] [0.000] [0.624] [0.000] [0.034] [0.000] [0.000] [0.000]

0.6292 −17.0469⁎⁎⁎ −0.3128 −14.2781⁎⁎⁎ −2.4050⁎⁎⁎ −14.7675⁎⁎⁎ 7.0327 −15.6705⁎⁎⁎ 3.3553 −6.9663⁎⁎⁎ 4.2597 −19.2515⁎⁎⁎ 9.5553 −14.5316⁎⁎⁎ −0.8828 −16.1779⁎⁎⁎ −3.0717⁎⁎⁎ −25.8182⁎⁎⁎

50.1426 765.7180⁎⁎⁎ 81.4016⁎⁎ 534.7180⁎⁎⁎ 486.2890⁎⁎⁎ 2424.9100⁎⁎⁎

Notes: ⁎⁎⁎, ⁎⁎, and ⁎ represent the significance at the 1%, 5%, and 10% levels, respectively.

Levin, Lin & Chu test −1.2610 −12.2402⁎⁎⁎ 0.0142 −4.0075⁎⁎⁎ −0.2754 −10.8268⁎⁎⁎ 0.5762 −19.3233⁎⁎⁎ −2.0238⁎⁎ −18.0620⁎⁎⁎ −2.2290⁎⁎ −16.6182⁎⁎⁎ 4.3287 −15.0032⁎⁎⁎ 2.0790 −8.0743⁎⁎⁎ 1.7278 −21.0204⁎⁎⁎ 9.1063 −15.0927⁎⁎⁎ −1.6954⁎⁎ −19.7171⁎⁎⁎ −2.4377⁎⁎⁎ −24.7503⁎⁎⁎

[0.104] [0.000] [0.506] [0.000] [0.392] [0.000] [0.718] [0.000] [0.023] [0.000] [0.013] [0.000]

[1.000] [0.000] [0.981] [0.000] [0.958] [0.000] [1.000] [0.000] [0.045] [0.000] [0.007] [0.000]

Breitung t-stat −1.2880⁎ −5.5089⁎⁎⁎ 2.1114 −5.3430⁎⁎⁎ −0.4365 −4.8221⁎⁎⁎ 1.1910 −9.4602⁎⁎⁎ −0.2255 −11.7779⁎⁎⁎ −3.5545⁎⁎⁎ −12.3707⁎⁎⁎ 6.8094 −6.1980⁎⁎⁎ 1.3095 −6.5015⁎⁎⁎ 6.8148 −4.6180⁎⁎⁎ 10.3541 1.8750 −1.7119⁎⁎ −11.2307⁎⁎⁎ −3.3042⁎⁎⁎ −17.0533⁎⁎⁎

[0.099] [0.000] [0.983] [0.000] [0.331] [0.000] [0.883] [0.000] [0.411] [0.000] [0.000] [0.000]

[1.000] [0.000] [0.905] [0.000] [1.000] [0.000] [1.000] [0.970] [0.044] [0.000] [0.001] [0.000]

O.K. Essandoh et al. / Science of the Total Environment 712 (2020) 136437 Table 6 Kao panel cointegration tests (1991–2014).

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Table 8 Pooled mean group with autoregressive distributed lag model: PMG-ARDL (1,1,1,1,1). Statistic

Prob.

Low-income countries ADF Residual variance HAC variance

−4.0461 0.0002 0.0002

0.0000

High-income countries ADF Residual variance HAC variance

−4.5615 0.0004 0.0004

0.0000

provide evidence of the presence of a cointegration relationship. In sum, the two cointegration tests suggest that there is a long-run relationship between the variables. 4.2. Long-run estimates and short-run dynamics

Low-income countries

High-income countries

Coefficient

Standard error

Coefficient

Standard error

0.0113 0.0111 0.0012 0.0103 0.1220

−0.0054 0.9883⁎⁎⁎ −0.0130⁎⁎⁎ −0.0210⁎⁎

0.0114 0.0163 0.0006 0.0087 0.0124

0.0542 0.0127 0.0634 0.0007 0.0128 0.0780 0.0327

−0.2583⁎⁎⁎ 0.0289 0.7469⁎⁎⁎

Long run equation lnRGDPPC 0.0625⁎⁎⁎ lnEC 0.9843⁎⁎⁎ RREC −0.0097⁎⁎⁎ TRADE −0.0160 FDI 0.2481⁎⁎ Short run equation ECT (−1) −0.1670⁎⁎⁎ lnRGDPPC −0.0013 lnEC 0.8830⁎⁎⁎ RREC −0.0113⁎⁎⁎ TRADE FDI Constant No. of obs.

0.0199 0.0117 0.0870⁎⁎⁎ 528

−0.0044

0.1281 0.0014 −0.0583 0.2818⁎⁎⁎ 720

0.0469 0.0242 0.0650 1.1067 0.0315 0.0448 0.0561

Notes: ⁎⁎⁎, ⁎⁎, and ⁎ represent the significance at the 1%, 5%, and 10% levels, respectively.

This study advances to examine the long-run estimates and shortrun dynamics by applying the PMG-ARDL approach. The application of the PMG estimator alone does not provide good accuracy for differences between countries. The PMG-ARDL allows for the homogeneity only in the long-run estimation, while the short-run estimators can vary from country to country. Table 8 shows the results of the PMG-ARDL estimations. Concerning the relationship between trade openness (TRADE) and CO2 emissions (lnCO2), the estimated results of the long-run equation show a significantly negative coefficient of TRADE for high-income countries, but an insignificant coefficient of TRADE for low-income countries. A 1% increase in trade openness (the ratio of trade flows to GDP) is associated with a 0.021% decrease in CO2 emissions for highincome countries. Developed countries often impose stringent environmental regulations on energy-intensity production units. Firms in developed countries comply with these regulations by outsourcing production units and importing final energy-intensity products, which helps sustain the environment in developed countries. Our results are in line with the finding of Kim et al. (2018) that trade leads to a reduction in CO2 emissions for advanced countries. The results of the shortrun equation present the insignificant short-run relationship between CO2 emissions and TRADE for high- and low-income countries, although the directions are similar to those of the long-run relationship. Regarding the relationship between FDI inflows (FDI) and CO2 emissions (lnCO2), the results of the long-run equation reveal a significantly positive coefficient of FDI for low-income countries, but an insignificant

coefficient of FDI for high-income countries. A 1% increase in the ratio of FDI inflows to GDP is related to a 0.248% increase in CO2 emissions for low-income countries. This suggests that developing countries receiving more FDI tend to experience environmental deterioration in terms of CO2 emissions. With the prevalence of global supply chains, developing countries attract FDI inflows related possibly to polluting industries and increasing CO2 emissions. These countries generally have their weak environmental standards and inadequate environmental management systems without advanced green technologies mitigating polluting trends, so that developed countries transfer or outsource their production units, including emission embodied products, to developing countries. The long-run positive relationship between FDI inflows and CO2 emissions for developing countries supports the pollution haven hypothesis, which coincides with the findings of several studies, including Sun et al. (2017) for China, Sarkodie and Strezov (2019) for the top 5 greenhouse gases emitting countries, and Sapkota and Bastola (2017) for 14 Latin American countries. The estimated coefficients of TRADE and FDI in the long-run equations imply that CO2 emissions are more sensitive to FDI inflows than trade openness. Moreover, the estimation of the short-run equation shows the insignificant short-run relationship between CO2 emissions and FDI for high- and low-income countries. Table 8 also shows the long- and short-run relationships between CO2 emissions and other variables (lnRGDPPC, lnEC, and RREC) for high- and low-income countries. The results reveal that CO2 emissions

Table 7 Pedroni panel cointegration tests (1991–2014). Within-dimension (panel statistics)

Low-income countries Pedroni (1999)

Pedroni (2004) Weighted Statistic

High-income countries Pedroni (1999)

Pedroni (2004) Weighted Statistic

Between-dimension (individual statistics)

Test

Statistic

Probability

Test

Statistic

Prob.

Panel v-statistic Panel rho-statistic Panel PP-statistic Panel ADF-statistic Panel v-statistic Panel rho-statistic Panel PP-statistic Panel ADF-statistic

−0.7283 3.5395 −2.3097 −4.9520 −1.6947 3.1703 −2.5345 −3.6108

0.7668 0.9998 0.0105 0.0000 0.9549 0.9992 0.0056 0.0002

Group rho-statistic Group PP-statistic Group ADF-statistic

4.6278 −2.4976 −4.7210

1.0000 0.0063 0.0000

Panel v-statistic Panel rho-statistic Panel PP-statistic Panel ADF-statistic Panel v-statistic Panel rho-statistic Panel PP-statistic Panel ADF-statistic

−1.0088 4.4232 −1.4666 0.5682 −1.3278 3.7312 −4.7701 −3.3009

0.8435 1 0.0712 0.7151 0.9079 0.9999 0 0.0005

Group rho-statistic Group PP-statistic Group ADF-statistic

5.4755 −6.3698 −3.1540

1.0000 0.0000 0.0008

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O.K. Essandoh et al. / Science of the Total Environment 712 (2020) 136437

Developing Countries Relationship

Developing Countries Relationship

Fig. 1. Long-run relationships among CO2 emissions, real GDP per capita, primary energy consumption, renewable energy consumption, trade openness, and FDI for developed (highincome) and developing (low-income) countries.

and real GDP per capita have a positive long-run relationship for lowincome countries and a negative, although less significant, long-run relationship for high-income countries. A 1% increase in real GDP per capita is associated with a 0.063% increase in CO2 emissions for lowincome countries and with a 0.005% decrease in CO2 emissions for high-income countries in the long-run. Given that a country's real GDP per capita measures its income level, this finding supports the EKC argument that the relationship between the income level and CO2 emissions is positive at an early development stage but is negative at the mature stage of development. In addition, the results present a positive

relationship between CO2 emissions and primary energy consumption in the long- and short-run for low- and high-income countries. A 1% increase in primary energy consumption is associated with a 0.984–0.988% increase in CO2 emissions in the long-run. Moreover, renewable energy penetration or the share of renewable energy consumption, is negatively correlated with CO2 emissions in the long-run for low- and high-income countries. This is consistent with the argument that renewable energy use would help reduce CO2 emissions in the long-run. Fig. 1 summarizes the long-run relationship among CO2 emissions, trade openness, FDI inflows, real GDP per capita, primary

Table 9 Dumitrescu and Hurlin panel causality tests. Null hypothesis

DLRGDPPC does not homogeneously cause DLCO2 DLCO2 does not homogeneously cause DLRGDPPC DLEC does not homogeneously cause DLCO2 DLCO2 does not homogeneously cause DLEC DRREC does not homogeneously cause DLCO2 DLCO2 does not homogeneously cause DRREC DTRADE does not homogeneously cause DLCO2 DLCO2does not homogeneously cause DTRADE DFDI does not homogeneously cause DLCO2 DLCO2 does not homogeneously cause DFDI DLEC does not homogeneously cause DLRGDPPC DLRGDPPC does not homogeneously cause DLEC DRREC does not homogeneously cause DLRGDPPC DLRGDPPC does not homogeneously cause DRREC DTRADE does not homogeneously cause DLRGDPPC DLRGDPPC does not homogeneously cause DTRADE DFDI does not homogeneously cause DLRGDPPC DLRGDPPC does not homogeneously cause DFDI DRREC does not homogeneously cause DLEC DLEC does not homogeneously cause DRREC DTRADE does not homogeneously cause DLEC DLEC does not homogeneously cause DTRADE DFDI does not homogeneously cause DLEC DLEC does not homogeneously cause DFDI DTRADE does not homogeneously cause DRREC DRREC does not homogeneously cause DTRADE DFDI does not homogeneously cause DRREC DRREC does not homogeneously cause DFDI DFDI does not homogeneously cause DTRADE DTRADE does not homogeneously cause DFDI

Low-income countries

High-income countries

W-stat.

Prob.

Causality

W-stat.

Prob.

Causality

1.7901⁎ 1.6946 1.3652 1.1526 1.4603 0.7025 1.0043 1.1287 2.2185⁎⁎⁎

0.0629 0.1100 0.4864 0.9096 0.3389 0.2631 0.7698 0.9615 0.0024 0.9768 0.4296 0.1327 0.2635 0.7693 0.6097 0.2044 0.9615 0.9156 0.7568 0.2749 0.7987 0.1223 0.1552 0.7707 0.4504 0.0143 0.3160 0.9811 0.3249 0.9063

RGDPPC → CO2

2.4383⁎⁎⁎ 1.5471 1.8495⁎⁎

0.0000 0.1632 0.0182 0.3050 0.0063 0.0000 0.9992 0.7584 0.9749 0.6370 0.6680 0.0000 0.0164 0.9110 0.2275 0.7848 0.0250 0.2770 0.0882 0.0099 0.0536 0.2210 0.0000 0.6293 0.5124 0.1424 0.0158 0.0793 0.0125 0.0696

RGDPPC → CO2

1.1217 1.3995 1.6600 0.7029 1.2182 1.2975 1.5744 1.1287 1.1498 0.9981 0.7125 1.2042 1.6752 1.6301 1.0047 0.8356 2.0050⁎⁎ 1.4772 1.1024 1.4705 1.1541

Notes: ⁎⁎⁎, ⁎⁎, and ⁎ represent the significance at the 1%, 5%, and 10% levels, respectively.

FDI → CO2

1.4318 1.9659⁎⁎⁎ 2.6690⁎⁎⁎ 1.1114 1.0150 1.1013 1.2587 0.9770 2.5572⁎⁎⁎ 1.8612⁎⁎ 1.0762 1.4884 1.1965 1.8120⁎⁎ 1.4509 1.6441⁎ 1.9169⁎⁎⁎ 1.7145⁎ 0.7285 2.8525⁎⁎⁎

RREC → TRADE

0.9602 0.9063 0.6526 1.8656⁎⁎ 1.6597⁎ 1.8916⁎⁎ 1.6783⁎

EC → CO2 RREC ← → CO2

RGDPPC → EC RREC → RGDPPC

FDI → RGDPPC RREC ← → EC TRADE → EC FDI → EC

FDI ← → RREC FDI ← → TRADE

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energy consumption, and renewable energy penetration for lowincome (developing) and high-income (developed) countries. 4.3. Granger causality tests Table 9 indicates the results of panel causality tests proposed by Dumitrescu and Hurlin (Dumitrescu and Hurlin, 2012). Concerning the group of high-income countries, the results show unidirectional or one-way Granger causal relationships from lnRGDPPC to lnCO2, from lnEC to lnCO2, from lnRGDPPC to lnEC, from TRADE to lnEC, from FDI to lnEC, from RREC to lnRGDPPC, and from FDI to lnRGDPPC. The analysis also reveals bidirectional or two-way Granger causal relationships between lnCO2 and RREC, between lnEC and RREC, between FDI and RREC, and between FDI and TRADE. Related to our main variables in this study (lnCO2, TRADE, and FDI), the tests show no clear evidence of the causal relationships between lnCO2 and TRADE and between lnCO2 and FDI, which are consistent with less significant results of the short-run equation of the PMG-ARDL model in the previous subsection. In addition, the one-way causal relationships from lnRGDPPC and lnEC to lnCO2 could be explained by the argument that intensified energy consumption associated with economic growth would affect CO2 emissions. Moreover, the two-way causal relationship between lnCO2 and RREC could be justified by the argument that in developed countries, renewable energy penetration would be one possible factor determining CO2 emissions, and changes in CO2 emissions would affect renewable energy penetration possibly though individuals' and firms' environmental behaviors as well as regulators' environmental policy. For low-income countries, the results show that the one-way Granger causality is observed from RREC to TRADE, from lnRGDPPC to lnCO2, and from FDI to lnCO2 in the short-run. The one-way causality from FDI to lnCO2 supports the argument that firms in developed countries tend to transfer their production units from their home countries with stringent environmental standards toward developing countries with low environmental standards. This is consistent with the pollution haven hypothesis, which helps shift pollution liabilities of consumption in developed countries (Borck and Pflüger, 2019). 5. Conclusion Applying the PMG-ARDL approach, this study has estimated the equilibrium (long-run) and short-run relationships among CO2 emissions, trade openness, FDI inflows, real GDP per capita, renewable energy consumption, and primary energy consumption. We have argued that these variables are strongly inter-related and the relationship should be examined in an integrated framework. The results have shown clear evidence of the existence of fairly robust relationships among these variables. The empirical results have suggested clear differences between low-income (developing) and high-income (developed) countries. For low-income countries, CO2 emissions have a significantly positive long-run relationship with FDI inflows, but less clear result related to a long-run relationship with trade openness. In contrast, for high-income countries, CO2 emissions have a significantly negative long-run relationship with trade openness, but less clear result related to a long-run relationship with FDI inflows. In keeping with our estimated results related to the trade-emission relationships, Kim et al. (2018) also mention the beneficial effects of trade openness for the reduction of CO2 emissions for developed countries. Developed countries have an efficient mechanism for the promotion of high-technology industries, so that environmental regulations in these countries are effective to control energy-intensive production. Developed countries outsource the production of energy-intensive units to developing countries with less stringent environmental policies and import the final energy-intensive products as part of the global supply chains. The favorable environmental effect of trade openness for developed countries may be achieved at the expense of developing countries. Since trade liberalization is an essential agenda for the

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regulators, our findings provide relevant information to consider trade management and policy formulation with better environmental quality. Our results have also shown that a 1% increase in FDI inflows is associated with approximately 0.25% increase in CO2 emissions for developing countries, suggesting that the reallocation of production units into developing countries through FDI could degrade environmental quality in support of the pollution haven hypothesis. This agrees with Shahbaz et al. (2015) and Aller et al. (2015), both of which argue that FDI results in environmental unsustainability in developing countries. As FDI inflows are vital for economic development, developing countries with their relatively weak environmental policies and poor management systems attract polluting industries with low green technology from developed countries through FDI inflows. Multinational enterprises find it more profitable to transfer energy-intensive industries to developing countries with weak environmental standards. Developed countries can benefit from the low environmental cost by importing the finished energy-intensive products from developing countries. Our results supporting the positive relationship between FDI inflows and CO2 emissions for developing countries may reflect the argument that FDI flows to developing countries relate to investment with less green technology. FDI with green technology and sound environmental management can mitigate emission-related problems and promote environmental sustainability in developing countries, where environmental issues are recognized as less important and cannot be solved by their technology and management. This suggests a need for a comprehensive cross-border transfer and diffusion of green technology. The environmental policy encouraging FDI with green technology and environmentally sustainable management systems is required to achieve sustainable development goals. Declaration of competing interest The authors state that there is no conflict of interest for this article. References Aller, C., Ductor, L., Herrerias, M.J., 2015. The world trade network and the environment. Energy Econ. 52, 55–68. Antweiler, W., Copeland, B.R., Taylor, M.S., 2001. Is free trade good for the environment? Am. Econ. Rev. 91, 877–908. Apergis, N., Payne, J.E., 2012. Renewable and non-renewable energy consumption-growth nexus: evidence from a panel error correction model. Energy Econ. 34, 733–738. Arellano, M., Bover, O., 1995. Another look at the instrumental variable estimation of error-components models. J. Econ. 68, 29–51. Balaguer, J., Cantavella, M., 2016. Estimating the environmental Kuznets curve for Spain by considering fuel oil prices (1874–2011). Ecol. Indic. 60, 853–859. Balsalobre-Lorente, D., Shahbaz, M., Roubaud, D., Farhani, S., 2018. How economic growth, renewable electricity and natural resources contribute to CO2 emissions? Energy Policy 113, 356–367. Baumert, N., Kander, A., Jiborn, M., Kulionis, V., Nielsen, T., 2019. Global outsourcing of carbon emissions 1995–2009: a reassessment. Environ. Sci. Pol. 92, 228–236. Bekun, F.V., Alola, Adewale Andrew, Sarkodie, S.A., 2018. Toward a sustainable environment: Nexus between CO2 emissions, resource rent, renewable and nonrenewable energy in 16-EU countries. Sci. Total Environ. 657, 1023–1029. Bekun, F.V., Emir, F., Sarkodie, S.A., 2019. Another look at the relationship between energy consumption, carbon dioxide emissions, and economic growth in South Africa. Sci. Total Environ. 655, 759–765. Bilgili, F., Koçak, E., Bulut, Ü., 2016. The dynamic impact of renewable energy consumption on CO2 emissions: a revisited Environmental Kuznets Curve approach. Renew. Sust. Energ. Rev. 54, 838–845. Borck, R., Pflüger, M., 2019. Green cities? Urbanization, trade, and the environment. J. Reg. Sci. 59, 743–766. Breitung, J., Das, S., 2005. Panel unit root tests under cross-sectional dependence. Statistica Neerlandica 59, 414–433. Bu, M., Li, S., Jiang, L., 2019. Foreign direct investment and energy intensity in China: firmlevel evidence. Energy Econ. 80, 366–376. Choi, I., 2001. Unit root tests for panel data. J. Int. Money Financ. 20, 249–272. Coondoo, D., Dinda, S., 2002. Causality between income and emission: a country groupspecific econometric analysis. Ecol. Econ. 40, 351–367. de Benedictis, L., Tajoli, L., 2011. The world trade network. World Econ. 34, 1417–1454. Dogan, E., Seker, F., 2016. The influence of real output, renewable and non-renewable energy, trade and financial development on carbon emissions in the top renewable energy countries. Renew. Sust. Energ. Rev. 60, 1074–1085. Dumitrescu, E.-I., Hurlin, C., 2012. Testing for Granger non-causality in heterogeneous panels. Econ. Model. 29, 1450–1460.

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