The impact of FDI in the OECD manufacturing sector on CO2 emission: Evidence and policy issues

Environmental Impact Assessment Review 77 (2019) 60–68

Contents lists available at ScienceDirect

Environmental Impact Assessment Review journal homepage: www.elsevier.com/locate/eiar

The impact of FDI in the OECD manufacturing sector on CO2 emission: Evidence and policy issues

T

Pasquale Pazienza University of Foggia, Department of Economics, L.go Papa Giovanni Paolo II n. 1, 71121 Foggia, Italy

A R T I C LE I N FO

A B S T R A C T

Keywords: Carbon dioxide Economic growth Environmental impact Foreign Direct Investment Globalisation

This work investigates the sign and the magnitude of the impact Foreign Direct Investment (FDI) inflowing in the manufacturing sector of the countries from the Organisation for Economic Co-operation and Development (OECD) exerts on the environment and, specifically, on the amount of CO2 from sectoral fuel combustion. By gathering data from various international institutions for those countries from 1989 to 2016, an equation model is built to take into separate account technique, scale and cumulative effects of FDI on CO2 and analysed through the panel data technique. The positive relationships found for all these effects would highlight a detrimental role of FDI on the environment. However, the very low magnitude of the estimated coefficients and the observation that the negative impact of FDI on CO2 decreases as the scale of its inflow increases, leads to a reconsideration of those arguments against the enforcement of international investment policies in the sector due to the environmental implications generally assumed. This positive environmental spillover is explained by referring to FDI as a driving force of technology innovation and, consequently, a way through which the implementation of more environmentally-friendly and cleaner production modes occurs. Results are consistent across different estimators and robust to a number of alternative specifications and additional co-variates.

JEL classification: C23 F18 F64 O44 P33 Q56 R11

1. Introduction Over the last decades, increasing trends of environmental degradation have been reported in several international documents (e.g. UNEP, 2012). As generally thought, this is caused by the widespread increase of economic activities resulting from globalisation (e.g. Huwart and Verdier, 2013). These heavily rely on the use of energy mainly produced from fuel combustion processes from which a significant amount of greenhouse gases – and, particularly, CO2 among these – are generated (e.g. IEA, 2007).1 The consideration that FDI is a significant part of the globalisation phenomenon raises concerns about its environmental implications.2 This becomes particularly important if the global data showing the rapid and almost contemporary increase of FDI and carbon dioxide is observed. FDI entering the world economies has enormously increased since the beginning of its phenomenon in the early 1970's. A focus on the last three decades shows us how FDI has moved from 196,315 billion US$ in 1990 to 1461 trillion in 2000 and, after a series of ample fluctuations, to 2437 trillion in 2016 (data. worldbank.org). Besides this, an increase of the CO2 emission level can also be appreciated; it has passed from 22.29 billion tones (Gt) in 1990

to 24.71 in 2000 and 33.5 in 2010 to reach 36.18 in 2016 (cdiac.essdive.lbl.gov). The understanding of the link between FDI and the environment has caught the attention of various analysts and researchers particularly since the mid-1990's. As will be seen later, the literature highlights a significant amount of works producing different and contradictory results. Therefore, whether and to what extent FDI can be considered detrimental or beneficial for the environment is a question yet to be answered and the possibility of contributing to this discussion is the main motivation of this work. Investigations in the FDI-environment relationship can be largely clustered into the following areas: 1) the effects of FDI on the environment; 2) the effects on environmental standards resulting from the competition for FDI; 3) the cross-border environmental performance. The research activity produced on these three themes is copious. Nevertheless, a general claim still exists that, so far, it has not yet generated any conclusive evidence (e.g. Cole et al., 2017; Pazienza, 2014). This is particularly true for the first thematic area for which further research is required (e.g. Shao, 2018; Zheng and Zheng, 2017; McAusland, 2010; OECD, 2002). As highlighted in the literature, one of the main aspects

E-mail address: [email protected]. About a third of the consumption of the world's energy and more that 36% of CO2 emissions are produced by manufacturing industries (IEA, 2007, 2011, 2017). 2 To the specific purpose of this study, it is worth highlighting how in 2016 the total amount of FDI inflowing the OECD area is equal to 1.241.463 million US$, that is about 63% of the World figure equal to 1.969.283 million US$ (www.oecd.org/investment/statistics). 1

https://doi.org/10.1016/j.eiar.2019.04.002 Received 27 November 2018; Received in revised form 11 March 2019; Accepted 4 April 2019 0195-9255/ © 2019 Published by Elsevier Inc.

Environmental Impact Assessment Review 77 (2019) 60–68

P. Pazienza

casuality between CO2 and FDI. In the same direction goes the evidence achieved by Kivyiro and Arminen (2014), who analyse a time series from 1971 to 2009 for six sub-Saharan African countries and observe that CO2 emissions and FDI (together with economic development and energy consumption) move along the same path in the long-run. Omri et al. (2014) also find – among other things – a bidirectional casuality between CO2 emissions and FDI while analysing a panel data of 53 countries over the period 1990–2011 through a dynamic simultaneousequation model. Further evidence of the positive relationships characterising the nexus between FDI and air pollution come from another couple of works focusing on China from the same author. In the first, the analysis of a simultaneous equation model built on data for the 29 Chinese provinces from 1994 to 2001 shows that FDI impacts positively on SO2 (He, 2006). In the second, the analysis with the GMM estimator of a simultaneous equation model based on data for 80 Chinese cities from 1993 to 2001 also proves the existence of a positive impact of FDI on the two pollutants considered (SO2 and total suspended particles), although for a small quantity (He, 2008). Bae et al. (2017) also find that FDI increases CO2 in their analysis of the determinants of CO2 emissions for 15 countries from the ex-Soviet region over the period 2000–2011. As for the opposite evidence, some other works find that FDI can be beneficial for the environment. It is the case of Liang (2008) who concludes her investigation of the Chinese city level data from 1996 to 2002 by suggesting that the overall effect of FDI on air pollution may be beneficial to the environment. In another work analysing a panel data containing statistics of the FDI inflow and air pollution for 286 Chinese cities between 2001 and 2007, a significant causal effect showing the beneficial role FDI plays in reducing air pollution is observed (Kirkulak et al., 2011). Acharyya (2009) reaches a similar conclusion in her work on India where the relationship between the FDI inflow and CO2 emission is analysed over the period from 1980 to 2003. It is the case to note that Shahbaz et al. (2015), already mentioned above, find that FDI is beneficial for the environment although only in high-income countries. More recently, working on a panel data with statistics from 2002 to 2015 of CO2 emission and FDI inflowing 28 subsectors of Chinese manufacturing, Sung et al. (2018) observed that FDI is positively correlated to the improvement of environmental quality. In the attempt to over-ride the hazardous limitation characterising all these works and represented by the use of aggregate data of FDI, here we proceed by following an investigation approach based on the sectoral analysis framework. Here, we analyse how FDI inflowing the “manufacturing” sector of the countries from the Organisation for the Economic Co-operation and Development (OECD) area impacts on the amount of CO2 emission caused by fuel combustion in the same sector. To this aim, an equation model considering technique, scale and cumulative effects is developed and econometrically analysed to understand the sign and magnitude of the impact. The work is structured as follows. Sections two is devoted to the presentation of the data and the method of the analysis. Section three reports on the results of the empirical analysis. Finally, in section four the findings of this work are discussed, and some conclusions drawn.

characterising FDI is that it does not impact the environment in isolation, but also interplays with a variety of other elements. For this reason, by following Grossman and Krueger (1995), regarding the analysis of the environmental impact of the North American Free Trade Agreement, various works have developed their analyses while distinguishing the effects of FDI on the environment into the following three groups: technique, scale and composition or structural effects (i.e. He, 2008; Liang, 2008; Cole and Elliott, 2003). The first type of effect refers to the consideration that the emission level per each unit of goods produced in the economy relies on the existing production techniques, which can be modified via policy and/ or technological avenues. In a free market situation, the liberal circulation of investment promotes allocative efficiency among countries and, in turn, the development, transfer and diffusion of more updated and efficient technology and/or the introduction of newer and tighter environmental legislation. As a result, the technique effect is predicted to be environmentally beneficial. The scale effect, instead, is presumed harmful to the environment since it is associated to the rise in the scale of the economy, which implies that more production means higher pollution levels. It is worth pointing out that the issue of the scale effect implicitly recalls the discussion on the Environmental Kuznets Curve (EKC). Various works have observed how the environmental deterioration expected from an increase in the scale of an economy is true but only up to a certain point. As the “turning point” is overtaken, environmental quality improvements are observed. Although the literature highlights the existence of countervailing views expressed on this evidence at global, country and local levels (e.g. Gill et al., 2018; Farhani et al., 2014; Aslandis and Iranzo, 2009; Stern, 2004), the general justification refers to the fact that wealthier countries are more capable of opting for newer and more efficient technologies. At the same time, their populations develop major levels of environmental sensitiveness for which they require tighter environmental rules. Finally, the composition (or structural) effect refers to a conversion of the industrial organisation of an economy resulting from a change in the order of its economic activity. It is envisaged to be environmentally advantageous considering that, in a free market context, investment pushes towards the allocative efficiency of resources among countries (OECD, 2001). By lowering tariff and non-tariff barriers, indeed, economic liberalisation decreases the relative prices of import-competing goods. In a given economy, this very likely leads to progressively substituting more polluting activity sectors with less polluting ones and the total emission level to fall. The literature on this issue also refers to the existence of counterviews. Cole and Elliott (2003), for example, show how in the situation of free trade, the sign characterising the composition effect can be negative or positive. This would depend on the competitive advantages of a considered country and its productive specialisation. The analysis of the literature also shows that the link between FDI and the environment has prevalently been analysed using aggregate data of FDI only. The development of analyses focusing on data disaggregated at the level of each specific single sector of activity to investigate the FDI-pollution relationship, instead, has been disregarded with the consequence that the evidence achieved so far might result hazardously deceptive. However, two are the arguments raised in the literature. On the one hand, FDI is considered to play a positive role for the environment of host countries. On the other hand, there is also evidence that FDI generates negative spillover effects on the environment of receiving countries. For example, through the employment of a three stage least square method to analyse, among other things, the impact of economic growth and the inflow of FDI on CO2 emission in Pakistan over the period from 1980 to 2014, Bakhsh et al. (2017) have found FDI positively related to CO2. Similarly, in analysing 99 heterogeneous (hig-, middle-, and low-income) countries over the period 1975–2012, through the fully modified ordinary least square method, Shahbaz et al. (2015) find evidence of the existence of a bidirectional

2. Data and method of the analyses By taking inspiration from He (2006, 2008), our analysis of the FDI3-CO2 relationship in the manufacturing sector of the OECD area relies on a purpose-built database containing 24 variables (mostly derived from our own computations on data gathered from the statistical databases of various international organisations) observed in relation to FDI is here considered in terms of flow and not stock. The stock refers to the FDI book value that is the direct investment position on a historical cost. Cantwell and Ballack (1998) highlight how the employment of the book value is not functional in international comparisons, since stock age distribution is not taken into consideration. 3

61

Environmental Impact Assessment Review 77 (2019) 60–68

P. Pazienza

30 OECD countries4 over a period of 28 years (from 1989 to 2016).5 Considering the country and time units in our database, the empirical development of our investigation is based on the use of the panel data econometric technique (Greene, 2012; Wooldridge, 2002). The 24 variables have all been tried in numerous estimation attempts, but only some of them have been found statistically useful. The functional form that best fits the relationships subject of our attention is represented by the following log-log type and with variables in first-differences6:

induced-FDI technique, scale and cumulative effects on our considered type of CO2 through β3, 2β4 FDIsctr and β3 + 2β4 FDIsctr respectively.9 The composition or total effect is observed in our model through the consideration of two different variables: the first is represented by the capitalisation levels of the considered economies and the second refers to the relevance of their “manufacturing” sector. Specifically, these respectively refer to the capital-labour ratio and to the sectoral-total GDP ratio (variables 6 and 7 in Table 1). A cross-product is also employed in our model to consider an additional nonlinear function in our analysis with the aim of empowering the test for the detection of ignored nonlinearities in our estimation (Wooldridge, 2002).

CO2 sctrit = α + β1 GDPsctrit + β2 GDPsctr 2 it + β3 FDIsctrit + FDIsctr 2 it + β4 GCFit + β5 SCTRrelit + β6 MKTopn it + β7 PROTit + β8 EDUC + β9 CRprit + εit

3. Results of the analysis

where i is the cross-sectional units in the analysis; t is the time units; ε is the error term. Table 1 reports a more specific description of each single variable in the model. For the identification in our model of the induced-GDP and the induced-FDI technique, scale and composition effects, we continue as follows.7 Starting from the induced-GDP effects, its technique effect occurs because of income variations and, therefore, it can be observed via the coefficient estimated for the per-capita GDP isolated variable (β1). The induced-GDP scale effect represents the growth of a considered country's economy. It is observed through 2β2 GDP, taken from the partial derivative of our equation with respect to GDP. The environmental-economic meaning of these two effects highlights how the percentage of the considered type of CO2 varies when GDP fluctuates by 1% (e.g. He, 2008; Liang, 2008; Cole and Elliott, 2003; Antweiler et al., 2001).8 Finally, the induced-GDP composition or total effect comes from taking into consideration both the technique and scale effects and is achieved by taking the partial derivative, with regard to GDP, of our equation. Its measure can be seen via the coefficients of β1 + 2β2 GDPsctr and calculated, as in our specific case, by substituting GDP with the mean income of our OECD countries in Table 2 (e.g. Managi et al., 2008; Liang, 2008; Cole and Elliott, 2003; Antweiler et al., 2001). The environmental-economic meaning of this highlights how the level of CO2 fluctuates as a percentage when there is a 1% variation of GDP. As we have done so far, we also observe the coefficients of the

The empirical analysis is developed by using Stata 14 software. Table 2 summarises the main statistics of the model variables.10 The model specification is tested for heteroskedasticity, autocorrelation and stationarity. By following Greene (2012), heteroskedasticity is tested by employing a LR test. This generates a chi2 = 1164.62 with a p-value = .0000 which confirm our model is heteroskedastic. Autocorrelation is verified via the test by Wooldridge (2002) for panel data. Its result shows a F value = 16.466 and a pvalue = .0004, which highlight the presence of autocorrelation problems in the considered model specification. A stationarity check is also performed through a Fisher test up to three lags (Maddala and Wu, 1999). Apart from the FDI variable, it highlights all the others as nonstationary.11 The table below (Table 3) reports on the analysis result and shows Ordinary Least Squares (OLS), Fixed Effects (FE) and Random Effects (RE) estimates corrected for heteroskedasticity and autocorrelation.12 On the consideration of the chi2 = 0.00 and the p-value = 1.0000 obtained from the Brush-Pagan test to choose between OLS versus RE/ FE models, we accept the first which becomes the reference for comments.13 We first observe the high statistical significance (p-value = .000) and the positive correlation (0.0223) between our considered type of CO2 and GDP. GDP also shows statistical significance (p-value = .041) and is positively related (0.0032) to CO2 even when considered in its squared form. As previously highlighted, these two results are respectively associated to the induced-GDP technique and scale effects on the pollutant considered as our dependent variable.14 Specifically, while

4 The database considers the whole set of the OECD countries except Chile, Estonia, Israel and Slovenia. Their statistics lacks an adequate time series, since their accession only occurred in 2010. 5 The end year is set on the availability of the FDI data, which is taken from the OECD international investment database (accessed through http://stats. ukdataservice.ac.uk) up to 2012 and the International Trade Centre (http:// www.intracen.org/itc/market-info-tools/foreign-direct-investment) up to 2016, both last checked on 20th October 2018. 6 All the variables are considered in natural log because of the exponential series and the different units of measures characterising the regressors in our model. It achieves the variables' coefficients in terms of elasticities (percentages), which represent a more unbiased measure. In addition, as will be highlighted in footnote 12, the variables are considered in first-differences [i.e. log(xit) – log(xit-1)] to repair the non-stationarity problem affecting the panel dataset. 7 It is worth noting that, technique and scale effects appear different from a theoretical point of view. In reality, they are alike and difficult to distinguish. Therefore, empirical analysts tend to refer to the specific variable (GDP or FDI as for our case study) taken in isolation to observe the technique effect and the same variable contemporarily considered with its square for the “scale” effect. 8 Antweiler et al. (2001) separately measure technique and scale through two different identities: the per-capita GDP for the earlier and the GDP per squared km for the scale. Cole and Elliott (2003) only use, instead, the per-capita GDP to catch both effects. By following these latter, only the sectoral GDP per-worker is considered in this work. The GDP per squared km., also used in the various estimation attempts, turns out to be insignificant. Furthermore, the consideration of the various versions of the GDP variable in exponential terms beyond the square generates insignificant results and reduces or invalidates the significance of other variables.

9 Even for FDI it is observed that the exponential conversion beyond the squared form produces statistically insignificant outcomes. 10 For the majority of the variables in the dataset, the number of observations is smaller than what could be normally expected. This is due to the lacunas in the source databases. For this reason, in agreement with Greene (2012), the dataset is handled as strongly unbalanced. 11 According to Engle and Granger (1987), a two-step procedure is used for cointegration analysis. The test is run in the OLS model with all the variables in level to check for stationarity among them and assess its residuals as a measure of disequilibrium. The ADF test on the residuals shows a p-value of the residuals equal to 0.0853, which induces the acceptance of the null hypothesis of nocointegration. As anticipated in footnote 6, this is the reason why the variables in the equation model are considered in first-differences. 12 This is done by following the estimation procedure proposed by Hoechle (2007). It generates standard errors, which are robust to forms of temporal and spatial dependency. 13 It is worth highlighting that this estimation shows a very high level of joint significance of the model variables since it performs a F-test with a pvalue = .0000. The joint significance of GDP and FDI with their respective squared forms is also tested. The joint significance of GDP and GDP square is confirmed by the p-value = .0065. FDI and FDI square are also jointly significant showing a p-value = .0027. This implies that also the joint consideration of these variables makes our model correctly specified. 14 The estimation result of the induced-GDP technique and scale effects

62

Environmental Impact Assessment Review 77 (2019) 60–68

P. Pazienza

Table 1 Variables specification.a No.

Variable

Description

Data sourceb

1 2 3 4 5 6 7 8 9 10 11

CO2sctr (dep. var.) GDPsctr GDPsctr2 FDIsctr FDIsctr2 GCF SCTRrel MKTopn PROT EDUC CRpr

Ratio = quantity of CO2 in mln. tons from sectoral fuel combustion / work force in the sector. One-year lag ratio = sectoral GDP (in real US$) / work force in the sector. Square of the ratio above at line no. 2. One-year lag ratio = sectoral inflow of FDI (in real mln. of US$) / work force in the sector. Square of the ratio above at line no 4. Ratio = Gross Capital Formation (in real US$) / total work force (in thousands). Ratio = sectoral GDP (in real US$) / total GDP (in real US$). Ratio: (f.o.b. export + f.o.b. import) in real US$ / total GDP (in real US$) considered in absolute terms. Protected area (in Km2). Average year of school. Cross-product = GCF (in real US$) x total inflow of FDI (in real mln. US$).

UN, IEA UN, OECD UN, OECD UN, OECD and ITC UN, OECD and ITC WB, ILO UN IMF, UN UN WB WB, OECD

a The financial data are in US$ and converted from current into real terms by recurring to the use of USA Gross National expenditure Deflator (base year = 2000) sourced from World Bank (http://databank.worldbank.org). b UN = United Nations; IEA = International Energy Agency; OECD = Organisation for the Economic Co-operation and Development; ITC = International Trade Centre; WB = World Bank; ILO = International Labour Organisation; IMF = International Monetary Fund.

the estimated β1 (0.0223) represents the first type of effect, 2β2 is the elasticity coefficient of the scale effect, which results positive and equal to about 0.0064. A first comment on the CO2-GDP relationship would make us note that a rise in environmental degradation is the result of an early stage of income increase. Further improvements in the income level, however, would still be detrimental to the environment but with a lower magnitude. The cumulative or total effect of such a relationship comes out equal to +0.1377 which is obtained through β1 + 2β2 LnGDP (namely 0.0223 + 0.0064 LnGDP) while considering the mean value of the GDPsctr variable (18.0264 as reported in the table giving the descriptive of the statistics). As already mentioned, the environmental-economic meaning of the estimated coefficients for the technique, scale and cumulative effects refers to the percentage variation of the CO2 emission as a result of a 1% variation of GDP. The main investigated relationship between FDI and CO2 emission is found significant (p-value = .003) and positive (+0.0058) when FDI is isolation.15 FDI squared also appears to be significant (p-value = .010) and highlights a positive correlation (0.0008) with CO2. Here again, we identify the technique effect of FDI on the dependent variable in the estimated coefficient (+0,0058) of the FDI in isolation. The coefficients of the scale and the cumulative effects are, instead, identified in +0.0016 LnFDI and + 0.0058 + 0.0016 LnFDI respectively with the cumulative effect computed at +0.0050 by using the sample mean of the FDI variable. As done before, the environmental-economic meaning of these coefficients represents the percentage variation of the CO2 level in response to a change of FDI of 1%. The relationship between the capitalisation level (namely, the GCF variable) of the considered countries' economies and CO2 is also found significant (p-value = .021) and positive (0.1673). While highlighting that this variable is used to catch one out of the two facets of the composition effect in our model, it is observed how an increase of the capitalisation degree produces an increase – although of low magnitude

Table 2 Summary statistics of the variables. Variable

Obs

Mean

Std. dev.

Min

Max

Id Year EDUC CO2sct (dependent var.) MKTopn GCF SCTRrel CRpr GDPsctr2 GDPsctr FDIsctr2 FDIsctr PRTarea

840 840 840 720

– – 2.13624 −12.54982

– – 0.2841542 0.5267211

1 1989 1.029552 −13.56774

30 2016 2.544327 −10.20213

712 707 721 658 641 640 531 530 530

−1.746345 22.68547 1.833355 31.9994 333.3453 18.0264 2.635225 −0.4833648 −7.110229

3.174612 0.6428644 0.2544623 11.38852 125.524 2.880987 4.245652 1.643241 1.917673

−14.79736 20.43952 −0.7751537 −36.13124 231.4395 15.21384 0.0000862 −5.067981 −9.219765

4.415158 23.76584 2.504032 40.98755 874.4762 29.64395 40.3875 6.42674 −1.7551

Table 3 Panel data estimation results. CO2sctr dep. var.

OLS

FE

RE

GDPsctr

0.0223* (0.0049354) 0.0032** (0.0012883) 0.0058* (0.0018812) 0.0008*** (0.00932432) 0.1673** (0.07553164) −0.1462** (0.065599) 0.0924*** (0.0582324) 0.0067 (0.0648771) 0.2644 (0.2513252) 0.0002 (0.0004244) −0.0153* (0.0046424)

0.0242* (0.0072641) 0.0036** (0.0014164) 0.0062* (0.0016632) 0.0008*** (0.0098567) 0.2024*** (0.0761431) −0.1641* (0.0532784) 0.1175** (0.0451927) 0.0198 (0.0561642) 0.0755 (0.05622549) 0.0002 (0.0002537) −0.0184* (0.0059426)

0.0223* (0.0061841) 0.0032** (0.0013941) 0.0058** (0.0031742) 0.0008*** (0.0092693) 0.1673 (0.1044384) −0.1462** (0.0612884) 0.0924 (0.0661523) 0.0067 (0.0646735) 0.2644 (0.2545501) 0.0002 (0.0001991) −0.0153* (0.0057731)

407

407 27 Rho 0.3074

407 27 Rho = 0

GDPsctr2 FDIsctr FDIsctr2 GCF SCTRrel MKTopn PROT EDUC CRpr Constant N. obs. N. groups R-squared Adj. R-squared

0.2459 –

(footnote continued) confirm the observation by Cole and Elliott (2003): in reality, the scale effect is synchronous, and the technique effect the result of an anterior dynamic. As a result, the diversification of their associated variables by using lagged forms is suggested. In agreement with this approach, in this analysis the coefficients of the induced-GDP technique and scale effects are found significant when considering the variable of the earlier at time t-1 and the variable of the latter at time t. The same is observed for the induced-FDI technique and scale effects as will be highlighted later. 15 In addition to what has been anticipated in the previous footnote, this FDI variable is considered with a lag of one year for a better response of the model estimation. The justification for this might lie in the time needed by investment to get up to speed and produce its actual impact on the considered pollutant.

Robust standard errors in parenthesis; P-value: * ≤ 1%, ** ≤ 5%; *** ≤ 10%.

63

Environmental Impact Assessment Review 77 (2019) 60–68

P. Pazienza

– of the level of our considered pollutant. The coefficient of the relationship between the relevance of the “manufacturing” sector and the dependent variable is also significant (p-value = .011), but negative (−0.1462). This result, representing the second facet of the composition effect in the equation would imply that a 1% growth of the sectoral relevance produces a decrease of CO2 emissions by 0.14%. The correlation between market openness and CO2 is also significant (p-value = .068) and positive (+0.0924), this implying that a 1% increase in the degree of openness would make the level of CO2 grow by about 0.1%. The surface of the protected area and the education characterising our set of countries are found statistically insignificant as well as the cross-product used in our model. 4. Discussion of the results To discuss the findings achieved from our analysis, we structure the following sub-sections for a more organised and clearer presentation of the insights we have produced. 4.1. The induced-FDI effects The analysis of the induced-FDI effects on CO2 emissions from the sectoral fuel combustion shows positive relationships. The coefficients of the technique, scale and cumulative effects achieved in our estimation are respectively equal to +0.0058, +0.0016 and + 0.0050. If we only focus on the signs of the coefficients, we would highlight the detrimental impact of FDI on the considered environmental feature. By looking at the magnitude of the coefficients, however, we should appreciate how FDI impacts minimally on CO2. Additionally, observing technique and scale effects together highlights that scale effects impact at a slower pace than technique effects. This means that, as the scale of the FDI inflow increases, the CO2-FDI relationship is still marginally positive but the impact of FDI on CO2 is reduced. Graph 1 illustrates the tendency of the induced-FDI technique, scale and cumulative effects on CO2 from sectoral fuel combustion and helps observe more clearly the dynamic of the CO2-FDI relationship derived from our estimation result. Once again, we observe that after an initial increase of a certain magnitude, the FDI-CO2 relationship tends to flatten at a later stage. At the beginning, CO2 increases in response to the rise of the sectoral inflow of FDI. Afterwards, CO2 still increases but at a slower pace with a significantly reduced magnitude. The cumulative effect reflects, of course, the same dynamic being the result of the first two. This evidence makes us go beyond what is generally reported in that part of the literature considering FDI as detrimental to the environment and of which some works have already been recalled in the introduction. However, if this is true (and our analysis confirms this at the initial stage of the FDI flow increase although with coefficients of very low magnitude), it is also true that FDI helps to reduce the environmental degradation as the scale of its inflow grows. In this other sense, our result also confirms what is reported in those other works – some of which are also recalled in the introductory section – where the environmentally positive role of FDI is highlighted. This is explained through the arguments that technology advances implicitly live in the investment activity and that FDI is, di per sé, a driver of technology innovation. This relevance of technology in FDI is confirmed in other analyses. In a recent work, Melane-Lavado et al. (2018) look at a sample of 4667 Spanish Small and Medium Enterprises (SMEs) and statistically prove how FDI generates positive spillovers especially by enhancing innovation and competition. They also observe how FDI makes the investigated set of SMEs more oriented to develop innovation based on sustainability principles. A similar conclusion is also reached by Xingang et al. (2019), who observe that FDI inflowing in 30 Chinese provinces over the period from 2005 to 2014 has brought more updated technologies, thus generating a higher level of energy intensity and a

Graph 1. Induced-FDI technique, scale and cumulative effects on CO2.

decrease in air pollution. 4.2. The induced-GDP technique, scale and cumulative effects The CO2-GDP relationship shows a technique effect equal to +0.0223, a scale effect equal to +0.0064 and cumulative effect equal to +0.1377. In broad terms, the positive signs of the investigated relationships would make us highlight that an increase of GDP is detrimental for the environment. More specifically, we observe that in a first phase (that corresponding to the consideration of the technique effect) the impact of the GDP growth on the environment is detrimental. This would make it impossible for us to accept the validity of those analyses which look at the technique effect as a driver of environmental quality improvement because of the consideration of the technological innovation and diffusion generally associated to increases in wealth. However, when GDP further increases (that is when the scale effect is considered), the impact on CO2 remains detrimental but its magnitude is considerably lower. The cumulative effects bring to a synthesis the 64

Environmental Impact Assessment Review 77 (2019) 60–68

P. Pazienza

allow us to argue in its favour. Our finding supports those analyses which find it hard or even impossible to confirm its validity. By using a variety of econometric techniques, for example, various authors have analysed the relationship between different pollutants and GDP without reaching any useful evidence to validate the EKC hypothesis (i.e. Stern, 2004; Perman and Stern, 2003; Yandle et al., 2002). With specific regard to the OECD countries, a work using a semi-parametric estimation method on data from 1960 to 2003 did not find any useful evidence to justify the GDP-CO2 relationship in the sense of the EKC. Its authors develop a model to account for technique, scale and composition effects and find that the technique effect is insufficient in reducing CO2 emissions, except for high-income countries (Tsurumi and Managi, 2010). Another analysis focused on Canada employed both semi-parametric and flexible non-linear parametric modelling estimation techniques without reaching any useful statistical support validating the EKC hypothesis in the CO2-GDP relationship (He and Richard, 2010). Aslandis and Iranzo (2009) also arrive at a similar conclusion in their investigation of a panel data containing CO2 emission data of nonOECD countries between 1971 and 1997. They employ a smooth transition regression technique and do not find any evidence of EKC. Similarly, a further work on Japan and China investigates, among other things, the GDP-CO2 relationship over a period of 30 years and finds no evidence of EKC (Yaguchi et al., 2007). The validity of the EKC is also rejected in a more recent study where MIKTA countries are analysed over the period from 1982 to 2011 (Bakirtas and Cetin, 2017). Apart from the development of a discussion strictly related to the validation or not of the EKC hypothesis – which incidentally arises in this work but is now its main aim – our finding shows that a GDP increase is beneficial for the environment, since its continuous increase impacts on the level of CO2 with a decreasing magnitude. This induces us to argue in the same terms as before for FDI and recognise the economic growth – namely, the increase of GDP – as a carrier of technological innovation and technology diffusion through which a more effective and efficient use of natural resources is pursued. 4.3. The composition effect In this work the consideration of the composition effect is twofold. On the one hand, we consider it in terms of economic systems composition and is proxied by the capital-labour ratio, namely the ratio composed of the Gross Capital Formation (GCF) and the number of workforces in the economy. On the other hand, we consider it as the relevance of the manufacturing sector in the whole economy represented by the sectoral GDP and the total GDP ratio. The correlation resulting from the first way of intending the composition effect, that is the CO2-GCF relationship, is equal to +0.1673. This outcome would imply that the more the capitalisation level of the considered economies increases, the more the detrimental impact on CO2. This agrees with those works which have observed that the increase and accumulation of fixed assets (plants and machinery, vehicles, buildings, etc.) turns out in higher production levels, more consumption and more pollution. Various authors have observed a positive correlation between capital and emission intensities while modelling with different pollutants (e.g. Mazzanti et al., 2007; He, 2006; Cole and Elliott, 2005). Antweiler et al. (2001) postulate a Factor Endowment Hypothesis (FEH) and investigate the environmental impact deriving from trade liberalisation. By working at a city-level panel data on ambient SO2 concentration, they find that a 1% growth in the capital-labour ratio of a country generates a 1% increase of SO2. In their view, the FEH predicts that liberalisation of trade leads to a rise of polluting emissions in those countries characterised by capital abundance and vice-versa. Later, Cole and Elliott (2003) replicate the analysis and take into consideration CO2, NOX and Biological Oxygen Demand (BOD). They also find positive correlations confirming that the higher the capital-labour ratio is, the higher the pollution intensity. Put in this way, the generally referred view for which capital

Graph 2. Induced-GDP technique, scale and cumulative effects on CO2.

dynamic of the technique and scale effects and shows how CO2 initially increases in response to an increase of GDP. At a further stage, when GDP further increases, CO2 still rises but at a slower pace because of the low magnitude of the coefficient of the scale effect. The graph below (Graph 2) helps to picture what we have just said. Although we are aware of various studies confirming the Environmental Kuznets's Curve (EKC) hypothesis,16 our result does not

16 In this sense, for instance, Shafik and Bandyopadhyay (1992) and Mazzanti et al. (2007) prove its validity while working on different sets of pollutants (with CO2 among these). In another work focusing on France, the GDP-CO2 relationship in the EKC sense is found statistically significant while using the autoregressive distributed lag (ARDL) approach to cointegration (Iwata et al., 2010). Some other studies find evidence to support that both the existence and inexistence of the EKC hypothesis depend on the geographical scale (whether local or global) at which a considered pollutant is taken into consideration (e.g. Lieb, 2003). Another recent work highlights the existence of an unconventional N-shaped EKC while analysing 17 OECD from 1990 to 2012 (Alvarez-Herranz et al., 2017).

65

Environmental Impact Assessment Review 77 (2019) 60–68

P. Pazienza

accumulation brings technological advances which, in turn, generate beneficial effects on the environment loses its full validity. While it is hard or impossible to deny the positive role technological progress plays on the environment and pollution abatement, our result can find its explanation in the consideration of the speed at which technological advances are absorbed in capital accumulation. If capital accumulation proceeds at a faster pace than its actual capability of absorbing technological advances, then its contribution to pollution abatement is slackened. In relation to the second way we have intended the composition effect in this work (namely, the relevance of “manufacturing” in the economies of the considered countries), our result displays a negative relationship (−0.1450) with the considered type of CO2. It highlights the beneficial role the manufacturing sector plays in reducing the emission of CO2. This result can be explained by the fact that, in a free trade and investment situation, countries are pushed towards a relocation process of their production factor endowments. From this, they develop comparative advantages among themselves and are induced towards an efficient specialisation of their economies. Consequently, they are expected to be less polluting as they employ reduced inputs per unit of output (OECD, 2001). In this sense, we should understand that the greater the specialisation in the “manufacturing” sector of the analysed countries the cleaner (in terms of CO2 reduction) are their economies as a result of some comparative advantages in the same sector. A recent study of the Indian manufacturing sector confirms our result. By using firm-level from 2009 to 2013, it finds a decline of 11% in CO2 emission intensity with a major contribution to this positive result coming from large and capital-intensive firms (Goldar et al., 2017). Besides this evidence, however, it is useful to recall Cole and Elliott (2003), who highlight that the environmental impact of the composition effect derives from the comparative advantages of countries. It can be beneficial or detrimental as a result of the economic specialisation each single country pursues. If the expanding sectors of economic activity are less polluting than those shrinking, then beneficial effects on the environment can be observed and vice versa.

Graph 3. Impact of market openness on CO2.

(e.g. Ghosh, 2007; OECD, 2002), it is worth stressing how the evidence achieved for the market openness variable would confirm the positive relationship between the FDI inflow and CO2. Although, in very general terms, this can be a valid association, the opportunity of keeping the reading of these two outcomes separated must be highlighted. In fact, the first outcome (the relationship between the FDI inflow and CO2) is associated to a sectoral dynamic. The second (the relationship between the level of market openness and CO2) considers the broader picture given by the total figures of import and export and does not specifically represent any sectoral dynamics. 5. Policy considerations From what has been highlighted in the discussion developed in the sub-sections above, we can derive the following policy considerations. Firstly, in relation to what has been said for the induced-FDI effects, we should focus on the capacity of FDI to transfer more modernised and environmentally-friendly technology and stress how this can help to exert a beneficial impact of FDI on the environment. As for a policy implication, then, we should reject those arguments expressing a position against the enforcement of FDI in the considered sector. Secondly, the discussion on the induced-GDP effects, which highlights that also economic growth – as FDI – can be considered as a transfer of technology innovation, should suggest the adoption of the same prescription derived from the EKC analysis, that is the increase of a country or population wealth per sé can be seen as a driver for pollution abatement. Thirdly, moving onto considering what has been discussed in relation to the composition effect, a policy indication should be formulated with a specific focus on the two different ways we have looked at it in our analysis. On the one hand, from the discussion developed for the composition effect intended as a capital-labour ratio, we should understand and recognise that capital accumulation can happen in ways which can be either advantageous or damaging for the environment. As we have already said, this might depend on the speed technology innovation is absorbed in capital accumulation processes. In the case this goes slowly, we cannot avoid considering the occurrence of negative externalities, which induces us to call for the adoption of environmental taxation mechanisms, despite the difficulty of monetising the environment broadly referred to in environmental economic studies. In other terms, attention should be paid to the importance of adopting schemes for the correct pricing of environmental assets and externalities (e.g. the selective business tax incentive) when implementing investment activities aimed at increasing the capitalisation level (namely, the accumulation of fixed assets) of countries. More specifically, the adoption of appropriate taxation mechanisms can ensure a more effective use of resources and orienting trade and investment towards more sustainable paths while avoiding their move in the direction of environmentally-damaging sectors. On the other hand, the discussion on the composition

4.4. Other evidence The other variables in our model referring to the surface of the protected area, the education level and the cross-product are not found statistically significant and, therefore, no comment is delivered. The only result worthy of comment in this section is represented by the market openness variable, which is found significant and positively correlated (+0.0924) to CO2. This positive sign would suggest that those countries having a higher trade openness are also those with a greater impact on our considered dependent variable. A similar conclusion is made by authors such as Feridun et al. (2006) and Hill and Magnani (2002), who have found positive correlations between pollution and market openness while analysing developed and developing countries. However, we can additionally observe that the result we have achieved is characterised by such a very low magnitude that the other view expressed in the literature is not gainsaid at all. In other works, belonging to the mainstream thinking, a virtuous circle in the relationship between market openness and environmental pollution is observed (e.g. Zhang et al., 2017; Ghosh, 2007; OECD, 2002). As already mentioned, this is explained by referring to the fact that free trade and investment pushes economies towards specialisation paths, which result in a major efficiency in the allocation of resources and a minor environmental impact (Akin, 2014; OECD, 2002; Lucas et al., 1992). The plotting of our estimation result helps to clarify what we are saying. In the graph below (Graph 3), it can be appreciated how the environmental impact of market openness is severe at an initial stage; afterwards it significantly reduces its magnitude and the trend of the impact flattens. Considering the strong correlation between trade and investment 66

Environmental Impact Assessment Review 77 (2019) 60–68

P. Pazienza

Funding

effect intended as the relevance of the manufacturing sector in the whole economy would induce us to recognise that the manufacturing sector holds comparative advantages which make its production cleaner (in terms of reduction of CO2 from fuel combustion). This consideration should push us towards encouraging the enforcement of investment in the sector once again. Lastly, the discussion developed on the other evidence achieved in this study and related to the impact of market openness on CO2 broadly reflects what we have already said about the FDI-CO2 relationship. Even in this case, the formulation of a policy indication should start from the consideration that free trade (and investment as a correlated phenomenon) pushes economies towards specialisation, resulting in a better efficiency of resources allocation and minor levels of environmental impact. Once again, we find a policy argument which is pro free trade and investment.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. References Acharyya, J., 2009. FDI, growth and the environment: evidence from India on CO2 emissions during the last two decades. J. Econ. Dev. 34 (1), 43–58. Akin, C.S., 2014. The impact of foreign trade, energy consumption and income on CO2. Intern. J. Energy Econ. Policy. 4 (3), 465–475. Alvarez-Herranz, A., Balsalobre-Lorente, D., Shahbaz, M., Cantos, J.M., 2017. Energy innovation and renewable energy consumption in the correction of air pollution levels. Energy Policy 105 (C), 386–397. https://doi.org/10.1016/j.enpol.2017.03.009. Antweiler, W., Copeland, B.R., Taylor, M.S., 2001. Is free trade good for the environment? Am. Econ. Rev. 91, 877–908. https://doi.org/10.1257/aer.91.4.877. Aslandis, N., Iranzo, S., 2009. Environment and development: is there a Kuznets curve for CO2 emissions? Appl. Econ. 41 (4–6), 803–810. https://doi.org/10.1080/ 00036840601018994. Bae, J.H., Li, D.D., Rishi, M., 2017. Determinants of CO2 emission for post-Soviet Union independent countries. Clim. Policy 17 (5), 591–615. https://doi.org/10.1080/ 14693062.2015.1124751. Bakhsh, K., Rose, S., Ali, M.F., Ahmad, N., Shahbaz, M., 2017. Economic growth, CO2 emissions, renewable waste and FDI relation in Pakistan: new evidence from 3SLS. J. Environ. Manag. 196 (1), 627–632. https://doi.org/10.1016/j.jenvman.2017.03.029. Bakirtas, I., Cetin, M.A., 2017. Revisiting the Environmental Kuznets Curve and pollution haven hypotheses: MIKTA sample. Environ. Sci. Pollut. Res. Int. 24 (22), 18273–18283. https://doi.org/10.1007/s11356-017-9462-y. Cantwell, J., Ballack, C., 1998. How important is Foreign Direct Investment? Oxford B. Econ. Stat. 60, 99–106. https://doi.org/10.1111/1468-0084.00088. Cole, M.A., Elliott, R.J.R., 2003. Determining the trade-environment composition effect: the role of capital, labor and environmental regulations. J. Environ. Econ. Manag. 46, 363–383. https://doi.org/10.1016/S0095-0696(03)00021-4. Cole, M.A., Elliott, R.J.R., 2005. FDI and the capital intensity of “dirty” sectors: a missing piece of the pollution haven puzzle. Rev. Dev. Econ. 9 (4), 530–548. https://doi.org/ 10.1111/j.1467-9361.2005.00292.x. Cole, M.A., Elliott, R.J.R., Zhang, L., 2017. Foreign direct investment and the environment. Annu. Rev. Environ. Resour. 42, 465–487. https://doi.org/10.1007/annurevenviron-102016-060916. Engle, R.F., Granger, C.W.J., 1987. Co-integration and error correction: representation, estimation and testing. Econometrica 55 (2), 251–276. https://doi.org/10.2307/ 1913236. Farhani, S., Mrizak, S., Chaibi, A., Rault, C., 2014. The Environmental Kuznets Curve and sustainability: a panel data analysis. Energy Policy 71, 189–198. https://doi.org/10. 1016/j.enpol.2014.04.030. Feridun, M., Ayadi, F.S., Balouga, J., 2006. Impact of trade liberalization on the environment of developing countries. J. Dev. Soc. 22 (1), 39–56. https://doi.org/10. 1177/0169796X06062965. Ghosh, I., 2007. The relationship between trade and FDI in developing countries. A panel data approach. Glob. Econ. J. 7 (3), 3–30. https://doi.org/10.2202/1524-5861.1272. Gill, A.R., Wiswanathan, K.K., Hassan, S., 2018. A test of Environmental Kuznets Curve (EKC) for carbon emission and potential of renewable energy to reduce Green House Gases (GHG) in Malaysia. Environ. Dev. Sustain. 20, 1103–1114. https://doi.org/10. 1007/s10668-017-9929-5. Goldar, B., Parida, Y., Sehdev, D., 2017. Reduction in carbon emission intensity and impact on export competitiveness: evidence from India manufacturing firms. J. Int. Commer. Econ. Policy. 8 (2), 1–30. https://doi.org/10.2202/1524-5861.1272. Greene, W.H., 2012. Econometric Analysis, 7th edition. Prentice Hall Inc, New Jersey, USA. Grossman, G.M., Krueger, A.B., 1995. Economic growth and the environment. Q. J. Econ. 110 (2), 353–377. https://doi.org/10.2307/2118443. He, J., 2006. Pollution haven hypothesis and environmental impact of foreign direct investment: the case of industrial emission of Sulfur Dioxide (SO2) in Chinese provinces. Ecol. Econ. 60, 228–245. https://doi.org/10.1016/j.ecolecon.2005.12.008. He, J., 2008. Foreign direct investment and air pollution in China: evidence from Chinese cities. Reg. Dev. 28, 131–150. He, J., Richard, P., 2010. Environmental Kuznets Curve for CO2 in Canada. Ecol. Econ. 69 (5), 1083–1093. https://doi.org/10.1016/j.ecolecon.2009.11.030. Hill, R.J., Magnani, E., 2002. An exploration of the conceptual and empirical basis of the Environmental Kuznets Curve. Aust. Econ. Pap. 41 (2), 239–254. https://doi.org/10. 1111/1467-8454.00162. Hoechle, D., 2007. Robust standard errors for panel regressions with cross-sectional dependence. Stat. J. 7 (3), 1–31. Huwart, J.Y., Verdier, L., 2013. Economic Globalization – Origins and Consequences. OECD Publishing, Paris, France. https://doi.org/10.1787/9789264111905-en. IEA, 2007. CO2 Emissions From Fuel Combustion 1971–2005. International Energy Agency, Paris, France. IEA, 2011. CO2 Emissions From Fuel Combustion Highlights. International Energy Agency, Paris, France. IEA, 2017. CO2 Emissions From Fuel Combustion Highlights. International Energy Agency, Paris, France. Iwata, H., Okada, K., Samreth, S., 2010. Empirical study on the Environmental Kuznets Curve for CO2 in France: the role of nuclear energy. Energy Policy 38 (8), 4057–4063. https://doi.org/10.1016/j.enpol.2010.03.031.

6. Concluding remarks By referring to a self-composed dataset with information on 30 OECD countries from 1989 to 2016, in this work we have developed an analysis to primarily understand how and with what magnitude FDI impacts on the environment. To this aim, a novel approach of analysis, based on the consideration of a specific sector of activity, has been used in the attempt to overtake the hazardous limitation represented – as can be widely seen in the literature produced insofar – by recurring to the use of aggregate data of FDI rather than disaggregate at the specific level of single activity sectors with the risk of producing misleading results. Our analysis of the technique, scale and cumulative effects of the FDI inflow in the manufacturing sector of OECD countries on the level of CO2 emission from the sectoral fuel combustion highlights positive relationships (+0.0058, +0.0016 and +0.0050 respectively). Apart from the observation of the positive signs of the relationships, which would lead us to speak in terms of the detrimental role of FDI on the environment, the very low magnitude of the coefficients together with the observation that the impact of FDI even decreases as the scale of its inflow increases, induce us to highlight the beneficial role FDI plays on the environment. The generation of positive environmental spillover from FDI is explained through its capability of being a driving force of technology innovation and, consequently, a tool through which the implementation of more environmentally-friendly and cleaner production modes occurs. This result induces us to reconsider those arguments which, by appealing to the negative effects of FDI on the environment, express an ideological position against the enforcement of international investment policies and, particularly, of those related to our considered sector. From an economic policy perspective, the main aspects characterising our finding induce us to stress the following. Firstly, the promotion of a free investment context would represent a necessary – although insufficient – condition to ensure that FDI exerts a positive impact on the environment, basically due to the technology innovation implicitly living in investment activities from which a more efficient allocation of resources and a minor negative environmental impact is derived. Secondly, whenever this is not the case because, as we have said in the discussion section, the investment activity contributes to a capital accumulation process, which is not accompanied by an appropriate absorption of technology innovation, then the need to adopt a Pigouvian taxation mechanism becomes the policy approach to be followed for the internalisation of negative externalities. We shape this conclusion while being aware of some limitations of this study. For example, although the dataset of the OECD countries we have analysed contains three developing countries (i.e. Korea Republic, Mexico and Turkey), no reflections have been developed to understand the specificities of their FDI-CO2 nexus. This, however, could be the subject of further research. 67

Environmental Impact Assessment Review 77 (2019) 60–68

P. Pazienza

Shao, Y., 2018. Does FDI affect carbon intensity? New evidence from dynamic panel analysis. Int. J. Clim. Chang. Strat. Manag. 10 (1), 27–42. https://doi.org/10.1108/ IJCCSM-03-2017-0062. Stern, D.I., 2004. The rise and fall of the Environmental Kuznets Curve. World Dev. 32 (8), 1419–1439. https://doi.org/10.1016/j.worlddev.2004.03.004. Sung, B., Song, W.Y., Park, S.D., 2018. How foreign direct investment affects CO2 emission levels in the Chinese manufacturing industry: evidence from panel data. Econ. Syst. 42 (2), 320–331. https://doi.org/10.101016/j.ecosys.2017.06.02. Tsurumi, T., Managi, S., 2010. Does energy substitution affect carbon dioxide emissionsincome relationship? J. Jpn. Int. Econ. 24 (4), 540–551. https://doi.org/10.1016/j. jjie.2010.01.005. UNEP, 2012. Global Environment Outlook GEO 5: Environment for the Future We Want. United Nations Environment Program, Nairobi, Kenya. Wooldridge, J.M., 2002. Econometric Analysis of Cross Section and Panel Data. MIT Press Cambridge, MA, USA. Xin-gang, Z., Yuan-feng, Z., Yan-bin, L., 2019. The spillovers of foreign direct investment and the convergence of energy intensity. J. Clean. Prod. 206, 611–621. https://doi. org/10.1016/j.jclepro.2018.09.225. Yaguchi, Y., Sonobe, T., Otsuka, K., 2007. Beyond the Environmental Kuznets Curve: a comparative study of SO2 and CO2 emissions between Japan and China. Environ. Dev. Econ. 12 (3), 445–470. https://doi.org/10.1017/S1355770X07003592. Yandle, B., Vijayaraghavan, M., Bhattarai, M., 2002. The Environmental Kuznets Curve: a primer. In: PERC Research Study. 02-1, pp. 1–24. Available at: http://www.perc. org/articles/article688.php. Zhang, S., Liu, X., Bae, J., 2017. Does trade openness affect CO2 emissions? Evidence from the new industrialized countries. Environ. Sci. Pollut. Res. 24 (21), 17616–17625. https://doi.org/10.1007/s11356-017-9392-8. Zheng, J., Zheng, P., 2017. The impact of foreign direct investment on the environment: market perspective and evidence from China. Economies 5 (1), 1–15. https://doi.org/ 10.3390/economies5010008.

Kirkulak, B., Qiu, B., Yin, W., 2011. The impact of FDI on air quality: evidence from China. J. Chin. Econ. Foreign Trade Stud. 4 (2), 81–98. https://doi.org/10.1108/ 17544401111143436. Kivyiro, P., Arminen, H., 2014. Carbon dioxide emissions, energy consumption, economic growth and foreign direct investment. Energy 74, 595–606. https://doi.org/10.1016/ j.energy.2014.07.25. Liang, F.H., 2008. Does Foreign Direct Investment Harm the Host Country's Environment? Evidence From China. SSRN Paper. 1479864. pp. 1–29. https://doi.org/10.2139/ ssrn.1479864. Available at: http://ssrn.com/abstract=1479864 (accessed 30 August 2018). Lieb, C.M., 2003. The Environmental Kuznets Curve. A Survey of the Empirical Evidence and of Possible Causes. Discussion Paper of the Economics Department of the University of Heidelberg. pp. 391. Available at: http://www.uni-heidelberg.de/md/ awi/forschung/dp391.pdf, Accessed date: 28 August 2017. Lucas, R.E.B., Wheeler, D., Hettige, H., 1992. Economic development, environmental regulation and the international migration of toxic industrial pollution 1960–1988. In: Low, P. (Ed.), International Trade and the Environment. World Bank, Washington D.C., USA. Maddala, G.S., Wu, S., 1999. A comparative study of unit root tests with panel data and a new simple test. Oxford B. Econ. Stat. 61, 631–652. https://doi.org/10.1111/14680084.0610s1631. Managi, S., Hibiki, A., Tsurumi, T., 2008. Does Trade Liberalization Reduce Pollution Emissions? RIETI Trade and Industry Discussion Paper Series. 08-E-013. pp. 1–45. Available at: http://www.rieti.go.jp/jp/publications/dp/08e013.pdf, Accessed date: 10 March 2017. Mazzanti, M., Montini, A., Zoboli, R., 2007. Economic dynamics, emission trends and the EKC hypothesis. New evidence using NAMEA and provincial panel data for Italy. In: Fondazione ENI Enrico Mattei (FEEM) Working Paper. vol. 24. pp. 1–29. https://doi. org/10.2139/ssrn.970270. McAusland, C., 2010. Globalisation's direct and indirect effects on the environment. In: OECD, Globalisation, Transport and the Environment. OECD Publishing, Paris, France. https://doi.org/10.1787/9789264072916-4-en. Melane-Lavado, A., Álvarez-Herranz, A., González-González, I., 2018. Foreign direct investment as a way to guide the innovative process towards sustainability. J. Clean. Prod. 172, 3578–3590. https://doi.org/10.1016/j.jclepro.2017.03.131. OECD, 2001. Environmental Outlook. OECD Publishing, Paris, France. https://doi.org/ 10.1787/9789264188563-en. OECD, 2002. Foreign direct investment for development. In: Maximising Benefits, Minimising Costs. OECD Publishing, Paris, France. https://doi.org/10.1787/ 9789264199286-en. Omri, A., Khuong Nguyen, D., Rault, C., 2014. Causal interactions between CO2 emissions, FDI, and economic growth: evidence from dynamic simultaneous-equation models. Econ. Model. 42, 382–389. https://doi.org/10.1016/j.econmod.2014.07. 026. Pazienza, P., 2014. The Relationship between FDI and the Natural Environment. Facts, Evidence and Prospects. Springer, New York, USA. https://doi.org/10.1007/978-3319-04301-2. Perman, R., Stern, D.I., 2003. Evidence from panel unit root and cointegration tests that the Environmental Kuznets Curve does not exist. Aust. J. Agric. Resour. Econ. 47 (3), 325–347. https://doi.org/10.1111/1467-8489.00216. Shafik, N., Bandyopadhyay, S., 1992. Economic growth and environmental quality: Time series and cross country evidence. In: World Bank Working Paper. vol. 904. pp. 1–55. Shahbaz, M., Nasreen, S., Afza, T., 2015. Does foreign direct investment impede environmental quality in high-, middle-, and low-income countries? Energy Econ. 51, 275–287. https://doi.org/10.1016/j.eneco.2015.06.014.

Consulted websites The Carbon Dioxide Information Analysis Centre (CDIAC) https://cdiac.ess-dive.lbl.gov/, Accessed date: January 2019. The International Energy Agency – Statistics https://www.iea.org/statistics/, Accessed date: June 2018. The International Labour Organization – Statistics https://www.ilo.org/ilostat, Accessed date: June 2018. The International Monetary Fund - Data https://www.imf.org/en/Data, Accessed date: June 2018. The International Trade Centre http://www.intracen.org/itc/market-info-tools/foreigndirect-investment/, Accessed date: June 2018. The OECD Investment Statistics http://www.oecd.org/investment/statistics, Accessed date: January 2019. The UK Data Service – International Direct Investment Statistics http://stats. ukdataservice.ac.uk, Accessed date: July 2018. The United Nations – Statistics Division https://unstats.un.org/home/, Accessed date: July 2018. The World Bank Development Data http://databank.worldbank.org, Accessed date: July 2018. The World Bank Open Data https://data.worldbank.org/, Accessed date: July 2018.

68