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Article

China’s Outward Foreign Direct Investment and the Environmental Performance of the “Belt and Road Initiative” Countries

by
Li Gao
* and
Fuyou Li
School of Economics and Finance, Xi’an Jiaotong University, Xi’an 710061, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(15), 11899; https://doi.org/10.3390/su151511899
Submission received: 24 May 2023 / Revised: 1 July 2023 / Accepted: 19 July 2023 / Published: 2 August 2023
(This article belongs to the Section Economic and Business Aspects of Sustainability)
Versions Notes

Abstract

:
Since the launch of the “Belt and Road Initiative” (BRI) in 2013, China’s outward foreign direct investment (OFDI) has grown rapidly. Moreover, the environmental protection issues introduced by these investment behaviors to BRI countries have attracted widespread attention from the international community. With the unbalanced panel data of 66 BRI countries from 2006 to 2020, this paper studied the impacts of China’s OFDI on the environmental performance of BRI countries from a systemic and partial perspective. We found that from a systemic perspective, China’s OFDI is conducive to the improvement of the comprehensive environmental performance of countries along the “Belt and Road”. From a partial perspective, the environmental performance influences of China’s OFDI in countries along the “Belt and Road” are threefold: (1) China’s OFDI can help mitigate climate change; (2) China’s OFDI improves wastewater treatment capacity; and (3) China’s OFDI has no significant impact on air quality. Therefore, China’s OFDI needs to continue its efforts to promote and improve the environmental performance of BRI countries to achieve their sustainable development goals. Some BRI developing countries should gradually change their extensive economic growth models; reduce their share of high energy-consuming, high-pollution, and inefficient industries in the national economy; and expand the proportion of their environmentally friendly industries while refraining from improving environmental performance by imposing high environmental pollution taxes.

1. Introduction

With the in-depth development of economic globalization, China’s OFDI share of global OFDI has frequently been listed among the top in the world in recent years [1]. The UNCTAD believes that foreign direct investment (FDI) is the largest source of external financing for developing countries and can complement the public investments needed to achieve the Sustainable Development Goals (SDGs), which is particularly important for developing countries [2]. The public expects business activities such as FDI to be consistent with the global SDGs [3], of which environmental sustainability goals are a subset. The BRI put forward by China in 2013 provided a cooperation platform for the investment and trade of participating countries, which greatly promoted common development among those countries. However, the international community worries that the vision of economic development under the BRI may conflict with the goal of environmental sustainability [4]. In recent years, the impact of China’s outward foreign investment on the environments of BRI countries has become especially concerning to the international community. Whether China’s large-scale OFDI in BRI countries has reduced the environmental performance of these countries remains inconclusive. The international community doubts that China’s large investments in BRI countries will cause serious environmental pollution to these countries [5]. However, in order to achieve the SDGs, the Chinese government has achieved a great deal of work on the environmental protection of China’s OFDI and is also actively promoting the construction of a green “Belt and Road” [6,7,8,9]. This mixture of factors that are both unfavorable and favorable to the environmental performance of BRI countries makes it difficult to draw a clear conclusion directly related to the environmental effects of China’s OFDI in BRI countries. Therefore, further academic discussions are needed on this topic.
Therefore, our research question is as follows: Does China’s OFDI reduce the environmental performance of BRI countries? Due to large differences in research methods and sample data, the existing literature exploring whether there is a definite relationship between China’s OFDI and environmental performance in BRI countries has not yet reached a conclusion [5,10,11]. In addition, we found that most of the existing literature uses a single environmental proxy variable from a partial perspective, instead of comprehensive environmental performance. However, environmental performance is a comprehensive multi-dimensional concept, and the conclusions drawn from a partial perspective are one-sided. For example, greenhouse gas emissions and carbon dioxide emissions can be classified as climate change variables. It is clearly not sufficient to use these single environmental proxy variables to represent a country’s environmental performance because the use of these single environmental proxy variables cannot fully reveal the multi-dimensional impact of FDI on a country’s environment [12]. Therefore, it is necessary to adopt a more comprehensive and multidimensional environmental performance index to study the relationship between China’s OFDI and BRI countries’ environments. We propose to study this issue under an analytical framework that combines a system perspective and partial perspectives of environmental performance.
The three main contributions of this paper are as follows. First, a multi-dimensional analytical framework is constructed for the relationship between China’s OFDI and the environmental performance of BRI countries. This framework not only studies the impact of China’s OFDI on the environmental performance of BRI countries from a system perspective but also considers the impact of OFDI on climate change mitigation, wastewater treatment, and air quality in BRI countries from partial perspectives. Secondly, the comprehensive and multidimensional Environmental Performance Index (EPI) is used as an overall environmental performance indicator to study the relationship between China’s OFDI and environmental performance in BRI countries. To our knowledge, existing studies do not apply comprehensive and multidimensional environmental proxy variables to study the relationship between China’s OFDI and environmental performance in BRI countries. Finally, we conclude that, from a systemic perspective, China’s OFDI is conducive to the improvement of the comprehensive environmental performance of countries along the “Belt and Road”. From partial perspectives, the environmental performance effects of China’s OFDI in countries along the “Belt and Road” are threefold: (1) China’s OFDI can help mitigate climate change; (2) China’s OFDI improves wastewater treatment capacity; and (3) China’s OFDI has no significant impact on air quality.
The above three contributions not only comprehensively respond to the concerns of the international community that China’s large OFDI in countries along the “Belt and Road” will cause environmental pollution but also contribute to the green development of China’s OFDI and BRI countries. The remaining sections of the paper are organized as follows. The second section is a literature review, the third section outlines the data and methodology, the fourth section provides a discussion of the empirical results, and the fifth section offers conclusions and policy implications.

2. Literature Review

One of the perspectives on the impact of FDI on environmental performance is the “Pollution Haven Hypothesis” (PHH). The pollution haven hypothesis, first proposed by Copeland, Taylor, and Chichilnisky, argues that developed countries have higher environmental regulatory standards, and multinational companies tend to shift polluting industries or sunset industries to developing countries with relatively low environmental regulatory standards in order to reduce the cost of implementing higher environmental standards, thereby worsening the environmental performance of host countries [13,14]. On this basis, Esty and Geradin found that in order to promote their own economic development and increase employment opportunities, developing countries take the initiative to lower their environmental protection standards or relax their environmental regulations to attract foreign investments, resulting in investor countries becoming more likely to transfer polluting industries to host countries and promoting the decline of environmental performance in developing countries; this phenomenon ultimately produces a “race to the bottom line” [15]. The emergence of a “race to the bottom line” in attracting foreign investment can significantly reduce the environmental standards of some countries [16], thereby losing the environmental protection role that environmental standards should play, which will further strengthen the “pollution haven” effect of international investments.
However, some researchers oppose the “pollution haven hypothesis”, arguing that environmental protection cost factors are not the only determinants of industrial transfer in developed countries and the location selection of multinational companies, and that corresponding analyses should also comprehensively consider other factors such as political and economic systems, factor endowments and prices, infrastructure conditions, education, and R&D when allocating global resources [17]. Most companies are committed to achieving SDGs in the economy and environment [18,19]. Mattera and Ruiz-Morales have indicated that multinational corporations contribute to sustainability and the SDGs more strongly than small–medium firms (SMEs) [18,20]. Under fierce international competition, multinational companies are willing to accelerate their research and development and promote technological upgrades to meet increasing environmental standards. Nidumolu and others found that environmentally friendly companies can take SDGs as the driving force, reduce environmental costs through innovation, and gain additional benefits, thus shaping sustainable competitive advantages [21]. Moreover, even in low-income countries, there are stricter environmental regulations [22], and multinational companies need to implement strict environmental standards that are globally uniform. The environmentally friendly and efficient management technologies promoted by such countries may have spillover effects on the environmental standards and technologies of host enterprises, thereby helping to reduce partial pollution emissions [23,24]. This model, representing the antithesis of PHH, is known as the Pollution Halo Hypothesis (PHL). The hypothesis holds that the higher environmental standards and more advanced technologies of multinational companies will allow host countries to learn from them, thus helping to improve the environmental performance of developing countries.
Some perspectives on this topic are more eclectic and argue that the impact of FDI on the host country’s environment is complex and multidimensional. Additionally, there may be both PPH and PPL effects between FDI and environmental performance, or there may be no obvious causal relationship. Kim and Adilov demonstrated that the impact of FDI on the environment varies according to the level of economic development, environmental policies, and geographic region of the host country; these findings support both PHH and PHL [25]. Chinese researchers have also studied the impact of FDI on China’s environmental performance, noting that the environmental effect of FDI is a “double-edged sword”. On the one hand, multinational companies transferring high-pollution and energy-consuming industries to China is not conducive to improving China’s environmental performance. On the other hand, this measure can produce positive effects that improve environmental performance through advanced technology and higher environmental protection standards [26,27]. Hoffmann et al. argued that for low-income countries, the degree of pollution caused by the entry of some multinational companies into these countries may be offset by the green technology transferred by other multinational companies to such countries [28].
In the context of the Belt and Road Initiative, some scholars have discussed the impact of BRI on the environment of participating countries. Some studies found that the implementation of BRI has a positive policy effect on the environmental quality of countries along the routes [5,29]. Furthermore, Cao and others confirmed that BRI can improve the environmental quality of countries along the routes by promoting technological progress and strengthening environmental regulation [30]. Zhang et al. argued that BRI will contribute to global prosperity and may also lead to an increase in global carbon emissions [31]. Additionally, some studies have also discussed BRI and SDGs. The UNDP believes that BRI can promote the sustainable transformation of countries and regions along the routes, focusing on poverty reduction, environmental sustainability and inclusive social development [32]. Yin studied the possibility of integrating SDGs into BRI, arguing that integrating SDGs into the implementation of BRI could accommodate the interests of different countries [33].
To explore whether China’s OFDI has positive environmental effects on host countries, studies in the existing literature use different proxy variables and sample ranges. Some studies used environmental proxy variables such as CO2 emissions, CO2 emissions per capita, GDP per unit of energy use, and CO2 intensity to study the environmental effects of China’s OFDI on BRI countries. Such studies have drawn different conclusions. Some studies found that the implementation of the BRI has a strong policy effect, which can effectively enhance the positive impact of China’s OFDI on the environments of host countries [5,29]. Additionally, the economic scale effects and industrial structure effects of China’s OFDI have increased the carbon emissions of BRI countries [11]. The production technology effect has inhibited the carbon emissions of BRI countries and played a leading role [11]. An et al. found that Chinese OFDI increases carbon emissions in countries with high and medium emissions. In countries with low-to-medium levels of pollution, people’s connectivity increases emissions, while innovation helps reduce carbon emissions [34]. Kamal et al. studied the environmental effects of the quality of institutions and China’s OFDI in BRI countries and found that when the quality of the host country’s institutions exceeds the threshold, China’s OFDI can reduce carbon emissions in the corresponding BRI region [35].
However, to the best of our knowledge, when studying the impact of Chinese OFDI on the environmental performance of host countries, most studies use single greenhouse gas emissions or CO2 emissions as proxy variables for environmental performance. Greenhouse gases and CO2 represent only one aspect of environmental performance (i.e., the mitigation of climate change) and do not reflect the multidimensional environmental effects of host countries. To study the combined environmental effects of China’s OFDI on BRI countries, it is necessary to replace a single variable with a more comprehensive and integrated environmental performance proxy variable. Additionally, the Environmental Performance Index (EPI) evaluates the environmental performance of many countries around the world using indicators of climate change mitigation, ecosystem vitality, and environmental health. This index is not only comprehensive but also reliable and was jointly implemented by Yale University’s Center for Environmental Law and Policy and Columbia University’s Center for International Earth Science Information Network (CIESIN) [36]. Therefore, in this study, we use the EPI as a comprehensive and multidimensional environmental performance proxy variable to study the impact of China’s OFDI on the environment performance of BRI countries from a system perspective and partial perspective.

3. Data and Methodology

3.1. Data and Variables Description

Considering the availability of data, this paper uses national-level unbalanced panel data from 66 countries along the Belt and Road (Table A1) from 2006 to 2020 as a sample to study the impact of China’s OFDI on the environmental performance of BRI countries. We deleted the missing values, removed the outliers, and ultimately used 376 observations in the benchmark regression. The explained variable EPI in this paper originated from the Yale Center for Environmental Law and Policy. The EPI is published every two years, and the latest data release year is 2022. As the data for other major variables in 2022 have not yet been published, the EPI data in 2022 were used in this study only to solve the endogenous problems caused by reverse causality. When the core explanatory variables lag by one period, the use of 2022 EPI data does not lose more observations due to the core explanatory variable’s single lag period. The explanatory variables in this paper were sourced from the WIND database and the World Bank. Observable control variables data were sourced from the UNCTAD, World Bank, IMF, Heritage Foundation, and the WIND database (Table A2).
(1) Explained variables: Firstly, we used the Environmental Performance Index (EPI) to reflect environmental quality from a system perspective. Based on the experience of Adeel-Farooq et al. in selecting environmental performance variables, we used the EPI as a proxy variable for the comprehensive environmental performance of host countries [12,37] to comprehensively evaluate the impact of China’s OFDI on the overall environmental performance of countries along the “Belt and Road”. The EPI provides country-level data calculated from corresponding sub-environmental performance indicators. Additionally, the EPI includes three policy objectives: climate change mitigation, environmental health, and ecosystem vitality. These policy objectives are divided into multiple issue categories such as climate change, air quality, drinking water health, heavy metal pollution, biodiversity and habitat, fisheries, agriculture, and wastewater treatment, and the issue categories are classified into more specific environmental performance indicators. On this basis, the EPI is calculated by the weights step by step [36]. The value range of the EPI index is (0–100), where the higher the EPI value, the higher the overall environmental performance. Secondly, we used climate change mitigation (CCH), wastewater treatment (WWT), and air quality (AIR) as environmental proxy variables from partial perspectives. (1) Climate change mitigation (CCH): To study the impact of China’s OFDI on climate change in BRI countries, this paper used the climate change mitigation index, a sub-indicator of the EPI, as a proxy variable of climate change in the host country [36]. This index is calculated from a combination of CO2 growth rate, CH4 growth rate, F-Gas growth rate, N2O growth rate, black carbon growth rate, per-capita GHG emissions, and GHG intensity trends. (2) Wastewater treatment (WWT): Water pollution is one of the most important factors affecting ecosystem vitality, and wastewater treatment can make a certain contribution to solving the problem of water pollution. To study the impact of China’s OFDI on wastewater treatment in countries along the Belt and Road, this paper used the sub-indicator wastewater treatment index of the EPI as a proxy variable for wastewater treatment in host countries [36]. (3) Air quality (AIR): Air quality is one aspect of environmental health. To study the impact of China’s OFDI on the air quality of BRI countries, this paper used the AIR index, a sub-indicator of the EPI, as a proxy variable for host country air quality [36]. The value range of the above CCH, WWT, and AIR indicators is 0–100, where the higher the index value, the better the environmental performance.
(2) Core explanatory variables: According to the research of Liu and Wang [5] and Ge et al. [11], we used the scale of foreign investment from China as a percentage of the host country’s GDP to measure the level of Chinese direct investment in host countries to avoid the heteroscedasticity caused by absolute amounts. Specifically, we used the flow of China’s OFDI as a percentage of the host country’s GDP (CFDIfGDP) as the core explanatory variable. At the same time, to obtain a more robust estimation, we used the stock of China’s OFDI as a percentage of the host country’s GDP (CFDIsGDP) instead of CFDIfGDP in the robustness test to estimate the impact of China’s OFDI on the environmental performance of host countries. The core explanatory variables are detailed in Table A2.
(3) Control variables: Based on existing research exploring the impact of foreign direct investment on the environmental performance of host countries, our control variables were as follows. To accurately distinguish the different effects of China’s OFDI and FDI from other countries on the environmental performance of host countries, we used the scale of FDI from other countries excluding China (eCFDIfGDP) based on the variable settings of LIU and DAI [38] to measure the impact of FDI from other countries on the environmental performance of host countries. In addition, comprehensively considering the important factors affecting the environmental performance of host countries, we controlled variables such as the level of economic development (lnGPC), industrial structure (IndaddGDP), population distribution (lnPDN), trade scale (eximGDP), average level of government governance capacity (meanWGI), environmental tax burden (Poltaxgdp), and the extent to which the government does not restrict investment (InvestFree) based on existing variable settings in the existing literature [11,12,38,39]. The meanWGI was derived from the worldwide governance index in the WGI database, which includes Voice and Accountability, Political Stability No Violence, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. We averaged the six variables to obtain the meanWGI indicator. Annual dummy variables and national dummy variables were also added to control annual and national fixed effects, respectively. The above observable control variables are detailed in Table A2.
Table 1 outlines the descriptive statistics and multicollinearity testing of the main variables. The mean and standard deviation of the host country EPI were 58.231 and 15.311. Additionally, the mean and standard deviation of China’s OFDI as a percentage of the host country’s GDP (CFDIfGDP) were 0.469 and 1.382. This phenomenon indicates that the EPIs of different countries and the scale of China’s OFDI in the host country are significantly different, providing a good data foundation for the quantitative analysis in this paper. To avoid the multicollinearity problem, a variance inflation factor (VIF) test was performed [40]. The results showed that the mean VIF of all variables involved in benchmark regression was less than 5 (Table 1). As a rule of thumb, a multicollinearity problem can be considered nonexistent when the mean VIF of all variables does not exceed 5 [41]. For this reason, the variables selected in this paper were considered not to have multicollinearity problems.

3.2. Model

This model uses panel data to estimate the impact of China’s OFDI on the environmental performance of BRI countries. According to the existing literature, cointegration tests are not suitable due to the short period (eight periods) of data in this study. Such tests usually cannot provide reliable results for datasets of shorter periods [12,50]. The ordinary least squares method cannot provide consistent results in panel data estimation because it cannot capture the unobserved heterogeneity among cross sections [51]. The methods of fixed effects and random effects can help overcome problems such as heterogeneity bias [12]. When estimating other conditional invariant effects, it is generally argued that FE is a more convincing estimator [52] in which the fixed-effects method has obvious advantages over the random-effects and ordinary least squares [12,53,54], helping to address endogenous problems caused by time-independent and unobservable variables. Therefore, to robustly estimate the impact of China’s OFDI on the environmental performance of BRI countries, we construct the following fixed-effect model:
E P I i , t = α 0 + α 1 CFDIfGDP i , t + α 2 eCFDIfGDP i , t + α 3 lnGPC i , t + α 4 IndaddGDP i , t                                                           + α 5 lnPDN i , t + α 6 eximGDP i , t + α 7 meanWGI i , t + α 8 Poltaxgdp i , t                                                           + α 9 InvestFree i , t + μ i + γ t + ϵ i , t
where i and t represent the country and year. EPIi,t represents the environmental performance of a country in a given year, which captures environmental performance from both a systematic perspective (measured by a comprehensive environmental performance index) and partial perspectives, such as climate change mitigation, wastewater treatment, and air quality. Here, α0 represents a constant term. CFDIfGDPi,t is the core explanatory variable, representing the scale of China’s investment in a country in a certain year. Additionally, α1 is the core explanatory variable coefficient in this study, representing the relationship between China’s OFDI and the environmental performance of BRI countries, while α2 to α9 are the coefficients of the observable control variables, which represent the degrees of impact of the observable control variable on environmental performance. The details of the observable control variables are shown in Table A2; µi is a fixed effect of the country dimension, which is used to control the unobservable factors of the country dimension and does not change over time; γt is the fixed effect of the year, which is used to control the impact of unobservable factors that change over time on environmental performance; and εi,t represents the residual term.

4. Empirical Results and Discussion

4.1. Analysis of Regression Results from a Systematic Perspective

In this part, we seek to answer whether China’s OFDI reduces the environmental performance of BRI countries from a systematic perspective. We estimated the environmental performance impact of China’s OFDI on BRI countries (M1 and M2 in Table 2). M1 lists the regression results without observable control variables. Additionally, M2 lists the regression results with all observable control variables. The coefficient of the core explanatory variable (CFDIfGDP) shows that China’s OFDI has a significant positive impact on the systemic environmental performance of BRI countries, regardless of whether the observable variables are controlled. Based on M2 with more comprehensive control variables, the coefficient of CFDIfGDP is 0.791, which is significant at a level of 1%. Therefore, for every 1% increase in China’s OFDI share of the host country’s GDP, the host country’s EPI increases by 0.791 points. Additionally, the coefficients of the control variables lnGPC and Poltaxgdp are significantly negative, while the coefficient of eximGDP is significantly positive.
We draw from the empirical results showing that China’s OFDI has a positive role in improving the overall environmental performance of host countries, which is in line with the “pollution halo hypothesis”. This result is mainly supported by the following three factors. On the one hand, institutions are an important driving force for multinational enterprises to engage with specific SDGs [55]. The Chinese government has accomplished a great deal of environmental protection work on OFDI for Chinese enterprises entering the global market [6,7,8,9]. The OFDI environmental protection policies formulated by the Chinese government explicitly encourage China’s OFDI enterprises to implement a green development model. On the other hand, multinational enterprises play a leading role in research and innovation related to technology-oriented topics such as clean technologies [56,57]. China’s OFDI enterprises are either state-owned and backed by strong state-owned capital, or private, with strong technological competitiveness in the international market. These two types of OFDI enterprises have excellent capital strength and the ability to invest sufficient money to develop advanced environmental technologies [11]. In addition, China’s OFDI enterprises need to shape their competitive advantages in environmental protection to achieve the SDGs [21], so they have a drive to develop advanced environmental technologies to improve the environmental performance of host countries.
Among the control variables, (1) we found that the economic growth of BRI countries reduced their environmental performance. The reason for the negative impact of economic growth on environmental performance may be that the majority of BRI countries are developing countries (52 developing countries out of 66 BRI countries, as shown in Table A1), and developing countries urgently need to develop their economies to improve the economic living standards of their residents. Accordingly, these countries prefer economic development over environmental protection. Developing countries have a relatively backward level of economic development and increases in their economic size and income are mainly dependent on the domestic consumption of natural resources and energy, which leads to an increase in polluting emissions and the deterioration of environmental performance. The environmental Kuznets curve theory also shows that economic growth can lead to deterioration of environmental performance in the early stages of development of a country or economy [58,59]. (2) Trading positively promotes the improvement of environmental performance. This factor partly demonstrates that BRI countries are working towards sustainable development goals in international trade. In addition, under the environmental rules in international trade agreements, import and export commodities need to meet the minimum standards stipulated in the environmental provisions of international trade agreements. Additionally, the green provisions related to environmental protection in these trade agreements help to restrict the import and export of high-pollution, high-emission, and low-value-added products, which is conducive to improving overall environmental performance. (3) The environmental tax burden has a significantly negative impact on the environmental performance of BRI countries, possibly because the transformation of polluting enterprises into environmentally friendly enterprises is a long process, and the increase in environmental pollution taxes will increase the environmental pressure of enterprises, which is not conducive to polluting enterprises investing more money to develop advanced environmental technologies or to improving environmental performance.

4.2. Analysis of Regression Results from Partial Perspectives

We also sought to determine whether China’s OFDI reduces the environmental performance of BRI countries from partial perspectives. That is, we analyzed the impacts of China’s OFDI on environmental performance in BRI countries from the perspective of climate change mitigation, wastewater treatment, and air quality (Table 3). The results show that China’s OFDI has significant positive impacts on climate change mitigation and wastewater treatment in BRI countries (M3 and M4 in Table 3). However, the impact of China’s OFDI on air quality in BRI countries is not significant (M5 in Table 3).
China’s OFDI is positive and significant in mitigating climate change in BRI countries, which is mainly attributed to the use of advanced carbon emission reduction technologies by Chinese OFDI enterprises in BRI countries. These technologies have significant carbon emission reduction effects on BRI countries and help to mitigate climate change in those countries. This positive effect is consistent with the results of Liu and Wang and Chen et al. [5,11]. Meanwhile, China’s OFDI enterprises do not increase the burden of wastewater treatment in host countries but instead improve the wastewater treatment capacity of BRI countries. This result is largely related to the technology spillover effects of China’s OFDI [23]. However, based on the available evidence, it is impossible to determine whether China’s OFDI contributes to improving the air quality in BRI countries.

4.3. Robustness Test

4.3.1. Endogenous Discussion

In this study, we applied a one-stage lag of explanatory variables, system GMM, and time-varying DID to solve the possible endogenous problems of the model. The estimation results considering endogeneity are shown in Table 4.
First, to cut off the reverse causal relationship between China’s OFDI and the environmental performance of the host country and thus avoid the problem of inaccurately estimating the empirical results caused by reverse causation, we referred to the existing literature [12], lagged the core explanatory variable CFDIfGDP by one period (d1CFDIfGDP), and treated all control variables with a lag of one period. The empirical results (M6 in Table 4) demonstrate that the coefficient of d1CFDIfGDP is significantly positive. This result means that after cutting off the possible reverse causality between the explained variable and the explanatory variables, the coefficient direction is the same as the results shown in Table 2. Additionally, this result demonstrates that China’s OFDI contributes to improving the environmental performance of BRI countries.
Secondly, we used the two-step robust system GMM estimation method to solve the possible endogenous problems. The research results of Blundell and Bond [60] showed that the difference GMM estimator is easily influenced by weak instrumental variables, resulting in large, limited sample deviation, while the system GMM has better limited sample properties. When using the system GMM estimation method, the explained variable, first-order, and even multi-order lag terms of the explanatory variables will reduce the sample size. To obtain robust results, we used interpolation to fill in the missing values to expand the sample size. M7 presents the empirical results of the two-stage robust system GMM (Table 4). The coefficient of CFDIfGDP is significantly positive, which is consistent with the results in Table 2.
Finally, since ignoring the policy variable of “the Belt and Road Initiative” in the model may lead to inconsistent estimation of the impact of China’s OFDI on the environmental performance of BRI countries, we included the policy variable of “the Belt and Road Initiative” in the model to control the impact of “the Belt and Road Initiative” on environmental performance. We also collected the time node data of all BRI countries in the sample together with the “the Belt and Road Initiative” and used interpolation to fill in the missing values. Considering that different countries were influenced by the “the Belt and Road Initiative” policy at different times, we used the method in [61] as a reference. Then, on the premise that the parallel trend test passes, we used the time-varying DID method to perform estimations. The estimation results show that the coefficient of CFDIfGDP is still significantly positive (M8 in Table 4) after considering the influence of “the Belt and Road Initiative” policy. This result means that China’s OFDI positively improves the environmental performance of BRI countries, which further demonstrates the robustness of the conclusions in this study.
In summary, after considering endogenous issues, this study uses the above three methods to re-estimate the impact of China’s OFDI on the environmental performance of BRI countries. We found that the impact of China’s OFDI on the environmental performance of BRI countries is significantly positive, which indicates that the results in Table 2 are robust.

4.3.2. Other Robustness Test Results

We also tested the robustness of the results in Table 2 by substituting core explanatory variables and using different samples. (1) Replacing the core explanatory variables: We replaced CFDIfGDP with the stock scale of China’s OFDI (CFDIsGDP), and the result indicates that (M9 in Table 5) the sign and significance of the estimated coefficient of CFDIsGDP are consistent with the results in Table 2. The results in Table 2 are robust and not affected by the specific form of the core explanatory variables. (2) Eliminating the influence of outliers: To eliminate the bias caused by outliers on model estimation, we winsorized all the variables at levels of 1% and 99%. The symbols of the variables after winsorization were EPI_w and CFDIfGDP_w. The result shows (M10 in Table 5) that the sign and significance of the CFDIfGDP_w coefficient are consistent with the results in Table 2. (3) Expanding the sample size: In this study, linear interpolation was used to complete the missing values of EPI in certain years to expand the sample size. The re-estimation was then based on the expanded sample size. The result demonstrates (M11 in Table 5) that the estimated coefficient of CFDIfGDP is significantly positive, which is consistent with the results in Table 2. (4) Applying different sample periods: We divided the sample into two groups according to the annual nodes that put forward the Belt and Road Initiative, namely the sample group before the Belt and Road Initiative and the sample group after the Belt and Road Initiative. The results demonstrate that the estimated coefficients for CFDIfGDP are significantly positive (M12 and M13 in Table 5), which is consistent with the results in Table 2.
In conclusion, after replacing the core explanatory variable, performing 1–99% tail reduction treatment on all variables, expanding the sample size, and regressing according to different sample periods, all results show that the regression coefficients are significantly positive, consistent with the conclusions drawn in Table 2. Overall, this outcome demonstrates that the results in Table 2 are robust.

5. Conclusions and Policy Implications

5.1. Conclusions

In this paper, we constructed an analytical framework combining a systematic perspective and partial perspective. Based on the unbalanced panel data of 66 BRI countries (Table A2) from 2006 to 2020, we used the fixed effect model to test the impacts of China’s OFDI on the environmental performance of BRI countries from systematic and partial perspectives. The general conclusions are as follows: From a systemic perspective, China’s OFDI promotes improvement of the overall environmental performance of BRI countries. Increasing the trade scale of BRI countries also helps to improve environmental performance, whereas economic growth and the imposition of environmental pollution taxes in BRI countries are not conducive to environmental performance. From partial perspectives, China’s OFDI helps to mitigate climate change and improve wastewater treatment capacity in BRI countries, but it has no significant impact on air quality.
Additionally, we found that the coefficient of the BRI policy was not significant. Based on the existing evidence, it is hard to judge whether BRI has promoted the improvement of host countries’ environmental quality. This is because BRI green development policy has been implemented for a short period of time, and its impact on the environmental performance of host countries has not yet appeared. The direct purpose of BRI in 2013 was to promote economic cooperation, such as investment and trade among participating countries to achieve common prosperity [62]. In April 2019, the BRI Green Development International Alliance was established, proposing to promote green infrastructure construction, green investment and trade, and constantly promote international consensus and common action on green development [63]. At that time, the implementation purpose of the BRI policy was officially expanded to the field of environmental protection by participating countries. Then the COVID-19 epidemic in 2020–2021 led to the decline or even stagnation of various international cooperation projects advocated by BRI [64]. Therefore, the effective time for the implementation of the BRI green development policy is less than two years. Considering that environmental protection in BRI countries is a complex and long-term project, it is not surprising that the short-term implementation effect of the BRI green development policy is not obvious.
Next, we discuss the differences between this paper and the existing literature. Existing related studies have fully discussed the impact of BRI policy on the economies and environments of relevant countries. Some studies have used a single variable to study the impact of China’s OFDI on the environment of BRI countries. However, unlike the existing literature, we discussed the environmental impact of China’s OFDI on BRI countries from different perspectives and revealed the multidimensional relationship between them. Ultimately, the conclusions regarding the impacts of China’s OFDI on overall environmental quality and the mitigation of climate change in BRI countries are consistent with some existing studies [5,11,38]. However, to our best knowledge, we found no published literature consistent with the conclusions in this study, i.e., that China’s OFDI has impacts on wastewater treatment in BRI countries. To a great extent, our research conclusions indicate that China’s OFDI has positive impacts on the environmental performance of BRI countries. These results comprehensively respond to the international community’s doubts that China’s massive OFDI in Belt and Road Initiative countries will cause environmental pollution.

5.2. Policy Implications

Based on the above conclusions, we propose some suggestions for the sustainable development of China’s OFDI and the improvement of environmental performance in BRI countries.
The Chinese government, China’s OFDI enterprises, and countries along the BRI need to implement the “Belt and Road Initiative” under a framework of high environmental standards. The Chinese government needs to take the initiative to align with the environmental quality standards of BRI countries, strongly advocate for green development policies in OFDI, and continue to promote construction of the green “Belt and Road”. It is also necessary to formulate a strict OFDI environmental performance supervision system, which would require China’s OFDI enterprises to be responsible for environmental protection in host countries. In the process of investment in host countries, China’s OFDI enterprises should continue to uphold the concept of green development, continuously improve environmental technical standards, and actively contribute to the environmental improvement of BRI countries. Developing clean energy and importing clean technology will not only achieve commercial benefits but also create tangible benefits for the people in BRI countries, in addition to other benefits. Moreover, BRI countries should strive to achieve sustainable development of resources and the environment while achieving their economic growth goals. To transform the extensive economic growth model, some BRI developing countries should reduce their share of industries with high energy consumption, high pollution, and low efficiency in their national economies and expand the proportion of their environmentally friendly industries. Meanwhile, BRI countries should not be encouraged to improve their environmental performance by levying high environmental pollution taxes. Since the transformation of polluting enterprises into environmentally friendly enterprises is a long process, the collection of high environmental pollution taxes would increase the environmental pressure on such enterprises. This pressure would not help polluting enterprises invest more money in developing advanced environmental technologies and would ultimately not improve environmental performance.

Author Contributions

Conceptualization, L.G. and F.L.; methodology, L.G.; validation, L.G. and F.L.; formal analysis, L.G.; data curation, L.G.; writing—original draft preparation, L.G.; writing—review and editing, L.G.; supervision, F.L.; project administration, F.L.; funding acquisition, F.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Major Project of the National Social Science Fund of China “Research on Improving Policies and Service Systems for Promoting Outward Foreign Investment under the Background of the ‘Belt and Road Initiative’” (Grant No. 20ZDA051).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

The appendix contains the List of 66 “Belt and Road” Countries (Table A1) and Variable Descriptions (Table A2).
Table A1. The List of 66 “Belt and Road” Countries.
Table A1. The List of 66 “Belt and Road” Countries.
“Belt and Road” Countries
AustriaCyprusCzech RepublicBoliviaMongolia
GreeceHungaryItalyCabo VerdeNiger
Korea, Rep.LithuaniaLuxembourgCambodiaNigeria
MaltaNew ZealandPolandCameroonPhilippines
SingaporeSlovak RepublicKazakhstanChadRwanda
AlbaniaCroatiaMalaysiaCongo, Rep.Senegal
ArgentinaDominican RepublicPeruCote d’IvoireTogo
BotswanaEcuadorNamibiaEgypt, Arab Rep.Tunisia
BulgariaEquatorial GuineaSamoaGhanaUganda
ChileFijiSerbiaKenyaUkraine
Costa RicaGeorgiaSeychellesKyrgyz RepublicPakistan
ThailandJamaicaSouth AfricaMadagascarMali
Venezuela, RBTurkeyUruguay
PanamaBangladeshMauritania
Note: Countries in bold are developed countries, and countries not in bold are developing countries. Source: The data were compiled manually by the authors according to ref. [49].
Table A2. Variable Descriptions.
Table A2. Variable Descriptions.
Variable ClassificationVariable NameSymbolVariable MeaningData Sources
The explained variable from system perspectiveEnvironmental performance index (range: 0–100)EPIThe comprehensive environmental performance of countries along the “Belt and Road”Yale Center for Environmental Law & Policy
The explained variables of partial perspectivesClimate change mitigation index
(range: 0–100)		CCHThe climate change mitigation performance for the Belt and Road countriesYale Center for Environmental Law & Policy
Wastewater treatment index
(range: 0–100)		WWTThe level of wastewater treatment of “Belt and Road” countriesYale Center for Environmental Law & Policy
Air quality index (range: 0–100)AIRThe air quality of countries along the “Belt and Road”Yale Center for Environmental Law & Policy
Core explanatory variablesThe flow of China’s OFDI as a percentage of the host country’s GDPCFDIfGDPThe flow scale of China’s OFDI in various countriesWind and the World Bank, GDP in current US dollars
The stock of China’s OFDI as a percentage of the host country’s GDPCFDIsGDPThe stock scale of China’s OFDI in various countriesWind and the World Bank, GDP in current US dollars
Control variablesThe flow of FDI from other countries as a percentage of the host country’s GDP (excluding China)eCFDIfGDPThe flow scale of FDI from other countries (excluding China) in each countryUNCTAD and Wind, GDP in current US dollars
GDP per capita (taking the logarithm in the empirical case)lnGPCThe level of economic development in each countryYale Center for Environmental Law & Policy
Industrial value added (% of GDP)IndaddGDPIndustrial structure in various countriesWDI
Exports and imports of goods and services (% of GDP)eximGDPThe scale of trade in each countryWDI
Investment Freedom (range: 0–100)InvestFreeThe extent to which the government does not restrict investment The Heritage Foundation
Government Governance Capability
(range: 0–100)		meanWGIThe average level of government governance capacity in various countriesWGI
Taxes on Pollution (% of GDP)PoltaxgdpThe environmental tax burden in each countryIMF
Population density (taking the logarithm in the empirical case)lnPDNPopulation distribution in various countriesYale Center for Environmental Law & Policy
Source: The data were compiled manually by the authors.

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Table 1. Descriptive Statistics for the Variables and Multicollinearity Test.
Table 1. Descriptive Statistics for the Variables and Multicollinearity Test.
Variables NameObsMeanSDMinMedianMaxVIF1/VIF
EPI37658.23115.31118.43058.36588.480
CFDIfGDP3760.4691.3820.0000.05613.9331.200.835973
eCFDIfGDP3765.75916.957−13.3452.622254.2121.230.812025
lnGPC3769.1971.0706.8899.27511.6403.140.318743
IndaddGDP37625.9768.99510.03525.13566.1791.410.709513
eximGDP37690.67558.74820.72375.094376.2951.920.520787
InvestFree37655.85118.9835.00055.00095.0002.230.447660
meanWGI37646.10823.1725.15344.14498.6314.240.235882
Poltaxgdp3760.0260.0630.0000.0010.4101.140.876548
lnPDN3764.1841.3700.5524.3729.0261.160.861909
Mean VIF 1.96
Sources: Authors’ estimations using data from ref. [36,42,43,44,45,46,47,48,49].
Table 2. Regression Results from a Systematic Perspective.
Table 2. Regression Results from a Systematic Perspective.
Explained Variable (EPI)
Explanatory variablesM1M2
CFDIfGDP0.497 **0.791 ***
(0.223)(0.199)
eCFDIfGDP −0.003
(0.015)
lnGPC −12.807 **
(5.044)
IndaddGDP 0.226
(0.148)
eximGDP 0.123 ***
(0.022)
Poltaxgdp −23.128 **
(9.308)
InvestFree −0.069
(0.055)
lnPDN 6.375
(8.026)
cons61.049 ***140.613 ***
(0.694)(47.247)
Control VariablesNoYes
Year/Country FEYesYes
observations805.000376.000
adj R20.6570.704
F155.94180.162
Standard errors in parentheses, ** p < 0.05, *** p < 0.01. Sources: Authors’ estimations.
Table 3. Regression Results from Partial Perspectives.
Table 3. Regression Results from Partial Perspectives.
Explained VariablesCCH (Climate Change
Mitigation)		WWT (Wastewater Treatment)AIR (Air Quality)
Explanatory variablesM3M4M5
CFDIfGDP2.334 *2.429 **−0.44
(1.384)(1.215)(0.630)
Control VariablesYesYesYes
Year/Country FEYesYesYes
observations376384385
adj R20.5170.5110.367
F20.23753.49220.385
Standard errors in parentheses, * p < 0.1, ** p < 0.05. Sources: Authors’ estimations.
Table 4. Robustness Test (considering endogeneity).
Table 4. Robustness Test (considering endogeneity).
Explained Variable (EPI)
Explanatory VariablesM6M7M8
One-Period Lag for Independent VariablesSYS-GMMTime-Varying DID
d1CFDIfGDP0.714 **
(0.34)
d1EPI1 0.511 ***
(0.109)
CFDIfGDP 0.595 *0.822 ***
(0.339)(0.220)
BRI policy −1.005
(1.192)
Control VariablesYesYesYes
Year/Country FEYesYesYes
observations412650507.000
adj R20.729 0.677
F52.006 94.445
AR(2) p value 0.56
Hansen p value 0.586
Standard errors in parentheses, * p < 0.1, ** p < 0.05, *** p < 0.01. Sources: Authors’ estimations.
Table 5. Robustness Test (replace the core explanatory variables, shrink the tail, expand the sample size, and apply different sample periods).
Table 5. Robustness Test (replace the core explanatory variables, shrink the tail, expand the sample size, and apply different sample periods).
Explained VariablesEPI EPI_wEPIEPIEPI
Explanatory variablesM9M10M11M12M13
Replace the core explanatory variablesWinsorize all variables at level 1% and 99%Expand the sample sizeApply different sample periods
2006–2013 (Before BRI)2014–2020 (After BRI)
CFDIsGDP0.427 *** 2.238 ***0.717 **
(0.115) (0.676)(0.307)
CFDIfGDP_w 1.162 ***
(0.357)
CFDIfGDP 0.407 **
(0.174)
Control VariablesYesYesYesYesYes
Year/Country FEYesYesYesYesYes
observations376376692168208
adj R20.7020.7090.6950.7950.732
F78.21793.71159.32681.97135.866
Standard errors in parentheses, ** p < 0.05, *** p < 0.01. Sources: Authors’ estimations.
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Gao, L.; Li, F. China’s Outward Foreign Direct Investment and the Environmental Performance of the “Belt and Road Initiative” Countries. Sustainability 2023, 15, 11899. https://doi.org/10.3390/su151511899

AMA Style

Gao L, Li F. China’s Outward Foreign Direct Investment and the Environmental Performance of the “Belt and Road Initiative” Countries. Sustainability. 2023; 15(15):11899. https://doi.org/10.3390/su151511899

Chicago/Turabian Style

Gao, Li, and Fuyou Li. 2023. "China’s Outward Foreign Direct Investment and the Environmental Performance of the “Belt and Road Initiative” Countries" Sustainability 15, no. 15: 11899. https://doi.org/10.3390/su151511899

APA Style

Gao, L., & Li, F. (2023). China’s Outward Foreign Direct Investment and the Environmental Performance of the “Belt and Road Initiative” Countries. Sustainability, 15(15), 11899. https://doi.org/10.3390/su151511899

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