Research on the nonlinear dynamic relationship between FDI and CO2 emissions in the “One Belt, One Road” countries

Gong, Mengqi; Zhen, Shiqi; Liu, Haiyun

doi:10.1007/s11356-021-12532-6

Cited by 21 publications

(9 citation statements)

References 29 publications

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“…However, the inflows of FDI at the 25th quantile distributions of CO 2 emissions enhanced environmental quality. These shreds of evidence connote heterogeneous effects of FDI on environmental quality in Brazil, hence corroborating the positions of Lateef et al (2021) in the case of SAARC countries and Gong et al (2021) in the case of One Belt–One Road countries. On account of this mixed evidence and not forgetting the potential growth benefits of FDI in developing economies, policies should be streamlined to ensure that the inflow of FDI remains within the quantile (25th) where it can effectively restrain pollution and guarantee a cleaner environment for Brazilians or where the pollution halo (a blessing on the environment) is assured.…”

Section: Empirical Results and Discussionsupporting

confidence: 80%

“…As highlighted earlier, there is still a lack of consensus among environmental scholars concerning the dynamics of FDI and environmental health. The extreme position maintained that the relationship between the two variables was unexplainable (Gong et al, 2021). In all cases, we envisaged that these existing studies have several shortcomings that may have heightened the ambiguities in their submissions.…”

Section: Introductionmentioning

confidence: 99%

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Re‐estimating the pollution haven–halo hypotheses for Brazil via a machine learning procedure

Uche,

Omoke,

Silva‐Opuala

et al. 2023

J of Intl Development

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In this study, we re‐examined the pollution haven and halo hypotheses in Brazil for approximately five decades (1970–2019) while controlling for the effects of income, renewable energy and natural resource depletion. For clearer insights, the study employed both the conventional autoregressive distributed lag (ARDL) and the enhanced kernel regularized least squares (KRLS) techniques. Notably, the KRLS is a flexible machine learning nonlinear analytical technique that explains the interactions of the regressand and the regressors both at the average and across a range of quantiles. After ascertaining cointegration through the bounds tests and the Bayer–Hanck procedures, the following empirical outcomes emerged: The ARDL result suggests the acceptance of the pollution haven hypothesis in Brazil in both the short and long runs. However, the KRLS technique reveals that foreign direct investment (FDI) could enhance environmental quality (pollution halo) within the 25th quantile of the distributions of CO2 emissions. However, at the 50th and 70th quantiles, the pollution haven hypothesis is rectified. This suggests the adoption of varying policy options to ensure continuous inflows of FDI without compromising environmental quality. Additionally, among the control variables, a U‐shaped environmental Kuznets curve (EKC) structure is revealed from the influence of gross domestic product (GDP); renewable energy ensures a clean environment at all times, while resource rent ensures a clean environment only at the 25th and 50th quantiles of the distributions. Policies that could lead to clean environments in Brazil have been provided.

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Section: Empirical Results and Discussionsupporting

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Re‐estimating the pollution haven–halo hypotheses for Brazil via a machine learning procedure

Uche,

Omoke,

Silva‐Opuala

et al. 2023

J of Intl Development

View full text Add to dashboard Cite

show abstract

Section: Previous Experimental Studiesmentioning

confidence: 96%

“…Regarding the impact of FDI on environmental pollution, studies by (Li et al, 2019), (Gong et al, 2021), (Abdo et al, 2020), (Wang et al, 2020) (Raihan, 2023)… all support the "pollution paradise" hypothesis when arguing that FDI is one of the causes of environmental quality deterioration in the host country. Most recently, Gong et al (2021) used data from countries along the "One Belt, One Road" as the sample and discovered that developing countries such as South Africa and Malaysia are still "polluted paradise." Qamruzzaman et al (2019) used an interprovincial panel to analyze the impact of foreign trade and FDI on the transfer of pollution intensive industries and pointed out that the more FDI was attracted by China's provincial administrative regions, the more significant the transfer of pollution-intensive industries; thus, China is still a "pollution paradise" for FDI.…”

Section: Previous Experimental Studiesmentioning

confidence: 96%

Impacts of FDI and Environmental Pollution in ASEAN Countries: The Role of Institutions

Nguyen,

Le,

Ngo

et al. 2023

IJEEP

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The study aims to examine the role of institutions in the relationship between foreign direct investment and environmental pollution in ASEAN countries from 2000 to 2019. With the General Moment method, the results show that foreign direct investment impacts increasing environmental pollution. At the same time, aspects of institutional quality such as the rule of law, government efficiency, political stability, quality regulations, and voice and accountability reduce environmental pollution. Furthermore, the study shows that institutional quality is essential in reducing the negative impact of foreign capital inflows on environmental pollution in ASEAN countries. From these results, the study recommends that ASEAN countries implement environmental protection plans when attracting foreign investment flows.

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“…Countries such as China, India and South Africa exhibit a nonlinear correlation between FDI and CO 2 emissions. FDI and CO 2 emissions in China are moving in tandem, and this suggests a structural breach in the underlying framework (Ahmad et al, 2023; Gong et al, 2021). Increased agricultural productivity reduces poverty, improves food security, and encourages economic growth.…”

Section: Introductionmentioning

confidence: 99%

Achieving sustainable development through economic growth, energy consumption, and agricultural productivity in China

Xia

Zhou²,

Xing

et al. 2023

Sustainable Development

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Large amount of carbon emission is the main hazard to environment and human health. The aim of this research is to investigate the impact of economic growth, forest zone, agricultural productivity, and energy consumption on environmental pollution in China using time series data from 1980 to 2020. The underline research utilized dynamic ordinary least‐squares (DOLS) method, Johansen and Engle–Granger cointegration tests, and the autoregressive‐distributed lag (ARDL) technique to analyze the varying data set. To compare findings, fully modified ordinary least squares (FMOLS) and canonical cointegrating regression (CCR) estimators are estimated. Energy use and population raise CO2 emissions with 1% economic development. Long‐term CO2 emissions may be reduced by 1% agriculture production and forestry zone growth. The Granger causality test showed causation between all variables. Results reveal that China might achieve sustainable development goals by cutting down CO2 emissions and mitigate global climate change. The underline research presents policy recommendations for attaining environmental sustainability in China through green investment by reducing carbon emissions. These policies highlight promoting use of renewable energy, financing green technological progression in agricultural productivity, and the ecological feasibility of China's forests.

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Research on the nonlinear dynamic relationship between FDI and CO2 emissions in the “One Belt, One Road” countries

Cited by 21 publications

References 29 publications

Re‐estimating the pollution haven–halo hypotheses for Brazil via a machine learning procedure

Re‐estimating the pollution haven–halo hypotheses for Brazil via a machine learning procedure

Impacts of FDI and Environmental Pollution in ASEAN Countries: The Role of Institutions

Achieving sustainable development through economic growth, energy consumption, and agricultural productivity in China

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