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This study aims to examine the relationship between foreign finance, economic growth and CO₂ to investigate if the environmental Kuznets curve (EKC) exists as an empirical evidence in 32 selected Organization for Economic Co-operation and Development (OECD) countries.

This study used quantitative analysis to test two main hypotheses: H1 is the U-shape relationship between foreign finance and environment, and H2 is the N-shaped association between economic growth and environment. In doing so, this study used panel data techniques. The panel set contained 32 countries over the period from 1990 to 2015, with 27 observations for each country. This study applied a panel OLS estimator via fixed-effects control to address heterogeneity and mitigate endogeneity. Generalized method of moments (GMM) with fixed effects-instrumental variables (FE-IV) and diagnostic tests were also used.

The results showed that foreign finance and environmental quality have an inverted U-shaped association. The three proxies’ foreign investment, foreign assets and remittance in the first stages contribute significantly to CO₂ emissions, but after the threshold point is reached, these proxies become “environmentally friendly” by their contribution to reducing CO₂ emissions. Also, a non-linear relationship denotes that foreign investment in OECD countries enhances the importance, as a proxy of foreign finance has greater environmental quality than foreign assets. Additionally, empirical results show that remittances received is linked to the highest polluted levels until a threshold point is reached, at which point it then helps reduce CO₂ emissions. The GMM and FE-IV results provide robust evidence on inverse U-shaped relationship, while the N-shaped relationship explains that economic growth produces more CO₂ emissions at the first phase of growth, but the quadratic term confirms this effect is negative after a specific level of GDP is reached. Then, this economic growth makes the environment deteriorate. These results are robust even after controlling for the omitted variable issue. The IV-FE results indicate an N-shaped relationship in the OECD countries.

Most studies have used different economic indicators as proxies to show the effects of these indicators on the environment, but they are flawed and outdated regarding the large social challenges facing contemporary, socio-financial economic systems. To overcome these disadvantages, the social, institutional and environmental aspects of economic development should also be considered. Hence, this study aims to explain this issue as a relationship with several proxies in regard to environmental, foreign finance and economic aspects.

This paper uses updated data sets for analyzing the relationship between foreign finance and economic growth as a new proxy for pollution. Also, this study simulates the financial and environmental future to show their effect on investments in different OECD countries. While this study enhances the literature by establishing an innovative control during analysis, this will increase to add value. This study is among the few studies that empirically investigate the non-linear relationship between finance and environmental degradation.

This paper aims to clarify the relationship between foreign direct investment (FDI) and carbon intensity. This study uses the dynamic panel data model to study and provide fresh evidence for the issue.

This study first uses the dynamic panel data model to consider the endogeneity problem, and applies a system-generalized method of moments estimator to study the effect of FDI on carbon intensity using the panel data of 188 countries during 1990-2013.

The result shows that FDI has a significant negative impact on carbon intensity of the host country. After considering the other factors, including share of fossil fuels, industrial intensity, urbanization level and trade openness, the impact of FDI on carbon intensity is still significantly positive. In addition, FDI also has a significant negative impact on carbon intensity of high-income countries and middle- and low-income countries.

This paper offers two contributions to the literature on the effect of FDI on carbon intensity. From a methodological perspective, this paper is the first to apply a dynamic panel data model to study the effect of FDI on carbon intensity using worldwide panel data. Second, this paper is the first to analyze the effect of FDI on carbon intensity in different countries with different income levels separately.

This study investigates the relationship between economic growth, international trade, and environmental degradation in Sub-Saharan Africa (SSA), focusing on the validity of the environmental Kuznets hypothesis (EKC), the pollution havens hypothesis (PHH), and the factor endowment hypothesis (FEH).

The study uses annual data for 41 SSA countries between 1990 and 2017 and employs the bias-corrected least square dummy variable (LSDVC) estimation techniques. Environmental degradation is indicated by carbon dioxide (CO₂), delicate particulate matter (PM2.5) emissions, and deforestation.

The results confirm the validity of the EKC hypothesis for PM2.5 emissions and deforestation but not for CO₂ emissions. The results also indicate that international trade reduces deforestation and that both the PHH and FEH are valid for CO₂ emission but not for PM2.5 emissions and deforestation.

In this paper, the authors are able to illustrate that both economic growth and international trade can harm the environment if unchecked. Therefore, the conclusion of this study offers policy options through which SSA countries can achieve desired economic growth goals without affecting environmental quality. The study can be a benchmark for environmental policy in the region.

The authors provide an in-depth discussion of the growth-trade-environmental degradation nexus in SSA. The EKC, PHH, and FEH’s validity confirm that economic growth remains a threat to the local natural environment in SSA. Hence, the need for a trade-off between economic growth needs and environmental degradation and understanding where to compromise to achieve SSA's economic development priorities.

This paper aims to identify variables that determine the differing levels of environmental quality on Java and other islands in Indonesia.

Using a quantitative approach, secondary data were sourced from the Central Statistics Agency and the Ministry of Environment and Forestry. The data were obtained through the collection of documentation from 33 provinces in Indonesia. The analytical approach used was discriminant analysis. The research variables are Trade Openness, Foreign Direct Investment (FDI), industry, HDI and population growth.

The variables that distinguish between the levels of environmental quality in Indonesian provinces on the island of Java and on other islands are Industry, HDI, FDI and population growth. The openness variable is not a differentiating variable for environmental quality. The most powerful variable as a differentiator of environmental quality on Java Island and on other islands is the Industry variable.

This study has not classified the quality of the environment based on the Ministry of Environment and Forestry's categories, namely, the very good, good, quite good, poor, very poor and dangerous. For this reason, further research is needed using multiple discriminant analysis (MDA).

Industry is the variable that most strongly distinguishes between levels of environmental quality on Java and other island, while the industrial sector is the largest contributor to gross regional domestic product (GDRP). Government policy to develop green technology is mandatory so that there is no trade-off between industry and environmental quality.

This study is able to identify the differentiating variables of environmental quality in two different groups, on Java and on the other islands of the Indonesian archipelago.

Based on the hypothesis of the environmental Kuznets curve (EKC), the purpose of this study is to investigate the relationship between environmental pollutants (as measured by CO₂ emissions) and GDP for India, over the period 1980–2012. The presence of an inverted “U” shape relationship is examined while controlling for factors such as the degree of trade openness, foreign direct investment, oil prices, the legal system and industrialization.

To verify whether the EKC follows a linear, quadratic or polynomial form, autoregressive distributed lag (ARDL) bounds testing approach for cointegration with structural breaks is adopted. The annual time series data for carbon emissions (CO₂), economic growth (GDP), industrial development (industrialization), foreign direct investment and trade openness have been obtained from World Development Indicators online database. Crude oil price (international price index) for the period is collected from the International Monetary Fund. Data for total petroleum consumption are collected from the US Energy Information Agency. Data for economic freedom variables are from the Fraser Institute's Economic Freedom Index's online database.

The findings support the existence of inverted U-shaped EKC in the short-run, but not in the long-run. A linear monotonic relationship has also been estimated in select model specifications. Additionally, trade openness has been estimated to reduce emissions in models, which incorporate FDI. Else, where significant, its impact on carbon emissions is adverse. A rise in fuel price leads to reduction in carbon emissions across model specifications. Further, the lower size of government degrades the environment both in the long-run and short-run.

Given the existence of the pollution haven hypothesis, wherein more trade and foreign direct investments cause environmental degradation, the paper proposes formulation of appropriate regulatory mechanisms that are environmentally friendly. Additionally, India's new economic policies, favoring liberalization, privatization and globalization, reinforces the need to strengthen environmental regulations.

Incorporation of economic freedom as measured by the “Size of Government” in the EKC model is unique. “Size of Government” deserves a special mention. The rationale for including this explanatory variable is to understand whether countries with lower government size are more polluting. After all, theory does suggest that goods and services, which have higher social cost vis-à-vis private cost, shall be overproduced in economies that adopt more market-friendly policies, necessitating government intervention. In the study, size of government is measured as per the definition and methodology adopted by Fraser Institute's Economic Freedom of the World Index.

The present study investigates the effect of foreign direct investment (FDI) and governance quality on carbon emissions in the Economics Community of West African States (ECOWAS).

To achieve the objective of this research, panel data for dependent and explanatory variables over the period 2005–2016, collected in the World Development Indicators (WDI) database and World Governance Indicators (WGI), are analyzed using the generalized method of moments (GMM). Also, the panel-corrected standard errors (PCSE) method is applied to the four segments of the overall sample to analyze the stability of the results.

The findings of this study are: (1) FDI inflows have a negative effect on carbon emissions in ECOWAS and (2) The interaction between FDI inflows and governance quality have a negative effect on carbon emissions. These results show the decreasing of environmental damage by increasing institutional quality. However, the estimation results on the country subsamples show similar and non-similar aspects.

This study suggests that policymakers in the ECOWAS countries should strengthen their environmental policies while encouraging FDI flows to be environmentally friendly.

The subject has rarely been explored in West Africa, with gaps such as the lack of use of institutional variables. This study contributes to the literature by drawing on previous work to examine the role of good governance on FDI and the CO2 emission relationship in the ECOWAS, which have received little attention. However, this research differs from previous work by subdividing the overall sample into four groups to test the stability of the results.

In a world characterised by increasing environmental and social awareness, the number of corporate social responsibility and sustainability initiatives has significantly grown. Among these, the United Nations Global Compact (UNGC) is one of the most important, involving more than 12,000 companies. The purpose of this study is to investigate the UNGC’s worldwide diffusion, both at country and industry level, to understand the reasons leading to the highlighted dissemination patterns, and to propose various future projections.

The study pursues its objectives by applying the logistic curve model to data provided by the United Nations. The analysis is complemented by adopting instability and concentration indexes.

Results suggest that, while human rights and environmental safeguard in some areas and industries will remain a controversial issue, UNGC adoption will continue growing and giving the participants the required legitimacy to compete in worldwide markets.

To the best of the authors’ knowledge, this is the first paper that analyses the UNGC’s worldwide diffusion and proposes a prediction model for its future dissemination. The findings are of considerable importance in extending the knowledge of the initiative and in understanding the potential values of its adoption.

With the Chinese marine economy developing rapidly, the environmental problem has been occurring frequently, which needs green finance that supports energy conservation, environmental protection, and sustainable development to solve.

In this paper, the entropy method is used to measure the development level of green finance, the DEA-ML index is used to measure the green total factor productivity which is used to indicate the high-quality development level of the marine economy in 11 coastal provinces (cities), then the grey correlation degree between them whose result shows that there is a certain correlation between the two variables is calculated. The fixed-effect model was used to analyze the relationship between them.

The results show that the development level of green finance can promote the high-quality development of the marine economy, but there are still some problems in the process of green finance supporting the marine economy.

This paper seeks new growth drivers, green finance, for the high-quality development of the marine economy, which few scholars have studied.

The purpose of this paper is to measure Chinese industries’ eco-efficiency during 2006-2013. The Chinese industry attained rapid achievement in recent decades, but meanwhile, overconsumption of energy and environmental pollution have become serious problems. To solve these problems, many research studies used the data envelopment analysis (DEA) to measure the Chinese industry’s eco-efficiency. However, because the target set by these works is usually the furthest one for a province to be efficient, it may hardly be accepted by any province.

This paper builds a new “closest target method” based on an additive DEA model considering the undesirable outputs. This method is a mixed-integer programming problem which can measure the ecological efficiency of provinces and more importantly guide the province to perform efficiently with minimum effort.

The results show that the eco-efficiency of Chinese provinces increased at the average level, but the deviations remained at a larger value. Compared to the “furthest” target methods, the targets by the approach proposed by this study are more acceptable for a province to improve its performance on both economy and environment counts.

This study is the first attempt to introduce the closest targets concept to measure the eco-efficiency and set the target for each provincial industry in China.

This study aims to analyze and compare the relationship between international trade in global value chains (GVC) and greenhouse gas (GHG) emissions for Brazil and China from 2000 to 2016.

The input-output method apply to multiregional tables from Eora-26 to decompose the GHG emissions of the Brazilian and Chinese productive structure.

The data reveals that Chinese production and consumption emissions are associated with power generation and energy-intensive industries, a significant concern among national and international policymakers. For Brazil, the largest territorial emissions captured by the metrics come from services and traditional industry, which reveals room for improving energy efficiency. The analysis sought to emphasize how the productive structure and dynamics of international trade have repercussions on the environmental dimension, to promote arguments that guide the execution of a more sustainable, productive and commercial development strategy and offer inputs to advance discussions on the attribution of climate responsibility.

The metrics did not capture emissions related to land use and deforestation, which are representative of Brazilian emissions.

Comparative analysis of emissions embodied in traditional sectoral trade flows and GVC, on backward and forward sides, for developing countries with the main economic regions of the world.