Testing the Environmental Kuznets Curve Hypotheses in Chinese Provinces: A Nexus between Regional Government Expenditures and Environmental Quality

Exploring the asymmetric effect of fiscal policy instruments in encountering environmental degradation: proposing an SDG framework for India

Asian countries are facing difficulties in attaining sustainable development goals (SDGs), and India is not an exception to it, with environmental degradation being one of the primary issues. Therefore, a policy-level reorientation may be required to address it. From this standpoint, fiscal policy instruments may come in handy towards fully integrating the SDGs into its agenda. The present investigation designs an SDG framework for India that could serve as an example for other Asian nations. This study introduces a new investigation exploring the relationship between fiscal policy instruments and environmental quality in India by examining the environmental Kuznets curve (EKC) hypothesis from 1990 to 2021. A nonlinear autoregressive distributed lag (NARDL) model is applied for empirical examination. The findings indicate that positive and negative shocks in fiscal policy instruments have significant impact on carbon emissions in both the long and short run. The study has also found evidence of an "inverted U-shape" EKC for India. These results are valuable from a policy perspective for India and other Asian countries to address environmental issues. The study has also outlined potential outcomes that may benefit India's fiscal policy in resolving environmental issues and attaining better economic growth. In the end, the study proposes a policy framework that supports SDG 7, SDG 8, SDG 12, SDG 13, and SDG 17 objectives.

Exploring the impact of public funds and eco-friendly innovations on reducing carbon pollution in North Africa

The main objective of this study is to examine the impacts of green energy and public investment on the CO2 emissions in North Africa. Moreover, the study also tests the existence of the N-shaped Environmental Kuznets Curve (EKC) hypothesis for North African countries between 1995 and 2018. These factors were analyzed using the Dynamic Ordinary Least Squares (DOLS), Fully Modified Ordinary Least Squares (FMOLS), and Pooled Mean Group (PMG) estimators to obtain estimations of heterogeneous parameters. The outcome of these tests and examinations showed that the N-shaped curve was confirmed. Secondly, The results of the study also demonstrate the effectiveness of renewable energy as an eco-friendly innovation in reducing carbon emissions. This finding highlights the positive impact that renewable energy sources can have in terms of emitting fewer carbon emissions compared to traditional energy sources. Moreover, public investment, which interprets government expenditure, and urbanization contribute to environmental degradation by increasing CO2 emissions in the case of North African countries. Furthermore, the findings also indicated a trade-off effect resulting from the correlation between CO2 emissions and economic development. Based on these findings, the study recommends that economic policymakers in North African countries prioritize transforming the structure of government expenditures to improve environmental quality, optimize the utilization of revenues from non-environmentally friendly energy resources to accelerate the energy transition, increase the exploitation of renewable energy, and promote environmental awareness in society. By implementing these recommendations, North African countries can balance economic growth and environmental quality while reducing their carbon footprint.

Moderating role of green innovation and fiscal expenditure towards achieving the Sustainable Development Agenda 2030 at provincial-level in China: policy implication from green total factor productivity

Amidst resource loss and environmental protection constraints, achieving green development necessitates enhancing green total factor productivity (GTFP) as a means of promoting rational and efficient resource allocation, thereby balancing economic growth and environmental preservation. Meanwhile, literature on the subject matter of GTFP from a sustainability viewpoint is minimal. As a result, this study employs the panel dataset from 30 provinces of China spanning the period 2005 to 2020 and utilizes the method of moments quantile regression (MMQR) developed by Machado and Santos Silva (2019) to analyze the heterogeneous role of green innovation, environmental regulations, and fiscal expenditure on GTFP. Moreover, the controlling variable for this study includes renewable energy and economic growth. Furthermore, this study investigates the heterogeneous combined impact of green innovation and fiscal expenditure (GTE*FSE) on GTFP. The findings of the MMQR reveal that green innovation has a positive impact on GTFP, while fiscal expenditure, environmental regulations, and renewable energy consumption have a negative impact. GTE*FSE has a positive and significant effect on GTFP, indicating that FSE can reinforce and increase the positive impact of GTE on GTFP in the long run. The study also reveals that economic growth has a mixed effect on GTFP, depending on the quantiles. Furthermore, environmental regulation has a significant and negative impact on GTFP, contradicting the Porter hypothesis. Likewise, the robustness of the findings is confirmed by the results of the fully modified OLS (FMOLS) and dynamic OLS (DOLS) estimations, which indicate a similar impact of the determinants on GTFP as observed in the MMQR analysis. This reinforces the validity of the findings and suggests that the observed relationships are robust to different estimation techniques. Furthermore, the findings of the Dumitrescu and Hurlin (D-H) panel causality test reveal significant bidirectional causality between renewable energy consumption and GTFP and fiscal expenditure and GTFP. Policy-makers need to channel a large chuck of their fiscal spending into green innovation so as to boost sustainability.

Green through finance: The impact of monetary policy uncertainty on inclusive green growth

For high-quality development, inclusive green growth (IGG) is a crucial strategic option. Given the deceleration in economic growth, monetary policy has several obligations, including stabilizing growth, changing structure, and mitigating risks. However, frequent revisions of monetary policy may diminish its regulatory efficacy due to resulting uncertainty. Accordingly, this study reveals the mechanism of the impact of monetary policy uncertainty (MPU) on IGG. Research suggests that MPU significantly inhibits IGG in the region, as demonstrated by robustness tests. Mechanism test reveal that MPU inhibits IGG by reducing green finance, ecological innovation, media attention, and employment levels. These four transmission mechanisms all show a masking effect. Additionally, further tests show that under different levels of green finance, ecological innovation, media attention, and employment, the impact of MPU on IGG is nonlinear. Heterogeneity analyses also indicate that the inhibition of MPU is weaker in cities with high scientific and technological progress and local fiscal expenditure. Finally, quantile regression demonstrates that the restraining effect of MPU diminishes before rising. This study contributes to the advancement of MPU research, provides a solid foundation for formulating, modifying, and executing monetary policy, and serves as a valuable resource for promoting IGG.

Effects of public expenditures on environmental pollution: evidence from G-7 countries

In this study, the effect of public expenditures and, their sub-components on environmental pollution is discussed in G-7 countries. Two different periods were used in the study. These are the period 1997-2020 for general public expenditure, and the period 2008-2020 for public expenditure sub-components. For cointegration, Westerlund cointegration test was used, and according to the analysis result there is a cointegration relationship between general government expenditure and environmental pollution. Panel Fourier Toda-Yamamoto causality test was used to determine the causality relationship between public expenditures and environmental pollution and the result indicates that there is bidirectional causality between public expenditures and CO₂ on a panel basis. For models estimation, System the Generalized Method of Moments (GMM) method was used. The findings of the study indicate that general public expenditures decrease environmental pollution. Considering at the results of the sub-components of public expenditures, housing and community amenities, social protection, health expenditure, economic affairs, recreation, culture & religion expenditures have a negative effect on environmental pollution. Other control variables generally  have a statistically significant effect on environmental pollution. Energy consumption and population density increase environmental pollution but environmental policy stringency index, renewable energy and GDP per capita reduce environmental pollution.

Moving towards Environmental Sustainability: Can Digital Economy Reduce Environmental Degradation in China?

In the context of environmental sustainability and accelerated digital technology development, China attaches great importance to the prominent role of digital economy in addressing environmental degradation. Utilizing Chinese provincial panel data from 2011 to 2019, this study investigates whether the digital economy can improve China's environmental sustainability proxy by reducing carbon emission intensity. Based on the fixed effects model, the findings reveal that the digital economy has a significant negative effect on carbon emission intensity and the conclusion remains robust after conducting several robustness checks. However, this impact shows regional heterogeneity, which is more effective in resource-based eastern regions and the Belt and Road provinces. Moreover, mediating effect analyses indicate that the transmission mechanisms are energy consumption structure, total factor energy productivity, and green technology innovation. Furthermore, the results based on the spatial Durbin model (SDM) demonstrate that digital economy development has a significant spatial spillover effect. Finally, on the basis of results analysis and discussion, policy recommendations are provided for achieving environmental sustainability.

The relationship between economic growth and environmental degradation: could West African countries benefit from EKC hypothesis?

There are growing concerns about environmental degradation and economic expansions in West Africa. Although there are several growth-environmental studies in Africa, there is limited empirical research exploring West African countries' potential of benefiting from the environmental Kuznets curve (EKC) hypothesis, with the few studies on this subject reporting diverse results based on selected West African countries. To fill this gap, this study explored the relationship between economic growth and environmental degradation within the EKC framework using 16 West African countries sub-grouped into low-income countries (LICs) and lower-middle-income countries (LMICs) between 1990 and 2018. This study implemented second-generation panel econometric estimators that are robust to cross-sectional dependent and parameter heterogeneity. The empirical results revealed that the data is cross-sectionally dependent, heterogeneous, integrated of order one, 1(1), and cointegrated. Controlling for other environmental determinants, panel estimates from the Augmented Meant Group and Common Correlated Effect Mean Group estimators revealed that economic growth accelerates environmental degradation in West African countries, with a greater impact on LMICs, followed by LICs in West Africa. The results also showed that West African countries especially LMICs could benefit from the EKC hypothesis. On the other hand, growth-environmental degradation among LICs in West Africa shows a monotonous increasing relationship. We found strong evidence to support for feedback hypothesis between economic growth and environmental degradation in LMICs, LICs, and West Africa as a whole. Based on the findings, policy recommendations that consider both LMICs and LICs and West Africa as a whole were offered to policymakers.

Carbon Neutrality in the Middle East and North Africa: The Roles of Renewable Energy, Economic Growth, and Government Effectiveness

Carbon neutrality is a 21st-century priority area, with the Middle East and North Africa (MENA) countries making significant investments in renewable energy and climate mitigation initiatives to attain it. However, carbon neutrality research in the MENA region is under-developed, particularly when considering the roles of renewable energy, economic growth, and effectiveness of government. To address this gap, this research investigates the roles of renewable energy, economic growth, and government effectiveness toward the MENA region's carbon neutrality goal. We implemented heterogeneous and second-generation panel data techniques that are resilient to cross-sectional dependency and slope heterogeneity to panel data spanning 16 MENA countries from 1996 to 2018. We discovered that MENA data are cross-sectionally dependent, heterogeneous, and cointegrated. We found that government effectiveness and renewable energy bring carbon neutrality closer, but economic growth initially delays it. We detected Environmental Kuznets Curve (EKC) in the MENA region, specifically in the High-Income Countries. Although there were signs of EKC in the Middle-Income Countries, this was not significantly validated. Finally, we found a one-way causal link from government effectiveness and renewable energy to carbon neutrality but a feedback mechanism between economic growth and carbon neutrality in the MENA region. As a result of these findings, it is recommended that the MENA region's policymakers prioritize renewable energies and improve the effectiveness of government to drive economic growth toward the carbon neutrality goal.

Can Economic Growth and Environmental Protection Achieve a "Win-Win" Situation? Empirical Evidence from China

Achieving a "win-win" situation regarding economic growth and environmental protection has become a common goal for sustainable development in all countries around the world. As the world's largest developing country and the second largest economy, China has been striving to maintain economic growth while improving environmental quality to achieve its sustainable development goals. Applying the decoupling approach, a model widely used to quantify the relationship between the environment and the economy, this study analyzed the relationship between the economy and the environment, examining the decoupling performance of economic growth and environmental impacts in 30 Chinese provinces, autonomous regions, and municipalities to investigate whether economic growth and environmental protection have achieved a "win-win" situation. Nighttime light (NTL) data were used to measure the performance of economic growth. In addition, an environmental pressure index (EPI) assessment framework covering 6 primary and 11 secondary indicators was constructed to measure the environmental quality of China over time. First, NTL data proved to be a valid data source for assessing decoupling performance; second, environmental pressure at both the national and provincial levels significantly decreased during the study period; third, the relationship between the economy and the environment has been further improved, and economic growth and environmental protection have achieved a "win-win" situation. These findings offer an in-depth analysis of the decoupling of the economy and the environment in China and serve as a guide for future implementation strategies for sustainable development in various regions.

The Non-Linear Impact of Financial Development on Environmental Quality and Sustainability: Evidence from G7 Countries

This paper analyses the impact of financial development on the environmental quality and sustainability for the group of G7 countries over the period 1990-2019 based on static panel data-fixed effect models. The objective is to explore if there exists a non-linear relationship between the whole financial system development and a wide array of measures of environmental sustainability and degradation, namely adjusted net savings, greenhouse gas, CO2, methane, nitrous oxide emissions and ecological footprint. We define a new Financial Environmental Kuznets Curve (FEKC) by introducing the square term of financial development on the environment-finance relationship. Empirical results prove the existence of non-linear relationships between the composite index of financial development and environmental degradation for the group of advanced economies. In the case of methane, we validate the presence of an inverted-U shape association in line with the FEKC hypothesis, while for greenhouse gas and CO2 the link follows a U-shaped pattern. The impact of financial development on environmental sustainability is monotonically positive and statistically significant while the ecological footprint is not statistically linked with the level of financial development within G7 countries. Economic growth, human capital, population density and primary energy consumption appear as significant drivers of environmental quality and sustainability.

Prediction and Trend Analysis of Regional Industrial Carbon Emission in China: A Study of Nanjing City

Based on the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, the impact factors of industrial carbon emission in Nanjing were considered as total population, industrial output value, labor productivity, industrialization rate, energy intensity, research and development (R&D) intensity, and energy structure. Among them, the total population, industrial output value, labor productivity, and industrial energy structure played a role in promoting the increase of industrial carbon emissions in Nanjing, and the degree of influence weakened in turn. For every 1% change in these four factors, carbon emissions increased by 0.52%, 0.49%, 0.17% and 0.12%, respectively. The industrialization rate, R&D intensity, and energy intensity inhibited the increase of industrial carbon emissions, and the inhibiting effect weakened in turn. Every 1% change in these three factors inhibited the increase of industrial carbon emissions in Nanjing by 0.03%, 0.07%, and 0.02%, respectively. Then, taking the relevant data of industrial carbon emissions in Nanjing from 2006 to 2020 as a sample, the gray rolling prediction model with one variable and one first-order equation (GRPM (1,1)) forecast and scenario analysis is used to predict the industrial carbon emission in Nanjing under the influence of the pandemic from 2021 to 2030, and the three development scenarios were established as three levels of high-carbon, benchmark and low-carbon, It was concluded that Nanjing’s industrial carbon emissions in 2030 would be 229.95 million tons under the high-carbon development scenario, 226.92 million tons under the benchmark development scenario, and 220.91 million tons under the low-carbon development scenario. It can not only provide data reference for controlling industrial carbon emissions in the future but also provide policy suggestions and development routes for urban planning decision-makers. Finally, it is hoped that this provides a reference for other cities with similar development as Nanjing.

Estimating Long-Run Relationship between Renewable Energy Use and CO2 Emissions: A Radial Basis Function Neural Network (RBFNN) Approach

The long-run relationship between economic growth and environmental quality has been estimated within the framework of the environmental Kuznets Curve (EKC). Several studies have estimated this relationship by using statistical models such as panel regression and time series regression. The current study argues that there is a nonlinear relationship between environmental quality indicators and economic and non-economic predictors and hence an appropriate nonlinear model is required to predict it. An adaptive and nonlinear model, namely radial basis function neural network (RBFNN) has been developed in this study. CO2 emission is used as the target output and renewable energy consumption share, real GDP, trade openness, urban population ratio, and democracy index are used as the predictors to estimate the EKC relationship for nineteen major CO2 emitting countries that account for 78% of the global emissions. The model developed in this study could predict the CO2 emissions of all the countries with more than 95% accuracy. This finding underlines the usefulness of the RBFNN model which can be used to predict emission levels of other pollution indicators at the global level. Further, comparing two models, one with all the predictors and the other excluding the renewable energy share, it was found that the model with renewable energy share predicts CO2 emissions more accurately. This reinforces the already strengthening campaign to encourage industries and governments to increase the share of renewable energy in total energy use.

Impact of globalization on CO2 emissions based on EKC hypothesis in developing world: the moderating role of human capital

In the last 3 decades, developing economies continuously have increased their manufacturing industries with an impressive growth rate. Rising the trend of globalization, these underdeveloped economies are receiving economic growth at the cost of environmental degradation. In this context, this study investigates the impact of globalization and human capital on carbon emissions (CO₂) in the 78 developing economies from 1990 to 2016. Our findings based on robust system generalized method of moments (GMM) indicate that human capital and political globalization significantly reduce environmental degradation while economic, social, and overall globalization decrease the environmental quality. Furthermore, our empirical results support the inverted U-shaped environmental Kuznets curve (EKC) hypothesis. However, globalization (without interactive term with human capital) appears to have no significant association with CO₂ emissions, while (with an interactive term) it appears to have a significant negative influence on environmental quality. Moreover, our results are robust to various robustness checks; I have performed for scrutiny the consistency of our findings. This study also offers useful policy implications for stakeholders, policymakers, and governments for promoting environmental sustainability.