Different impacts of democracy and income on carbon dioxide emissions: evidence from a panel quantile regression approach

The impact of Chinese foreign direct investment and environmental tax revenues on air degradation in Europe: a spatial regression approach, 2000-2020

This study analyzes air pollution through the effects of China's FDI in 27 European countries over a 20-year period, with a focus on the impact of environmental tax revenues (ETRs) and the environmental context in China. The relationship is estimated through spatial regressions that account for the presence of air pollutants in neighboring countries. The findings suggest that China's FDI in Europe does not contribute to air pollution but rather has a positive impact. The presence of environmental charges filters out non-polluting investments, which has a non-linear relationship with PM2.5 pollution rates. The study also concludes that air pollution is closely linked to the global environmental context, highlighting the positive effects of international agreements in the fight against climate change. Specifically, the study finds a link between China's efforts to address its polluting activities and their impact on European air quality.

An empirical examination of the environmental sustainability-influencing mechanisms of renewable energy: contextual evidence from Next Eleven countries

Recognizing the environmental development-related commitments made by the Next Eleven countries at 26th Conference of Parties (COP26), this study scrutinizes the repercussions accompanying good democratic governance, renewable energy transition, economic growth, and the ratification of the Kyoto Protocol on carbon emission figures of these emerging nations. In this regard, the period of analysis considered spans from 1990 to 2018 while the econometric analyses involve application of both parametric and non-parametric panel data estimators. Among the key findings, firstly, the outcomes from the parametric estimation methods verify that establishing better democratic governance and undergoing renewable energy transition, both independently and jointly, curb carbon emission levels, while higher economic growth and the signing of the Kyoto Protocol are responsible for boosting emissions the Next Eleven countries. Secondly, the findings derived using the non-parametric methods reveal a great deal of heterogeneity when compared with the results obtained from the parametric analysis. Notably, better democratic governance is seen to reduce carbon emissions in less and moderately polluted. Next Eleven nations, while renewable energy transition curbs emissions only in the moderately and highly polluted ones. Additionally, these variables jointly inhibit emissions only in the Next Eleven nations that are moderately polluted. Besides, better democratic governance is observed to mediate the renewable energy transition-carbon emissions nexus only for the less-polluted Next Eleven nations, while the environmental impacts of economic growth and the signing of the Kyoto Protocol vary across different emission quantiles. Accordingly, relevant policies are recommended for helping the Next Eleven countries to comply with their pledges made at the COP26.

Democracy, green energy, trade, and environmental progress in South Asia: Advanced quantile regression perspective

Unquestionably, the industrial revolution of the twenty-first century contributes to global warming. Excessive amounts of carbon emissions into the atmosphere are responsible for global warming. Therefore, this research aims to assess the impact of GDP, green energy consumption, population, trade openness, and democracy on CO2 emissions in four selected South Asian countries from 1990 to 2019. This research also attempts to evaluate the EKC hypothesis in terms of economic growth (GDP2). The unit root of panel data and cointegration tests are executed in this study as a prelude to the regression analysis. Quantile regression for panel data, which (Powell, 2016) devised to deal with the fixed effect problem, is used in this study, and (Powell, 2016) empirical findings are the main focus. The estimated coefficient of GDP is positively significant, demonstrating that economic activity increases the burning of fossil fuels and upsurges atmospheric CO2 emissions. After attaining economic development, the reversed U-shaped EKC theory is valid for four selected South Asian countries. Economic development encourages these countries to use green technology, which helps mitigate CO2 emissions. The research, however, reveals that green energy is to blame for CO2 emissions. Burning biomass releases carbon dioxide that negatively impacts the quality of the environment. The study confirms that human activities are the leading contributor to environmental deterioration. Population growth has a worsening effect on the environment. The association between population and CO2 emissions is positively significant. The estimated coefficient of trade openness is positive, which increases CO2 emissions significantly. The estimated coefficient of democracy is quite negative. Therefore, this study suggests prioritizing democracy to reduce CO2 emissions. Citizens who live in democracies are better informed, more organized, and able to protest, all of which contribute to increased government responsiveness to environmental preservation. The results of the Wald test support the differential effects at various quantiles. The Dumitrescu-Hurlin (2012) panel causality tests are also used in this analysis to check causality between variables. Based on the findings, this research makes many policy suggestions for lowering carbon emissions.

Regional Differences and Convergence of Carbon Emissions Intensity in Cities along the Yellow River Basin in China

Since the ecological protection and high-quality development of the Yellow River Basin (YRB) in China have become a primary national strategy, the low-carbon economy is crucial. To formulate effective emission mitigation policies for the YRB, we need to comprehensively understand the characteristics of the spatial agglomeration of the carbon emissions intensity in the YRB and its regional heterogeneity. Therefore, based on the relevant data from 2005 to 2017, we first scientifically measure the carbon emissions intensity of 57 cities along the YRB. Then, we analyze the spatial agglomeration characteristics and long-term transfer trends of carbon emission intensity using exploratory spatial data analysis methods and Markov chains. Finally, the Dagum Gini coefficient and the variation coefficient method are used to study the regional differences and differential evolution convergence of the carbon emissions intensity in the YRB. The results show that the carbon emissions intensity of the YRB has dropped significantly with the spatial distribution characteristics “high in the west and low in the east”, and there is a significant spatial autocorrelation phenomenon. In addition, the probability of a shift in urban carbon intensity is low, leading to a “club convergence” and a “Matthew effect” in general and across regions. Inter-regional differences have always been the primary source of spatial differences in carbon emissions intensity in the YRB, and the intra-regional differences in carbon emissions intensity in the lower YRB show a significant convergence phenomenon. The research results may provide a reference for the regional coordinated development of a low-carbon economy in the YRB, and serve to guide the win-win development model of ecological environment protection and economic growth in the YRB.

Coupling Coordination and Spatiotemporal Evolution between Carbon Emissions, Industrial Structure, and Regional Innovation of Counties in Shandong Province

Industrial structure and regional innovation have a significant impact on emissions. This study explores, from the multivariate coupling and spatial perspectives, the degree of coupling coordination between three factors: industrial structure, carbon emissions, and regional innovation of 97 counties in Shandong Province, China from 2000 to 2017. On the basis of global spatial autocorrelation and cold and hot spots, this article analyzes the spatial characteristics and aggregation effects of coupled and coordinated development within each region. The results are as follows. (1) The coupling degree between carbon emissions, industrial structure, and regional innovation in these counties fluctuated upward from 2000 to 2017. Coupling coordination progressed from low coordination to basic coordination. Regional differences in coupling coordination degree are evident, showing a stepped spatial distribution pattern with high levels in the east and low levels in the west. (2) During the study period, the coupling coordination showed a positive correlation in spatial distribution. Moran’s I varies from 0.057 to 0.305 on a global basis. Spatial clustering is characterized by agglomeration of cold spots and hot spots. (3) The coupling coordination exhibited significant spatial differentiation. The hot spots were distributed in the eastern part, while the cold spots were located in the western part. The results of this study suggest that the counties in Shandong Province should promote industrial structure upgrades and enhance regional innovation to reduce carbon emissions.