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This study examined the relationship between the air quality index (AQI), COVID-19, and the oil market using China’s provincial data for the first four months of the pandemic (1 January–22 April 2020). The results show that air quality improved significantly during this period. Our income and regional group analyses further clarified that the middle-income group and Eastern and Central provinces, which were most affected by COVID-19, showed a more robust relationship between AQI and COVID-19 cases than other Chinese provinces. The national oil market, which saw a 57% decline in the oil price over the period of 13 January to 22 April, was also found to be insignificantly related to AQI. All in all, our paper implies that an improvement in air quality due to a drastic decline in economic activity. We provide some future research directions in the paper.

Governments actively encourage renewable energy use to deal with climate change and achieve carbon emission reduction targets. It is crucial to find out the driving factors that affect the utilization of renewable energy. Therefore, based on China’s 2010–2016 input–output table, this paper uses the input–output model and structural decomposition analysis (SDA) to analyze the driving factors of renewable energy changes in the production end, household end, and the aggregate economy. The results show that the changes in the consumption structure (F) is the most crucial factor for renewable energy use, followed by technology progress (T) and final demand per capita (V). Sector SEHW (supply of electric power, heat power, and water) and MCRP (manufacture of coke and refined petroleum products) are the two vital sectors to achieve China’s energy transition of the production level. However, as for households, the proportion of renewable energy has been declining. Hence, the government should promote renewable energy use and achieve the green transition in production and household levels.

The influence of taxation on the economy and society is everywhere. Given the fact that CO2 emission is closely connected with human activities, the question naturally arises whether the tax burden affects the carbon emissions. To address this issue, based on the data of 21 OECD countries over the period from 1991–2014, we use the ratio of tax revenue to GDP as a proxy of the tax burden and employ a panel quantile model with the non-additive fixed effects for analysis. The results suggest that the impact of the tax burden on carbon emissions is heterogeneous across countries based on the level of carbon emissions. Moreover, we find a stable U-shaped relationship between the tax burden and carbon emissions whether for countries with a high or low level of carbon emissions. Our findings suggest that moderate taxes could help reduce carbon emissions, but the effect of excessive taxation is the opposite.

Determine the main factors affecting carbon emissions of the Chinese steel industry is indispensable commitments to achieve the sustainable development of China. Hereby, based on the Stochastic Impacts by Regression on Population, Affluence and Technology (STIPRAT) model, this paper combines the economic growth function, carbon emission production function, and the FDI function of the Chinese steel industry, and uses the three-stage least square equation model (3SLS) to analyze the relationship between China’s economic growth, carbon emissions in the steel industry, and FDI (foreign direct investment) inflows. The results document a complete two-way causal relationship of three variables in the whole country and the Western region, while the relationship in the Eastern region and the Central region is not complete. Moreover, there are no bidirectional causal relationship between carbon emissions and FDI in the Eastern region, while only bidirectional causality between carbon emissions and FDI in the Central region. These findings are of great significance for the Chinese steel industry to formulate effective emission reduction policies.

More countries have made carbon neutral or net zero emission commitments since 2019. Within this context, re-examining the environmental Kuznets curve (EKC) hypothesis plays an essential role in sizing up the global economic development situation and realizing the global carbon emission reduction target. A methodological challenge in testing the EKC hypothesis, which states that increasing income makes CO2 emissions begin to decline beyond a turning point, lies in determining if this benchmark point exists. The EKC hypothesis between income and CO2 emissions is reassessed by applying a new kink regression model for the G7 countries from 1890 to 2015. Results reveal the inverted U-shaped nexus does not exist for US, Germany, Italy, Canada and Japan. For these five countries, the EKC curve has a turning point, but the positive impact of incomes on CO2 emissions becomes significantly smaller after the turning point. We describe this relationship as a pseudo-EKC. K.U.K. and France are the only exceptions, fitting the EKC hypothesis. Further analysis indicates that the relationship between income and SO2 emissions presents an inverted U-shaped curve. Moreover, we observe that the turning point occurs at different points in time for the different G7 countries. Therefore, environmental policies targeting pollutant emission reduction should consider the different characteristics of different pollutants and regions.

We investigate the influence of local Confucian culture on firms’ commitment to environment, social, and governance (ESG). We hypothesize that local Confucian culture will encourage firms to increase their commitment and effort to ESG practices, as a basic idea of Confucianism is akin to the golden rule, which dictates that one must treat people fairly and well if one wishes to be treated the same manner. Using geographic distances to local Confucian temples and centers to create geographic proximity-based measures of local Confucian culture, we confirm that local Confucian culture in the vicinity of corporate headquarters is positively associated with firms’ ESG performance. Further, we hypothesize and find that the role of local Confucian culture is more prominent in state-owned firms, larger firms, firms with greater media coverage and analyst following, and in more recent years, when environmental, social, and governance practices are more relevant and receive greater attention. Our research shed light on studies evaluating the influence of local cultural norms on investment decisions within the framework of ESG.

Governments actively encourage renewable energy use to deal with climate change and achieve carbon emission reduction targets. It is crucial to find out the driving factors that affect the utilization of renewable energy. Therefore, based on China's 2010-2016 input-output table, this paper uses the input-output model and structural decomposition analysis (SDA) to analyze the driving factors of renewable energy changes in the production end, household end, and the aggregate economy. The results show that the changes in the consumption structure (F) is the most crucial factor for renewable energy use, followed by technology progress (T) and final demand per capita (V). Sector SEHW (supply of electric power, heat power, and water) and MCRP (manufacture of coke and refined petroleum products) are the two vital sectors to achieve China's energy transition of the production level. However, as for households, the proportion of renewable energy has been declining. Hence, the government should promote renewable energy use and achieve the green transition in production and household levels.

Determine the main factors affecting carbon emissions of the Chinese steel industry is indispensable commitments to achieve the sustainable development of China. Hereby, based on the Stochastic Impacts by Regression on Population, Affluence and Technology (STIPRAT) model, this paper combines the economic growth function, carbon emission production function, and the FDI function of the Chinese steel industry, and uses the three-stage least square equation model (3SLS) to analyze the relationship between China's economic growth, carbon emissions in the steel industry, and FDI (foreign direct investment) inflows. The results document a complete two-way causal relationship of three variables in the whole country and the Western region, while the relationship in the Eastern region and the Central region is not complete. Moreover, there are no bidirectional causal relationship between carbon emissions and FDI in the Eastern region, while only bidirectional causality between carbon emissions and FDI in the Central region. These findings are of great significance for the Chinese steel industry to formulate effective emission reduction policies.

Since the reform and opening-up, China’s CO2 emissions have increased dramatically, and it has become the world’s largest CO2 emission and primary energy consumption country. The manufacturing industry is one of the biggest contributors to CO2 emission, and determining the drivers of CO2 emissions are essential for effective environmental policy. China is also a vast transition economy with great regional differences. Therefore, based on the data of China’s provincial panel from 2000 to 2013 and the improved STIRPAT model, this paper studies the impact of economic growth, foreign direct investment (FDI) and energy intensity on China’s manufacturing carbon emissions through the fixed-effect panel quantile regression model. The results show that the effects of economic growth, FDI and energy intensity on carbon emissions of the manufacturing industry are different in different levels and regions, and they have apparent heterogeneity. In particular, economic growth plays a decisive role in the CO2 emissions of the manufacturing industry. Economic growth has a positive impact on the carbon emissions of the manufacturing industry; specifically, a higher impact on high carbon emission provinces. Besides, FDI has a significant positive effect on the upper emission provinces of the manufacturing industry, which proves that there is a pollution paradise hypothesis in China’s manufacturing industry, but no halo effect hypothesis. The reduction of energy intensity does not have a positive effect on the reduction of carbon emissions. The higher impact of the energy intensity of upper emission provinces on carbon emissions from their manufacturing industry, shows that there is an energy rebound effect in China’s manufacturing industry. Finally, our study confirms that China’s manufacturing industry has considerable space for emission reduction. The results also provide policy recommendations for policymakers.

The five major power generation corporations dominate the power industry in China, and play vital roles in China's carbon trading scheme. Under this circumstance, this paper studies the allocation of carbon emission quotas to China's five major power generation corporations based on the fairness and efficiency principles, which proves the primary prerequisite for setting up the national carbon market. Specifically, a bi-level programming model is developed to optimally allocate carbon emission quotas to the corporations, and then a zero sum gains data envelopment analysis (ZSG-DEA) model is adopted to evaluate the efficiency of the allocation so as to adjust towards the optimal solution. The results indicate that power generation has significantly positive influence on the allocation of carbon emission quotas. Moreover, the difference between the sum of optimal carbon emissions and the sum of carbon quotas allocated is 1.431 billion tons, which implies that the emission reduction potential of the five major power generation corporations is tremendous. Finally, the bi-level model in this paper can well capture the context of China's power industry and provide effective results for the allocation of carbon quotas.

Macroeconomic factors are regarded as important determinants of corporate leverage decisions, although their importance during good and bad times is less understood. We examine the effect of macroeconomic factors on total, long-term and short-term corporate leverage (debt to total assets) decisions during: (1) the expansionary and contractionary phases of the business cycle; and (2) the financial crisis (FC) and non-FC period. This paper explains leverage decisions for S&P 500 non-financial firms over the period 1985–2017. Our main results are as follows. Short-term leverage and interest rate are positively related. Long-term leverage, on the other hand, is negatively associated with inflation. Interest rate and inflation effects are stronger during the expansionary phases of the business cycle and non-FC period, while the impact of US economic activity is stronger during the contractionary phases of the US business cycle.

In response to the recent growth of multitudes of theoretical literature analysing the corruption impact on the economy and environment, this paper subjects the corruption–carbon emission relationship in China to a detailed empirical examination through the autoregressive distributed lag modelling approach and panel quantile regressions. Based on panel data from Chinese provinces, spanning the period 1998–2016, this study explores the impact of long- and short-term corruption on per capita carbon emissions by considering the heterogeneous distribution of those emissions. The results document that corruption increases per capita carbon emissions in Chinese provinces in the short run, reducing per capita carbon emissions in the long run. Moreover, an increase in corruption leads to an increase in carbon emissions per capita in all quantiles, indicating that these emissions increase with corruption severity. The coefficients in low quantiles are slightly larger than those in high quantiles, indicating that corruption leads to more carbon emissions in provinces with lower per capita carbon emissions.

Climate change is one of the most important global problems faced by the international community. It is generally believed that increasing the consumption of renewable energy is an effective measure to promote CO2 emissions reduction. Therefore, renewable energy consumption has become one of the best alternative strategies for sustainable development. Based on this, this paper employs the 3SLS model to conduct an empirical study on the relations among real output, renewable energy consumption, and CO2 emissions of BRICS countries (except Russia) in 1999−2014. The empirical results support, for BRICS group, the complete tri-variate relationships (energy-output-emission nexus), and renewable energy had a significant positive impact on the real output, and vice versa. Besides, compared with other countries, Brazil also has the same tri-variate relationships as BRICS group. However, China has no relationship from real output to renewable energy consumption and from real output to CO2 emissions; India does not have the relationship from real output to renewable energy consumption and the bilateral relationship between real output and CO2 emissions; the relationship between variables in South Africa only occurs in the energy output chain. Finally, according to the estimation results of the simultaneous equation, the BRICs governments should consider the importance of human capital level and financial development when controlling the real output level and pollution. In addition, it should be noted that effective energy policies help to reduce carbon dioxide emissions without compromising real output.

A noteworthy feature of U.S. politics in recent years is serious partisan conflict, which has led to intensifying polarization and exacerbating high policy uncertainty. The US is a significant player in oil and gold markets. Oil and gold also form the basis of important strategic reserves in the US. We investigate whether U.S. partisan conflict affects the returns and price volatility of oil and gold using a parametric test of Granger causality in quantiles. The empirical results suggest that U.S. partisan conflict has an effect on the returns of oil and gold, and the effects are concentrated at the tail of the conditional distribution of returns. More specifically, the partisan conflict mainly affects oil returns when the crude oil market is in a bearish state (lower quantiles). By contrast, partisan conflict matters for gold returns only when the gold market is in a bullish scenario (higher quantiles). In addition, for the volatility of oil and gold, the predictability of partisan conflict index virtually covers the entire distribution of volatility.

In this paper, the time-varying volatility feedback of nine series of energy prices is researched by employing the time-varying parameter stochastic volatility in mean (TVP-SVM) model. The major findings and conclusions can be grouped as follows: Significant differences exist in the time-varying volatility feedback among the nine major energy productions. Specifically, crude oil and diesel’s price volatility has a remarkable positive time-varying effect on their returns. Yet the returns, for natural gas and most petroleum products are negatively affected by their price volatility over time. Furthermore, obvious structural break features exist in the time-varying volatility feedback of energy prices, which coincide with the breakpoints in the energy volatility. This indicates that some factors such as major global economic and geopolitical events that cause the sudden structural breaks in the energy volatility may also affect the volatility feedback of the energy price. Moreover, the volatility feedback in energy price will become weak and even have no impact on energy returns in some special periods when the energy price volatility is extremely high.