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Existing studies mostly discussed the impact of transportation infrastructure on the economy and society. However, the environmental performance of digital infrastructure has been discussed less. This study explores the effect of digital infrastructure construction on green economic transformation based on theoretical analysis. Using the Broadband China policy as a quasi-natural experiment, the authors construct a staggered difference-in-difference (DID) model and empirically assess the effect of digital infrastructure on green economic development with panel data of Chinese 271 cities from 2003 to 2019. First, the main results indicate that digital infrastructure can enable green economic performance in Chinese cities. The results remain robust after considering the heterogeneous treatment effects, placebo test, and excluding the effect of other policies. Second, the heterogeneity results indicate that green economic performance in eastern and economically developed cities benefits more from digital infrastructure construction. Finally, by enhancing energy efficiency, fostering digital industrialization, and stimulating green technology innovation, the digital infrastructure indirectly contributes to the urban green economy development. This study put forward some constructive policy suggestions to promote green economic transformation from the digital infrastructure construction perspective.

ABSTRACT Expanding the supplies of new energy can not only reduce CO2 emissions, but also alleviate energy shortage. This paper applies the quantile regression to investigate the new energy industry in China. The results show that economic growth exerts the greatest effect on the new energy industry in the lower 10th quantile province. This is because these provinces have the developed economies, demand for a higher ecological environment and new energy resources. Foreign energy dependence has a minimal impact on the new energy industry in the 25th–50th quantile province, due to their minimal oil importation. The contribution of technological progress to the upper 90th quantile province is the lowest, because their R&D capabilities are the weakest. The impact of energy consumption structure decreases in steps from the lower 10th quantile provinces to the upper 90th quantile provinces. The agricultural sector promotes the new energy industry in most provinces.

Green investments (GIs) in the energy industry are crucial for driving a clean energy transition and fostering environmental sustainability. In the digital economy era, insufficient attention has been paid to digital finance’s (DF’s) influence on GIs in energy enterprises, potentially underestimating its impact. Our study utilized a two-way fixed-effects model, analyzing data from 108 listed energy firms from 2011 to 2020, to empirically investigate the influence of DF on GIs in China’s energy industry. The research findings are as follows: (1) An increase of one unit in DF can improve the intensity of GIs in the energy industry by 0.03% by alleviating financing constraints, increasing cash flow, and correcting financial mismatches. (2) DF has a significant threshold effect on GIs, with market incentive- and command-and-control-based environmental regulations having thresholds of 16.98 and 0.98, respectively. (3) The GI performance of large state-owned energy enterprises in regions with a higher marketization benefits more from DF. We suggested tailored policy suggestions according to these findings.

This study addresses a crucial gap in the existing literature by exploring the intricate relationship between gender, disability, and energy poverty. While prior research has shown that females and persons with disabilities are more vulnerable to energy poverty, our study adopts an intersectionality framework to investigate how these identities interact with other variables, including life dissatisfaction, food insecurity, and energy subsidy, to shape the experience of energy deprivation. Using a series of robust techniques, our analysis of the General Household Survey in South Africa reveals several noteworthy findings. First, while females are less likely to be energy poor, the intersection between females and disability significantly amplifies their risk of energy poverty by 2.6%. Our mediation analysis further elucidates that life dissatisfaction and food insecurity serve as critical mechanisms through which this intersection exacerbates energy poverty. Importantly, we also find that the impact of energy subsidy is most effective when targeted toward females with disabilities, highlighting the need for tailored interventions. We call for policymakers and stakeholders to prioritize targeted energy subsidy schemes for persons with disabilities and females, recognizing the critical role such policies can play in mitigating energy poverty and promoting equity.

Increasing social funding is essential for adapting to climate change and driving low-carbon transformation. Employing a panel two-way fixed effects model, we investigate the response of institutional investors’ shareholding preferences to the green information disclosure of held companies in the Chinese stock market. Our main regression results demonstrate that institutional investors prefer “green” investments in their shareholding behavior and place greater trust in Bloomberg ESG disclosures. In addition, green information disclosure can effectively reduce market risk, alleviate information asymmetry, and improve the performance of listed companies. Specifically, compared with the Bloomberg ESG rating, the impact of green information disclosed in the annual reports of listed companies on financial indicators is statistically more significant. Moreover, different types of institutional investors have varying shareholding preferences on the basis of the green information disclosure of invested companies. Notably, institutional investors show low levels of trust in the independent green disclosures of energy state-owned enterprises while expressing high levels of trust in ESG disclosures. Finally, we conclude with targeted policy implications to guide corporate information disclosure.

The green credit policy (GCP) is an essential financial policy tool for solving the problem of environmental pollution, and urban energy conservation is an effective way to achieve the goal of carbon neutrality. However, existing research has not verified the energy-saving effects of green credit (GC) at the city level. Based on panel data from 283 cities in China, this study aims to investigate whether GC can effectively reduce urban energy intensity (EI), which is an important complement to existing research. In terms of research methods, to better evaluate the effect of the policy and exclude the influence of other relevant factors, this study considers the promulgation of the Green Credit Guideline (GCG) in 2012 as the basic event, uses the difference-in-differences (DID) model to investigate the impact of GC on EI, and discusses the main impact mechanism. The key results are follows. (1) GC can effectively reduce urban EI. (2) Public environmental demand positively regulates the negative correlation between GC and EI. (3) GC reduces EI through three main channels: government support, capital investment, and technological innovation; however, the mechanism of industrial structure has no significant effect. (4) The effect of GC is more significant in areas with large urban scales, low environmental regulation intensity, and high industrial agglomeration. Based on the above results, this study presents puts forward targeted policy recommendations to strengthen the role of GC in urban sustainable development.

Carbon tax and some other policies are designed to reduce emissions; resource tax can raise the energy price from the supply side to achieve the purpose of emission mitigation. Based on previous studies, this paper abstracts mitigation policies into supply-control (resource tax as an example) and demand-control (carbon tax as an example). The effects of these policies have been divided into the direct and the indirect effects. A dynamic recursive computable general equilibrium model is applied to simulate different impact path of the two policies. The research shows that if there is no foreign trade and the market is completely market-oriented, the effect of the demand control and the supply control may be equivalent. But this is not the real case. Under the same level of CO 2 emission, carbon tax can significantly reduce the energy demand of enterprises and restrain energy imports. However, resource tax can significantly increase domestic energy prices firstly, and then enterprises will be more willing to use cheaper imported energy. Regardless of energy security, relatively low energy use costs ease the economic costs of emission mitigation. Therefore, if every country in the world is required to reduce emissions compulsorily, resource tax may be a better policy of reducing emissions while obtaining “excess profits”.

Coal accounts for more than 60% of China’s primary energy consumption. Due to the demand decline since 2013, the coal industry was facing the dilemmas of falling prices, overcapacity, and high debt ratios. Reduction of overcapacity of the coal industry has become a crucial task in China’s supply-side structural reform. This paper attempts to explain several issues related to overcapacity reduction in the coal industry. First, we analyze the characteristics of China’s coal market and the causes of over-capacity in the coal industry. It is revealed that the aggregate coal demand of China is price inelastic, and the coal enterprises own market power. In addition, we illustrate that current overcapacity is the result of enterprises’ rational expansion in the context of rapid growth in demand in the previous period. Second, different capacity reduction schemes are compared. The results suggest that some of the inefficient production capacity should be temporarily withdrawn from the market, rather than ordering all coal mine to limit production capacity in the same proportion. Third, we conduct a regression model to describe the long-term price trend of coal and establish a mean-reverting model to simulate the motion path of the coal price. According to the Monte Carlo simulation, we estimate the value of the real option of coal capacity and find it is higher than the capacity replacement cost. This demonstrates that the real option is economically feasible in application.

There is a consensus within the international community that replacing traditional fossil energy with renewable energy, such as photovoltaic energy, will help mitigate climate change. However, the literature addressing the rapid development issues of the photovoltaic industry and related carbon dioxide abatement costs is limited. China is currently the largest photovoltaic producer and consumer in the world, hence suitable as our research object. In this paper, a fixed effect panel model with provincial panel data during the period 2012–2016 is applied to study the factors that influence China’s photovoltaic industry. The empirical results indicate that carbon dioxide emission mitigation requirements, government subsidies, technological progress, energy substitution, economic growth, and illumination resources promote the development of the photovoltaic industry. We further adapt the cost estimation model to estimate the average carbon dioxide abatement cost of photovoltaic electric power in China at 679.72 yuan/ton in 2015 and 681.88 yuan/ton in 2016. Compared with wind power and biomass energy, photovoltaic electric power is currently less economical for carbon dioxide emission reduction. Moreover, the future carbon dioxide abatement cost is predicted using a scenario analysis at 118.94–259.42 yuan/ton in 2025 and 42.63–171.95 yuan/ton in 2030. Since the carbon dioxide abatement cost in 2030 is in line with the future price level of the carbon trading market, it will be both economical and feasible to use photovoltaic electric power to decrease carbon dioxide emissions in the future.

China is facing huge pressure on CO2 emissions reduction. The heavy industry accounts for over 60% of China’s total energy consumption, and thus leads to a large number of energy-related carbon emissions. This paper adopts the Log Mean Divisia Index (LMDI) method based on the extended Kaya identity to explore the influencing factors of CO2 emissions from China’s heavy industry; we calculate the trend of decoupling by presenting a theoretical framework for decoupling. The results show that labor productivity, energy intensity, and industry scale are the main factors affecting CO2 emissions in the heavy industry. The improvement of labor productivity is the main cause of the increase in CO2 emissions, while the decline in energy intensity leads to CO2 emissions reduction, and the industry scale has different effects in different periods. Results from the decoupling analysis show that efforts made on carbon emission reduction, to a certain extent, achieved the desired outcome but still need to be strengthened.