Recently, green innovation efficiency, which considers innovation and environmental factors, is gradually becoming important for the sustainable development of Chinese heavy polluting industries because of the increasing strictness in China’s environmental regulations. Previous studies ignore the impact of external environmental factors on the efficiency of green industry innovation and fail to explain the complex relationship between environmental and technical efficiency fully. Therefore, a non-radial directional distance function-data envelopment analysis (DDF-DEA) three-stage green innovation efficiency evaluation model was constructed to measure the green innovation efficiency of China’s heavy polluting industries objectively and explore the impact mechanism of external factors. Then, the aforementioned model was used to conduct an empirical test on China’s heavy polluting industries. Results indicate that the green innovation efficiency of heavy polluting industries is generally low in China, and the entire industry is in the transitional stage of “effective innovation but not green.” The uncertainty of the effect of the environmental regulation policy, the over-reliance on external technologies, and the scale diseconomies of industries, which are the key factors in improving the green innovation efficiency of China’s heavy polluting industries, have a significant negative impact on green innovation efficiency. The conclusions of this study can provide a useful reference for China and other emerging markets to formulate reasonable environmental regulations and green transition of heavy polluting industries.

Transportation is an important part of social and economic development and is also a typical high-energy and high-emissions industry. Achieving low-carbon development in the transportation industry is a much-needed requirement and the only way to achieve high-quality development. Therefore, based on the relevant data of 30 provinces in China from 2010 to 2018, this research uses the static panel model, panel threshold model and spatial Durbin model to conduct an empirical study on the impact and mechanism of digital innovation on carbon emissions in the transportation industry, and draws the following conclusions. (1) Carbon emissions in the transportation industry have dynamic and continuous adjustment characteristics. (2) There is a significant inverted U-shape non-linear relationship between the level of digital innovation and carbon emissions in the industry. In regions with a low level of digital innovation, the application of digital technology increases carbon emissions in this industry, but as the level of digital innovation continues to increase its application suppresses carbon emissions, showing an effect of carbon emission reduction. (3) The impact of digital innovation on carbon emissions in the transportation industry has a spatial spillover effect, and its level in one province significantly impacts carbon emissions in other provinces’ transportation industry through the spatial spillover effect. Therefore, it is recommended to further strengthen the exchange and cooperation of digital innovation in the transportation industry between regions, improve the scale of digitalization in this industry, and accelerate its green transformation through digital innovation, thus promoting the green, low-carbon, and sustainable development of China’s economy.

China is currently in a stage of high-quality economic development, but the high energy consumption and high pollution production methods of the construction industry are no longer adaptable to the country’s economic development goals in the new era. As one of the important tools for the government to regulate high-quality advancement, taxation plays a vital role in the green development of the construction industry. This research uses panel data of 26 provinces in China from 2008 to 2017 and constructs a multiple intermediary effect model to conduct an empirical test on the impact of green taxes on the carbon emission efficiency of the construction industry and its mechanism. The results show that green taxation promotes carbon emission efficiency by accelerating the promotion of fixed capital investment in this industry, accelerating the flow of technological elements and technological research and development. This study further verifies that green taxation and carbon emission efficiency present an inverted U-shape relationship, and that the path mechanism of green taxation, fixed capital investment and technological progress-improving carbon emission efficiency of the construction industry has an intermediary effect. On this basis, suggestions are offered to rationally adjust the corporate tax burden, optimize the industrial structure, and actively guide the green transformation of the construction industry.

In this study, The Global Climate Risk Index, a global climate risk score, along with firm-level data are used to investigate how climate risk affects corporate financial performance and financial policy in various nations. To evaluate the impact of climate risk on various parts of a firm, this study uses the capabilities of SPSS (version: 28.0.1.1(15)) to perform correlation and regression analysis between climate risk indicators and the firm’s operational data. We obtained financial information from the Bloomberg database for companies in 37 different countries from 2017 to 2021, including return on assets, cash from operations, sales growth, short-term debt, long-term debt and short- and long-term debt. This is combined with a climate risk index to analyze the impact of climate risk on companies in different countries. The financial performance of a firm is found to be adversely, but not significantly, associated with climate risk in this study using correlation and regression analysis, whereas the long-term debt of the company is found to be favorably related to climate risk. From this investigation, the following findings can be derived. A lower returns on assets is the indicator of the comparatively poor financial performance of businesses in nations with increased climate risk. Businesses in nations with greater climate risk typically retain more long-term debt. Companies in nations with lower climate risk, on the other hand, typically retain less long-term debt. Lastly, this study contributes to the understanding of how climate risk affects different countries and how climate risk affects corporate financing strategies.

Utilizing the local-density approximation (LDA) + Gutzwiller method, we have studied the $\alpha \text{−}\gamma$ transition in cerium. Our results indicate that the volume collapse transition between $\alpha$ and $\gamma$ phases is present at zero temperature with negative pressure. By further providing a newly finite temperature generalization of the LDA + Gutzwiller method (using the mean-field potential approach), the entropy contributed by both electronic quasiparticles and lattice vibration included, we obtain the Gibbs free energy at a given volume and temperature, from which we get the $\alpha \text{−}\gamma$ transition at finite temperature and pressure. Our results indicate that the electronic entropy and lattice vibrational entropy both play important roles in the $\alpha \text{−}\gamma$ transition. We also calculated the equation of state and phase diagram of cerium, finding good agreement with the experiments.

In this era of rapid economic development, it is inevitable that economic activities eventually cause serious damage to the environment’s air quality, making it the focus of global public health. If the treatment efficiency of medical accidents can be improved, then this can significantly stabilize society and improve production efficiency. Past research has mainly focused on work safety and health issues, seldom discussing economic, social, medical, and environmental pollution issues together, and, most generally, adopted static methods that fail to recognize how air pollution affects the overall economy, society, medical care, and external environment. In order to more deeply understand the changes among social, economic activities, and environmental issues due to air pollution, this study proposes a meta-two-stage undesirable dynamic DDF (Direction Distance Function) that, under an exogenous model, divides the 30 provinces of China into high-income regions and middle-income regions and explores the economic, social, medical, and environmental efficiencies between the two areas to resolve the lack of related static analyses. The empirical results are as follows. (1) The AQI (air quality index) significantly impacts the efficiency of medical injuries in various regions. (2) When the AQI is considered, the medical insurance expenditure efficiency score value of high-income areas is lower than the value without the AQI. (3) When the AQI is considered, the efficiency value of the number of work injury insurance benefits in the middle-income area is lower than the efficiency value without the AQI.