This paper explores the impact of corporate social responsibility (CSR) and financial distress on corporate financial performance (CFP) in Chinese listed companies of the manufacturing industry. Covering a total of 1445 manufacturing observations from 2013 to 2018 by matching the China Stock Market & Accounting Research Database (CSMAR) and Ranking CSR Ratings (RKS) database and regression models, we find that CSR has a significant positive impact on CFP, and the relationship is more pronounced for firms that are more stable. Further, the win-win relationship of CSR and CFP is also stronger in state-owned enterprises (SOEs). These empirical results suggest that enterprises should actively embrace CSR in response to the call of the country. At the same time, corporate stability should be increased to enhance the role of CSR in promoting CFP. We provide a quantitative analysis of the CSR, CFP, and financial distress of listed firms, and help to alleviate managers’ concern of CSR fulfillment and risk control.

In order to explore the impact of environmental regulation on the coordinated development of energy and the environment with the background of governance transition, we propose a three-stage integrated approach and use the panel data of China’s manufacturing industry 27 sub-sectors during the period of 2006–2015. In the first stage, according to the environmental pollution intensity, the manufacturing industry is divided into heavily polluting industry, moderately polluting industry, and lightly polluting industry. The second stage is employed the slacks-based measure (SBM)-undesirable method to study the sub-industries’ green energy-environmental efficiency under different environmental pollution intensities. Besides, the dynamic changes of technical innovation and efficiency among different industries are analyzed through the Malmquist productivity index. For the purpose of investigating the transmission mechanism of the Porter’s hypothesis and exploring the compound effects of environmental regulation and governance transition on green development, in the third stage, we use the panel data analysis to conduct more in-depth research on the relationship between environmental regulation, governance transition, and technical innovation. Results show that the highest average green energy-environmental efficiency is lightly polluting industry, which is 0.52, followed by the heavily polluting industry at 0.40, and the lowest is the moderately polluting industry, which is 0.32. By decomposing total factor productivity, heavily polluting industry is at the forefront of technical innovation. Panel data analysis results indicate that investment in research and development and governance transition could promote the growth of total factor productivity for manufacturing.

An accurate short-term prediction of wind power is crucial for grid reliability and optimized power generation allocation. Currently, deep learning-based methods for wind power prediction (WPP) focus excessively on exploiting spatial-temporal correlations of multivariate time series and neglect potential confounding factors. However, these factors can introduce spurious correlations, diminishing the reliability and robustness of the methods solely based on correlations. Specifically, traditional deep learning methods lack generalizability across varied power output scenarios due to the interference from confounding factors, such as spatial distribution differences and turbine disturbances. Therefore, this paper proposes a novel method, called Temporal Causal Attention Network (TCAN) for short-term WPP, leveraging the physically meaningful internal-external causality to model interactions among factors, and enhancing generalizability across power output scenarios. In this method, a multiscale temporal transformer is proposed to capture the internal causality of a dynamic time series, and a causal graphs-based mechanism is introduced to identify the external causal path among different factors. In addition, a causal attention-based information updating method is proposed to measure the impact of cause nodes on effect nodes. The real data of eight wind turbines from Asia's largest wind power producer are used to evaluate the proposed method. Results show that compared to the second-best method in the experiment, the average RMSE, MAAPE, and R-Squared for the proposed method for 10-min WPP are enhanced by 35.34%, 32.73%, and 4.73%, respectively.