The energy sector’s digital evolution is a critical micro-reflection of the digital economy’s architecture and an essential tactical pathway for achieving sustainable development goals. However, the value of digital change in regard to how effectively energy firms’ core business functions is not yet apparent. This research utilizes textual analysis to quantify the textual intensity of energy enterprises’ digitization. Applying data gathered from A-share listed firms in Shanghai and Shenzhen from 2010 to 2021 and based upon the fixed-effects panel model and mediated effects model, we assess the impact of digitization on critical business performance and evaluate the research themes’ variability from business and geographical viewpoints using a full-sample strategy. We derive three notable findings. First, the digital development of energy enterprises significantly improves the performance of their core businesses and exhibits some fluctuating characteristics. Second, the digitization of energy enterprises greatly increases the input and output of enterprise innovation, thereby improving the efficiency of their core business in the two main ways of breakthrough innovation. Third, there is a nonlinear relationship between the performance of energy enterprises’ core businesses and their digital transformation, meaning there is just one threshold consequence that diminishes after crossing the threshold. Digital transformation enables energy companies to carry out cross-border cooperation and integrate innovative resources, thereby improving corporate performance and promoting sustainable development. This paper offers relevant insights for more effective fostering of digital transformation and can help spur enterprises to seek out distinctive and ground-breaking innovation models.

The high carbon emissions and pollution of China’s thermal power industry chain have exacerbated environmental and climate degradation. Therefore, accelerating the green transformation process is of great significance in promoting the sustainable development of enterprises. This study selected 30 listed thermal power enterprises in China as research objects, analyzed their data from 2018 to 2022, set targeted input–output indicators for different stages, and used a two-stage dynamic data envelopment analysis (DEA) model to evaluate and measure the efficiency of the green transformation of Chinese thermal power enterprises. In addition, this study also uses the logarithmic mean Divisia index (LMDI) method to analyze the driving effects of green transformation. The results indicate that in terms of overall efficiency, there is a significant difference in the overall performance of these 30 thermal power enterprises, with a large difference in average efficiency values. Efficiency values are related to enterprise size. In terms of stage efficiency, the average efficiency value of thermal power enterprises in the profit stage was significantly higher than that in the transformation stage, and the profitability of Chinese thermal power enterprises was better. In terms of sub-indicator efficiency, the efficiency of each indicator shows a “U”-shaped trend, and there is a certain correlation between the operating costs and revenue of thermal power enterprises, the market value of green transformation, and related indicators. In addition, the most important factor affecting the efficiency of green transformation is the sewage cost they face, whereas their operational capabilities have the least impact on their green transformation. In this regard, thermal power enterprises should increase their investment in the research and development of key technologies for thermal power transformation and continuously optimize their energy structure. The government will increase financial support for thermal power green transformation enterprises and correspondingly increase emission costs.

Land urbanization enables a thorough perspective to explore the decoupling of land use environmental efficiency (LUEE) and energy use, thereby supporting the shift into low-carbon land use by emphasizing energy conservation and reducing carbon emissions. This paper first calculates LUEE from 2011 to 2021 by using the EBM-DEA model in China. The geographical detector model is used to examine the driving factors of land use environmental efficiency. The results show the following: (1) China’s LUEE is high in general but shows a clear pattern of spatial differentiation internally, with the highest values in the eastern region represented by Beijing, Jiangsu, and Zhejiang, while the central and western regions show lower LUEE because of their irrational industrial structure and lagging green development. (2) Energy consumption, economic development, industrial upgrading, population size, and urban expansion are the driving factors. Their explanatory power for the spatial stratification heterogeneity of land use environmental impacts varies. (3) Urban expansion has the greatest impact on the spatial differentiation of land use environmental effects, while energy consumption also shows significant explanatory strength. In contrast, economic development and population size exhibit relatively weaker explanatory effects. (4) The interaction of the two driving factors has a greater impact on LUEE than their individual effects, and the interaction is a two-factor enhancement. Finally, we make targeted recommendations to help improve land use environmental efficiency.

Wind power is a core component of a clean energy system. The efficiency of a wind power system evolves through coordinated interactions. These interactions occur among three regional subsystems: resource subsystem, technology subsystem, and economy subsystem. To reveal the operational mechanisms of its internal subsystems, this study analyzes the comprehensive efficiency of the wind power system in China from 2010 to 2022. The two-stage EBM model, the Tobit regression model and the feedforward neural network model are employed in combination. The results show that: (1) The comprehensive efficiency of the wind power system has gradually improved, but shows spatiotemporal variations due to uneven subsystem coordination. (2) The improvement of efficiency is characterized by stages. The optimization of technology subsystems drives the development stage, while economic scaling dominates the operation stage (though operation and maintenance technologies remain deficient). (3) The correlation between development and operation stages is suboptimal, and the coordination of subsystems remains weak. (4) Technology innovation and electricity demand boost comprehensive efficiency, while human resources hinder it. Extreme weather exerts either a contributing or an interfering effect on the system. (5) Future projections show continued efficiency growth. The study concludes with cross-system coordination strategies to enhance the contribution of wind power in clean energy.

Based on panel data from 30 provinces in mainland China from 2011 to 2017, this research investigates the spatial spillover effect and nonlinear impact of digital economy (DE) on green and high-quality manufacturing industry (GHMI) using the spatial econometric and panel threshold model. It is found that both the DE and GHMI have a significant positive spatial correlation. The spatial econometric model under different weights all illustrate that regional DE has not significantly promoted GHMI in local region, but produced a significant positive influence on GHMI in neighboring regions. The east region has a higher degree of DE, while the level of DE in the central and west regions are both lower and close to each other. The level of GHMI shows a decreasing pattern in the east, central and west regions in that order. In addition, the impact of DE on GHMI presents a significant single threshold effect. With the improvement of DE, GHMI has a U-shaped change trend. Attention should be paid to the threshold effect of DE on GHMI, control the development speed of DE within a reasonable range, scientifically allocate DE resources according to different regional economic development levels in order to promote GHMI.