Improving energy utilization efficiency is a crucial means to achieve energy conservation, emission reduction, and green development. At present, to establish a high-quality development framework and satisfy the growing need for a better life among all its people, China must steadfastly pursue the path of green development. Although China’s substantial economic scale and achievements in ecological civilization construction provide favorable conditions for green transformation, there remains a significant gap compared to developed countries in the application of green and clean technologies. Confronted with technological bottlenecks, leveraging emerging technologies such as industrial robots from the new round of scientific and technological revolutions to improve the green total factor energy efficiency (GTFEE) is of critical importance to China’s green development. This study explores the potential impact of industrial robots on enhancing China’s GTFEE. It begins by reviewing the current research landscape in this field, highlighting its shortcomings, and theorizing potential impact pathways of industrial robots. Subsequently, the paper analyzes data from 2010 to 2019 on the usage of industrial robots and GTFEE across 276 cities at the prefectural level or above in China. Through empirical regression models that incorporate control variables and interaction terms, the study investigates the specific impacts of industrial robots on energy efficiency and their mechanisms of action. The results indicate that industrial robots significantly enhance the GTFEE of Chinese cities, especially in the Northeastern region. Industrial robots notably improve the GTFEE in resource-based cities, old industrial bases, and low-carbon pilot cities. Additionally, robots indirectly boost GTFEE by increasing labor productivity. Enhanced levels of green innovation and environmental regulations also positively moderate the effectiveness of industrial robots in improving energy efficiency. The findings of this research can assist local government agencies in coordinating and implementing policies that are conducive to green development, making better use of industrial robots to serve the people, and are of significant importance for promoting the transformation of China’s economy and society towards high-quality development.

Based on data from the International Federation of Robotics (IFR) and Chinese A-Share Listed Companies 2011–2019, this paper evaluates how the penetration of industrial robots in China affects the labor income share from both theoretical and empirical perspectives. We first develop a theoretical framework considering robots in the task model, consider different tasks matching different types of labor, construct a labor income share model including robots based on the task model, and perform a theoretical analysis finding that industrial robots can improve labor income. Then, we explore the mechanism of labor price distortion as an intermediate variable that can reduce the labor income share, finding that robots are able to effectively reduce the negative impact of labor price distortions on labor income share. After correcting for potential endogeneity problems, the results confirm a positive, significant, and lasting impact of industrial robot penetration on labor income share, confirming that labor price distortion is the underlying mechanisms through which the robots can effectively reduce the negative impact of labor price distortions on labor income share. At the same time, the impact of robots on firms’ labor income shares varies significantly across different regions, external financial dependence, and skill premiums. Our findings can help the government to provide a decision-making basis for how industrial robots can better serve people in the new century, ensuring that people can share in the achievements of economic development.

Rural revitalization plays an important role in reducing urban–rural disparities and promoting inclusive regional development in China. This study appraises the coordinated development of agricultural new-quality productive forces and rural revitalization across five provincial-level regions in Northwest China—Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang—from 2013 to 2022. Thirty-two indicators (16 in each subsystem) are weighted by the entropy method and evaluated using TOPSIS, grey relational analysis, a coupling coordination degree model, an overall Dagum Gini coefficient (without within- or between-group decomposition), and an obstacle degree model. The empirical results show a clear regional hierarchy: the mean coupling coordination degree (D) is 0.977 in Shaanxi, 0.882 in Xinjiang, 0.854 in Gansu, 0.707 in Ningxia, and 0.700 in Qinghai. In Xinjiang, the agricultural new-quality productive-forces score fell from 0.984 in 2013 to 0.748 in 2022, the rural-revitalization score fell from 0.721 to 0.705, and D fell from 0.918 to 0.852, showing a fluctuating relative trajectory rather than steady improvement. Agricultural means of production remain the largest criterion-level obstacle (37.74% on average across the five regions), while agricultural science and technology personnel constitute Xinjiang’s largest indicator-level obstacle (17.50%). The results support differentiated regional strategies and demonstrate that composite-index findings should be interpreted as descriptive and relative rather than causal.