Based on panel data from 30 Chinese provincial administrations during 2001-2016, this study uses the Global Malmquist-Luenberger (GML) index to measure provincial green productivity and employs a spatial econometric model to examine the impact of environmental regulation on green productivity. The results indicate that China’s overall green productivity has increased in trend during the research period, but there are significant regional differences, with those in the east showing greater increase than those in the mid-west. Beyond that, green productivity has significant spatial correlation in most years of the study period. Considering spatial effects, we find that there is a threshold for the impact of environmental regulation on green productivity. As regulation intensity crosses the inflection point, its effect on green productivity will change from negative to positive. Furthermore, environmental regulations may indirectly improve green productivity through technological innovation and foreign direct investment (FDI), but the effect depends upon the kind of environmental regulation imposed. Empirical results indicate that expenditure-type environmental regulation might improve green productivity by enhancing the positive effect of FDI, while investment-type environmental regulation might raise green productivity by promoting technological innovation. The results have important guiding significance for future environmental policy making.

Fine particulate matter (PM_2.5) has been an important environmental issue because it can seriously harm human health and can adversely affect the economy. It poses a problem worldwide and especially in China. Based on data of PM_2.5 concentration and night light data, both collected from satellite remote sensing during 1998–2013 in China, we identify the socio-economic determinants of PM_2.5 pollution by taking into account the spatial flow and diffusion of regional pollutants. Our results show PM_2.5 pollution displays the remarkable feature of spatial agglomeration. High concentrations of PM_2.5 are mainly found in Eastern China (including Shandong, Jiangsu, and Anhui provinces) and the Jing-Jin-Ji Area region in the north of China (including Beijing, Tianjin, and Hebei provinces) as well as in the Henan provinces in central China. There is a significant positive spatial spillover effect of PM_2.5 pollution, so that an increase in PM_2.5 concentration in one region contributes to an increase in neighboring regions. Whether using per capita GDP or nighttime lighting indicators, there is a significant N-shaped curve that relates PM_2.5 concentration and economic growth. Population density, industrial structure, and energy consumption have distinct impacts on PM_2.5 pollution, while urbanization is negative correlated with PM_2.5 emissions. As a result, policies to strengthen regional joint prevention and control, implement cleaner manufacturing techniques, and reduce dependence on fossil fuels should be considered by policy makers for mitigating PM_2.5 pollution.

We constructed an internet development index and used inter-provincial panel data from China between 2006 and 2016 to empirically test the impact and the mechanism by which internet influenced green productivity. Internet strongly promoted green productivity growth in China, and its influence showed clear regional heterogeneity, with positive effects in the eastern and central regions but no effect in the western region. Internet not only promoted green productivity directly, but also had an indirect positive effect on green productivity by accelerating technological innovation and industrial structure upgrades. Nonetheless, its direct effects far exceeded its indirect effects. More importantly, we found that the positive effect of internet on China’s green productivity showed significant non-linear characteristics. When the development level of internet crossed a critical threshold, its influence was significantly enhanced. At the same time, R&D investment and industrial structure upgrading can further strengthen the network effect of internet. These findings provide important theoretical guidance for the application of internet and associated strategies to promote green economic growth in China.

Enhancing Energy-Embedded Green Utilization Efficiency of Urban Land (E-GUEUL) is crucial for reconciling economic growth with carbon neutrality targets, with the Integration of the Digital–Real Economy (IDRE) emerging as a key driver. This study measures city-level E-GUEUL using the super-efficiency SBM–Malmquist index model. To rigorously identify the causal effect of IDRE on E-GUEUL and address potential model misspecification and high-dimensional confounding factors, a Double Machine Learning (DML) framework is employed. Findings reveal a robust and significant positive effect of IDRE on E-GUEUL, a conclusion that holds across a series of robustness checks and endogeneity controls. Heterogeneity analysis indicates that the efficiency enhancement is more pronounced in non-resource-based, digitally developed, and eastern or central cities. Mechanism analysis reveals that optimizing Energy Consumption Intensity acts as a short-term driver, while Green Technology Innovation and Environmental Regulation serve as long-term sustainers. Furthermore, moderating effects reveal that Marketization exerts a positive moderating influence. This study provides empirical evidence and policy insights for leveraging IDRE to advance green growth through tailored approaches.