Water pollution in China has expanded from regions to river basins. It is of great significance to study the behaviors of regulated subjects from the perspective of river basins for protecting the water environment and improving the effectiveness of environmental regulations. This paper constructed a basin spatial regression design to discuss the governance effect and economic effect of water pollution from industry in relation to the central environmental protection goal of China, as well as the response and strategy of local government. Considering the validity of the data, the annual average chemical oxygen demand (COD) concentration of each monitoring station was adopted as the ambient water quality index, and there was direct evidence that the ambient water quality index influenced water-polluting industries. We found that, if the downstream COD concentration in the last year rose by 1 mg/L, the water-polluting industries reduced their pollution and output value by 1.9% and 0.2%, respectively. After using the upstream COD concentration as the tool variable, the result was still robust. However, the results of local government’s two strategies, which we can call regulatory constraint force and fiscal incentive force, were opposites. Specifically, the governance effect of polluting industries under regulatory constraints was more significant, while fiscal incentives encourage water-polluting industries to create more value. In addition, we estimated the impact of ambient water quality on water-polluting industries in the Yangtze River Basin and the Yellow River Basin, and revealed that the governance effect exceeded the average value of seven river basins. After further distinguishing the relative upstream cities and relative downstream cities, the results show that the response of relative upstream cities to water quality was significantly higher, and the gap between regions expanded further after 2013.

The technological attributes of urban digital infrastructure (UDI) are transforming labor skill structures in the market, thereby altering changes in skill premiums. This study investigates the relationship between UDI and skill premiums by developing a theoretical model that incorporates both digital and material capital. Using data from the China Labor Force Dynamic Survey and urban statistics, we examine the underlying mechanisms. The findings indicate that UDI exhibits skill-biased technological attributes, thereby increasing the skill premium. UDI development raises the demand for high-skilled labor across both skill-intensive and non-skill-intensive industries, altering the labor skill structure and consequently elevating the skill premium. This effect stems from the complementarity between UDI-related digital capital and high-skilled labor. Compared to material capital, deepening digital capital enables high-skilled labor to contribute more significantly to output.

Based on the SBM-Malmquist model of unexpected output, the paper estimates and decomposes the total-factor water efficiency (TFWE) of 11 provinces and cities in Yangtze-river Economic Belt from 2000 to 2017. The study finds that: (i) TWFE in the Yangtze-river Economic Belt has been significantly improved during the sample time, which has an average value of 0.70, the highest value of 0.91 in 2017 and the lowest value of 0.56 in 2006; (ii) provinces or cities from the eastern region of Yangtze-river Economic Belt including Shanghai, Zhejiang and Jiangsu, tend to have higher efficiency than the central and western regions at the same time; (iii) the index of technical change (Tech) obtained by further decomposition of the efficiency is the main contributing factor of efficiency improvement.

Improving resource allocation efficiency and fostering innovation are important dimensions of sustainable urban economic development. Public data trading platforms may contribute to these objectives by facilitating data circulation, reducing information frictions, and improving the coordination of productive resources. However, empirical evidence on their implications for urban productivity remains limited. This study examines the effects of public data trading platform establishment on urban total factor productivity (TFP), drawing on prefecture-level city panel data from China covering 2010–2024. By exploiting the staggered establishment of these platforms as a quasi-natural experiment, we employ a staggered difference-in-differences (DID) framework to estimate their productivity effects and investigate potential transmission mechanisms. The results indicate that public data trading platform establishment is positively associated with urban TFP after controlling for city and year fixed effects. The findings also reveal a positive association between platform establishment and technological innovation. Further mechanism analyses suggest that high-level human-capital agglomeration and the spatial agglomeration of scientific and technological resources may represent additional pathways linking data-market institutions to urban productivity. These findings highlight the potential role of institutional infrastructure for data exchange in supporting innovation-driven growth and more efficient resource utilization. From a sustainability perspective, the study provides evidence relevant to the socioeconomic and institutional dimensions of sustainable urban development and suggests that policies promoting data accessibility, effective data governance, and complementary innovation capabilities may help strengthen the foundations of long-term urban productivity. The results do not directly measure environmental sustainability and should therefore be interpreted primarily in terms of economic and institutional sustainability.