To explore the impact of new urbanization development on industrial water utilization efficiency, the epsilon-based measure model and the entropy method were used to calculate the industrial water utilization efficiency and the development of new urbanization based on the panel data of 30 provincial cities in China from 2011 to 2020. Then the spatial Durbin and the panel threshold models were constructed to explore the spatial spillover and threshold effects of new urbanization development on industrial water utilization efficiency. The results show that there are large regional differences in the development of new urbanization and industrial water utilization efficiency. New urbanization development has a positive spillover effects on the improvement of industrial water efficiency. The promotion effect of new urbanization development on industrial water efficiency is affected by the level of new urbanization development, thus showing different results. According to the findings, it is recommended that local governments at all levels should increase their efforts to build new urbanization. And boost high-level and high-quality development of towns and cities while promoting the rationalization of industrial structure and optimizing resource allocation, thereby improving the efficiency of industrial water utilization.

Megacity clusters are the key battlegrounds for carbon emission reduction in China, and the polycentric spatial structure of these clusters has a profound impact on their carbon emission intensity. This paper focuses on five major megacity clusters: the Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD), Pearl River Delta (PRD), the middle reaches of the Yangtze River (MRYR), and the Chengdu–Chongqing (CY) City Clusters. We construct an inter-period panel dataset spanning from 2002 to 2023 and utilize an index of polycentric spatial structure, which equally considers both morphology and functionality. A fixed-effects model is employed, and the Lind–Mehlum U-shape test is applied to identify the nonlinear relationship. Additionally, a two-step approach is used to examine the mediating effect of industrial agglomeration, while interaction terms help identify the moderating effects of technological innovation and transport infrastructure. The results indicate a significant U-shaped relationship between the polycentric structure of megacity clusters and carbon emission intensity. When the polycentric spatial structure index reaches a specific threshold, carbon emission intensity is minimized, suggesting that a moderate degree of polycentricity is most conducive to carbon reduction. Mechanism analysis reveals that industrial agglomeration functions as a significant mediator, whereas technological innovation and transport infrastructure serve as critical moderators in this relationship. Based on these findings, we propose several policy recommendations: to guide the moderate adjustment of the polycentric structure of city clusters with stage-specific targets, optimize the mechanism of industrial synergy and transfer, differentiate the allocation of innovation resources, and achieve a fine-tuned alignment between the transport system and spatial structure. These measures will support the high-quality, low-carbon transformation of city clusters.

Haze episodes recur in industrial regions worldwide and slow progress towards the health and urban air-quality targets of the Sustainable Development Goals. Regulators need to know where episodes start, how they spread and which plants to inspect first. We combined hourly PM_2.5 data for 107 counties in Shaanxi, China, with hourly stack monitoring of 346 industrial facilities for 2017–2022. From 102 haze episodes, we built a county network of the order in which pollution rises and ranked facilities by their association with counties that pass pollution on. Episodes usually started in Xianyang, Weinan and Tongchuan and reached Xi’an hours later, and Fuping, Weicheng and Jingyang were most critical for spread. The 20 largest emitters accounted for 42% of emissions but 11% of the propagation-weighted score. Nine facilities, stable across 150 re-estimations and scoring above 95% of placebo scores with time-shifted emissions, form a first-priority list (led by coal-fired power plants and heat suppliers) that applies mainly to the heating season. Network weighting changed the list and extended priority to downwind prefectures but barely improved prediction. The list can guide which plants to inspect first, and targeted controls at these plants are an option that requires plant-specific verification.

Accurately predicting future carbon emissions is of great significance for the government to scientifically promote carbon emission reduction policies. Among the current technologies for forecasting carbon emissions, the most prominent ones are econometric models and deep learning, but few works have systematically compared and analyzed the forecasting performance of the methods. Therefore, the paper make a comparison for deep learning model, machine learning model, and the econometric model to demonstrate whether deep learning is an efficient method for carbon emission prediction research. In model mechanism, neural network for deep learning refers to an information processing model established by simulating biological neural system, and the model can be further extended through bionic characteristics. So the paper further optimizes the model from the perspective of bionics, and proposes an innovative deep learning model based on the memory behavior mechanism of group creatures. Comparison results show that the prediction accuracy of the heuristic neural network is higher than that of the econometric model. Through in-depth analysis, the Heuristic neural network is more suitable for predicting future carbon emissions, while the econometric model is more suitable for clarifying the impact of influencing factors on carbon emissions.