Rapid industrialization and urbanization have presented a dilemma to traditional environmental policies, and severe haze pollution has attracted particular attention in China. This paper employed the exploratory spatial data analysis technique to analyze global and local spatial auto-correlation of environmental regulations (ERS) and haze pollution. Static and dynamic spatial panel data models were further established to investigate the influence of ERS and its spatial spillover effect on haze pollution across 31 provinces of China in 2005-2015. Results showed that significant spatial autocorrelation existed for both ERS and haze pollution in China, forming different aggregation regions in terms of geographical distribution. There was strong evidence for the “inverted-U”-shaped EKC relationship between ERS and haze pollution regardless of whether it was the static or dynamic spatial econometric model. In addition, haze pollution was not only affected by economic growth, population size, and urbanization, but also by the haze pollution in the previous period. Conclusions and control strategies that would be of great significance for implementing effective management measures were synoptically discussed.

Effectively reducing manufacturing carbon dioxide (CO₂) emissions is a vital strategy for China to curb its rapidly rising carbon footprint. Features of such a reduction in manufacturing include an increase in the share of high-tech manufacturing and optimization of the energy consumption structure. This study aims to analyze the case of Shenzhen, a unique post-industrial innovative manufacturing megacity, for its leading experience in China’s manufacturing transition. Disaggregated manufacturing emissions data of Shenzhen, including 27 sub-sectors in four categories, were collected, and driving factors were identified by the logarithmic mean Divisia index (LMDI) method. The results suggest that: (1) CO₂ emissions from Shenzhen’s manufacturing show a phased difference between 2008–2012 and 2012–2020. CO₂ emissions embodied in electricity consumption have increased by over 30% in the former period and have remained stable at a high level of over 90%. (2) Significant heterogeneity of CO₂ emissions in various manufacturing sectors is revealed, with the largest emissions sources being factories that make communication equipment, computers, and other electronic equipment. (3) Lower carbon intensity is the primary factor in reducing CO₂ emissions, while the economic activity effect of manufacturing possesses a stimulating impact. (4) The marginal impact of restructuring on CO₂ emissions is insignificant since the manufacturing and energy structures of Shenzhen have been upgraded to a low carbon level. Therefore, strengthening the power saving management and improving the energy efficiency of the manufacturing, rather than optimizing the manufacturing and final energy structures, will be a necessary potential solution to the problem of how to reduce CO₂ emissions in Shenzhen’s manufacturing.

This research focuses on the spatial distribution and spatial correlation of environmental regulations (ERS) with exploratory spatial data analysis technique (ESDA). The panel data of 31 provinces in China during the period of 2005-2015 is used. Finds show that the most intensive ERS region is located around Beijing-Tianjin-Hebei region. The values of global Moran's I vary from 0.1104 to 0.2145, indicating that ERS in China is not distributed randomly, but has spatial agglomeration effects. The High–High agglomeration type is mainly distributed in north China.