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.

With severe haze pollution continuing to frequently occur in many regions of China in recent years, increasing research effort has been made to study the source and control of haze pollution using socio-economic factors. Based on the urban panel data of 21 cities in Guangdong Province, South China, from 2005 to 2016, this paper analyzes the spatial and temporal evolution trend, spatial autocorrelation and influencing factors of Guangdong’s haze pollution through spatial econometric methods with inverse distance weight. First, the results show that the haze pollution of 21 cities in Guangdong Province has strong spatial autocorrelation and spatial clustering phenomenon. Aggregation areas of heavy air pollution are located in the Pearl River Delta region centered on Guangzhou, Foshan, Zhongshan and Dongguan. Those cities should be targeted as priority haze-pollution-reduction areas. Secondly, significant spatial dependence and spatial spillover effects of haze pollution in Guangdong are demonstrated by the spatial econometric models. Joint prevention and control of haze pollution between cities are therefore important and necessary. Thirdly, the direct and indirect effects of energy consumption on haze pollution are positive and significant at the 1% level. Meanwhile, population, foreign direct investments (FDI) and the ratio of the secondary industry (e.g., manufacturing and construction industries) relative to the service industry also play important roles in haze pollution, and all of them have positive effects on air pollution. However, the environmental Kuznets curve (EKC) hypothesis is not supported by the economy-emission data of Guangdong. Economic growth and car density are not significant factors in deciding the level of haze pollution. To effectively improve the local air quality, the government is recommending the introduction of policies for adjusting the structures of energy, economy and FDI, promoting the research, development and application of clean technologies, and enhancing the awareness of energy conservation among residents and enterprises.