Environmental regulation and green innovation are two main fulcrums in the realization of green transition of industrial growth. However, few studies have done an empirical analysis of the impact of environmental regulation and green innovation on green development. Based on the theory of systematic interduality, regional industrial green development is regarded as a dynamic system composed of two subsystems: the state and the process subsystem. Using provincial industrial panel data from 2007–2015 and the spatial Durbin model under the unified analysis framework, this paper examines the role and mechanism of environmental regulation (divided into administrative environmental regulation, market-based environmental regulation, and public participation environmental regulation) in the impact of green innovation (divided into green product innovation and green craft innovation) on industrial green development. The results indicate a sharp fluctuating trend in China’s overall industrial green development performance, and that China’s 30 provinces can be divided into four categories, based on the development levels of two subsystems of industrial green development. There is a clear positive spatial correlation between the industrial green development performance in different provinces. Considering the impact of environmental regulation on industrial green development performance, different types of environmental regulation have different regional influences. Considering the impact of green innovation on industrial green development performance, in the absence of environmental regulation constraints, green product innovation shows a certain promotional role, and green craft innovation has a significant inhibitory effect. However, under environmental regulation constraints, market-based environmental regulation through the encouragement of green craft innovation rather than green product innovation achieves a positive impact on industrial green development.

This paper adopts 2009 to 2015 panel data from 27 manufacturing industries in China. A Super-SBM model is used to measure the green innovation efficiency (GIE) of China’s manufacturing industry. A panel data model is then built to systematically examine the impact of environmental regulation (ER) and two-way foreign direct investment (FDI) on the GIE of China’s manufacturing industry under a unified analysis framework. The results are as follows: (1) the overall level of the green innovation efficiency in China’s manufacturing is low, and there is still great potential for improvement. Considering industry heterogeneity, the green innovation efficiency of patent-intensive manufacturing is significantly higher than that of non-patent-intensive manufacturing; (2) in terms of the whole manufacturing industry, ER and the interaction between ER and outward foreign direct investment (OFDI) have significantly negative effects on GIE, OFDI has significantly positive effects on GIE. (3) when considering industry heterogeneity, for patent-intensive manufacturing, ER and the interaction between ER and inward foreign direct investment (IFDI) have significantly negative effects on GIE, while IFDI has significantly positive effect on GIE. For non-patent-intensive manufacturing, ER and the interaction between ER and OFDI have significantly negative effects on GIE, while IFDI and the interaction between ER and IFDI have significantly positive effects on GIE.

Low-carbon transition stands as a vital strategy for the global community to address the challenge of climate change, inevitably affecting residents’ daily lives. However, there is a notable gap in the quantitative analysis of the low-carbon transition’s impact on energy poverty in developing countries, limiting policymakers’ understanding of the inherent mechanism and their ability to take informed actions. This study investigates the low-carbon city pilot (LCCP) policy, China’s key low-carbon initiative, as a quasi-natural experiment, using the difference-in-differences (DID) method to examine its impact on residents’ energy poverty conditions. Utilizing panel data from 4807 households in the CHARLS dataset, this study effectively integrated household-level and city-level data. Benchmark regression indicates that the LCCP policy exacerbates energy poverty among residents. Further analysis reveals the pivotal role of energy infrastructure and expenditure in bridging the nexus between the LCCP policy and energy poverty, providing crucial insights into the potential pathways through which this policy impacts energy poverty. Additionally, heterogeneity analysis indicates that the impacts of LCCP policy are more pronounced in eastern cities, non-resource cities, and high administrative-level cities, as well as in the communities suffering from subpar governance quality. By leveraging reliable survey data and robust quantitative methods, this study not only broadens the methodology of energy poverty studies but also offers valuable insights for developing countries to safeguard residents’ energy welfare amid low-carbon transitions.

Recently, the Chinese government released the Outline of the Development Plan for the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), raising the development of the GBA urban agglomeration to a national strategy. An efficient technology transfer network is conducive to promoting the integrated and coordinated development and enhancing the scientific and technological innovation capabilities of the GBA urban agglomeration. Therefore, this study uses the patent transaction data for three years (2010, 2014, and 2018), integrates data mining, and uses complex network analysis, based on full-flow and net-flow networks, from the overall characteristics, network node strength, network association, network node importance, and network communities to reveal the structural characteristics and spatial patterns of the technology transfer network in the GBA. The results revealed that: (1) Technology transfer networks (full-flow and net-flow) in the GBA show heterogeneity. (2) Full-flow network presents a clear hierarchy within the GBA, showing a “two poles and two strong” pattern, and technology transfer has the same city preference; outside the GBA, there are close technology transfer regions that have technical and geographical proximity characteristics; the net-flow network presents a “one pole, two strong” pattern, and Guangzhou has become the core region of the net-flow network. (3) Connection objects of the technology transfer network have path dependence and spatial preference. Coexistence of concentration and decentralization characterizes the spatial flow. (4) Spatial distribution of the hub and authority of the technology transfer network is heterogeneous and hierarchical. Each city in the GBA has its own technological advantages. (5) Spatial clustering characteristics of the community within the technology transfer network are obvious. (6) The GBA is dominated by the transfer of patented technology in the high-tech industry, while the transfer of patented technology in the traditional manufacturing industry also plays an important role.

The Sino–US trade war has prompted China to re-examine the development of manufacturing, while the energy crisis restricts such development. Scientifically planning industrial energy allocation is important for supporting industrial transformation and the upgrading of manufacturing. The embodied energy flow in China’s manufacturing was investigated by reconstructing the energy flow network; taking a systems perspective, a fine-grained analysis of the emerging patterns and evolution of these flows in the internal and external manufacturing industries was performed, thus providing useful insights for energy planning. The results show that in the internal and external networks of Chinese manufacturing, most of the embodied energy convergence and transmission is concentrated in a few industries Moreover, it is clear that industries with stronger embodied energy convergence and conductivity are generally more likely to be associated with industries with weak convergence and conductivity. Preferential selection is an important mechanism for the generation of embodied energy flow paths. The choices of the embodied energy flow paths of various industries exhibit the preference that ‘the rich get richer,’ and newly generated flow paths are more likely to be chosen for connectivity to a path of strong convergence or conductivity. The embodied energy flow patterns of the internal network of manufacturing mainly include two-focus and multi-focus convergence patterns, while that of the external network of manufacturing is mainly a two-focus transmission pattern. Within in-edge networks, communities of high-end manufacturing have gathered most of the embodied energy, while in out-edge networks, communities of traditional manufacturing have been key in the transmission of embodied energy. The impacts of the internal and external network types, and of the in-edge and out-edge types on the stability of the embodied energy flow pattern are separate, and the embodied energy flow pattern is stable. Based on these findings, an ‘energy-related industrial cluster’ model is proposed here to aid in energy convergence and transmission, as well as to realize network cluster synergy.