Comparing the carbon emission efficiency (CEE) of resource and non-resource-based cities in the Yellow River Basin (YRB) can guide their synergistic development and low-carbon transition. This study used the super-efficiency slacks-based measure (super-SBM) model to measure the CEE of cities in the YRB. Kernel density estimation and Theil index decomposition methods were used to explore the spatiotemporal evolutionary patterns, and a panel regression model was established to analyze the influencing factors of CEE. The research results showed that the CEE of the two types of cities have an overall upward trend in time, with a widening regional gap. Resource-based cities mainly displayed the characteristics of decentralized regional agglomeration, while non-resource-based cities mainly showed the characteristics of convergent regional agglomeration. Panel regression results showed that the levels of economic development, indus-trial structure, and population density are significantly positively correlated with CEE in the YRB, while foreign direct investment and resource endowment are significantly negatively correlated with CEE. Except for economic development and industrial structure, there is some variability in the contribution of the remaining influencing factors to the CEE of the resource and non-resource-based cities. The research results suggest developing classification measures for low-carbon transition in the YRB.

Urban agglomerations have become the core areas for carbon reduction in China since they account for around 75% of its total emissions. Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and the Pearl River Delta (PRD), which are its most important poles of regional development and technological innovation, are key to achieving China’s carbon peak emissions target. Based on the panel data of these three major urban agglomerations from 2003 to 2017, this study estimated the carbon emission efficiency (CEE) by the super-efficiency slacks-based measure (super-SBM) model and analyzed its spatiotemporal distribution pattern. The Dagum Gini coefficient was used to evaluate the difference in CEE between the three major agglomerations, while panel data models were established to analyze the impact of technological innovation on the three agglomerations. The overall CEE showed an upward trend during the study period, with significant spatial and temporal variations. Additionally, the main source of urban agglomeration difference in CEE evolved from inter-regional net differences to intensity of transvariation. While technological innovations are expected to significantly improve CEE, their effect varies among urban agglomerations. These results provide policymakers with insights on the collaborative planning of urban agglomerations and the low-carbon economy.

Urban activities are a significant source of global carbon emissions. Balancing economic development and environmental protection during urban construction and development has become a common concern worldwide. The urban form structure, as the spatial foundation of urban activities, is critical for both current and future urban development. In this study, an urban economic activities perspective is taken to examine the relationship between the urban form structure and carbon emission efficiency in 63 prefecture-level cities within China’s three major urban agglomerations from 2013 to 2022. Two dimensions are considered: land resource development and the urban spatial layout. The research findings indicate that (1) the built-up areas of the three major urban agglomerations in China generally exhibit a pattern of “core cities expanding outwards and peripheral cities emerging sporadically”. (2) Various urban form structure indicators have different effects on carbon emission efficiency, with interaction detection via geographic detectors showing a dual-factor enhancement effect. (3) Urban form structure influences carbon emission efficiency through the mediating pathway of economic agglomeration. This study enriches empirical research on the impact of the urban form structure on carbon emission efficiency from an economic activities perspective and provides empirical evidence for urban spatial planning and achieving sustainable development.

Green technology innovation (GTI) is crucial for achieving synergistic development in reducing pollution and carbon emissions (CEs). The spatio-temporal evolutionary aspects of carbon emission intensity (CEI) in resource-based cities (RBCs) and the heterogeneity of the carbon emission reduction effects of GTI from zoning, grading, and classification perspectives are investigated using kernel density estimation, Markov chains, and panel regression models. Our results are as follows: the CEI of RBCs displays a fluctuating downwards trend from 2006 to 2022. Spatially, the main feature is that the north is higher than the south. Second, GTI has significantly reduced the CEI of RBCs through structural optimization, energy savings, and efficiency improvement, as verified in different development stages and dominant resource types. In addition, national high-tech zones (NHTZs) have significantly contributed to reducing CEI in RBCs. The proposed countermeasures include increasing investment in GTI, establishing an exchange platform for GTI, and implementing differentiated policies according to local conditions, which are important for constructing an ecological civilization in RBCs.

Instead of being merely an important embodiment of regional productivity, the manufacturing industry also serves as a significant sector of economic operation and the supply chain system that is highly dependent on resources and the environment. Studying the spatial pattern of the manufacturing industry and its environmental effect is extremely significant for optimizing the spatial layout of urban industry, allocating production factors in a rational manner, and promoting the green transformation of industry. In this regard, this study aimed to further reveal the spatial pattern characteristics of the regional manufacturing industry and its impact on PM_2.5. Using data from micro-enterprises in the manufacturing industry in the Yangtze River Economic Belt, its spatial pattern characteristics are explored and an econometric model is constructed to analyze the impact of the manufacturing industry on PM_2.5 by comprehensively applying approaches including kernel density estimation, nearest proximity index, and Dagum Gini coefficient decomposition. Three research conclusions were drawn: (1) an obvious “core-edge” feature is present in the spatial distribution of the manufacturing industry in the studied area showing an apparent pattern of “high in the east and low in the west”. The core density of the manufacturing industry in the central cities is significantly higher than that in the surrounding cities. (2) In the manufacturing industry and its subdivisions, the characteristics of spatial agglomeration are unveiled, while the agglomeration and spatial differences are diminished during the study period for the spatial equilibrium of the manufacturing industry. (3) A significantly positive impact is exerted on PM_2.5 pollution that is not limited to local cities by the manufacturing industry, which, due to the development differences within the study region, is also heterogeneous. In view of this, policy proposals for aspects such as forging a green manufacturing cluster area, establishing an industrial integration development platform, giving play to regional advantages and technological potential, etc., are put forward in this study, so as to provide a useful reference for optimizing the industrial pattern and promoting the green transformation of industries.