Changjian Wang

Enhancing urban carbon productivity (UCP) is crucial for achieving the dual carbon goals in China. This study investigates the spatiotemporal patterns and underlying drivers of UCP in the Yangtze River Economic Belt (YREB) from 2010 and 2020. Utilizing a comprehensive dataset of 110 cities, we employ kernel density estimation, spatial autocorrelation analysis, and the Spatial Durbin Model (SDM). The results reveal a significant overall improvement in UCP alongside intensified internal disparities and a fundamental spatial restructuring—from a monocentric eastern-led pattern to a multipolar network driven by the Yangtze River Delta, middle Yangtze, and Chengdu-Chongqing agglomerations. The SDM decomposition reveals a shift in core drivers towards green technological innovation and advanced industrial structure, while energy consumption remains the primary constraint. Crucially, complex spatial spillover effects are identified: factors like advanced industrial structure and digital governance are associated with positive synergistic spillovers, whereas government intervention (government public budget expenditure) and urban sprawl exhibit negative competitive spillovers, collectively corresponding to the polarized regional pattern. Furthermore, urban form shows strong spatial externalities: urban compactness is linked to a “local-neighborhood” double dividend, while urban sprawl is associated with a “local-neighborhood” double curse. The influence of digital factors appears to evolve from early widespread spillovers to later localized deepening. The findings suggest the necessity of implementing spatially differentiated policies, strengthening regional collaborative governance to manage spatial externalities, and promoting compact regional spatial planning to foster synergistic and equitable low-carbon transitions across the YREB.

Enhancing carbon productivity is fundamental to achieving carbon neutrality while sustaining economic growth. Utilizing a comprehensive dataset of Chinese cities from 2010, 2015, and 2020, this study investigates the spatiotemporal patterns and underlying drivers of urban carbon productivity (UCP). Methods including kernel density estimation, spatial autocorrelation analysis, and the spatial Durbin model (SDM) are employed. The results reveal that China’s UCP has improved significantly overall, yet with increasing internal disparities among cities. The SDM decomposition indicates a fundamental shift in driving mechanisms. Green technological innovation has supplanted generalized R&D expenditure as the most dependable core driving force for improving local carbon productivity. Moreover, the economic development level also exerts positive spatial spillover effects in the later stage, which jointly contribute to the formation of a multi-centered pattern. Urban form metrics exert dual influences: urban compactness (ENN_MN) shows a stable positive local effect, whereas urban fragmentation (PD) and urban sprawl (CONTAG) exhibit a paradoxical “local inhibition–neighborhood promotion” effect, highlighting intricate inter-city spatial interactions. The findings underscore the necessity for differentiated local practices, namely, policy must target differentiated city roles and manage spatial spillovers for synergistic regional green and sustainable transition.

Cities serve as the primary arenas for achieving the strategic objectives of “carbon peak and carbon neutrality”. This study employed the LMDI method to systematically analyze the evolution trend of energy-related carbon emissions in Hong Kong and their influencing factors from 1980 to 2023. The main findings are as follows: (1) Hong Kong’s energy consumption structure remains dominated by coal and oil. Influenced by energy prices, significant shifts in this structure occurred across different periods. Imported electricity from mainland China, in particular, has exerted a promoting effect on the optimization of its energy consumption mix. (2) Economic output and population concentration are the primary drivers of increased carbon emissions. However, the contribution of economic growth to carbon emissions has gradually weakened in recent years due to a lack of new growth drivers. (3) Energy consumption intensity, energy consumption structure, and carbon intensity are the primary influencing factors in curbing carbon emissions. Among these, the carbon reduction impact of energy consumption intensity is the most significant. Hong Kong should continue to adopt a robust strategy for controlling total energy consumption to effectively mitigate carbon emissions. Additionally, it should remain vigilant regarding the potential implications of future energy price fluctuations. It is also essential to sustain cross-border energy cooperation, primarily based on electricity imports from the Pearl River Delta, while simultaneously expanding international and domestic supply channels for natural gas.

Encouraging cities to take the lead in achieving carbon peak and carbon neutrality holds significant global implications for addressing climate change. However, existing studies primarily focus on the urban scale, lacking more comprehensive county-level analyses, which hampers the effective implementation of differentiated carbon mitigation policies. Therefore, this study focused on the Pearl River Delta urban agglomeration in China, adopting nighttime light data and socio-economic spatial data to estimate carbon emissions at the county level. Furthermore, trend analysis, spatial autocorrelation analysis, and Geodetector were adopted to elucidate the spatiotemporal patterns and influencing factors of county-level carbon emissions. Carbon emissions were predominantly concentrated in the counties on the eastern bank of the Pearl River Estuary. Since 2010, there has been a deceleration in the growth rate of carbon emissions in the region around the Pearl River Estuary, with some counties exhibiting declining trends. Throughout the study period, construction land expansion consistently emerged as a predominant factor driving carbon emission growth. Additionally, foreign direct investment, urbanization, and fixed asset investment each significantly contributed to the increased carbon emissions during different development periods.

Studying the expansion of urban construction land is necessary to promote rational land use and scientific territorial spatial planning. To reveal urban built-up areas, this study uses 1990–2020 Landsat remote sensing images, superimposed with NPP/VIIRS nighttime light. To extract urban construction land, support vector machines are then used to conduct classification experiments. The spatial-temporal features are analyzed using the expansion index, the shift of the center of gravity, and expansion direction, while influencing factors are analyzed using a Geodetector. The results show the following: (1) Urban construction land in Nanchang continued expanding from 1990 to 2020, by 385.22 km², with an average annual expansion intensity of 0.18% and an average annual growth rate of 6.2%. (2) During this time period, the expansion of urban construction land in Nanchang underwent three development stages from: low-strength with low-speed, low-strength with medium-speed, and medium-strength with low-speed expansion. The types of urban construction land expansion were primarily found to be edge expansion and outlying expansion. (3) The overall center of gravity of urban construction land shifts northwest, with significant expansion SW, NW, S, N, and W. (4) Urban planning policy is the dominant driving factor for urban expansion, whereas natural geographic factors have the weakest influence. The results suggest that planning policies should focus on strengthening the rational use and protection of land resources, and promoting the integration and coordinated development of urban functional spaces.

Cities are regarded as the main areas for conducting strategies for energy sustainability and climate adaptation, specifically in the world’s top energy consumer—China. To uncover dynamic features and main drivers for the city-level energy consumption, a comprehensive and systematic city-level total energy consumption accounting approach was established and applied in China’s megacity, which has the highest industrial electricity consumption. Compared with previous studies, this study systematically analyzes drivers for energy consumption based on industrial and residential perspectives. Additionally, this study analyzes not only the mechanisms by which population size, economic growth, and energy intensity affect energy consumption but also the effects of population and industry structural factors. According to the extended Logarithmic mean Divisia index (LMDI) method, the main conclusions drawn from this research are as follows: (1) The total energy consumption of Suzhou presented an overall increasing trend, with 2006–2012 as a rapid growth stage and 2013–2016 as a moderate growth stage. (2) The energy consumption structure was mainly dominated by coal, which was followed by outsourced electricity and natural gas. (3) Scale-related factors have dominated changes in energy consumption, and structural and technological factors have had profound effects on energy consumption in different development periods. (4) Population size and economic output were the main drivers for increments in industrial energy consumption, whereas energy intensity and economic structure performed the important curbing effects. The income effect of urban residents was the biggest driver behind the increase in residential energy consumption, whereas energy intensity was the main limiter. These findings provide a scientific basis for an in-depth understanding of the determinants of the evolution of urban energy consumption in China’s megacity, including similar cities or urban areas in the developing world.

Population migration, especially population inflow from epidemic areas, is a key source of the risk related to the coronavirus disease 2019 (COVID-19) epidemic. This paper selects Guangdong Province, China, for a case study. It utilizes big data on population migration and the geospatial analysis technique to develop a model to achieve spatiotemporal analysis of COVID-19 risk. The model takes into consideration the risk differential between the source cities of population migration as well as the heterogeneity in the socioeconomic characteristics of the destination cities of population migration. It further incorporates a time-lag process based on the time distribution of the onset of the imported cases. In theory, the model will be able to predict the evolutional trend and spatial distribution of the COVID-19 risk for a certain time period in the future and provide support for advanced planning and targeted prevention measures. The research findings indicate the following: (1) The COVID-19 epidemic in Guangdong Province reached a turning point on January 29, 2020, after which it showed a gradual decreasing trend. (2) Based on the time-lag analysis of the onset of the imported cases, it is common for a time interval to exist between case importation and illness onset, and the proportion of the cases with an interval of 1–14 days is relatively high. (3) There is evident spatial heterogeneity in the epidemic risk; the risk varies significantly between different areas based on their imported risk, susceptibility risk, and ability to prevent the spread. (4) The degree of connectedness and the scale of population migration between Guangdong’s prefecture-level cities and their counterparts in the source regions of the epidemic, as well as the transportation and location factors of the cities in Guangdong, have a significant impact on the risk classification of the cities in Guangdong. The first-tier cities — Shenzhen and Guangzhou — are high-risk regions. The cities in the Pearl River Delta that are adjacent to Shenzhen and Guangzhou, including Dongguan, Foshan, Huizhou, Zhuhai, Zhongshan, are medium-risk cities. The eastern, northern, and western parts of Guangdong, which are outside of the metropolitan areas of the Pearl River Delta, are considered to have low risks. Therefore, the government should develop prevention and control measures that are specific to different regions based on their risk classification to enable targeted prevention and ensure the smooth operation of society.

The residential location choice of the highly educated population is an important consideration to construct a livable city. While landscape and environment are important factors, few studies have deeply analyzed the spatial heterogeneity effects of landscape and environment on the residential location choices of a highly educated population. Taking Guangzhou as the sample, we built a livability-oriented conceptual framework of landscape and environment, and constructed datasets for highly educated population proportion, landscape, and environment factors, and other influencing factors for Guangzhou’s 1364 communities. Global regression and geographically weighted regression (GWR) models are used for analysis. The GWR model is more effective than the global regression model. We found spatial heterogeneity in the strength and direction of the relationship between the highly educated population proportion and landscape and environment. We find that landscape and environment exert spatial heterogeneity effects on the residential location choice of the highly educated population in Guangzhou. The conclusions will be of reference value to further understand how the spatial limitations of landscape and environment affect residential location choices. This study will help city managers formulate spatially differentiated environment improvement policies, thereby increasing the city’s sustainable development capabilities.