Coordinated development and driving factor heterogeneity of different types of urban agglomeration carbon emissions in China

The mechanism of China's renewable energy utilization impact on carbon emission intensity: Evidence from the perspective of intermediary transmission

Renewable energy (RE) plays a crucial role in global energy transformation, and a thorough study of the potential impact of RE on regional carbon emissions is of great significance. This is particularly relevant to China, which needs to clarify its path to carbon reduction. Using the sample data of 30 provinces in China from 2000 to 2021, this paper uses the Granger causality test to verify the causal relationship between carbon emission intensity (CEI) and other factors. It builds a mediation effect model on this basis to explore the direct impact effect and indirect transmission path of renewable energy utilization (REU) on CEI. The results show that REU has a one-way causal relationship with CEI. REU can directly and indirectly reduce CEI by improving social wealth and changing the direction of energy investment. In addition, REU indirectly increases CEI through the transmission paths of investment in the energy industry - social affluence and industrial level-social affluence. The CEI is indirectly reduced through the conduction paths of (social affluence-Urbanization rate), (Investment in the energy industry-Urbanization rate), (Industrial level-Urbanization rate), and (Industrial level-Investment in the energy industry). These conclusions will assist policymakers in exploring targeted pathways for low-carbon power development, providing a reference for strategic and sustainable carbon reduction policies.

The effect of haze pollution on rural-to-urban migrants' long-term residence intentions

Severe haze pollution in China threatens human health, and its negative effect hampers rural-to-urban migrants' settlement intentions in destination cities. Using the 2017 China Migrants Dynamic Survey Data (CMDS), the satellite data of PM2.5, and city-level data, this study investigates the impact of haze pollution on rural migrants, long-term residence intentions in Chinese context with IV-probit model, and mediating effect model. Overall, we find an inverted U-shaped relationship between the level of haze pollutants and rural migrants' long-term settlement intentions. Robustness check using multi-measures and thermal inversion as the instrumental variable supports this conclusion. The mediating effect model shows haze pollution plays its role through two opposite mechanisms: signal effect and health effect. When the size of signal effect is larger than health effect, rural migrants are inclined to settle down in their host cities; otherwise, they show lower settlement willingness. The turning point appears when PM2.5 concentration reaches 38.5 μg/m3; migrants have the highest long-term residence intentions. Currently, the national average PM2.5 concentration is 40.98 μg/m3, indicating that China is at the stage where the health effect of haze pollution holds a dominant position. Haze pollution has heterogeneous impacts on migrants' residence intentions. From the individual level, the younger generation, female, and higher-educated migrants have a higher tolerance for polluted air. From the city level, migrants who work in the city with 5 to 10 million dwellers have the highest long-term residence intention and are less sensitive to haze pollution. Thus, we propose stringent environmental regulations and more inclined public service policies to migrants.

Regional differences and driving factors of carbon emission intensity in China's electricity generation sector

As an industry with immense decarbonization potential, the low-carbon transformation of the power sector is crucial to China's carbon emission (CE) reduction commitment. Based on panel data of 30 provinces in China from 2000 to 2019, this research calculates and analyzes the provincial CE intensity in electricity generation (CEIE) and its spatial distribution characteristics. Additionally, the GTWR model based on the construction explains the regional heterogeneity and dynamic development trend of each driving factor's influence on CEIE from time and space. The main results are as follows: CEIE showed a gradual downward trend in time and a spatial distribution pattern of high in the northeast and low in the southwest. The contribution of driving factors to CEIE has regional differences, and the power structure contributes most to the CEIE of the power sector, which promotes regional CE. Concurrently, most provinces with similar economic development, technological level, geographic location, or resource endowment characteristics show similar spatial and temporal trends. These detections will furnish broader insights into implementing CE reduction policies for the regional power sector.

Characteristics, regional differences, and influencing factors of China's water-energy-food (W-E-F) pressure: evidence from Dagum Gini coefficient decomposition and PGTWR model

Water, energy, and food security are global concerning issues especially in China. To promote regional environmental management cooperation as well as find resource security influencing factor differences among regions, this paper calculates the water-energy-food (W-E-F) pressure, find W-E-F pressure's regional differences, and the influencing factors by Dagum Gini coefficient decomposition and geographically and temporally weighted regression model for panel data (PGTWR). First, the temporal trend of W-E-F pressure is decreasing and then increasing during 2003-2019; pressure in the eastern provinces is significantly higher than in other provinces and structurally energy pressure is the dominant resource pressure in W-E-F in most provinces. Besides, inter-regional differences are the main source of regional differences in China's W-E-F pressure, particularly for the inter-regional differences between eastern regions and other regions. In addition, there are obvious spatio-temporal heterogeneity effects of population density, per capita GDP, urbanization, energy intensity, effective irrigated area, and forest cover on W-E-F pressure. Balancing regional development gaps and developing differentiated resource pressure mitigation strategies based on the characteristics of different regional drivers are of great importance.