Heterogeneous and Interactive Effects of Multi-Governmental Green Investment on Carbon Emission Reduction: Application of Hierarchical Linear Modeling

Sustainability, Journal Year: 2025, Volume and Issue: 17(3), P. 1150 - 1150

Published: Jan. 31, 2025

Although both prefectural governmental green investment (GGI_city) and provincial (GGI_prov) have potentially diverse impacts on cities’ carbon emission reduction (CER), previous studies rarely examined the effects of (GGI) different indicators CER such as total dioxide emissions (CE), intensity (CEI) per capita (PCE) in context cities nested provinces China. In our research, six hierarchical linear models are established to investigate impact GGI_city GGI_prov, well their interaction, CER. These consider eight control factors, including fractional vegetation coverage, nighttime light index (NTL), proportion built-up land (P_built), so on. Furthermore, heterogeneous across groups based area, terrain, economic development level considered. Our findings reveal following: (1) The three GGI exhibit significant spatial temporal variations. coefficient variation for CEI PCE shows a fluctuating upward characteristic. (2) Both lnGGI_city lnGGI_prov promoted CER, but strength lnCE lnPCE is more pronounced than that lnGGI_city. GGI_prov can strengthen effect significantly lnCE. Diverse variables exerted albeit with considerable effects. (3) upon conducting grouped analysis by area size, terrain complexity, level. interaction term lnGGI_city:lnGGI_prov stronger small group simple group. Among variables, Development Level (GDPpc), logarithm gross fixed assets (lnFAI), NTL, P_built particularly differences groups. This study provides robust understanding interactive aiding promotion sustainable development.

Language: Английский

Spatiotemporal Patterns and Influencing Factors of Carbon Emissions in the Yangtze River Basin: A Shrinkage Perspective

Sustainability, Journal Year: 2025, Volume and Issue: 17(5), P. 2112 - 2112

Published: Feb. 28, 2025

This study categorizes 45 cities into four types based on population dynamics using census data (2000–2020). Methods such as ArcGIS10.8, carbon emission estimation, LISA clustering, and association analysis are employed to explore the spatiotemporal distribution of shrinking emissions. analyzes patterns influencing factors for city provides policy recommendations. The findings follows: (1) Lasting-growth show a “two-end mass, middle-point” pattern, while stage-growth stage-shrinking “point” distributed. Lasting-shrinking mainly distributed in middle lower reaches Yangtze River. (2) Total emissions rising, showing two clusters high-value areas. Carbon intensity is falling quickly, being higher west east. (3) have fastest direct growth rate, energy-related indirect undergoing increase rate other In terms reduction, lasting-growth perform best, whereas worst. (4) Regional GDP, per capita regional urban construction area, hospital beds 10,000 people promote reduction types, number industrial enterprises inhibits it. Birth aging mortality no significant impact. addresses gaps previous research by considering dynamic nature processes analyzing patterns.

Language: Английский