Analysis of the coupling characteristics of land transfer and carbon emissions and its influencing factors: A case study of China

Abstract

The rapid and disorderly expansion of urban construction land has exacerbated the contradiction between land use and low-carbon development. In this paper, we use the spatial autocorrelation model and coupling model to analyze the spatial characteristics of the coupled coordination degree of land transfer and carbon emissions in 291 cities in China. The multi-scale geographically weighted regression (MGWR) model is used to explore the spatial heterogeneity of the influence of socioeconomic factors on their coupled coordination degree. The results show that: from 2005 to 2015, the scale of land transfer and carbon emissions has been increasing quantitatively and spatially showing a shift from the southeast coast to the central and western regions. In 2005, 2010, and 2015, the global Moran’s I of the coupled coordination degree are .3045, .3725, and .3388, respectively, indicating that the coupled coordination degree between land transfer and carbon emissions has a significant positive spatial autocorrelation. The MGWR model indicates that the influence of socioeconomic factors on the coupling coordination degree has significant spatial heterogeneity at different time nodes. In 2005 and 2015, the coefficients of the NGR on the coupling coordination of land transfer and carbon emissions have obvious stratification characteristics, with the coefficients decreasing from northeast to southwest. In 2010, the high coefficient (.924∼.989) of GPC is mainly distributed in the central region. The coefficient of the PD ranges from .464 to .918, but the difference of influence degree between the southeast coast and the northwest is obvious. This study may provide new clues for sustainable urban development and carbon reduction.

Land Use Carbon Emissions or Sink: Research Characteristics, Hotspots and Future Perspectives

“…For instance, on a time scale, based on the theoretical basis of IPAT (impact, population, affluence, technology) [61] or KAYA [62], scholars further extended it to STIRPAT (stochastic impacts by regression on population, affluence and technology) [63], LMDI (logarithmic mean index method) [58] and other models [64,65], as well as mainly discussing the anthropogenic impacts (economic, social and demographic factors) on land use carbon emission [66]. From the spatial scale, scholars analyzed the spatial heterogeneity of the impact factors of land use carbon emission by using GWR (geographically weighted regression) [67,68], MGWR (multi-scale geographically weighted regression) [69,70] and spatial econometric models [71], etc. For example, Kang et al, (2023) pointed that construction land expansion did not monotonously increase carbon emissions, but it possibly changed with geographic location; the results described relationships between land use, carbon emissions and urban morphology, which enhanced our knowledge on this relationship and gave the scientific basis for policy-making [68].…”

Spatiotemporal evolution patterns and driving factors of arable land in Huaihai Economic Zone, China

“…In addition, the spatial weight matrix was constructed based on the arable land map of HEZ from 2005 to 2020 using Geoda software. Finally, the arable land area proportion was selected as a variable to calculate the global Moran's I index, with the formula defined as follows (Anselin, 1995;Zhang et al, 2023):…”

Spatio-temporal heterogeneity of transportation carbon emissions and its driving factors in China’s main urban agglomerations

“…In addition, the degree of transportation and economic activities among different regions lead to a certain spatial correlation and heterogeneity of regional carbon emissions from transportation [19] . Combined with the spatial econometric model, the interaction between social development and human activities such as population size, passenger and freight turnover, regional GDP and carbon emissions from transportation in space can be explored, and the effects of different factors on carbon emissions from transportation can be revealed and predicted [20] . There are obvious spatial differences in China's economic development, population distribution and industry.…”

“…In this paper, 30 provinces, municipalities and autonomous regions in China (except Tibet Autonomous Region, Hongkong, Macau and Taiwan Province) are taken as the research objects, and the carbon emissions from transportation of the three major urban agglomerations, namely Beijing-Tianjin-Hebei Region, Yangtze River Delta Region and Pearl River Delta Region, are emphatically analyzed. Using ESDA and geographically weighted regression (GWR), the spatiotemporal distribution and heterogeneity of carbon emissions from transportation are explored, and their influencing factors are revealed [20] , and corresponding opinions are put forward in terms of carbon emissions reduction from transportation.…”