Spatiotemporal Continuity and Spatially Heterogeneous Drivers in the Historical Evolution of County-Scale Carbon Emissions from Territorial Function Utilisation in China: Evidence from Qionglai City

Dinghua Ou (), Jiayi Wu, Qingyan Huang, Chang Shu, Tianyi Xie, Chunxin Luo, Meng Zhao, Jiani Zhang and Jianbo Fei ()
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Dinghua Ou: College of Resources, Sichuan Agricultural University, Chengdu 611130, China
Jiayi Wu: College of Resources, Sichuan Agricultural University, Chengdu 611130, China
Qingyan Huang: College of Resources, Sichuan Agricultural University, Chengdu 611130, China
Chang Shu: College of Resources, Sichuan Agricultural University, Chengdu 611130, China
Tianyi Xie: College of Resources, Sichuan Agricultural University, Chengdu 611130, China
Chunxin Luo: College of Resources, Sichuan Agricultural University, Chengdu 611130, China
Meng Zhao: Faculty of Public Administration, Sichuan Agricultural University, Yaan 625014, China
Jiani Zhang: College of Resources, Sichuan Agricultural University, Chengdu 611130, China
Jianbo Fei: College of Resources, Sichuan Agricultural University, Chengdu 611130, China

Land, 2025, vol. 14, issue 10, 1-36

Abstract: County-level administrative areas serve as fundamental components in China’s territorial spatial governance, and the precision and consistency of their carbon emission reduction policies are directly linked to the efficacy of the “dual-carbon” strategy’s execution. However, the spatiotemporal evolution characteristics, future trends, and driving factors of carbon emissions from territorial spatial function (TSF) utilisation at the county level remain unclear, posing a fundamental theoretical issue that local governments urgently need to address when formulating carbon reduction policies. This study developed a framework to simulate the spatial distribution of carbon emissions resulting from land use at the county level. It simulated the carbon emissions in Qionglai City from 2009 to 2023, analysed the spatial-temporal evolution characteristics and future trends using global Moran’s I, the Getis-Ord G i * index, and the Hurst index, and employed the Geographically and Temporally Weighted Regression (GTWR) model for analysis. The findings indicated the following: (1) From 2009 to 2023, the city’s total carbon emissions increased from 852,300 tonnes to 1,422,500 tonnes, showing a significant phased trend. Among these, rural production spaces (RPSs) were the primary carbon sources, accounting for over 70% of annual carbon emissions each year. (2) County carbon emissions exhibit a pronounced positive geographical correlation and aggregation distribution, characterised by notable regional heterogeneity. (3) From 2009 to 2023, the city’s regional carbon emissions rose dramatically by 65.69%, while 29.66% of the areas experienced negligible increases; 99% of the regions are projected to maintain the historical growth trend, but this continuity exhibits spatial and temporal variations. (4) Economic growth, industrial structure, and development intensity are the core driving factors of carbon emissions at the county level, with spatial variations in their impact. The research findings not only provide a basis for Qionglai City, China, to formulate precise and sustainable carbon reduction policies (such as developing differentiated carbon emission control measures based on the spatiotemporal heterogeneity of carbon emissions and their driving factors), but also offer insights for similar regions worldwide in controlling carbon emissions and addressing global climate change (for example, by optimizing land spatial function utilisation, reducing carbon sources, and maximizing carbon sink capacity).

Keywords: carbon emissions; carbon mitigation policy; utilisation of territorial spatial functions (TSFs); spatiotemporal heterogeneity; Hurst index; GTWR (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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