Exploring the spatiotemporal heterogeneity and influencing factors of agricultural carbon footprint and carbon footprint intensity: Embodying carbon sink effect

被引:77
作者
Cui, Yu [1 ,3 ]
Khan, Sufyan Ullah [2 ]
Sauer, Johannes [3 ]
Zhao, Minjuan [1 ]
机构
[1] Northwest A&F Univ, Coll Econ & Management, Yangling 712100, Shaanxi, Peoples R China
[2] Univ Stavanger, UiS Business Sch, Dept Econ & Finance, N-4036 Stavanger, Norway
[3] Tech Univ Munich, Agr Prod & Recourse Econ, Alte Akad 14, D-85354 Freising Weihenstephan, Germany
基金
中国国家自然科学基金;
关键词
Carbon footprint; Carbon footprint intensity; Spatiotemporal heterogeneity; Influencing factors; Carbon sink; YANGTZE-RIVER DELTA; POPULATION-DENSITY; CO2; EMISSIONS; INPUT-OUTPUT; CHINA; URBANIZATION; DYNAMICS; PROVINCE; IMPACT; POLICY;
D O I
10.1016/j.scitotenv.2022.157507
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Due to the combined effects of carbon emission and carbon sink, agriculture is acknowledged as an essential contributor to achieve the Chinese government's carbon neutrality goal of 2060, and carbon footprint (CF) and carbon footprint intensity are substantial indicators to reveal the carbon emission level. For these reasons, the Theil index technique and extended STIRPAT model were employed to evaluate their spatiotemporal heterogeneity and influencing factors using panel data from 31 provinces for the period 1997-2019. The findings revealed that the CF showed an increasing trend with an annual growth rate of 24.6 %. The carbon footprint intensity (CFI) indicated an evident spatiotemporal heterogeneity and transferred over time, with an average growth rate of 19.82 %. The CFI Theil index and its contribution rate both confirmed that intra-regional difference is the main source of the overall difference, among which, the CFI Theil index displayed the distribution feature of "western (11.50 %) > central (11.12 %) > eastern (10.56%) > northeast (6.61 %). The contribution rate of CFI illustrated the spatial pattern of "eastern (33.74 %) > central (21.07 %) > western (19.87%) > northeast (5.24 %). Furthermore, the influencing effects of GDP per capita, planting structure, population density and urbanization level on CF and CFI also demonstrate evident spatiotemporal heterogeneity.
引用
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页数:13
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