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Exploring the spatiotemporal heterogeneity and influencing factors of agricultural carbon footprint and carbon footprint intensity: Embodying carbon sink effect.
Cui, Yu; Khan, Sufyan Ullah; Sauer, Johannes; Zhao, Minjuan.
Afiliação
  • Cui Y; College of Economics and Management, Northwest A&F University, Yangling 712100, Shaanxi, China; Agricultural Production and Recourse Economics, Technische Universität München, Alte Akademie 14, 85354 Freising, Germany. Electronic address: yu.cui@tum.de.
  • Khan SU; Department of Economics and Finance, UiS Business School, University of Stavanger, 4036 Stavanger, Norway. Electronic address: sufyan@nwsuaf.edu.cn.
  • Sauer J; Agricultural Production and Recourse Economics, Technische Universität München, Alte Akademie 14, 85354 Freising, Germany. Electronic address: jo.sauer@tum.de.
  • Zhao M; College of Economics and Management, Northwest A&F University, Yangling 712100, Shaanxi, China. Electronic address: minjuan.zhao@nwsuaf.edu.cn.
Sci Total Environ ; 846: 157507, 2022 Nov 10.
Article em En | MEDLINE | ID: mdl-35870582
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies País/Região como assunto: Asia Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies País/Região como assunto: Asia Idioma: En Ano de publicação: 2022 Tipo de documento: Article