周一凡, 李彬, 张润清. 县域尺度下河北省农业碳排放时空演变与影响因素研究[J]. 中国生态农业学报 (中英文), 2022, 30(4): 570−581. DOI: 10.12357/cjea.20210624
引用本文: 周一凡, 李彬, 张润清. 县域尺度下河北省农业碳排放时空演变与影响因素研究[J]. 中国生态农业学报 (中英文), 2022, 30(4): 570−581. DOI: 10.12357/cjea.20210624
ZHOU Y F, LI B, ZHANG R Q. Spatiotemporal evolution and influencing factors of agricultural carbon emissions in Hebei Province at the county scale[J]. Chinese Journal of Eco-Agriculture, 2022, 30(4): 570−581. DOI: 10.12357/cjea.20210624
Citation: ZHOU Y F, LI B, ZHANG R Q. Spatiotemporal evolution and influencing factors of agricultural carbon emissions in Hebei Province at the county scale[J]. Chinese Journal of Eco-Agriculture, 2022, 30(4): 570−581. DOI: 10.12357/cjea.20210624

县域尺度下河北省农业碳排放时空演变与影响因素研究

Spatiotemporal evolution and influencing factors of agricultural carbon emissions in Hebei Province at the county scale

  • 摘要: 县域是农业碳排放基本单元, 研究县域农业碳排放的时空演变与驱动因素对制定区域差异化农业减排政策有重要意义。本文测算了2009—2019年河北省168个县农业碳排放量, 基于探索性空间统计与空间计量方法, 研究了县域农业碳排放的时空演变与影响因素并讨论了空间溢出的边界。研究表明: 河北省农业碳排放整体呈下降趋势, 农业碳排放中土地管理、畜禽肠道发酵和粪便管理的碳排放分别占33.00%、42.57%和24.33%, 县域尺度农业碳排放呈现高度空间集聚的特点。农业碳排放的热点分布与农业产业结构有密切关系, 土地管理引发的农业碳排放热点在冀东南的深州、武强、饶阳等县, 冀北丰宁、围场、滦平和隆化县为畜牧业排放热点。县域农业碳排放有显著的空间溢出效应, 邻近地区农业碳排放对本地区碳排放有正向作用。农业经济发展是农业碳排放增加的主要驱动力, 农业产业结构、机械化程度、化肥施用强度、农村能源消费和农民收入是驱动农业碳排放增长的重要因素。城镇化率对农业碳排放有反向影响。农业碳排放受空间外溢与边界效应双重影响, 碳排放空间溢出范围大概在6~8个邻近县。本研究为建立区域农业碳减排机制提供了政策依据和定量研究工具。

     

    Abstract: Global climate change, caused by greenhouse gas emissions, is a common challenge for human society. Counties are the smallest administrative units covered by statistics in China, and are also the basic unit at which agricultural carbon emissions data are collected. Studying the spatiotemporal evolution and drivers of agricultural carbon emissions in counties is important for improving the inventory of agricultural carbon emission data, establishing agricultural carbon emission monitoring systems, and formulating regional emission reduction policies. A county-level greenhouse gas emission inventory was established by combining the agricultural greenhouse gas inventory with the characteristics of county data in this study. The agricultural carbon emissions of 168 counties in Hebei Province from 2009 to 2019 were measured firstly, and the spatial and temporal evolution and drivers of agricultural carbon emissions in counties were analyze then from the perspective of spatial spillover using exploratory spatial statistics and spatial measurement methods. The boundary effects of spatial spillover were investigated finally by using a dynamic spatial model to reveal the regular changes induced by the spatial spillover of agricultural carbon emissions in counties with increasing distance. The study results showed that agricultural carbon emissions in Hebei Province were decreasing during the study duration, with land management, livestock and poultry enteric fermentation, and manure management accounting for 33.00%, 42.57%, and 24.33% of agricultural carbon emissions, respectively. A high spatial agglomeration of agricultural carbon emissions at the county scale was found, and the distribution of agricultural carbon emissions hotspots was closely related to the structure of the agricultural industry. The hotspots of agricultural carbon emissions caused by land management were in Shenzhou, Wuqiang, and Raoyang counties in the south of Hebei, whereas the hotspots of livestock emissions were in Fengning, Weichang, Luanping, and Longhua counties in the north of Hebei. County agricultural carbon emissions had a significant spatial spillover effect, and agricultural carbon emissions in neighboring areas increased the overall carbon emissions in the region. Agricultural economic development was the main driver for the increase in agricultural carbon emissions. The agricultural industry structure, mechanization, fertilizer application intensity, rural energy consumption, and farmers’ income were important factors that increased the agricultural carbon emissions. The urbanization rate had an inverse effect on agricultural carbon emissions. Agricultural carbon emissions were affected by both spatial and boundary factors. The spatial spillover of agricultural carbon emissions in the county showed an increasing trend within 30 km, and a decreasing trend within 30–85 km, and the spatial spillover boundary of agricultural carbon emissions was thus approximately 30 km. The spatial spillover of carbon emissions occurred in approximately 6–8 neighboring counties. This study provides a basis and data foundation for establishing regional agricultural carbon emission reduction policies.

     

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