| 摘要: |
| 为探究农田生态系统碳排放演化趋势，制定合理的碳减排政策，以面板数据为依据，估算2007—2020年中国农田生态系统碳排放量及强度；运用空间自相关分析碳排放强度空间格局；建立GM(1，1)预测模型，对未来碳排放强度进行预测，并对预测结果进行空间自相关分析，以研究其发展趋势。结果表明:1)中国农田生态系统碳排放量在研究期间呈波动下降趋势，并于2013年达到峰值；2)碳排放强度总体呈下降趋势，表现为“北冷南热”的空间格局，未来将持续下降，由2020年的0.107 kg/yuan下降至2027年的0.054 kg/yuan，总体空间格局保持稳定，但热点区域扩大，冷点区域缩小。因此，为加快低碳农业发展，应根据地区差异，通过制定差异化农业管理策略，降低农田生态系统碳排放强度。 |
| 关键词: 碳排放 农田生态系统 时空格局 空间自相关 |
| DOI：10.11841/j.issn.1007-4333.2023.01.08 |
| 投稿时间：2022-07-14 |
| 基金项目:国家社会科学基金项目(19BJY169)；农业农村部种养结合重点实验室开放课题 |
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| Spatial-temporal pattern and trend analysis of carbon emission from farmland ecosystem |
|
WANG Ying1,2,YANG Juan1,WANG Jiali1*
|
| (1.College of Engineering, Northeast Agricultural University, Harbin 150030, China;2.Laboratory of Planting and Breeding of Ministry of Agriculture and Rural Affairs, Harbin 150086, China) |
| Abstract: |
| In order to explore the evolution trend of carbon emission in farmland ecosystem and formulate reasonable carbon emission reduction policies, the carbon emission and intensity of China's farmland ecosystem from 2007 to 2020 were estimated, and the spatial pattern of carbon emission intensity was analyzed by spatial autocorrelation. The GM(1, 1)prediction model was established to predict the future carbon emission intensity. Based on the prediction result, its development trend was analyzed by spatial autocorrelation. The results show that: The carbon emission of farmland ecosystem showed a fluctuating downward trend during the study period, and reached peak in 2013; The carbon emission intensity generally showed a downward trend, which displayed the spatial pattern of “north cold and south hot”. It will continue to decline in the future and estimated will drop from 0. 107 kg/yuan in 2020 to 0. 054 kg/yuan in 2027. The overall spatial pattern remains stable, but the hot spot area expands and the cold spot area shrinks. Therefore, in order to accelerate the development of low-carbon agriculture, it is necessary to formulate differentiated agricultural management measures according to regional differences and reduce the carbon emission intensity of farmland ecosystem. |
| Key words: carbon emission farmland ecosystem spatial-temporal pattern spatial autocorrelation |
