| 摘要: 了解湖南省农业碳排放特征及影响因素，可为湖南省绿色低碳农业发展提供科学依据。通过查阅《湖南统计年鉴》以及各地市统计年鉴对农田土壤利用量、农业物资投入量及畜禽养殖量进行数据整合，利用IPCC经典碳排计算理论计算湖南省2007-2020年农业碳排放量。在kaya碳排恒等式基础上，采用LMDI法分析影响因素，并引入灰色预测模型GM（1，1）预测湖南省2021-2040年碳排放量。计算结果表明，湖南省2020年碳排放量为6.15×107 t，碳排放强度为1.01 t·(104￥)-1，并于2015年达到顶峰。湖南省农业碳排放量呈现出3段式变化：因2008年特大冰雪灾害对农业产生严重影响，农业碳排放量在2007-2008年下降；2009-2015年稳步上升并于2015年达到顶峰；2015-2020年呈现整体下降。农业碳排放源排放占比大小:农田土壤利用>畜禽养殖>农资投入。农业碳排放强度逐年下降。地区经济发展水平、劳动力水平和农村总用电量对增加农业碳排放量起主要作用，其中地区经济发展水平、农村总用电量是主要影响因素；农业生产效率，农业产业结构，地区产业结构，农村居民人均用电量在农业碳排放量减少的过程中起重要作用。研究表明，湖南省农业碳排放量在2015年达到峰值，但不同地市存在明显差异；长沙，湘潭，衡阳，邵阳、岳阳、常德、益阳于2015年前后达峰；而其他地市未能在2030年前完成碳达峰。提出优化产业结构、各地因地制宜地推动绿色创新、加强政府职能等建议，为湖南省农业碳减排决策提供参考。 |
| 关键词: 农业碳排放量 碳排放强度 影响因素 LMDI模型 灰色预测 |
| 中图分类号:F323;X196 |
| 基金项目:无 |
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| Characteristics of agricultural carbon emissions and carbon peak analysis in Hunan Province |
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dengweizhong, xuzhongjian
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Hunan University of Science and Technology
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| Abstract: Understanding the characteristics and influencing factors of agricultural carbon emission in Hunan Province can provide scientific basis for realizing the development of green and low-carbon agriculture in Hunan Province. Through consulting statistical yearbooks of of Hunan Province and its cities, the data of farmland soil utilization, agricultural material input and livestock and poultry breeding were integrated, and the IPCC classic carbon emission calculation theory was used to calculate the agricultural carbon emissions of Hunan Province from 2007 to 2020. Based on the kaya carbon emission identity, the LMDI method was used to analyze the influencing factors, and the gray prediction model GM(1,1) was introduced to predict the carbon emissions of Hunan Province from 2021 to 2040. In 2020, Hunan Province"s carbon emissions is 6.15×107 t, and the carbon emission intensity is 1.01 t· (104￥)-1. The agricultural carbon emissions in Hunan Province showed a three-stage changes: decline from 2007 to 2008, mainly due to the huge impact of the 2008 mega-ice and snow disaster on agriculture; rose steadily from 2009 to 2015 and peaked in 2015; and overall decline from 2015 to 2020. The proportion of agricultural carbon emission sources is farmland soil utilization> livestock and poultry breeding > agricultural input. The agricultural carbon emission intensity showedan overall decline. Regional economic development level, labor level and total rural electricity consumption play a major role in increasing agricultural carbon emissions, among which regional economic development level and total rural electricity consumption are the main influencing factors. Agricultural production efficiency, agricultural industrial structure, regional industrial structure, and per capita electricity consumption of rural residents play an important role in the process of reducing agricultural carbon emissions. The results showed that agricultural carbon emissions in Hunan Province peaked in 2015, but there are significant differences among different cities. Changsha, Xiangtan, Hengyang, Shaoyang, Yueyang, Changde, Yiyang reached the peak around 2015; Other cities will not reach their carbon peak by 2030. Suggestions on optimizing industrial structure, promoting green innovation according to local conditions and strengthening government functions were put forward to provide reference for agricultural carbon emission reduction decision-making in Hunan Province. |
| Keyword: Carbon emissions from agriculture Carbon emission intensity Influencing factors LMDI model Gray forecasting model |