郑博福, 梁涵, 万炜, 刘忠, 朱锦奇, 吴之见. 江西省县域农业碳排放时空格局及影响因素分析[J]. 农业工程学报, 2022, 38(23): 70-80. DOI: 10.11975/j.issn.1002-6819.2022.23.008
    引用本文: 郑博福, 梁涵, 万炜, 刘忠, 朱锦奇, 吴之见. 江西省县域农业碳排放时空格局及影响因素分析[J]. 农业工程学报, 2022, 38(23): 70-80. DOI: 10.11975/j.issn.1002-6819.2022.23.008
    Zheng Bofu, Liang Han, Wan Wei, Liu Zhong, Zhu Jinqi, Wu Zhijian. Spatial-temporal pattern and influencing factors of agricultural carbon emissions at the county level in Jiangxi Province of China[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2022, 38(23): 70-80. DOI: 10.11975/j.issn.1002-6819.2022.23.008
    Citation: Zheng Bofu, Liang Han, Wan Wei, Liu Zhong, Zhu Jinqi, Wu Zhijian. Spatial-temporal pattern and influencing factors of agricultural carbon emissions at the county level in Jiangxi Province of China[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2022, 38(23): 70-80. DOI: 10.11975/j.issn.1002-6819.2022.23.008

    江西省县域农业碳排放时空格局及影响因素分析

    Spatial-temporal pattern and influencing factors of agricultural carbon emissions at the county level in Jiangxi Province of China

    • 摘要: 农业生产是碳排放的主要来源之一,在碳达峰碳中和的时代背景下,厘清区域农业碳排放现状并分析其时空变化和影响因素具有重要意义。江西省是农业大省,近几十年来农业的快速发展伴随着农业碳排放量的升高。因此基于本区域水稻种植、农资投入、土壤利用及畜禽养殖4类主要碳源,构建农业碳排放测算体系,评估2000-2020年农业碳排放量,分析县域农业碳排放空间格局及其驱动机制。结果表明:1)江西省农业碳排放量总量范围在1 098.32万~1 471.94万t;种植业碳排放强度整体呈下降趋势,范围在2.50~3.87 t/万元,畜牧业碳排放强度整体亦呈下降趋势,范围在0.76~2.03 t/万元;各碳源碳排放总量和其占农业碳排放总量的比例大小依次为:水稻种植(806.72万t,61.15%)、畜禽养殖(243.57万t,18.57%)、农资投入(237.39万t,18.02%)、农田土壤利用(29.60万t,2.26%);2)江西省县域农业碳排放量空间特征明显,高碳排放区均集中于鄱阳湖平原地区以及吉泰盆地;农业碳排放强度空间分布由相对离散到集中在赣北地区;整体上江西省碳排放总量的重心向北移动;3)农业碳排放效率是影响农业碳排放的最重要的因素,各因素对农业碳排放减少量和其占总农业碳排放减少量的比例大小依次为:农业生产效率因素(1 828.13万t,56.57%)、地区产业结构因素(1 265.29万t,39.15%)、农业产业结构因素(86.12万t,2.66%)、农村总人口因素(52.12万t,1.62%)。整体上,各因素减少农业碳排放总量绝对值由大到小为:赣北、赣中、赣南。研究结果可为江西省乃至全国其他粮食主产区农业碳排放的测算以及农业碳减排政策的制定提供科学参考。

       

      Abstract: Agricultural carbon emission has been one of the major sources of carbon emission in the era of peak carbon and carbon neutrality. Therefore, it is of great significance to clarify the status quo of agricultural carbon emission in recent years, particularly for the spatial-temporal changes and influencing factors. Taking Jiangxi Province of China as the typical study area, the measurement system of agricultural carbon emission was established to calculate the agricultural carbon emission in 81 counties from 2000 to 2020. Four major carbon sources were selected as the paddy field planting, agricultural investment inputs, soil plowing, as well as livestock and poultry farming. The spatial pattern of agricultural carbon emission was analyzed by the spatial autocorrelation and center of gravity transfer at county level. The relevant influence factors were then determined by logarithmic mean Divisia index (LMDI). The results were summarized as follows: 1) The agricultural carbon emission in the study area was ranged from 10.98 to 14.72 million tons. An outstanding increasing trend was found in the agricultural carbon emission on the whole, whereas the carbon emission intensity was in an overall decreasing trend. The carbon emission intensity of planting industry showed a decreasing trend, ranging from 2.50 to 3.87 tons per ten thousand Yuan. Similarly, the carbon emission intensity of animal husbandry also showed a decreasing trend, ranging from 0.76 to 2.03 tons per ten thousand Yuan. Particularly, the total carbon emissions of each carbon source and the proportion in total agricultural carbon emissions were ranked in the descending order of: the paddy field planting (8.07 million tons, 61.15%), livestock and poultry (2.43 million tons, 18.57%), agricultural investment inputs (2.37 million tons, 18.02%), soil plowing (0.27 million tons, 2.26%); 2) There was the apparent spatial characteristic of agricultural carbon emissions. For example, the high carbon emission areas were concentrated in the Poyang Plain and the Jitai Basin. Furthermore, the spatial distribution of agricultural carbon emission intensity was from the relative dispersion to concentration in the Northern, whereas, the carbon emission intensity in The Southern was relatively low. The center of gravity of total carbon emissions shifted northward, where the carbon emission in The Northern was higher than that in The Southern Jiangxi Province. 3) Agricultural production efficiency improvement was the most important factor to restrain the sustained growth of the agricultural carbon emissions. The factors were ranked on the agricultural carbon emission reduction and the proportion in the total agricultural carbon emission reduction: the agricultural production efficiency (18.28 million tons, 56.57%), regional industrial structure (12.65 million tons, 39.15%), rural population size (0.86 million tons, 2.66%), agricultural industrial structure (0.52 million tons, 1.62%). Overall, the area order was given in the absolute value of agricultural carbon emission reduction by each factor from large to small: the North, the Middle, the South. The finding can provide the scientific strategy to estimate agricultural carbon emissions in Jiangxi Province, even the major grain producing areas.

       

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