赵洪亮, 陈思越, 谢立勇. 辽宁省种植业碳排放影响因素及预测分析[J]. 中国生态农业学报 (中英文), 2024, 32(11): 1−12. DOI: 10.12357/cjea.20240157
引用本文: 赵洪亮, 陈思越, 谢立勇. 辽宁省种植业碳排放影响因素及预测分析[J]. 中国生态农业学报 (中英文), 2024, 32(11): 1−12. DOI: 10.12357/cjea.20240157
ZHAO H L, CHEN S Y, XIE L Y. Influence factors and prediction analysis of carbon emissions from planting industry in Liaoning Province[J]. Chinese Journal of Eco-Agriculture, 2024, 32(11): 1−12. DOI: 10.12357/cjea.20240157
Citation: ZHAO H L, CHEN S Y, XIE L Y. Influence factors and prediction analysis of carbon emissions from planting industry in Liaoning Province[J]. Chinese Journal of Eco-Agriculture, 2024, 32(11): 1−12. DOI: 10.12357/cjea.20240157

辽宁省种植业碳排放影响因素及预测分析

Influence factors and prediction analysis of carbon emissions from planting industry in Liaoning Province

  • 摘要: 随着经济社会的不断发展, 气候变化问题也走进了人们的视线。中国力争2030年前二氧化碳排放达到峰值, 2060年前实现碳中和目标。构建以降碳为重要内容的社会主义生态文明, 需要各个行业都做出一定的碳减排贡献。农业是温室气体排放的重要碳源, 而种植业是农业的重要组成部分。因此, 针对种植业碳排放进行研究具有重要意义。基于农地利用和农田土壤利用2类碳源, 采用因子系数法综合测算2003—2021年辽宁省种植业碳排放量, 运用对数平均迪氏指数法(LMDI模型)对辽宁省种植业碳排放进行影响因素分解, 并根据可拓展的随机性的环境影响评估模型(STIRPAT模型)对2022—2030年种植业碳排放进行预测。结果表明: 1)辽宁省种植业碳排放总体呈先上升后下降的变化趋势, 于2013年达最高。农地利用中主要碳源是化肥,农田土壤中主要碳源是水稻,玉米的碳排放量增长较快,并于2015年首次超过水稻。2)LMDI模型分解影响因素结果表明,经济水平是促进辽宁省种植业的最主要因素, 生产效率是抑制辽宁省种植业碳排放的最主要因素。产业结构和农业人口的影响方向不确定。3)在设定的基准情景与低碳情景下, 2022—2030年辽宁省种植业碳排放均呈下降趋势。最后, 本文针对辽宁省种植业的碳减排提出以下政策建议: 短期来看, 推进机械化升级的同时对农业机械进行绿色升级; 长期来看, 在保障粮食安全的基础上对农村产业结构进行合理调整, 推广低碳种植技术, 全面提升种植业绿色生产水平。

     

    Abstract: With the continuous development of the economy and society, climate change has also come into people’s sight. On September 22, 2020, President Xi Jinping announced at the 75th United Nations General Assembly that China will strive to peak carbon dioxide emissions before 2030 and strive to achieve carbon neutrality before 2060. Building a socialist ecological civilization with carbon reduction as an important task requires all industries to make certain contributions to carbon reduction. Agriculture is the main carbon source of greenhouse gas emissions, and planting is an important component of agriculture. Hence, carrying out research pertaining to carbon emissions stemming from the planting industry holds immense significance. Based on two types of carbon sources, namely agricultural land use and farmland soil use, the factor coefficient method was used to comprehensively calculate the carbon emissions of the planting industry in Liaoning Province from 2003 to 2021. The Logarithmic Mean Divisia Index (LMDI model) was used to decompose the influencing factors of the carbon emissions of the planting industry in Liaoning Province, and the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT model) was used to predict the carbon emissions of the planting industry from 2022 to 2025. The results showed that: 1) The overall carbon emissions of the planting industry in Liaoning Province showed a trend of increasing first and then decreasing, achieved carbon peak in 2013. The main carbon source in agricultural land utilization is chemical fertilizer, and the main carbon source in farmland soil is rice. The carbon emission of corn has a rapid growth and exceeded that of rice for the first time in 2015. 2) The results of LMDI model decomposition of influencing factors show that economic level is the most important factor to promote planting industry in Liaoning Province, and production efficiency is the most important factor to restrain the carbon emission of planting industry in Liaoning Province. The direction of the influence of industrial structure and agricultural population is uncertain. 3) Under both the baseline and low-carbon scenarios, the carbon emissions from the planting industry in Liaoning Province showed a downward trend from 2022 to 2030. Finally, short-term and long-term policy recommendations were proposed for carbon reduction in the planting industry in Liaoning Province. In the short term, while promoting mechanization upgrading, the green upgrade of agricultural machinery and machinery. In the long run, on the basis of ensuring food security, reasonable adjustments should be made to the rural industrial structure, low-carbon planting technologies should be promoted, and the green production level of the planting industry should be comprehensively improved.

     

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