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新疆农业源非二氧化碳温室气体排放变化趋势预测

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基于 2000-2020 年新疆农作物产量、化肥投入、稻田播种面积等统计数据,根据IPCC排放因子法,核算新疆农业源非CO2温室气体(GHG)排放总量,通过对数平均迪氏指数(LMDI)模型对农业源非CO2 GHG排放驱动因素进行分解,使用蒙特卡洛模拟结合情景分析法预测其排放趋势。结果表明,研究期内新疆农业源非CO2 GHG排放呈现波动上升趋势,增幅达到 34。43%,畜牧养殖是主要排放源。驱动因素研究结果表明,2000-2020 年,农业经济发展水平和城镇化水平的提高促进了农业源非CO2 GHG的排放,贡献排放量分别达4211。74×104tCO2eq和 1016。08×104tCO2eq。农业非CO2 GHG排放强度、乡村人口以及农业结构的降低抑制了农业源非 CO2 GHG 的排放,2000-2020 年减排量分别为 4163。36×104tCO2eq、224。84×104tCO2eq 和130。64×104tCO2eq。农业源非CO2 GHG排放在基准和规划情景下均呈上升趋势,但规划情景下的增速快于基准情景,在低碳情景下通过提高农业非CO2 GHG排放强度、改善生产结构有效减缓增速,可能在 2035 年实现负增长。实现农业源非CO2 GHG排放达峰需要强化对减排政策的落实,强制约束农业源非CO2 GHG排放;降低农业非CO2 GHG排放强度,控制因农业生产技术落后所导致的农业源非CO2 GHG排放;通过完善减排惩戒激励机制,鼓励技术突破引导新疆农业的低碳转型。
Predicting Trend of Agricultural Non-CO2 Greenhouse Gas Emissions in Xinjiang
Agriculture represents a significant contributor to non-carbon dioxide(CO2)greenhouse gas(GHG)emissions in China.Effectively managing non-CO2 GHG emissions from agriculture is crucial for achieving China's"dual-carbon"targets,providing valuable insights into guiding the green transformation of agriculture and promoting sustainable development.Utilizing statistical data on crop yield,fertilizer input,and paddy sowing area in the Xinjiang Uygur Autonomous Region from 2000 to 2020,the authors applied the IPCC coefficient method to assess total agricultural non-CO2 GHG emissions.The Logarithmic Logarithmic Mean Divisia Index(LMDI)model was used to analyze the drivers of non-CO2 GHG emissions from agricultural sources.Additionally,a Monte Carlo simulation,coupled with scenario analysis,was conducted to predict the emission trends of agricultural non-CO2 GHG emissions in Xinjiang during the study period.The findings revealed that:(1)agricultural non-CO2 GHG emissions in Xinjiang exhibited a fluctuating upward trend,experiencing a 34.43%increase,with animal husbandry identified as the primary emission source.(2)Key contributors to agricultural non-CO2 GHG emissions were identified as the level of agricultural economic development and urbanization,contributing to emissions of 4211.74×104tCO2eq and 1016.08×104tCO2eq,respectively.Conversely,the efficiency of Agricultural non-CO2 GHG emission intensity,rural population,and the agricultural structure acted as inhibitors,contributing to emission reductions of 4163.36×104tCO2eq,224.84×104tCO2eq and 130.64×104tCO2eq,respectively,during the period from 2000 to 2020.(3)Agricultural non-CO2 GHG emissions exhibited an increasing trend in both baseline and planning scenarios.However,the growth rate in the planning scenario surpassed that in the baseline scenario,and a slowdown in the growth rate is anticipated in the low-carbon scenario,with the potential for negative growth by 2035.(4)To effectively control non-CO2 GHG emissions from agriculture,there is a need to reinforce the implementation of emission reduction policies,continually enhance the efficiency of agricultural production,improve incentive mechanisms for emission reduction,and encourage technological breakthroughs.These measures are essential to guide the green transformation of Xinjiang's agriculture and foster its sustainable development.

Agricultural non-CO2 GHGDriversScenario projectionsLMDI model

于爽、赵直、徐晗、李健、张雪艳、马欣

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中国农业科学院农业环境与可持续发展研究所,北京 100081

新疆师范大学地理科学与旅游学院,乌鲁木齐 830017

德勤有限公司气候变化与可持续发展研究院,北京 100026

中国科学院地理科学与资源研究所/中国科学院陆地表层格局与模拟院重点实验室,北京 100101

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农业源非CO2温室气体 驱动因素 情景预测 LMDI模型

国家重点研发计划国家重点研发计划国家自然科学基金国家自然科学基金中国科学院战略咨询项目中国农科院科技创新工程项目

2021xjkk09032019YFA06074033227163832171561XDA20010302ASTIP-CAAS

2024

中国农业气象
中国农业科学院农业环境与可持续发展研究所

中国农业气象

CSTPCD
影响因子:1.679
ISSN:1000-6362
年,卷(期):2024.45(4)
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