甘肃省农业碳排放驱动因素与脱钩效应研究

LZUFE-KMS > 经济学院

作者	梁蒙蒙
姓名汉语拼音	Liang Mengmeng
学号	2020000001026
培养单位	兰州财经大学
电话	15637916145
电子邮件	1459896368@qq.com
入学年份	2020-9
学位类别	学术硕士
培养级别	硕士研究生
学科门类	经济学
一级学科名称	理论经济学
学科方向	人口、资源与环境经济学
学科代码	020106
第一导师姓名	武翠芳
第一导师姓名汉语拼音	Wu Cuifang
第一导师单位	兰州财经大学
第一导师职称	教授
题名	甘肃省农业碳排放驱动因素与脱钩效应研究
英文题名	A Study on the Driving Factors and Decoupling Effects of Agricultural Carbon Emissions in Gansu Province
关键词	农业碳排放甘肃省 LMDI 模型脱钩效应低碳农业
外文关键词	Agricultural carbon emissions; Gansu Province; LMDI model; Decoupling effect; Low-carbon agriculture
摘要	气候变化是人类面临的全球性问题，碳排放的急剧增加使温室效应愈演愈烈。冰川消融、灾害频发，气温升高正在影响人类生活的方方面面。农业既受气候变化的影响又影响气候变化。一方面，农业系统十分脆弱，极易受到气候变化的影响，全球每年因气候变化而减产的稻米、小麦、大豆大约为 300 万吨、900 万吨和 200 万吨，气候变化可以解释全球粮食产量波动的 32%-39%。另一方面，人口增加导致农业生产的扩大，增加了温室气体排放，加剧了气候变化，全球至少五分之一的温室气体来自农业产生的排放，包括种植业、林业、渔业和畜牧养殖。农业不仅是主要的温室气体排放源，也是国家发展的支柱产业，在全社会节能降碳大环境下，农业低碳转型也成为业内关注的热点，如何实现农业减排目标和实现其可持续发展是当前迫切需要解决的问题。甘肃省是全国主要的粮食作物和畜牧业生产基地，其低碳农业的发展对促进“双碳”目标的实现具有重大的意义。本文根据甘肃省种植业和畜牧业的发展现状，合理构建了甘肃省农业碳排放体系，选择 17 个农业碳源对甘肃省 2005～2020年间农业碳排放进行了测算；其次，从时间和空间视角对甘肃省农业的碳排放变化特征、结构变化特征、农业的碳排放强度变化特征进行了研究。在此基础上，利用 LMDI 模型研究甘肃省农业碳排放的驱动因素，并利用 TAPIO 脱钩模型对农业碳排放与农业经济发展之间的脱钩效应进行研究。本文具体研究结果如下：碳排放总量方面：2005～2020 年甘肃省农业碳排放总量总体呈波动上升趋势，其中畜牧业碳排放增幅最大，增幅为 42.39%，种植业的碳排放在此期间上升了 35.57%。截止到 2020 年，种植业和畜牧业碳排放分别占甘肃省农业碳排放的 29.15%和 70.85%。碳排放强度方面：甘肃省农业碳排放强度逐年递减，下降幅度为 37.75%；甘肃省 14 地市农业碳排放量及碳排放强度呈现一定的空间差异性，以畜牧业为主、粮食作物种植面积占比大的地市碳排放量大、碳排放强度高，工业相对发达城市农业碳排放强度低。碳排放结构方面：畜牧业碳排放占农业碳排放的比重在六成左右，碳源以牛和羊为主，两者分别占畜牧业碳排放的 55.44%和 34.77%，种植业占农业碳排放的四成左右，主要碳源是化肥和农膜，两者分别占 39%和 24.24%；驱动因素方面：通过 LMDI 模型分析得出，总效应呈上升状态，农业生产效率因素和农业劳动力因素对农业碳排放具有抑制作用，农业经济发展水平因素和产业结构因素对农业碳排放具有正向驱动作用。其中，农业生产效率因素对碳排放的抑制作用最强，农业经济发展水平因素对碳排放的驱动作用最强，是甘肃省农业碳排放增加的主要驱动因素。脱钩效应方面：2005～2020 年甘肃省农业碳排放与农业经济增长之间的脱钩状态出现弱脱钩、扩张链接和强脱钩 3 种类型，呈现波动期—强脱钩期—弱脱钩期的发展趋势，整体以弱脱钩和强脱钩为主，由弱脱钩逐渐向强脱钩转变，脱钩状态良好。本文对甘肃省农业碳排放做了全面综合的分析，基于得出的研究结论，提出关于甘肃省农业碳减排的对策建议如下：1）推进科技运用，提升畜禽养殖业低碳发展水平；2）提高农业物资利用的效率，减少单位农业物资的投入；3）尊重区域差异，因地制宜制定碳减排措施；4）立足资源优势，积极调整农业产业结构；5）加大技术创新力度，提高农业生产效率。
英文摘要	Climate change is a global issue facing humanity, and the sharp increase in carbon emissions has intensified the greenhouse effect.Glacier melting, frequent disasters, and rising temperatures are affecting all aspects of human life. Agriculture is influenced by both climate change and climate change. On the one hand, the agricultural system is extremely fragile and vulnerable to the impact of climate change. The annual reduction in global production of rice, wheat, and soybeans due to climate change is approximately 3 million tons, 9 million tons, and 2 million tons. Climate change can explain 32% -39% of global food production fluctuations. On the other hand, the increase in population has led to the expansion of agricultural production, increased greenhouse gas emissions, and exacerbated climate change. At least one fifth of global greenhouse gas emissions come from agriculture, including farming, forestry, fisheries, and animal husbandry. Agriculture is not only the main source of greenhouse gas emissions, but also a pillar industry for national development. In the context of energy conservation and carbon reduction in the entire society, the low-carbon transformation of agriculture has become a hot topic of industry attention. How to achieve agricultural emission reduction goals and achieve sustainable development is an urgent problem that needs to be solved. Gansu Province is the main production base for grain crops and animal husbandry in China, and the development of low-carbon agriculture in Gansu Province is of great significance for promoting the achievement of the "dual carbon" goal. Based on the current development status of planting and animal husbandry in Gansu Province, this article reasonably constructed the agricultural carbon emission system in Gansu Province, and selected 17 agricultural carbon sources to calculate the agricultural carbon emissions in Gansu Province from 2005 to 2020;Secondly, the characteristics of carbon emission changes, structural changes, and carbon emission intensity changes in agriculture in Gansu Province were studied from a temporal and spatial perspective. On this basis, the LMDI model is used to study the driving factors of agricultural carbon emissions in Gansu Province, and the TAPIO decoupling model is used to study the decoupling effect between agricultural carbon emissions and agricultural economic development. The specific research results of this article are as follows: In terms of total carbon emissions: From 2005 to 2020, the total agricultural carbon emissions in Gansu Province showed a fluctuating upward trend, with the largest increase in carbon emissions from animal husbandry at 42.39%, while carbon emissions from planting increased by 35.57% during this period. As of 2020, carbon emissions from planting and animal husbandry accounted for 29.15% and 70.85% of agricultural carbon emissions in Gansu Province, respectively. In terms of total carbon emissions, the intensity of agricultural carbon emissions in Gansu Province has been decreasing year by year, with a decrease of 37.75%; There are certain spatial differences in agricultural carbon emissions and carbon emission intensity among 14 cities in Gansu Province. Cities with animal husbandry as the main industry and a large proportion of grain crop planting area have higher carbon emissions and carbon emission intensity, while cities with relatively developed industries have lower agricultural carbon emission intensity. In terms of carbon emission structure: The proportion of carbon emissions from animal husbandry to agricultural carbon emissions is about 60%, with cattle and sheep as the main carbon sources, accounting for 55.44% and 34.77% of the carbon emissions from animal husbandry, respectively. Planting accounts for about 40% of agricultural carbon emissions, with fertilizers and agricultural film as the main carbon sources, accounting for 39% and 24.24% respectively; In terms of driving factors: Through LMDI model analysis, it is found that the total effect is on the rise, agricultural production efficiency factors and agricultural labor factors have inhibitory effects on agricultural carbon emissions, and agricultural economic development level and industrial structure factors have a positive driving effect on agricultural carbon emissions. Among them, agricultural production efficiency factors have the strongest inhibitory effect on carbon emissions, while agricultural economic development level factors have the strongest driving effect on carbon emissions, making them the main driving factor for the increase in agricultural carbon emissions in Gansu Province. In terms of decoupling effect: From 2005 to 2020, there were three types of decoupling states between agricultural carbon emissions and agricultural economic growth in Gansu Province: weak decoupling, expansion linkage, and strong decoupling, showing a trend of fluctuation period - strong decoupling period - weak decoupling period. Overall, weak decoupling and strong decoupling were the main forms, gradually transitioning from weak decoupling to strong decoupling, and the decoupling state was good. This article provides a comprehensive and comprehensive analysis of agricultural carbon emissions in Gansu Province. Based on the research findings, suggestions for reducing carbon emissions in agriculture in Gansu Province are proposed as follows: 1) Promote the application of technology to enhance the low-carbon development level of livestock and poultry breeding industry; 2) Improve the efficiency of agricultural material utilization and reduce the input of agricultural materials per unit; 3) Respect regional differences and develop carbon reduction measures tailored to local conditions; 4) Based on resource advantages, actively adjust the agricultural industrial structure; 5) Increase technological innovation efforts and improve agricultural production efficiency.
学位类型	硕士
答辩日期	2023-05
学位授予地点	甘肃省兰州市
语种	中文
论文总页数	60
参考文献总数	42
馆藏号	0004760
保密级别	公开
中图分类号	F062.1/62
文献类型	学位论文
条目标识符	http://ir.lzufe.edu.cn/handle/39EH0E1M/34399
专题	经济学院
推荐引用方式 GB/T 7714	梁蒙蒙. 甘肃省农业碳排放驱动因素与脱钩效应研究[D]. 甘肃省兰州市. 兰州财经大学,2023.

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