全球农业贸易中隐含碳排放转移及其影响因素分析

凌再莉, 乔立达, 王润雨, 黄韬

中国环境科学 ›› 2025, Vol. 45 ›› Issue (10) : 5894-5906.

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中国环境科学 ›› 2025, Vol. 45 ›› Issue (10) : 5894-5906.
碳排放控制

全球农业贸易中隐含碳排放转移及其影响因素分析

  • 凌再莉1, 乔立达2, 王润雨3, 黄韬3
作者信息 +

CO2 emission transfer embodied in global agricultural trade and impact factors

  • LING Zai-li1, QIAO Li-da2, WANG Rui-yu3, HUANG Tao3
Author information +
文章历史 +

摘要

随着全球农业贸易的快速增长,农业已成为贸易中隐含碳转移的重要部门之一,然而少有研究关注到全球农业贸易隐含碳排放转移及其影响因素,因此,本研究基于区域间投入产出模型和GTAP(Global Trade Analysis Project)全球投入产出数据定量评估了2004、2007、2011和2014年全球141个国家及地区农业贸易中隐含碳排放,明晰全球农业部门生产侧和消费侧的碳排放特征,探讨全球农业贸易中隐含碳排放转移路径,并利用结构分解分析方法定量分析了全球农业贸易中隐含碳排放的影响因素.结果表明,2004~2014年期间,生产侧碳排放量较高的地区为中国、美国、拉美地区等全球主要粮食产区,而消费侧碳排放量较高的地区为中国、美国、中东北非等主要农产品消费地.6个农业部门中,生产侧和消费侧碳排放量最大的均是肉类产品部门;因农业部门的最终需求导致能源部门的碳排放量最大,占导致所有部门总排放量的40%以上;全球农业贸易中隐含碳净流出地主要有拉美地区和美国,隐含碳净流入地区主要有西欧、中国和中东北非等地区;投入产出结构、最终需求结构以及最终需求规模是促进全球农业贸易中隐含碳排放量增加的影响因素,导致10a间全球农业贸易中隐含碳排放量共增加459.6Mt,其中最终需求规模贡献量最大,占总增加量的87.8%.全球碳排放强度的降低是主要的抑制因素,导致10a间全球农业贸易中隐含碳排放量降低了458.4Mt.研究结果可从需求驱动的角度为减少全球农业贸易中隐含碳排放提供数据支撑和政策建议.

Abstract

With the rapid growth of global agricultural trade, agriculture has become one of the key sectors of dioxide carbon (CO2) emission embodied in trade. However, few studies have focused on the transfer of virtual CO2 emission embodied in global agricultural trade and its driving factors. This study quantitatively estimated CO2 emissions embodied in agricultural trade for 141 countries and regions worldwide in 2004, 2007, 2011, and 2014, by using the multi-regional input-output (MRIO) model and the GTAP 10 database. The production-based and consumption-based CO2 emission of the global agricultural sector and virtual CO2 transfer pathways associated with global agricultural trade were investigated. In addition, the structural decomposition analysis (SDA) method was used to analyze the impact of major driving factors on the CO2 emissions embodied in global agricultural trade. The results show that the regions with highest production-based CO2 emissions were the major agricultural producers in the world, including China, the United States, and Latin America. The regions with highest consumption-based CO2 emissions were the predominantly major agricultural consumers, including China, the United States and the Middle East and North Africa. The meat sector exhibited the largest production-based and consumption-based CO2 emissions in the six agricultural sectors. The energy sector was the largest contributor to CO2 emissions due to final demand from the agricultural sector, accounting for more than 40% of total CO2 emissions induced by agricultural sector. The regions with the net CO2 outflow embodied in the agricultural trade were the Latin America and the United States, while the Western Europe, China and the Middle East and North Africa were the net CO2 inflow regions. The input-output structure, final demand structure, and final demand scale are the key drivers that lead to the growth of virtual CO2 emissions embodied in global agricultural trade, leading to increase of 459.6Mt CO2 emissions embodied in agricultural trade over the past decade. The final demand scale was the largest driver factors, accounting for 87.8% of total growth of virtual CO2 emissions. The reduction in global CO2 emission intensity has served as the predominant inhibiting factor for embodied carbon emissions in agricultural trade, resulting in a decrease of 458.4Mt in CO2 emissions over the past decade. The results can provide data support and policy recommendations for reducing CO2 emissions embodied in global agricultural trade from a demand-oriented perspective.

关键词

全球农业贸易 / 碳排放 / 多区域投入产出模型 / 结构分解分析

Key words

global agricultural trade / CO2 emission / multi-regional input-output model / structural decomposition analysis

引用本文

导出引用
凌再莉, 乔立达, 王润雨, 黄韬. 全球农业贸易中隐含碳排放转移及其影响因素分析[J]. 中国环境科学. 2025, 45(10): 5894-5906
LING Zai-li, QIAO Li-da, WANG Rui-yu, HUANG Tao. CO2 emission transfer embodied in global agricultural trade and impact factors[J]. China Environmental Science. 2025, 45(10): 5894-5906
中图分类号: X24   

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基金

甘肃省科技计划项目(22JR11RA135);国家自然科学基金资助项目(42107404);甘肃省自然科学基金资助项目(21JR7RA686)

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