全球产业链下可再生能源部门隐含碳驱动因素研究

doi:10.12006/j.issn.1673-1719.2025.105

摘要/Abstract

摘要：

气候变化推动全球能源体系向可再生能源过渡，但伴随其产业链上下游排放增长的新挑战。文中从全球价值链视角出发，重点考虑贸易关联因素，基于多区域投入产出模型和结构分解分析技术，系统探究了可再生能源发电部门的隐含碳驱动因素，并分析了不确定性信息对核算结果的影响。研究发现：(1)从2016到2022年，全球可再生能源发电部门CO₂净转移量减少2.49%。(2)总体需求增长是隐含碳排放上升的主导因素，生产结构优化效应在降碳方面发挥显著作用。(3)风能和太阳能热能发电部门的碳排放强度效应呈现先减后增趋势，反映了技术进步与规模扩张的复杂关联。(4)中国、美国、德国及墨西哥等国在进口商品过程中隐含可再生能源电力显著增加，其中德国作为出口国，其出口产品导致可再生能源部门隐含碳排放增排0.27 Mt，凸显了国际贸易在能源转型及降碳中的重要作用。本研究为理解可再生能源在全球能源转型中的复杂角色提供了新视角，并为制定精准减排策略提供了科学依据。

关键词: 可再生能源发电部门, 全球产业链, 贸易关联, 隐含CO₂, 驱动因素, 结构分解分析（SDA）

Abstract:

Climate change is promoting the global energy system to transition to renewable energy, but it brings new challenges along with the increase in emissions in the upstream and downstream of its industrial chain. From a global industry chains perspective, this study systematically investigated the dynamics and driving mechanisms of embodied carbon emissions in the renewable energy power generation sector using multi-regional input-output model (MRIO) and structural decomposition analysis (SDA), with a focus on the impact of trade linkages among key countries. Furthermore, the impact of uncertain information on accounting outcomes was examined. The results are as follows. (1) From 2016 to 2022, the global net transfer of CO₂ in the renewable energy power generation sectors decreased by 2.49%. (2) Overall demand growth was the predominant factor driving the increase in embodied carbon emissions, the optimization effect of production structure played a significant role in reducing carbon emissions. (3) The carbon emission intensity effect of wind and solar thermal power generation sectors initially decreased before increasing, reflecting the intricate interplay between technological advancement and scale expansion. (4) Renewable energy power products in the upstream production stages of China, the United States, Germany, Mexico, and other countries saw significant increases. Notably, Germany, as an exporter, experienced a 70% rise in embodied carbon emissions within the renewable energy sector due to its exported products, underscoring the critical role of international trade in energy transition and carbon reduction. This study offers new insights into the complex role of renewable energy in the global energy transition and provides a scientific foundation for formulating precise emission reduction strategies.

Key words: Renewable energy power sector, Global industry chains, Trade linkages, Embodied CO₂, Driving factors, Structure decomposition analysis (SDA)

郭晨雨, 陈聪, 赵书园, 董聪. 全球产业链下可再生能源部门隐含碳驱动因素研究[J]. 气候变化研究进展, 2025, 21(6): 789-806.

GUO Chen-Yu, CHEN Cong, ZHAO Shu-Yuan, DONG Cong. Analyzing drivers of embodied carbon in renewable energy sector within global industry chains[J]. Climate Change Research, 2025, 21(6): 789-806.

图/表 13

表1 碳排放影响因素解析

Table 1 Analysis of factors influencing carbon emissions

表2 NAMAD和EXIOBASE数据库部门匹配

Table 2 NAMAD and EXIOBASE database sector matching

图1 2016、2019和2022年全球可再生能源发电部门在生产端和消费端的碳排放情况

Fig. 1 Global carbon emissions from the renewable energy generation sector on the production and consumption sides in 2016, 2019 and 2022

图2 全球可再生能源发电部门隐含CO2 2016、2019、2022年转移特征注：基于自然资源部标准地图服务网站下载的审图号为 GS(2016)1665 号的标准地图制作，底图无修改。黑色箭头方向表示路径流向，显示某一地区向另一地区出口的可再生能源产品隐含碳排放量。地图底色分别表示该地区可再生能源相关碳排放的净出口：红色表示净排放为正，绿色表示净排放为负，颜色深浅反映绝对值大小；0和缺失值为空白。

Fig. 2 Characteristics of embodied carbon dioxide transfer in global renewable energy generation sectors in 2016, 2019, and 2022

图3 4类可再生能源发电部门前十大转移路径注：图中26、28、29、79分别代表风力发电、太阳能光伏发电、太阳能热能发电、其他可再生能源发电；图中左侧数据为各地区可再生能源发电部门CO2流出量，右侧数据为各地区CO2流入量，单位都为kt。

Fig. 3 Top ten transfer paths for four types of renewable energy power generation sectors

图4 可再生能源发电部门四因素驱动因素分析(a)总体贡献，(b)地区贡献注：(b)图横坐标Δf为碳排放强度效应，ΔL为生产结构效应，ΔY为总需求量效应，ΔV为需求规模效应；数字1代表2016—2019年，数字2代表2019—2022年。例如，Δf1指2016—2019年的碳排放强度效应，Δf2指2019—2022年的碳排放强度效应。

Fig. 4 Analysis of four driving factors in the renewable energy power generation sector. (a) Overall contribution, (b) region contribution

图5 不同发电部门四因素分析(a)风电，(b)光伏，(c)太阳能热能，(d)其他可再生能源

Fig. 5 Analysis of four driving factors in different power generation sectors. (a) Wind power, (b) solar photovoltaic, (c) solar thermal, (d) other renewable energy

图6 可在生能源发电部门十二因素分析(a)中国，(b)美国，(c)德国，(d)墨西哥

Fig. 6 Analysis of twelve factors in the renewable energy power generation sector. (a) China, (b) the United States, (c) Germany, (d) Mexico

图7 4类可再生能源发电部门驱动因素分析 1 :排放强度效应 2:本国内部生产结构 3:后向联系 4:前向联系 5:其他国家内部生产结构 6:其他国家贸易 7:本国需求量8:本国需求规模 9:本国需求来源 10:其他需求量 11:其他需求规模 12:其他需求来源

Fig. 7 Analysis of driving factors for four types of renewable energy power generation sectors

图8 碳排放强度的蒙特卡罗模拟结果（基础情景）注：横坐标“数值”为蒙特卡罗模拟中碳排放强度可能出现的数值，下同。

Fig. 8 Monte Carlo simulation results of carbon emission intensity (baseline)

图9 基于碳排放强度模拟的隐含碳排放结果注：折线是当年基准数据的碳排放，竖线是模拟当年碳强度之后的上限和下限。

Fig. 9 Carbon emission results based on carbon emission intensity simulation

图10 碳排放强度的蒙特卡罗模拟结果（SDA）

Fig. 10 Monte Carlo simulation results of carbon emission intensity (SDA)

图11 蒙特卡罗模拟后各驱动因素的变化情况

Fig. 11 Changes in drivers after Monte Carlo simulation

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