广西细分行业碳排放驱动因素及脱钩分析：基于多元因素考量

谭招帆; 令狐大智

doi:10.13205/j.hjgc.202505023

广西细分行业碳排放驱动因素及脱钩分析：基于多元因素考量

doi: 10.13205/j.hjgc.202505023

谭招帆¹,
令狐大智^2, ,

1. 广西大学经济学院, 南宁 530004;
2. 广西大学工商管理学院, 南宁 530004

基金项目:

广西哲学社会科规划基金项目（23BGJ001）

详细信息

作者简介:
谭招帆（2001-），男，硕士研究生，主要研究方向为环境经济管理。2252651477@qq.com

通讯作者:
令狐大智（1979-），男，教授，主要研究方向为环境经济管理。linghuzhi@126.com

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出版历程
- 收稿日期: 2024-03-24
- 录用日期: 2024-05-30
- 修回日期: 2024-04-15
- 网络出版日期: 2025-09-11

Driving factors and decoupling analysis on carbon emissions of Guangxi's industrial sub-sectors based on multiple factors

TAN Zhaofan¹,
LINGHU Dazhi^{2
, ,}

1. School of Economics, Guangxi University, Nanning 530004, China;
2. School of Business Administration, Guangxi University, Nanning 530004, China

摘要

摘要: “双碳”目标下，从行业异质性角度研究显著碳排放驱动因素及脱钩分析对于广西“双碳”实现和产业绿色低碳化转型具有重要战略意义。首先，基于2012—2022年广西细分行业化石能源消耗量测算其碳排放量；其次，运用LMDI分解法建立生产、人口、经济、贸易、能源5个类别共12个有效驱动因素，从正负显著因素视角切入，探讨其对行业碳排放影响；最后，采用Tapio脱钩指数构建生产总值脱钩和驱动因素脱钩弹性，在把握行业碳排放与经济脱钩背景下分析碳排放显著因素与经济发展脱钩的关系。结果表明：11年间广西整体碳排放总量增长34.9%，达8246.67万t CO₂，碳排放强度下降29.5%，各行业变化与广西整体趋同；其中工业碳排放基数最大，2022年仅交通运输业和工业碳排放强度分别高于全区82.3%、135.4%。广西整体及行业间的前两个正负显著因素总效应存在共性，出口、经济发展是行业增加碳排放显著因素，效应分别达11.5406亿，1.4232亿t CO₂；技术、能源强度是促进行业碳减排主要因素，分别为-9803.85万，-6019.90万t CO₂，但不同时期行业正负显著因素也存在一定差异。生产总值脱钩表明：2021年以来广西整体及各行业主要呈负脱钩类型；且显著因素脱钩状态较为稳健，具体表现为正显著因素拉动负脱钩、连结脱钩类型，负显著因素推动正脱钩类型。
- “双碳”目标 /
- 细分行业碳排放 /
- LMDI模型 /
- Tapio脱钩指数 /
- 广西
Abstract: Under the "dual carbon" goal， achieving carbon peaking and carbon neutrality in Guangxi has become an urgent issue for the entire region. From the perspective of industry heterogeneity， it is of great strategic significance to study the significant carbon emission drivers and conduct decoupling analysis for realizing the "dual carbon" goals and achieving green and low-carbon transformation of Guangxi's industrial sectors. Firstly， carbon emissions from Guangxi's industrial sub-sectors（2012—2022） were estimated based on their fossil energy consumption. Secondly， the LMDI model was used to establish a total of 12 effective driving factors in five categories of production， population， economy， trade， and energy， with their effects on industrial carbon emissions discussed from the perspective of positive and negative significant factors. Finally， the Tapio decoupling index was used to construct the decoupling elasticity between GDP and driving factors， and the relations between significant carbon emission factors and economic development were analyzed under the background of industrial carbon emissions and economic decoupling. The results showed that the total carbon emissions of Guangxi increased by 34.9% to 82.4667 million tonnes of CO₂， while the carbon emission intensity decreased by 29.5%. The trends of all industries were similar to those observed in Guangxi overall. Among them， the industrial sector had the largest carbon emission base， and in 2022， only the transportation sector's and industrial sector's carbon emission intensities were 82.3% and 135.4% higher than the whole region， respectively. The total effects of the top two significant positive and negative factors in Guangxi as a whole and among industries showed similarities. Export and economic development were significant factors for increasing carbon emissions of industries， contributing 1154.06 million and 142.32 million tons of CO₂， respectively. Technological advancement and energy intensity were the main factors to promote carbon emission reduction in the industry， with reductions of -98.0385 million and -60.199 million tons of CO₂， respectively. However， there were some differences in the positive and negative significant factors affecting the industry in different periods. The GDP decoupling analysis showed that since 2021， Guangxi as a whole and all its industrial sectors predominantly exhibited negative decoupling； moreover， the decoupling status of significant factors remained relatively stable. This was specifically reflected in the fact that positive significant factors drove negative decoupling and linked decoupling types， while negative significant factors promoted positive decoupling types. It is suggested to intensify industrial adjustment， optimize the industrial energy consumption structure， promote low-carbon development by industry and focus， and adopt an overall planning to promote the rectification of the industry.
- "dual carbon" goals /
- carbon emissions in industrial sub-sectors /
- LMDI model /
- Tapio decoupling index /
- Guangxi

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