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

TAN Zhaofan; LINGHU Dazhi

doi:10.13205/j.hjgc.202505023

Volume 43 Issue 5

May 2025

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TAN Zhaofan, LINGHU Dazhi. Driving factors and decoupling analysis on carbon emissions of Guangxi's industrial sub-sectors based on multiple factors[J]. ENVIRONMENTAL ENGINEERING , 2025, 43(5): 207-220. doi: 10.13205/j.hjgc.202505023

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TAN Zhaofan, LINGHU Dazhi. Driving factors and decoupling analysis on carbon emissions of Guangxi's industrial sub-sectors based on multiple factors[J]. ENVIRONMENTAL ENGINEERING , 2025, 43(5): 207-220. doi: 10.13205/j.hjgc.202505023

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

doi: 10.13205/j.hjgc.202505023

TAN Zhaofan¹,
LINGHU Dazhi^{2
,
,}

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

Received Date: 2024-03-24
Accepted Date: 2024-05-30
Rev Recd Date: 2024-04-15

Available Online: 2025-09-11

Abstract

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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