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Analysis of the Driving Factors and Decoupling of Carbon Emissions from Energy Consumption in Hainan Province, China

中国海南省におけるエネルギー消費由来炭素排出の駆動要因とデカップリングの分析 (AI 翻訳)

Xiaoning Wang, Yamei Chen, Qiong Chen, Xin Lin, Jingwen Zhao, Qian Jin, Yuying Zhao

Sustainability📚 査読済 / ジャーナル2026-08-05#エネルギー転換Origin: CN対象セクター: cross_sector
DOI: 10.3390/su18157961
原典: https://doi.org/10.3390/su18157961
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🤖 gxceed AI 要約

日本語

本研究は中国海南省を対象に、LMDI法とTapioモデルを用いて2007〜2022年のエネルギー消費由来CO2排出の駆動要因とデカップリング状態を分析した。経済成長と人口増加が排出を促進する一方、エネルギー強度と産業構造の改善が排出削減に寄与した。近年は弱いデカップリングから強いデカップリングへ移行しつつあり、自由貿易港や観光島としての政策に示唆を与える。

English

This study analyzes the driving factors and decoupling of energy-related CO2 emissions in Hainan Province, China, from 2007 to 2022 using LMDI decomposition and the Tapio model. Economic growth and population increase drive emissions, while improvements in energy intensity and industrial structure contribute to reductions. The region is transitioning from weak to strong decoupling, offering insights for Hainan's development as a free trade port and tourism island.

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

📝 gxceed 編集解説 — Why this matters

日本のGX文脈において

日本のGX文脈では、SSBJ開示やカーボンプライシング導入が進む中、地域レベルの排出要因分解は自治体や企業のScope 1・2排出削減計画の策定に参考となる。特にエネルギー集約度改善の効果を定量的に示す点は、省エネ投資の優先順位付けに有用。

In the global GX context

In the global GX context, this study contributes to the literature on regional carbon emission decomposition and decoupling, relevant for countries pursuing net-zero targets. The LMDI and Tapio methods are widely applicable for assessing the effectiveness of climate policies and identifying key drivers of emissions, supporting evidence-based policy design.

👥 読者別の含意

🔬研究者:Provides a methodological template for decomposing emission drivers and decoupling analysis in a subnational context.

🏢実務担当者:Offers insights into how energy intensity and industrial structure improvements can reduce emissions, useful for corporate decarbonization planning.

🏛政策担当者:Highlights the importance of energy structure and intensity policies in achieving decoupling, relevant for regional climate strategy.

📄 Abstract(原文)

High-energy-consuming and high-emission industries have made enormous contributions to economic development, but their carbon emissions are also substantial. To achieve the “dual carbon” goals as early as possible, this study takes Hainan Province, China, as the study area and employs the LMDI method to decompose the factors influencing carbon emissions. The Tapio model is also used to analyze the decoupling relationship between the driving factors and carbon emissions. The results show the following: (1) Carbon emissions in Hainan Province from 2007 to 2022 exhibited an overall upward trend, with an average annual growth rate of 5.75%. Various oil products accounted for an average share of over 40.11%, but the share of electricity increased, while that of oil decreased. The sectors, ordered from the highest to lowest carbon emissions, are: industry > transportation > residential > agriculture, forestry, animal husbandry, and fishery. (2) The decomposition results indicate that economic output, energy structure, and population size have positive effects on carbon emissions, while energy intensity and industrial structure have negative effects. At the sectoral level, the energy structure factor has a negative effect only on the transportation sector, and positive effects on all other sectors. The energy intensity factor has negative effects on all sectors except “other sectors” and the residential sector, with a cumulative contribution of 2003.65 × 104 tonnes of carbon emissions. The industrial structure factor has negative effects on carbon emissions across all sectors, with a cumulative contribution of 1568.23 × 104 tonnes. The economic output factor promotes emissions in all sectors, with a cumulative increase of 5787.35 × 104 tonnes, of which 2740.88 × 104 tonnes are from the industrial sector. The population factor also promotes emissions across all sectors, with a cumulative contribution of 549.21 × 104 tonnes. (3) The decoupling model analysis shows that from 2007 to 2008, the decoupling state was predominantly an unfavorable negative decoupling. From 2008 to 2010, it shifted to a favorable positive decoupling, but from 2010 to 2011 it returned to an unfavorable negative decoupling. From 2011 to 2022, the decoupling index declined from 1.43 to 0.13, indicating an overall favorable weak decoupling state. (4) The decoupling effects of individual influencing factors reveal that in the 2007–2008 period, the carbon emission decoupling index was mainly composed of the energy intensity effect and the economic output effect. In the 2012–2013 period, the energy structure effect did not change significantly and remained in a weak decoupling state, while the energy intensity effect declined markedly, changing the decoupling state from weak to strong decoupling. The industrial structure effect remained in a strong decoupling state. In 2017–2018, the economic output effect changed from an expansive coupling state to a weak decoupling state, while the other effects all showed relatively favorable positive decoupling states. In 2021–2022, all effects exhibited favorable positive decoupling states, among which the energy structure and energy intensity effects showed strong decoupling. Finally, this study provides a case study for the development of Hainan as a Free Trade Port, a tourism island, a petroleum- and aviation-fuel-intensive province, and a pilot ecological civilization zone.

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