[STIRPAT-based Prediction and Multi-scenario Optimization of Carbon Emissions in Yunnan].
STIRPATモデルに基づく雲南省の炭素排出予測とマルチシナリオ最適化 (AI 翻訳)
Jing-Gang Du, Jin-Li Wang, Xun-Cheng Zhu
🤖 gxceed AI 要約
日本語
本研究は拡張STIRPATモデルを用いて雲南省の炭素排出要因を分析し、2023〜2035年の排出量を6つのシナリオで予測した。二次産業比率と石炭消費が排出促進要因、一次電力比率が抑制要因である。グリーン発展シナリオで2035年に基準年比20.1%削減、低成長保全シナリオで31.5%削減可能と示した。
English
This study uses an extended STIRPAT model to analyze carbon emission drivers in Yunnan Province, projecting emissions from 2023 to 2035 under six scenarios. Key findings: secondary industry share and coal consumption promote emissions, while primary electricity share inhibits. Green Development Scenario achieves 20.1% reduction by 2035, and Low-growth Conservation Scenario achieves 31.5%.
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
中国雲南省の事例だが、STIRPATモデルを用いた地域レベルの排出予測手法は日本でも自治体の脱炭素計画策定に応用可能。特に水力や風力など再生可能エネルギー比率の高い地域への示唆がある。
In the global GX context
This paper contributes to regional carbon forecasting literature by applying an extended STIRPAT model to a province with high clean energy share. Its multi-scenario optimization approach is relevant for policymakers worldwide designing regional decarbonization pathways.
👥 読者別の含意
🔬研究者:Provides a STIRPAT-based forecasting framework applicable to regions with high renewable energy penetration.
🏢実務担当者:Offers scenario analysis for corporate carbon reduction planning in regions like Yunnan.
🏛政策担当者:Highlights the need for integrating clean energy restructuring and industrial decarbonization in regional carbon peak planning.
📄 Abstract（原文）
As one of China's provinces with the highest proportion of clean energy， Yunnan exhibits distinctive regional characteristics in its carbon emission driving mechanisms and decarbonization pathways. To explore implementation paths for achieving the "Dual Carbon" goals， this study establishes a carbon emission forecasting system based on an extended STIRPAT model， integrating multidimensional factors including population， economy， energy， industry， and technology. Six development scenarios were simulated to project carbon emission trajectories from 2023 to 2035. Key findings include： ① Among driving factors， the proportion of secondary industry output demonstrated the most significant promoting effect on emissions （the elasticity coefficient was 0.348）， followed by coal consumption share and degree of prosperity. Conversely， increasing the proportion of primary electricity showed notable inhibitory impacts （the elasticity coefficient was -0.297）. ② Scenario simulations revealed that the Green Development Scenario， through energy structure decarbonization and industrial low-carbon transition， could reduce 2035 emissions by 20.1% compared to the baseline year. The Low-growth Conservation Scenario， enhanced by technological empowerment and accelerated industrial structural transformation， further decreased emissions to 33.369 1 million tons， achieving a 31.5% reduction. ③ Achieving carbon peaking in Yunnan requires prioritizing clean energy restructuring and industrial decarbonization. Strategic recommendations emphasize leveraging hydropower and wind energy advantages to upgrade energy-intensive industries， while enhancing digital twin technology applications and ecological carbon sequestration capacity to establish a sustainable development paradigm under the "Dual Carbon" framework.
🔗 Provenance — このレコードを発見したソース
- openalex https://pubmed.ncbi.nlm.nih.gov/42161793first seen 2026-05-23 05:37:08
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