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都市規模での長江経済帯の生活廃棄物炭素排出の要因分解と削減ポテンシャル

[Factors Decomposition and Carbon Reduction Potential of Domestic Waste Carbon Emission in the Yangtze River Economic Belt at the Urban Scale]. (原題)

Yu-Zhu Chen, Ming Gao

PubMedジャーナル2026-08-15#エネルギー転換Origin: CN対象セクター: waste_management
DOI: 10.13227/j.hjkx.202506299
原典: https://pubmed.ncbi.nlm.nih.gov/42670101

🤖 gxceed AI 要約

日本語

長江経済帯の111都市のパネルデータを用い、生活廃棄物の炭素排出量を推計し、都市類型別に影響要因を分解。埋立が主要排出源で、リサイクル削減効果が増大。BPニューラルネットワークとシナリオ分析で削減ポテンシャルを予測し、都市類型により大きな差があることを示した。

English

Using panel data from 111 cities in the Yangtze River Economic Belt, this study estimates carbon emissions from domestic waste, decomposes influencing factors by city type, and predicts reduction potential using BP neural networks and scenario analysis. Landfill is the main emission source, while recycling's reduction effect grows. Reduction potential varies greatly across city types.

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

中国の廃棄物管理政策に基づく実証研究であり、日本の自治体の廃棄物削減策やカーボンニュートラル戦略に示唆を与える。特に、都市特性に応じた対策の重要性は、日本の地域差を考慮した政策立案に参考となる。

In the global GX context

This empirical study on waste carbon emissions in Chinese cities offers insights for global waste management and climate policy. It demonstrates the importance of city-specific strategies, which is relevant for countries developing localized decarbonization pathways.

👥 読者別の含意

🔬研究者:Provides a methodological framework for decomposing waste emission factors and predicting reduction potential using neural networks.

🏢実務担当者:Highlights the role of recycling and waste treatment structure in reducing emissions, useful for corporate waste management strategies.

🏛政策担当者:Emphasizes the need for tailored waste reduction policies based on city characteristics, informing local climate action plans.

📄 Abstract(原文)

Promoting carbon emissions reduction of urban domestic waste is an important measure to achieve the "dual carbon" goals and achieve coordinated pollution reduction and carbon emission reduction. Based on the panel data of 111 cities above prefecture level in the Yangtze River Economic Belt from 2010 to 2020, this study calculated the carbon emissions of urban domestic waste, including the "source reduction + terminal disposal" process. The cities were clustered into four types based on population and economic characteristics, and the influencing factors of carbon emissions from urban domestic waste were decomposed. Furthermore, the BP neural network model and scenario analysis method were used to predict the carbon reduction potential of domestic waste in each type of city. The results show that: ① The total carbon emissions of domestic waste in the Yangtze River Economic Belt had been increasing year by year during the observation period. From the perspective of the carbon emission structure, landfill was always the main carbon source with the largest proportion, followed by incineration, and the proportion of biodegradation emissions was the lowest, while the growth rate of carbon reduction from recycling was the highest, and the reduction efficiency was continuously enhanced. ② The impact effects of treatment structure, economic development, urbanization level, and total population size on the carbon emissions of domestic waste were all positive, while the impact effects of waste generation intensity and industrial structure optimization were negative, and they showed different effects in various cities. ③ All types of cities had great carbon reduction potential in domestic waste, but there were great differences. The carbon reduction potential of the second type of city was far higher than that of other cities, while the first type of city had the lowest carbon reduction potential overall. The change degree of carbon reduction potential of the third and fourth types of cities was similar, showing a trend of increasing first and then decreasing. All localities should formulate and implement carbon reduction measures for domestic waste disposal according to local conditions.

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