gxceed
← 論文一覧に戻る

The Spatiotemporal Evolution of Carbon Dioxide Emission Reduction Costs in China’s Industrial Sector and Its Influencing Factors: Evidence Based on DDF and SBM Methods

中国産業部門における二酸化炭素排出削減コストの時空間的進化とその影響要因：DDFおよびSBM法に基づく証拠 (AI 翻訳)

Shaohui Zou, Shen Kong

Sustainability📚 査読済 / ジャーナル2026-06-05#炭素価格Origin: CN
DOI: 10.3390/su18115767
原典: https://doi.org/10.3390/su18115767

🤖 gxceed AI 要約

日本語

本研究は、DDFとSBM法を用いて中国産業部門のCO2限界削減費用の時空間的進化を分析。2010～2022年の省別パネルデータから、削減費用は上昇傾向にあり、省によって大きな格差があることを発見。炭素排出強度が削減費用にU字型の影響を与え、都市化はコスト圧力を高める一方、貿易開放は削減コストを低減することを示した。

English

This study uses DDF and SBM methods to analyze the spatiotemporal evolution of marginal CO2 abatement costs in China's industrial sector. Using provincial panel data from 2010 to 2022, it finds that abatement costs have increased and show significant provincial heterogeneity. Carbon emission intensity has a U-shaped effect on costs, while urbanization increases cost pressure and trade openness reduces abatement costs.

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

中国の排出削減費用の実証分析は、日本のカーボンプライシング政策や産業別削減費用の推計に参考となる。SSBJや有報での気候関連開示において、削減費用の地域差や要因分析は投資家の判断材料として有用。

In the global GX context

This empirical study of China's industrial abatement costs contributes to the global literature on marginal abatement cost estimation. Its findings on the U-shaped relationship between emission intensity and costs, and the role of urbanization and trade openness, are relevant for carbon pricing design and climate disclosure under ISSB and CSRD.

👥 読者別の含意

🔬研究者:Provides a non-parametric approach to estimate marginal abatement costs and identifies key drivers, useful for further research on carbon pricing and industrial decarbonization.

🏢実務担当者:Offers insights into cost drivers and regional heterogeneity that can inform corporate carbon management and investment decisions in emission reduction technologies.

🏛政策担当者:Highlights the increasing cost of emission reductions and the importance of structural adjustments and trade openness, informing carbon pricing and industrial policy design.

📄 Abstract（原文）

Given the combined limitations of carbon peaking and carbon neutrality goals, the economic cost of industrial emission reduction in China has become increasingly prominent and regionally differentiated. This study evaluates the shadow prices of CO2 within the Chinese sector and examines the spatiotemporal evolution of carbon abatement costs across provinces, as well as the underlying influencing mechanisms. To capture the evolution of marginal abatement costs (MAC), we use two non-parametric frameworks based on provincial panel data from 2010 to 2022: slack-based measure (SBM), and the directional distance function (DDF) that accounts for unwanted outcomes. In addition, a fixed effects model with regional and temporal effects was constructed to determine the key determinants of marginal carbon reduction costs. Empirical evidence suggests that: (1) From 2010 to 2022, China’s industrial carbon abatement marginal cost has clearly increased, indicating that emission reduction has gradually shifted from a low-cost stage driven by efficiency improvement to a high-cost stage relying on structural adjustment and advanced technologies. (2) Carbon abatement costs exhibit significant provincial heterogeneity by a small number of high-cost provinces (mainly in developed regions) and a majority of low-cost regions, reflecting differences in industrial structure and technological constraints. (3) The industrial carbon emission reduction cost curves in some provinces of China have obvious similar evolution paths, and some areas also show a lagging phenomenon. (4) Carbon emission intensity is the dominant factor influencing abatement costs and presents a significant U-shaped relationship, while urbanization increases cost pressure and trade openness helps reduce abatement costs through structural optimization and technology spillovers.

🔗 Provenance — このレコードを発見したソース

🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。

gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。