Does Enhanced Carbon Emission Efficiency Mitigate Urban Climate Risk?
炭素排出効率の向上は都市の気候リスクを軽減するのか？ (AI 翻訳)
Fengqian Chen, Xiaoyong Huang, Zhi Li, Hanchen Xie, Yifei Wu
🤖 gxceed AI 要約
日本語
本研究は、中国163都市のパネルデータを用い、XGBoostとSHAP分析、地理的・時間的加重回帰（GTWR）を統合し、炭素排出効率（CEE）が気候物理リスク指標（CPRI）に与える非線形かつ空間的に不均一な影響を検証した。結果は、CEEが短期的に気候リスクを悪化させつつ長期的に緩和する二段階動態を示し、都市レベルや気候タイプごとに異なるパターンが確認された。
English
Using panel data from 163 Chinese cities (2006-2022), this study integrates XGBoost with SHAP and GTWR to examine the nonlinear and spatially heterogeneous effects of carbon emission efficiency (CEE) on the Climate Physical Risk Index (CPRI). Results reveal a two-stage dynamic pattern where CEE initially exacerbates then mitigates climate risk, with variations by city level and climate type. The strongest risk reduction effects appear in peripheral cities and extreme rainfall-dominated areas, with a north-south gradient.
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
本論文は中国の都市を対象とするが、日本の気候変動適応策や都市の炭素効率改善にも示唆を与える。特に、非線形効果や地域差を考慮した政策設計の重要性を強調しており、日本の国土交通省や自治体が策定する気候リスク対応計画に参考となる。
In the global GX context
This paper provides empirical evidence on the nonlinear relationship between carbon efficiency and climate physical risk, offering insights for global urban climate adaptation policies. The spatially heterogeneous results underscore the need for tailored land-use strategies, relevant to ISSB and TCFD's focus on scenario analysis and risk management.
👥 読者別の含意
🔬研究者:Provides a novel empirical framework combining ML and spatial econometrics to study carbon efficiency-climate risk linkages, useful for future research on nonlinear and spatial effects.
🏛政策担当者:Highlights the need for differentiated land-use policies based on city type and climate conditions to mitigate climate risk through carbon efficiency improvements.
📄 Abstract（原文）
Extreme climate events have emerged as a critical threat to the economic resilience and environmental sustainability of urban systems. As a central pillar of the low-carbon transition, improvements in carbon emission efficiency (CEE) are increasingly recognized as a potential pathway to mitigate the occurrence and intensity of such events. Drawing on a balanced panel dataset of 163 cities from 2006 to 2022, this study integrates an Extreme Gradient Boosting (XGBoost) model augmented with SHAP (Shapley Additive Explanations) analysis and a Geographically and Temporally Weighted Regression (GTWR) framework to examine the nonlinear and spatially heterogeneous effects of CEE on the Climate Physical Risk Index (CPRI). The results reveal a distinct two-stage dynamic pattern, in which CEE initially exacerbates and subsequently mitigates climate risk, indicating a nonlinear transition from short-term intensification to long-term alleviation. This relationship shows clear differences across city levels and climate types. The strongest effects appear in peripheral cities and in areas with extreme rainfall dominance (ERD). Spatial analysis based on GTWR also shows a clear north–south pattern. The effect of CEE in reducing risk becomes stronger from the south to the north. Based on these results, the study suggests different land-use policy strategies for different city types and climate conditions. The results give actionable insights for designing targeted carbon governance policies. These policies aim to deal with the growing challenges caused by extreme climate events under ongoing climate change.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/land15061068first seen 2026-06-19 05:04:23
🔔 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。