Urban–Rural Spatial Patterns, Landscape Configuration, and Carbon Emission Performance: A County-Level Analysis in Henan Province, China
都市・農村空間パターン、景観構成、炭素排出性能：中国河南省の郡レベル分析 (AI 翻訳)
Xicheng Zhang, Xiaoyang Guo, Xicheng Zhang, Chen Li, Chenming Zhang
🤖 gxceed AI 要約
日本語
本研究は中国河南省157郡を対象に、2013年、2018年、2023年の炭素排出性能（CEP）を測定し、空間的・時間的変化を分析した。UN_SBMモデルと超効率SBMモデルを用い、景観パターン指標（TA、AWMSI、SPLITなど）がCEPに与える影響を回帰分析で検証。結果、全体のCEPは改善傾向にあるが地域差が大きく、総景観面積と形状指標は正の効果、分割指数は負の効果を持つことが示された。
English
This study measures carbon emission performance (CEP) of 157 counties in Henan Province, China, for 2013, 2018, and 2023 using UN_SBM and super-efficiency SBM models. It analyzes the impact of landscape pattern indicators (TA, AWMSI, SPLIT) on CEP via regression. Results show overall CEP improvement but significant spatial heterogeneity; total landscape area and shape index positively affect CEP, while splitting index inhibits it.
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
中国河南省の事例だが、日本の地方自治体における土地利用と炭素排出の関係理解に示唆を与える。特に、都市計画や地域開発における低炭素化施策の有効性評価に役立つ。
In the global GX context
This county-level analysis provides empirical evidence on how urban-rural spatial patterns affect carbon emission performance, relevant for global urban decarbonization strategies and spatial planning for climate mitigation.
👥 読者別の含意
🔬研究者:Offers a methodology combining carbon performance measurement with landscape metrics, applicable to other regions.
🏢実務担当者:Provides insights for urban planners and local governments on optimizing land use for carbon reduction.
🏛政策担当者:Highlights the importance of spatial configuration in achieving carbon targets, informing regional low-carbon policies.
📄 Abstract（原文）
Against the backdrop of global climate change and increasing pressure to mitigate carbon emissions, counties serve as critical units for urban–rural spatial development and carbon governance. However, their carbon emission performance (CEP) and underlying spatial mechanisms remain insufficiently understood. This study focuses on 157 counties in Henan Province, selecting three time points: 2013, 2018, and 2023. The study measures the CEP and analyzes its spatiotemporal differentiation characteristics. First, considering that carbon emissions are undesirable outputs generated during the economic production process, this study employs the undesirable output slack-based measure (UN_SBM) model and the super-efficiency slack-based measure model with undesirable outputs (Un_Super_SBM) to evaluate county-level carbon emission performance. Second, landscape pattern indicators, including expansion, complexity, and compactness, are selected, and regression models are constructed to explore the influence of different factors on carbon emission performance. The results show the following: (1) The overall CEP of counties in Henan Province improved from 2013 to 2023, but there were significant spatial differences. (2) Both “Total landscape area” (TA) and “Area-weighted mean shape index” (AWMSI) had significant positive impacts on CEP, whereas the “Splitting index” (SPLIT) inhibited CEP. (3) The effects of vegetation cover and transportation conditions varied, reflecting the heterogeneity of development stages and spatial functional positioning across different counties. This study reveals the relationship between urban–rural spatial form and carbon emission performance at the county level, providing empirical evidence for optimizing construction land spatial structure, enhancing CEP, and promoting regional low-carbon development.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/land15061021first seen 2026-07-05 05:08:14
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