[Spatial-temporal Evolution and Driving Factors of Carbon Footprint in Gansu Province].
甘粛省におけるカーボンフットプリントの時空間的進化と駆動要因 (AI 翻訳)
Q F Liu, Ai-di HUO, Zhixin Zhao, Xuan-Tao Zhao, Jia-Lu An, Yuan-Jia Huang
🤖 gxceed AI 要約
日本語
本論文は中国甘粛省のカーボンフットプリントの時空間的変化とその駆動要因を分析。2000年から2020年のデータを用い、炭素吸収の比率が高い地域（隴南・甘南）と低い地域の差異を明らかにした。人口、GDP、都市化率が主要な影響因子であり、社会経済因子と自然因子の複合効果が個別因子より大きいことを示した。地域の排出削減策策定に資する知見を提供。
English
This paper analyzes the spatial-temporal evolution and driving factors of carbon footprint in Gansu Province, China from 2000 to 2020. It finds that areas like Longnan and Gannan have high carbon absorption and ecological support coefficients (4.39-8.80), while others have low coefficients (0.12-0.16). Key drivers include population, GDP, and urbanization rate, with combined socio-economic and natural factors having greater impact than individual factors. The study provides a reference for regional carbon emission reduction policies.
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
中国の省レベルでのカーボンフットプリント分析事例であり、日本の都道府県レベルでの同様の分析や地域排出削減策に参考となる可能性がある。ただし、日本と中国では炭素吸収源の状況や統計データの特性が異なるため、直接適用には留意が必要。
In the global GX context
This regional carbon footprint study from China offers methodological insights for subnational emission analysis, relevant to global efforts in localizing climate action. The decomposition of driving factors (population, GDP, urbanization) is consistent with findings in other regions, while the ecological support coefficient provides a metric for carbon absorption capacity. It contributes to the literature on spatial carbon accounting and can inform similar studies in other countries.
👥 読者別の含意
🔬研究者:Provides a methodology for carbon footprint calculation and driver decomposition at the provincial level, useful for regional carbon accounting research.
🏢実務担当者:Offers insights for local governments on key factors influencing carbon footprint, aiding in the design of targeted emission reduction measures.
🏛政策担当者:Highlights the importance of socio-economic factors and natural resource endowment in shaping regional carbon footprints, relevant for subnational climate policy formulation.
📄 Abstract（原文）
， which was the highest value， while the ecological support coefficient only remained at 0.12-0.16. Longnan and Gannan were rich in natural resources such as vegetation and water. They had the largest proportion of carbon absorption， and their ecological support coefficient ranged from 4.39 to 5.23 and from 6.80 to 8.80， respectively. ③ The change in carbon footprint was influenced by multiple factors such as society， economy， and nature. Population， GDP and urbanization rate were the key factors leading to the change in carbon footprint in Gansu Province， followed by industrial structure and energy consumption. The combined effect of socio-economic factors and natural factors was greater than that of individual factors on the change of carbon footprint. Controlling carbon emissions to slow down global warming and ensure the healthy development of human society has become an issue of common concern worldwide. Based on the calculation of the carbon footprint and ecological support coefficient， it can provide reference for the reasonable formulation of carbon emission reduction measures in Gansu Province.
🔗 Provenance — このレコードを発見したソース
- openalex https://pubmed.ncbi.nlm.nih.gov/42473356first seen 2026-07-22 05:16:31
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