Spatial Correlation Analysis of Forest Carbon Sequestration Efficiency in China
中国における森林炭素固定効率の空間相関分析 (AI 翻訳)
Tian Peng
🤖 gxceed AI 要約
日本語
本論文は、中国30省の2003〜2022年のデータを用い、DEA-BCCモデルと空間統計手法で森林炭素固定効率を評価。効率は段階的に向上し、地域間で不均一だが正の空間的自己相関が確認され、80％の省で高-高または低-低クラスターが形成された。政策への示唆を提供。
English
This study assesses provincial forest carbon sequestration efficiency across 30 Chinese provinces from 2003-2022 using DEA-BCC and spatial analysis. Results show phased improvement, regional heterogeneity, and significant positive spatial autocorrelation, with 80% of provinces exhibiting HH or LL clusters. Findings inform regional forestry carbon 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
中国の森林炭素吸収効率の空間分析は、日本における森林由来カーボンクレジット制度（J-クレジット）や地域間格差の検討に示唆を与える。ただし、制度や森林管理が異なるため直接適用は困難。
In the global GX context
This paper provides spatial evidence on forest carbon efficiency in China, offering methodological insights for global nature-based solution accounting. However, its China-specific data limits direct transferability to other regions.
👥 読者別の含意
🔬研究者:Spatial econometric approaches for forest carbon efficiency can be applied to other regions or compared with alternative measurement methods.
🏢実務担当者:Companies with forest-based carbon credits may use similar efficiency metrics to evaluate project performance.
🏛政策担当者:Highlights the importance of regional clustering in forest carbon policies; relevant for designing spatially targeted incentives.
📄 Abstract（原文）
Global warming has become a major challenge faced by human society, and reducing greenhouse gas emissions while enhancing the carbon sequestration capacity of forest ecosystems are key measures to address the climate crisis. As the world's largest carbon emitter, China actively fulfills its emission reduction commitments. Forests, as the largest terrestrial carbon reservoir, play a crucial role in ecological security and climate regulation through their carbon sequestration efficiency. This study examines 30 provinces (cities and autonomous regions) in China from 2003 to 2022, employing an output-oriented DEA-BCC model to scientifically assess provincial forest carbon sequestration efficiency. Using exploratory spatial data analysis (ESDA) methods, combined with global Moran's I and local Moran's I, the study systematically analyzes the spatial correlation and clustering characteristics of forest carbon sequestration efficiency. The findings reveal that China's forest carbon sequestration efficiency—encompassing comprehensive technical efficiency, pure technical efficiency, and scale efficiency—has shown a phased improvement and gradual enhancement, with significant regional heterogeneity. The global Moran's I values were all positive and passed significance tests, indicating significant positive spatial autocorrelation in forest carbon sequestration efficiency. In 2022, 80% of provinces exhibited high-high (HH) and low-low (LL) spatial clustering patterns, demonstrating persistent spatial aggregation. These conclusions provide important theoretical support and practical insights for refining regional forestry carbon sequestration policies, optimizing factor input structures, and promoting balanced and efficient development of forest carbon sequestration.
🔗 Provenance — このレコードを発見したソース
- crossref https://doi.org/10.30560/sdr.v8n1p26first seen 2026-05-14 23:40:05
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