中国における気候変動リスクが持続可能な作物生産に与える影響：外生変数を組み込んだEBM-DEAモデル

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:気候リスクを効率評価モデルに外生変数として組み込む手法と地域間格差の動態分析の参考になる。

🏢実務担当者:農業・食品サプライチェーン企業が気候物理リスクを生産性評価に織り込む視点を得られる。

🏛政策担当者:農業政策の優先順位を生産拡大からグリーン・低炭素への転換へ移す根拠として参照できる。

As agriculture continues its green, low-carbon transformation, evaluating the efficiency of sustainable crop production in China under climate risk is increasingly important. Using 2019–2023 panel data for 31 Chinese provinces, this study incorporates the Climate Physical Risk Index (CPRI) as an exogenous variable into a parallel two-stage Epsilon-Based Measure–Data Envelopment Analysis (EBM-DEA) model to assess sustainable crop production efficiency and examine regional disparities and dynamic evolution. The results show that the national mean overall efficiency declines from 0.7518 to 0.7420 after CPRI is incorporated, indicating that ignoring climate risk may modestly overestimate efficiency. Overall efficiency is highest in central China, followed by eastern and western China; this regional ranking remains broadly stable after CPRI is incorporated, although the efficiency scores and relative positions of some provinces change. At the stage level, Crop Production Efficiency (CPE) is markedly higher than Sustainable Development Efficiency (SDE), while at the substage level, Green, Low-Carbon Efficiency (GLE) is lower than Livelihood Improvement Efficiency (LIE), identifying the sustainable development stage – particularly the green, low-carbon stage – as the principal constraint on further efficiency improvement. Overall efficiency disparities narrow after 2020; inter-regional disparities contribute the most, although the contribution of transvariation density increases in later years. Markov analysis indicates that high- and low-efficiency categories exhibit state dependence, transitions occur mainly between adjacent categories, and stable convergence has not emerged. These findings support incorporating climate risk management into crop production efficiency assessments, shifting policy priorities from production expansion to green, low-carbon transformation, and adopting region-specific measures. Incorporate climate risk as an external constraint into crop production efficiency assessments and routine risk management.Shift policy priorities from production efficiency alone to SDE, particularly GLE.Tailor policy instruments to province-specific constraints across the crop production, livelihood improvement, and green, low-carbon stages.Establish multi-stage dynamic monitoring and cross-regional technology diffusion mechanisms to address persistence and state lock-in in low-efficiency categories. Incorporate climate risk as an external constraint into crop production efficiency assessments and routine risk management. Shift policy priorities from production efficiency alone to SDE, particularly GLE. Tailor policy instruments to province-specific constraints across the crop production, livelihood improvement, and green, low-carbon stages. Establish multi-stage dynamic monitoring and cross-regional technology diffusion mechanisms to address persistence and state lock-in in low-efficiency categories.

• openalex https://doi.org/10.6084/m9.figshare.34067966first seen 2026-10-25 04:38:14 · last seen 2026-11-04 04:38:33