説明可能な機械学習に基づく新質生産力が農業のグリーン・低炭素転換に与える影響
The impact of new quality productivity driven on green and low-carbon transformation of agriculture based on explainable machine learning (原題)
Xue Zhu, Ran Gong, Na Gong, Sheng Qu, Shidi Shao, Xiwu Shao, Zhuo He
🤖 gxceed AI 要約
日本語
中国30省の2011〜2023年パネルデータを用い、カーネル密度推定・空間分析・ベイズ最適化XGBoostで新質生産力が農業のグリーン低炭素転換に与える影響を分析。新質生産力の寄与率は27.4%で、水準0.2207を超えると転換が顕著に加速する。土地生産性が調整要因となり、食糧生産機能区・経済区により異質性がある。
English
Using 2011–2023 panel data from 30 Chinese provinces, this study applies kernel density estimation, spatial analysis, and a Bayesian-optimized interpretable XGBoost model to examine how 'new quality productivity' drives agriculture's green low-carbon transition. New quality productivity contributes 27.4%, with a threshold of 0.2207 accelerating the transition; land productivity moderates the effect, with heterogeneity across grain-production and economic regions.
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
中国の農業脱炭素政策と新質生産力概念の実証研究であり、日本の農業GXやSSBJ開示とは直接の接点は薄い。ただし、地域別の脱炭素進展を機械学習で評価する手法は、日本の地域農業政策や自治体の気候計画立案に応用可能な示唆を含む。
In the global GX context
This paper contributes to the growing literature on AI-driven regional decarbonization assessment, offering a replicable framework for evaluating agricultural transition pathways. While focused on China, its interpretable ML approach and spatial clustering analysis are relevant to global climate policy design and could inform agricultural transition finance and disclosure metrics under frameworks like ISSB and CSRD.
👥 読者別の含意
🔬研究者:機械学習と空間分析を組み合わせた地域脱炭素評価手法として、農業以外のセクターへの応用可能性を検討する価値がある。
🏢実務担当者:農業関連企業や食品サプライチェーン企業が、地域別の脱炭素進展度を評価し、Scope 3やサプライヤー管理に活用できる可能性がある。
🏛政策担当者:地域別の農業脱炭素政策を設計する際、新質生産力の閾値や空間クラスターを考慮したターゲティングの参考になる。
📄 Abstract（原文）
As an important source of carbon emissions in China, how to promote its green and low-carbon transformation of agriculture is an important starting point for policy formulation, and the emergence of new quality productivity, has brought new opportunities for agriculture to achieve green transformation. In order to deeply explore the influence of new quality productivity on green and low-carbon transformation of agriculture, based on the panel data of 30 provinces in China from 2011 to 2023, this paper uses kernel density estimation, global Moran index, standard deviational ellipse and Bayesian-optimized interpretable XGBoost model for analysis, and draws the following conclusions: First, the green and low-carbon transformation of agriculture in China exhibits a significant growth trend, with regional development showing a polarized pattern. Meanwhile, this transformation demonstrates pronounced spatial clustering characteristics, evolving from “single provincial agglomeration” to “multi-provincial agglomeration”, the spatial difference gradually diminishes. Second, the contribution rate of new quality productivity to green and low-carbon transformation of agriculture is as high as 27.4%. When the development level of new quality productivity is exceeds the threshold value of 0.2207, it will significantly accelerate the green and low-carbon transformation of agriculture. In addition, agricultural new-quality workers, agricultural new quality labor materials, and agricultural new quality labor inputs all significantly contribute to the green and low carbon transformation of agriculture. Third, the impact of new quality productivity on the green and low-carbon transformation of agriculture is moderated by land production efficiency. Finally, the impact of new quality productivity on the green and low-carbon transformation of agriculture exhibits heterogeneity across the functional region of grain production and and economic region. Based on the above conclusions and with corresponding policy recommendations proposed, the aim is to enhance the development level of new quality productivity and promote the sustainable development of China’s agricultural economy.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1038/s41598-026-70420-wfirst seen 2026-09-14 04:41:54
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