Fertilizer planning strategies supporting low-emission transitions in regulated agricultural systems
規制された農業システムにおける低排出移行を支援する肥料計画戦略 (AI 翻訳)
Clément Bamogo, Mustapha Oudani, Amine Belhadi, Sofiène Dellagi, Zied Achour
🤖 gxceed AI 要約
日本語
肥料計画における低排出肥料への移行を最適化する二段階確率モデルを提案。Q学習と可変近傍探索を組み合わせた解法で計算効率を向上。中国の紅茶生産を事例に、キャップアンドトレード制度の経済的有効性を示した。
English
This paper proposes a two-stage stochastic optimization model for fertilizer planning that supports the transition to low-emission fertilizers under cap-and-trade regulation. A reinforcement Q-learning-enhanced variable neighborhood search algorithm achieves high solution quality with low optimality gaps. A case study on tea production in subtropical China demonstrates economic viability and emission reductions.
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
日本では農業分野のGHG排出削減が政策課題となっており、キャップアンドトレード制度の導入検討が進んでいる。本研究は肥料計画における低排出肥料の導入判断にAI最適化を応用しており、日本の農業政策や炭素市場設計に示唆を与える。
In the global GX context
This paper offers a decision-support framework for agricultural planning under carbon pricing, relevant to global efforts like the EU's Common Agricultural Policy and voluntary carbon markets. The combination of RL and optimization is novel for agricultural decarbonization.
👥 読者別の含意
🔬研究者:The RL-enhanced optimization approach for agricultural planning under carbon regulation is a methodological contribution for those working on AI applications in sustainability.
🏢実務担当者:Agricultural producers can use the model to evaluate investment in low-emission fertilizers under cap-and-trade, identifying cost-effective emission reduction pathways.
🏛政策担当者:The case study illustrates how cap-and-trade can incentivize low-emission fertilizer adoption, informing carbon market design for the agricultural sector.
📄 Abstract（原文）
Agricultural production relies heavily on chemical fertilizers (CFs), which contribute significantly to greenhouse gas (GHG) emissions. Transitioning to low-emission fertilizers (LEFs) may reduce these emissions but involves additional investment and operational costs. This article studies fertilizer planning under a cap-and-trade carbon regulation that incentivizes the adoption of LEFs. We propose a two-stage stochastic mixed-integer linear programming model that determines the optimal timing and capacity of LEF installations and the corresponding production decisions under demand, carbon emissions, and price uncertainties. The first stage represents strategic investment decisions in LEF capacity, while the second stage determines production quantities using conventional (CF) or LEFs and the trading of carbon credits. To address the computational complexity of the problem, we develop a reinforcement Q-learning-enhanced variable neighborhood search (QL-VNS). Computational experiments on a set of benchmark instances show that the proposed approach yields high-quality solutions with average optimality gaps below 0.2%, while significantly reducing computational time compared with classical heuristics. A case study based on tea production in subtropical China illustrates how cap-and-trade mechanisms can make LEF systems economically viable, reduce emissions, and generate additional revenue through carbon credit trading. The results provide insights for policymakers and agricultural producers seeking cost-effective pathways towards low-emission farming.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1080/01605682.2026.2691991first seen 2026-07-19 04:50:56
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