モラルハザードと逆選択下における公平な小規模農家炭素農業のためのプリンシパル・エージェント契約の学習
Learning Principal-Agent Contracts for Equitable Smallholder Carbon Farming under Moral Hazard and Adverse Selection (原題)
Rishi Bharadwaj, Y. Narahari
🤖 gxceed AI 要約
日本語
農業土壌の炭素貯留を促す炭素農業において、小規模農家が実践から排除される構造を契約設計の観点から分析。集約業者が提示するプール契約をPOMDPとして定式化し、強化学習で動的利潤最大化契約を学習した。その結果、利潤最大化契約は小規模農家の排除を増幅し、大規模農場で87.7%の採用率に対し小規模農家では8.2%にとどまった。MRVコストを面積比例にした反実仮想では格差が解消される。
English
This study examines why smallholder farmers are largely excluded from carbon farming programs through the lens of contract design. Modeling an aggregator's pooled contract as a POMDP with adverse selection and moral hazard, reinforcement learning learns a dynamic profit-maximizing contract. Results show the aggregator amplifies exclusion: 87.7% adoption on large farms versus 8.2% on smallholdings, driven by per-hectare MRV cost gradients. Making MRV costs purely area-proportional eliminates the disparity.
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J-クレジット制度や農業分野の炭素貯留（環境保全型農業）が注目されるが、小規模農家の参加促進は課題。本論文の契約設計とMRVコストの知見は、日本の農業炭素クレジット制度設計や集約業者モデルに示唆を与える。
In the global GX context
Globally, agricultural soil carbon is a key frontier for carbon removal and voluntary carbon markets. This paper contributes to the design of equitable carbon farming contracts and MRV cost structures, relevant to Article 6.4, VCM integrity, and just transition debates in climate finance.
👥 読者別の含意
🔬研究者:契約理論と強化学習を組み合わせた炭素農業の公平性分析の手法と知見を提供。
🏢実務担当者:炭素農業プログラム設計時にMRVコスト構造と契約形態が小規模農家参加に与える影響を考慮すべき。
🏛政策担当者:小規模農家を包摂する炭素農業政策には、MRVコストの面積比例化や契約設計の規制が有効。
📄 Abstract（原文）
Agricultural soils are a major untapped carbon sink. Carbon farming is emerging as a promising practice for tapping this potential. Smallholder farmers, who dominate agriculture across South Asia and sub-Saharan Africa, are key to scaling climate mitigation via carbon farming. It is ironic that real-world carbon programs largely fail to reach them. We study this important gap through the lens of contract design. An aggregator offers a single pooled contract to a heterogeneous population of smallholder farmers who have private adoption costs (adverse selection) and exert unobserved effort (moral hazard), with agronomic outcomes evolving over multiple seasons. We formulate this evolving contracting problem as a POMDP and use reinforcement learning to learn a dynamic profit-maximising contract. We analyse the performance of the aggregator under various conditions. We find that a profit-maximising aggregator does not merely inherit the exclusion of smallholders, it amplifies it. On large farms the aggregator realises 87.7% of achievable adoption, against only 8.2% on smallholdings. Per-hectare Measurement, Reporting and Verification (MRV) costs fall as farm size rises, and the aggregator's pooling contract compounds this gradient rather than offsetting it. A counterfactual that makes MRV costs purely area-proportional eliminates this disparity. Our results and simulation can guide contract and policy design that opens carbon income to smallholders while enabling agricultural soils to contribute to climate mitigation at scale.
🔗 Provenance — このレコードを発見したソース
- openalex https://arxiv.org/abs/2609.20404first seen 2026-10-08 04:47:10
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