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arXiv Machine Learning By Rishi Bharadwaj, Yadati Narahari

Learning Principal-Agent Contracts for Equitable Smallholder Carbon Farming under Moral Hazard and Adverse Selection

Read the original on arXiv Machine Learning →

The paper investigates how a single pooled contract offered by an aggregator to heterogeneous smallholder farmers can be designed to maximize profit while addressing private adoption costs and unobserved effort over multiple seasons. Using a POMDP framework and reinforcement learning, the authors find that profit‑maximizing contracts disproportionately favor large farms, achieving 87.7% of possible adoption on large farms versus only 8.2% on smallholdings, largely due to higher measurement, reporting, and verification costs on smaller plots. The study suggests that adjusting MRV cost structures could reduce this disparity and help scale carbon farming to smallholders.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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