AI-Driven Risk Pricing and Transition Finance in Agriculture: Evidence from Punjab
農業におけるAI駆動のリスク価格付けと移行金融：パンジャブの証拠 (AI 翻訳)
Amandeep Kaur Sangra
🤖 gxceed AI 要約
日本語
本研究は、インド・パンジャブ州の800農場のパネルデータを用いて、農業移行リスクを価格付けするAI指標（AI_Price）を開発。ランダムフォレストとLSTMで非線形リスクと時間変動を捉え、2019年のグリーンクレジット政策を自然実験として固定効果操作変数法で因果効果を推定。その結果、AI価格付けは排出強度を12%削減し、収量を6%向上させ、環境と生産性のトレードオフを否定した。
English
This study develops an AI-driven risk pricing index (AI_Price) using Random Forest and LSTM to price transition risks in agriculture. Using a panel of 800 Punjab farms and exploiting the 2019 Green Credit Policy rollout as a quasi-experiment, fixed-effects IV estimation shows that AI pricing reduces emissions intensity by 12% while increasing yields by 6%, rejecting environmental-productivity trade-offs. The paper offers a hybrid AI-econometric framework for transition finance and causal evidence from a developing context.
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
日本でも農業分野のGX（みどりの食料システム戦略）や地域金融でのサステナビリティ連動融資が注目される中、AIを用いたリスク価格付けの実証手法は、SSBJ開示や有報での気候リスク分析にも示唆を与える。パンジャブの事例は小規模農家の金融包摂という点で日本の中山間地域にも参考になる。
In the global GX context
This paper provides a novel AI-econometric framework for pricing agricultural transition risks, relevant for global climate disclosure (TCFD/ISSB) and transition finance design. The causal evidence from India demonstrates that sustainability-linked lending can simultaneously reduce emissions and improve productivity, offering lessons for blended finance and emissions trading schemes worldwide.
👥 読者別の含意
🔬研究者:The hybrid AI-econometric approach for causal inference in transition finance offers a methodological template for future studies on green credit and agricultural sustainability.
🏢実務担当者:Financial institutions can use the AI_Price index to design sustainability-linked loans for agri-SMEs, improving risk assessment while incentivizing decarbonization.
🏛政策担当者:The Green Credit Policy evidence supports designing credit-based climate policies that align with smallholder realities, informing green finance guidelines in emerging economies.
📄 Abstract（原文）
Agriculture's 20-24% share of global emissions demands innovative transition finance, yet smallholder farmers in Punjab face credit gaps amid groundwater depletion and input intensification. This study develops an AI_Price index—integrating Random Forest for nonlinear risks and LSTM for temporal dynamics—to overcome limitations of traditional models like CAPM in pricing agricultural transition risksUsing a 2018-2025 panel of 800 Punjab farms, we apply fixed-effects IV estimation exploiting the 2019 Green Credit Policy rollout. Results show AI-driven pricing reduces emissions intensity by 0.25 tCO₂e/ha (12%) while increasing yields by 0.16 t/ha (6%), rejecting environmental-productivity trade-offs. Robustness checks confirm causality.We contribute a hybrid AI-econometric framework for transition finance, causal evidence from developing contexts, and policy designs for sustainability-linked lending. Findings generalize to blended finance and emissions trading, supporting global decarbonization while enhancing smallholder resilience.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.5281/zenodo.19736904first seen 2026-05-15 18:10:19
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