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Design and Implementation of a Dual-LLM Prescriptive ESG Reporting System in the Indonesian Palm Oil Industry

インドネシアのパーム油産業向けデュアルLLM規範的ESG報告システムの設計と実装 (AI 翻訳)

Niko Firzi Anansyah, Ratu Mutiara Siregar, Andi Prayogi, Muhammad Akbar Syahbana Pane

Journal of Deep Learning Computer Vision and Digital Image Processing📚 査読済 / ジャーナル2026-07-08#AI×ESG経営インパクト: コスト削減対象セクター: agriculture
DOI: 10.61255/decoding.v4i2.1404
原典: https://doi.org/10.61255/decoding.v4i2.1404
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🤖 gxceed AI 要約

日本語

この研究は、パーム油産業向けにデュアルLLM（Groq Llama 3とGemini）を用いた自動ESG報告システムを開発した。厳格な重み付けルールにより、Ganoderma感染率が20%を超える場合にESGスコアを50に制限するプログラム上の制約を導入し、AI生成レポートの正確性とリスク管理を向上させた。このシステムは報告業務の効率化とリアルタイムの環境リスク警告を可能にする。

English

This study develops an automated ESG reporting system for the palm oil industry using a dual-LLM architecture (Groq Llama 3 primary, Gemini backup). It introduces a programmatic constraint that caps the ESG score at 50 if Ganoderma infection exceeds 20%, improving accuracy and risk control. The system accelerates reporting workflows and provides real-time environmental risk warnings.

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SSBJ対応が進む中、AIを活用したESG報告自動化はコスト削減と透明性向上に直結する。特にパーム油などサプライチェーン上の環境リスク評価において、本システムのプログラム制約アプローチは参考になる。

In the global GX context

Globally, with ISSB and CSRD driving mandatory ESG disclosure, this system demonstrates how LLMs can automate reporting while embedding domain-specific risk controls to prevent hallucination-driven score inflation. It offers a template for sectors with high environmental sensitivity (e.g., palm oil, forestry) to produce accountable, auditable AI-assisted ESG reports.

👥 読者別の含意

🔬研究者:Pioneers dual-LLM failover and programmatic constraint for ESG scoring; relevant to AI×ESG and disclosure automation research.

🏢実務担当者:Provides a working prototype for automating palm oil ESG reporting with risk control; can inspire similar systems in other commodity supply chains.

🏛政策担当者:Illustrates how programmatic safeguards can complement AI in ESG reporting, supporting regulatory oversight and preventing greenwashing.

📄 Abstract（原文）

Purpose – This study aims to develop an automated Environmental, Social, and Governance (ESG) reporting information system based on Large Language Model (LLM) for the palm oil industry to overcome low efficiency, data inconsistencies, and analysis limitations inherent in manual reporting processes.Methods – This applied research employs the Design Science Research (DSR) paradigm, encompassing needs analysis, system design, implementation, and black-box testing. The system was developed using the Laravel MVC framework and integrated a Dual-LLM API failover architecture (Groq Llama 3 as primary and Gemini as backup). The case study was conducted at PT Surya Mata Ie.Findings – The developed system successfully automated ESG indicator extraction and prescriptive narrative generation. It utilizes a Strict Weighting Rule, a programmatic safeguard capping the ESG score at 50.0 (as a proof-of-concept testing constraint) if Ganoderma infection exceeds a 20% threshold (supported by agronomic research). During prototype evaluation, this rule intercepted an overly optimistic raw LLM score of 60.0 and corrected it to 50.0. This demonstrates the system's capability to function as a risk-control mechanism, mitigating potential hallucination-driven score inflation and supporting mathematically accountable outputs.Research implications – The implementation of this system significantly accelerates reporting workflows and serves as an early warning instrument for environmental risks, thereby enhancing real-time managerial decision-making and corporate transparency in complying with global sustainability standards.Originality – This study pioneers the integration of a Dual-LLM failover mechanism within a Laravel framework tailored for the palm oil sector. It introduces a novel programmatic constraint approach in JSON object parsing to maintain strict mathematical accountability in AI-generated ESG drafts.

🔗 Provenance — このレコードを発見したソース

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