Technological Maturation and Methodological Bias Mitigation in the Renewable Energy Ecosystem: A Strategic Review of Indian R&D Institutions
再生可能エネルギーエコシステムにおける技術的成熟と方法論的バイアス軽減：インドの研究開発機関の戦略的レビュー (AI 翻訳)
Rajendra Prasad B S, Rachateshwar Rachateshwar, Nandini B
🤖 gxceed AI 要約
日本語
本論文は、インドのNet-Zero 2070目標達成に向け、認知バイアス（過信、確証バイアスなど）が再生可能エネルギーの起業家精神に与える影響を分析。NISE、NIWE、SSS-NIBEなどの研究機関が標準化試験やデータ駆動型意思決定支援を通じてバイアスを軽減する役割を考察。機械学習と分析の統合も提案し、技術的デバイアスシステム（TDS）フレームワークを提示する。
English
This paper analyzes how cognitive biases (overconfidence, confirmation bias, etc.) influence entrepreneurial decisions in India's renewable energy sector, focusing on the role of research institutions like NISE, NIWE, and SSS-NIBE in mitigating biases through standardized testing and data-driven support. It proposes a Technological Debiasing System (TDS) framework integrating machine learning and analytics.
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
インド特有の制度を対象とするが、SSBJや有報とは直接関係しない。日本においても、再生可能エネルギー投資の意思決定におけるバイアス軽減の枠組みは参考になる可能性がある。
In the global GX context
While focused on Indian institutions, the paper offers a framework for mitigating cognitive biases in renewable energy entrepreneurship that could be adapted to global contexts, complementing TCFD/ISSB decision-usefulness discussions.
👥 読者別の含意
🔬研究者:Provides a behavioral economics lens on renewable energy innovation and a framework for studying bias in technology adoption.
🏢実務担当者:Suggests how institutional infrastructure and data analytics can improve investment decisions in renewable energy projects.
🏛政策担当者:Highlights the role of R&D institutions in de-risking renewable energy ventures and supporting evidence-based policy.
📄 Abstract（原文）
Abstract India's transition toward its Net-Zero 2070 target requires not only technological innovation but also improved decision-making within the renewable energy ecosystem. This paper investigates how cognitive biases, including overconfidence, confirmation bias, anchoring bias, and availability bias, influence entrepreneurial decisions in renewable energy ventures. It further examines the role of India's leading renewable energy research institutions—National Institute of Solar Energy (NISE), National Institute of Wind Energy (NIWE), and Sardar Swaran Singh National Institute of Bio-Energy (SSS-NIBE)—in mitigating these biases through standardized testing, scientific validation, and data-driven decision support. Using a qualitative analysis of the Ministry of New and Renewable Energy (MNRE) Annual Report (2023–2024), this study maps institutional research outputs to behavioral economic principles and demonstrates how these organizations function as technological debiasing mechanisms. The paper also explores the integration of machine learning and analytics for objective forecasting while highlighting the potential risk of automation bias. Based on these findings, a Technological Debiasing System (TDS) framework is proposed to support evidence-based innovation and improve the reliability of renewable energy entrepreneurship. The study concludes that combining institutional infrastructure, advanced analytics, and behavioral insights can significantly strengthen decision-making, reduce cognitive bias, and accelerate sustainable renewable energy innovation in India.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.5281/zenodo.20924063first seen 2026-07-18 05:18:34
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