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成熟と方法論的バイアスの軽減：インドのR&D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、再生可能エネルギー事業における認知バイアス（過信、確証バイアスなど）の影響を調査。NISE、NIWE、SSS-NIBEといった主要研究機関が標準化テスト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分析し、技術的デバイアシングシステム（TDS）フレームワークを提案する。
English
This paper investigates how cognitive biases affect entrepreneurial decisions in India's renewable energy ecosystem, analyzing the role of key R&D institutions (NISE, NIWE, SSS-NIBE) in mitigating these biases through standardized testing and data-driven support. It proposes a Technological Debiasing System (TDS) framework to improve decision-making and accelerate sustainable innovation.
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
インドの再生可能エネルギー政策と研究機関の役割に焦点。日本のGX文脈では、SSBJや有報との直接の関連は薄いが、インド市場参入や技術協力を検討する日本企業にとって、現地の意思決定プロセスを理解する参考となる。
In the global GX context
The paper offers insights into India's renewable energy ecosystem and institutional debiasing mechanisms. Globally, it contributes to behavioral economics in energy transitions, though it does not directly address disclosure frameworks like TCFD/ISSB. It may inform international investors on Indian R&D dynamics.
👥 読者別の含意
🔬研究者:Behavioral economics applied to renewable energy decision-making; a framework for debiasing through institutional support.
🏢実務担当者:Not directly applicable; useful for R&D managers in renewable energy firms seeking to reduce bias in project selection.
🏛政策担当者:Implications for designing institutional mechanisms that support evidence-based innovation in energy sectors.
📄 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.20924062first seen 2026-07-17 04:47:46
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