Toward Sustainability Through Carbon Neutrality: Predicting Innovation Quality in New Energy Firms from the Business Environment
カーボンニュートラルによる持続可能性へ：ビジネス環境からの新エネルギー企業のイノベーション品質予測 (AI 翻訳)
Shan Liu, Xiaozhen Wang
🤖 gxceed AI 要約
日本語
本研究は、機械学習を用いてビジネス環境が新エネルギー企業のイノベーション品質を予測できるかを検証。人的資源、金融環境、公共サービスが重要要素であり、特に人的資源とイノベーション品質は上昇傾向を示す。所有権や所在地による差異も明らかに。
English
This study uses machine learning to assess whether the business environment predicts innovation quality in new energy firms. Human resources, financial environment, and public services are key predictors, with human resources showing a positive trend. Heterogeneity across ownership and location is observed.
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
日本でも新エネルギー分野のイノベーション促進はGX政策の柱。ビジネス環境の整備が企業の技術力向上に寄与する示唆は、日本の産業政策や地域活性化にも応用可能。
In the global GX context
This paper provides evidence on how business environment factors drive innovation in new energy firms, relevant for global energy transition policy. The machine learning approach offers a methodology that can be adapted for other regions and sectors.
👥 読者別の含意
🔬研究者:Demonstrates a machine learning framework to predict innovation quality from business environment variables, applicable to sustainability transitions research.
🏢実務担当者:Highlights which business environment elements (human resources, financial services) are critical for fostering innovation in new energy firms.
🏛政策担当者:Provides evidence for optimizing business environment policies to support innovation in the renewable energy sector.
📄 Abstract（原文）
Achieving sustainable development and carbon neutrality requires continuous technological upgrading in the new energy sector. Improvement of innovation quality in new energy firms therefore plays a significant role in sustainability transitions. However, whether and how the business environment supports the innovation quality in the new energy sector remains unclear. Using machine learning, our study assesses the predictive ability of the business environment for innovation quality in new energy firms, distinguishes the importance of different elements, and then portrays predictive patterns of critical elements. The results show that the business environment provides substantial predictive ability for innovation quality, increasing out-of-sample R2 from 0.6200 to 0.7001, which represents an improvement of 0.0801. Among the focal explanatory variables, human resources, financial environment, and public services emerge as relatively important elements. Furthermore, we find that human resources and innovation quality exhibit an overall upward trend, whereas public services and financial environment have a complex relationship with innovation quality. Heterogeneity analysis reveals that the predictive ability of the business environment for innovation quality varies significantly across firms with different ownership and locations. Our study provides evidence for policy design and business environment optimization to strengthen the institutional foundations of sustainable development.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/su18073459first seen 2026-05-17 07:28:49
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