Studies on innovative energy startups’ topological roles and their correlation with success: based on temporal networks

Introduction Innovative energy startups are expediting the energy transition through the adoption of emerging technologies, including blockchain, fintech, artificial intelligence, and crowdfunding. However, existing research primarily focuses on technological capabilities at the startup level and macro-level national applications to explore the current state of innovative energy adoption. In contrast, limited attention has been paid to analyzing the role attributes of innovative energy startups and their correlations with potential success, which are critical for understanding their development trajectories within the energy market. Methods This study develops a temporal investment information network for global energy startups, drawing on data from energy enterprises worldwide between 2005 and 2024. The research examines the role attributes of startups and explores the temporal topological characteristics of the network. We propose a success evaluation model based on the features of successful startups to assess the potential of innovative energy startups. Results and Discussion The findings indicate that, despite their relatively small market share, innovative energy startups exert significant influence. Notably, successful startups typically exhibit higher betweenness centrality and lower closeness centrality. Moreover, factors such as network degree, centrality, and government administrative capacity play crucial roles in determining the success of innovative energy startups. In the evaluation model constructed using these factors, network structural characteristics contribute the most, achieving an evaluation accuracy of 0.984. This study provides valuable insights for policymakers evaluating innovative energy development trends and for investors assessing the potential of startups.

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Publication Details

Journal
Frontiers in Physics
Published
2025-07-30
DOI
https://doi.org/10.3389/fphy.2025.1610832
Primary Topic
Innovation Diffusion and Forecasting
Type
article
Field-Weighted Citation Impact
0.00

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article

Studies on innovative energy startups’ topological roles and their correlation with success: based on temporal networks

Ying Wang, Qing Guan
Frontiers in Physics
Innovation Diffusion and Forecasting
article

Studies on innovative energy startups’ topological roles and their correlation with success: based on temporal networks

Ying Wang, Qing Guan
article en

Abstract

Introduction Innovative energy startups are expediting the energy transition through the adoption of emerging technologies, including blockchain, fintech, artificial intelligence, and crowdfunding. However, existing research primarily focuses on technological capabilities at the startup level and macro-level national applications to explore the current state of innovative energy adoption. In contrast, limited attention has been paid to analyzing the role attributes of innovative energy startups and their correlations with potential success, which are critical for understanding their development trajectories within the energy market. Methods This study develops a temporal investment information network for global energy startups, drawing on data from energy enterprises worldwide between 2005 and 2024. The research examines the role attributes of startups and explores the temporal topological characteristics of the network. We propose a success evaluation model based on the features of successful startups to assess the potential of innovative energy startups. Results and Discussion The findings indicate that, despite their relatively small market share, innovative energy startups exert significant influence. Notably, successful startups typically exhibit higher betweenness centrality and lower closeness centrality. Moreover, factors such as network degree, centrality, and government administrative capacity play crucial roles in determining the success of innovative energy startups. In the evaluation model constructed using these factors, network structural characteristics contribute the most, achieving an evaluation accuracy of 0.984. This study provides valuable insights for policymakers evaluating innovative energy development trends and for investors assessing the potential of startups.

Frontiers in PhysicsVol. 13
China University of Geosciences (Beijing) (CN), Beijing Academy of Artificial Intelligence (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 78%
Innovation Diffusion and Forecasting
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