How generative artificial intelligence empowers research in the energy economy field: a scientific knowledge map analysis based on CiteSpace
Year: 2025
Type: article
Abstract: Generative artificial intelligence (GAI), as a breakthrough in the field of AI in recent years, is reshaping the production mode and creativity boundary of human society in an unprecedented way. This paper examines 12,114 SCI/SSCI documents included in Web of Science from 1988 to 2024 as the research object. It uses CiteSpace software to explore the development lineage, research hotspots, empowerment paths, and future trends of GAI in the field of energy economy research in terms of temporal distribution, institutional distribution, keyword co-occurrence, and theme clustering. The research results show that: (1) In terms of the timeline perspective, GAI-empowered energy economy research as a whole shows a fluctuating upward trend, beginning to sprout in 1988, entering a period of development with steady growth from 2009, and showing a boom period of explosive growth from 2017 to the present. (2) In terms of spatial distribution, the distribution of countries and regions, and research institutes shows a multipolar trend; and the inclusion of journals shows a trend of thematic refinement, diversification, and interdisciplinary integration. (3) In terms of research hotspots, “renewable energy development” and “utilization”, “model construction”, and “system optimization” are the core research content in this field. Among them, the terms “model”, “optimization”, “design”, and “renewable energy sources” are the core buzzwords about the research in the field of GAI-empowered energy economy; “carbon neutrality” becomes the keyword with the highest surge intensity in 2022−2024. (4) In terms of empowering paths, GAI has empowered energy economy through capabilities ranging from high-precision data generation, material design discovery, and complex model construction, multimodal data fusion, strategy simulation and scenario generation and optimization, and fault diagnosis and warning. (5) In terms of future trends, energy economics research topics ranging from early energy shortages and environmental issues to the discovery and use of renewable energy sources and model optimization, and in the future, it will be deeply engaged in deep-learning artificial intelligence technologies and hotspots such as carbon neutrality and energy efficiency and other hotspots.
Source: Carbon Management
Authors: Yujie Wang, Muyao Qin, Qing Miao, Xinru Wang
Language: English
Subfield: Statistics, Probability and Uncertainty
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,590
OpenAlex ID: W4417180771