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Top1. Introduction
Under the dual pressures of the global economy and the environment, energy transition has become an issue driving sustainable development (Hall and Vredenburg, 2003; Song et al., 2020; Swilling et al., 2022). With the excessive extraction and consumption of fossil fuels, as well as their profound impact on climate change, the construction of a clean and low-carbon energy system is urgent (Kabeyi and Olanrewaju, 2022; Siciliano et al., 2021). The energy transition encompasses not only the optimization of energy structures but also necessitates innovation in economic models, the promotion of technological advancements, and the transformation of social operational modes (Su et al., 2023). This historic shift aims to achieve a smooth transition from reliance on non-renewable energy sources such as coal and oil to renewable sources like wind and solar energy, ensuring the security of energy supply and the sustainability of the environment (Omer, 2011; Prasad et al., 2021). In this context, digital and intelligent technologies are highly anticipated due to their demonstrated potential for enhancing energy production efficiency and reducing energy consumption intensity, thereby offering new perspectives and tools for the energy transition (Xu, 2024). Digital and intelligent technologies are considered cutting-edge for improving production efficiency and reducing energy intensity (Liu et al., 2023; Ye et al., 2024). AI plays a pivotal role by enabling real-time monitoring, predictive analytics, and advanced diagnostics of wind turbines and solar photovoltaic systems. These capabilities not only streamline the generation and distribution of energy but also pave the way for proactive maintenance, thereby minimizing downtime and maximizing the overall efficiency of renewable energy infrastructure (Dogo et al., 2019; Liu et al., 2021). Moreover, AI-driven intelligent control strategies have proven to significantly enhance the power generation efficiency and reliability of these systems. By optimizing operational parameters and automating decision-making processes, AI contributes to a more resilient and sustainable energy grid. This integration of AI also facilitates the integration of renewable energy with smart grids, enhances demand-side management, and supports the transition towards a decentralized and decarbonized energy future (Pappas et al., 2023).
Research indicates that AI technology, which underpins the era of Industry 4.0, may potentially inhibit energy transition (Fan et al., 2023; Vinuesa et al., 2020). As a pioneering technology driving the technological revolution, the role of AI in enhancing energy efficiency and optimizing energy production is unequivocal (Xie and Yan, 2024). However, the deployment of AI technology requires substantial electricity consumption. The operation of AI is contingent upon extensive data processing, which necessitates substantial computational power typically provided by servers and storage devices in data centers. The continuous operation of these devices consequently incurs significant electricity consumption (Zamponi and Barbierato, 2022). Additionally, advancements in technology often lead to reduced energy costs, which may, paradoxically, stimulate additional energy demand (Du et al., 2023; Han and Zhou, 2024). This phenomenon, known as the Energy Rebound Effect, highlights the need for a nuanced perspective when evaluating AI’s impact on the energy transition. While it is crucial to acknowledge AI’s positive contributions to promoting clean energy utilization and improving the efficiency of energy systems (Wang et al., 2023), it is equally important to address the potential energy consumption issues that may arise from implementing AI technologies (Kong et al., 2023).