Digital Intelligence Transformation of Energy Conservation Management in China’s Public Institutions: Evolution, Innovation Approach, and Practical Challenges

Energy conservation management in public institutions is a critical area of administrative affairs, playing a leading and exemplary role in implementing China’s “green development strategy” and accelerating the transition to green and low-carbon development. The evolution of energy conservation management in public institutions has generally progressed from behavioral energy conservation and policy-driven energy conservation to digital and intelligent energy conservation. Each stage is characterized by distinct conceptual foundations, tool selections, key tasks, and value orientations. From a theoretical perspective, the innovative practices of digital intelligence transformation in energy conservation management are deeply driven in China by problem solving, environmental factors, and technological advancements. This transformation is the result of the interplay between the broader context of digital government construction and the specific challenges and structural adjustments within energy conservation management in public institutions, combined with the strong momentum of innovation diffusion in energy conservation management informatization. The innovative practices of digital intelligence transformation in energy conservation management in China can be categorized into four models: the “Technology Demonstration + Digital Platform” model, the “Edge–Cloud Data Middle Platform” model, the “Operation + Platform” split front–back-end model, and the “Intelligent Function Aggregation Platform” model. Each model has unique functional characteristics and applicable scenarios, yet faces various inherent challenges. Currently, the digital intelligence transformation of energy conservation management in China’s public institutions is constrained by the tension between innovation pressure and limited grassroots resources, the diminishing marginal returns and internalization costs of digital intelligence transformation, the inverted hierarchy dilemma, and the “floor effect” of digital energy conservation under traditional governance norms.