1. Introduction
The pursuit of rapid industrialization in developing nations often creates a tension between economic growth and environmental protection, manifesting in challenges like global warming and air pollution (Onwe et al., 2024a, 2024b). This duality underscores the urgency of adopting the eco-efficiency principle – a metric that quantifies sustainability by optimizing economic output per unit of environmental input. Crucially, such alignment resonates with SDG-9’s call for sustainable infrastructure that integrates resource-efficient technologies. Here, digital infrastructure (5G/artificial intelligence [AI]/cloud computing) emerges as a nexus: it operationalizes eco-efficiency by enabling “more with less” (Lei et al., 2024), while simultaneously addressing SDG-9’s industrial innovation targets and developing country stakeholders’ dual needs for stable growth and excellent environments (Onwe et al., 2024a, 2024b). China’s experimentation with digital solutions for pollution control exemplifies this synergy, offering a replicable model where digitalization serves as both an economic catalyst and an environmental governance tool, thus reconciling the traditionally competing paradigms of development and sustainability.
Originating from the German scholar Schaltegger, the concept of eco-efficiency encapsulates the input–output efficiency, taking into account the utilization of resources like capital, labor and energy, alongside diverse outputs, particularly undesirable ones such as carbon emissions (Sehaltegger and Sturm, 1990). As an extension of the concept, green eco-efficiency not only emphasizes the harmonious interplay among economic society and ecological protection but also offers the scientific basis for the improvement of the construction of ecological civilization (Maxime et al., 2006), and functions as a commonly used indicator for measuring sustainable development. The existing scholarship on green eco-efficiency can be summarized in terms of measuring methods and influencing factors (Xu et al., 2022). Regarding measurement methodologies, existing studies have mainly adopted the ratio method (Jiang and Tan, 2020), material flow analysis (Kuosmanen and Kortelainen, 2005), data envelopment analysis (Moutinho et al., 2020; Charnes et al., 1978) and stochastic frontier analysis (Song and Chen, 2019) to assess eco-efficiency. Among them, the super-efficiency DEA method that contains the SBM model with the undesirable output (Yasmeen et al., 2020) has gradually become the mainstream method in this field because...
