Computing Corridor or Ecological Cost Conveyor? Regional Equity in China’s East‐Data‐West‐Computing Project

Over the past decade, advances in artificial intelligence and related digital technologies have increased demand for computing power. Because the artificial intelligence sector relies heavily on electricity, land, and water, its resource footprint has raised growing concerns regarding pressures on environmental carrying capacity and infrastructure resilience. In response, China launched the East-Data-West-Computing Project in 2020. With eight national computing hubs designated, the project seeks to redirect both large-scale data center clusters and latency-tolerant computing tasks from eastern regions, where demand is concentrated, to central and western China. It aims to ease eastern capacity constraints by reallocating infrastructure and workloads, but it may also reshape the geographic distribution of ecological costs within China, potentially intensifying pressures in central and western regions. Such a pattern may emerge because ecological costs remain insufficiently visible under existing project-by-project assessment frameworks, while efficiency targets are more readily translated into enforceable metrics than equity and coordination objectives. As a result, central and western regions may face growing belated ecological risks, together with resource and fiscal bottlenecks, while the benefits they receive may not be commensurate with the responsibilities they bear. If this mismatch persists, central and western regions may get stuck into less sustainable development paths, which would reinforce development disparities between eastern and western China. To address this challenge, this article proposes a three-pronged institutional framework: MRV-based ecological cost accounting with interregional compensation and benefit-sharing mechanisms; adaptive governance supported by monitoring, disclosure, and participation that enables periodic adjustment; and technological innovation as a transitional measure to reduce unit-level impacts.