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Coal-to-nuclear (C2N) repowering, which replaces coal-fired units with nuclear reactors while reusing existing site infrastructure, offers a unique solution to the challenge of large-scale coal retirement in China’s low-carbon transition. However, the availability of suitable sites, which fundamentally determines C2N potential, remains insufficiently quantified at high spatial resolution, and the key factors constraining siting suitability remain poorly understood. This study develops a unit-level C2N siting model integrating a database of coal-fired units with multi-source geospatial constraint layers at 1-km resolution. A four-dimensional framework encompassing geological stability, cooling water availability, population distribution, and unit capacity is applied to screen candidate sites for large reactors (LRs) and small modular reactors (SMRs). Results show that 44 units (30.80 GW) pass the geospatial screening criteria for LRs, while 1,657 units (718.36 GW) pass the criteria for SMRs in China, indicating SMRs may represent a potentially suitable option under geospatial constraints. Shapley-value decomposition quantitatively attributes each constraint’s contribution to site exclusion, identifying population as the dominant factor. Plant capacity is the second-largest contributor for LRs, whereas cooling water and geological conditions are the next most influential factors for SMRs. Regional Shapley analysis reveals pronounced spatial heterogeneity in binding constraints across China’s seven power regions. Sensitivity analyses across alternative population regulatory frameworks and cooling-water distance thresholds confirm the robustness of SMR siting advantages while showing that shifts in these standards can substantially reshape regional technology mix and siting patterns. These findings provide high-precision spatial inputs for power system planning and differentiated policy design toward decarbonization of coal-fired power plants in China.

This is an open access article under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0, http://creativecommons.org/licenses/by/4.0/).
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