Ground-coupled heat pump (GCHP) systems can provide comfortable indoor environments, but also inevitably contribute to greenhouse gas emissions and other impacts on human health, ecosystems, and resources. Life cycle assessment (LCA) methodology has been widely adopted to estimate the environmental impacts associated with GCHP systems, with operational electricity consumption being the largest contributor among most categories. Given the data-intensive nature of LCA, operational electricity consumption should be prioritised to refine the accuracy of LCA results. Previous studies, however, have relied on static COP (Coefficient of Performance) and annual average data to obtain the environmental impacts and have disregarded the effect of long-term performance degradation caused by building thermal load imbalances. In this paper, to bridge this research gap, a dynamic operational environmental impact assessment (DOEIA) method was proposed to improve the precision of LCA results by incorporating higher temporal resolution data of electricity mix and real-time performance modelling of the reversible GCHP system. Impacts of different temporal resolutions (i.e. monthly, trimestral, and annual) on the accuracy of LCA results was examined and the operational performance degradation resulting from imbalanced building heating and cooling loads was considered. Results demonstrate that while performance degradation effects are evenly distributed across impact categories, gaps due to temporal resolutions vary significantly between different categories. Subsequent analysis of spatial variations in the latter further emphasises the importance of accounting for higher temporal resolutions. Finally, life cycle impact assessment (LCIA) results for reversible GCHP systems in different locations were obtained with the support of DOEIA method and the results underscore the necessity of a cleaner energy mix. The proposed DOEIA method can be applied to other energy systems for comparative analyses. It is replicable in other countries and regions and therefore is expected to provide methodological guidance for future decision-makers.