A Generation-Weighted Modelling Framework for Life Cycle Assessment of Low-Carbon Electricity Mixes: Scenario Simulation, Boundary Diagnostics and Regional Proxy Analysis

Abstract

Installed-capacity shares are widely used to describe power-sector transition, but per-kWh life cycle assessment (LCA) depends on delivered generation. This study develops and tests a static, annual-average generation-weighted structural diagnostic framework linking capacity-to-generation conversion, technology impact factors, scenario simulation, uncertainty analysis, optimization-boundary diagnostics, and regional proxy analysis. The lcpy Simple LCA capacity mix supplies six pedagogical S0–S5 stress-test scenarios, while UK official capacity and generation observations provide a 2020–2024 observational backcast. Relative to raw capacity shares, fixed 2024 capacity-factor weighting reduces mean generation-share error by 60.1%, and a prior-year capacity-factor model reduces it by 72.3%; these improvements are interpreted as arithmetic and structural evidence, not as evidence of dispatch-model forecasting skill. In the scenario set, generation weighting lowers GWP100 by 13.55–22.59%; the low-fossil S2 scenario gives the lowest GWP100, 0.09011 kg CO2-eq kWh−1, 50.01% below S0, and remains lowest in the tested climate-change sensitivity analyses. Multi-indicator rankings are less stable, with S2, S3, and S4 forming a low-burden group rather than a method-invariant optimum. Applying the same weighted-sum calculation to 2024 generation structures for China, the UK, and the EU gives a central-proxy China GWP100 of 0.7497 kg CO2-eq kWh−1; this is a proxy-based structural diagnostic, not a validated regionalized LCA. The results relocate the assessment focus from installed capacity to delivered generation while identifying time-varying utilization and region-specific inventories as necessary extensions.