The study addresses a critical challenge for the wind industry: rapidly evolving turbine designs are increasing uncertainty in component reliability, with significant consequences for both financial viability and environmental performance during operations. This is relevant, because operations and maintenance (O&M) decisions influence both operational costs and vessel-related emissions, but these two aspects are often evaluated separately in practice. To tackle this, a methodology is developed that loosely couples a discrete-event O&M simulation model with a life-cycle assessment (LCA) model, enabling quantification of annualized O&M costs (k€/MW/year) and \(\text {CO}_2\) -equivalent emissions (kt/year) across the operational lifetime. The O&M model accounts for stochastic failures, weather-limited access, logistics and resource constraints (vessels and technicians), and repair durations, while the LCA translates operational activity (e.g., transit and on-site work and waiting) into \(\text {CO}_2\) -equivalent emissions using the Global Warming Potential (GWP) method with a 100-year time horizon. The methodology is demonstrated through a case study of the planned IJmuiden Ver wind farm, using wind farm and operational parameters derived from publicly available tender documentation and consultations with offshore stakeholders. The analysis focuses on the operational phase and isolates reliability-driven effects by keeping the remaining wind-farm and maintenance assumptions consistent across scenarios. Reliability uncertainty is captured through two scenarios: (i) constant failure rates and (ii) non-constant, component-specific Weibull patterns, both using identical mean times to failure (MTTF) values. Results show that average O&M costs increase from 71.5 k€/MW/year to 77.5 k€/MW/year \((+8.4\%),\) while average emissions rise from 16.5 kt/year to 19.0 kt/year \((+15.2\%)\) when moving from scenario (i) to scenario (ii). In addition to shifting mean values, the Weibull-based scenario increases year-to-year variability by concentrating failures in certain periods, which increases the likelihood of peak repair demand and associated vessel activity. These differences highlight how reliability uncertainties can be particularly detrimental, as they may trigger unplanned financial spikes in annual budgets and risk breaching annual emissions thresholds that are increasingly subject to monitoring and compliance. The main contribution is a framework that links reliability assumptions directly to both economic (O&M cost) and environmental \((\text {CO}_2\) -equivalent) indicators within one modeling workflow, enabling risk-informed comparison of O&M strategies for next-generation offshore wind farms. The findings aim to support developers, investors, and policymakers in designing offshore wind projects with holistic, risk-informed O&M strategies that are both economically viable and environmentally sustainable.
