The European Union aims to decarbonise its energy system by replacing fossil fuels with renewable energy technologies (RET). Among these, wind energy stands out for its low cost and climate change (CC) impact, particularly with innovations enabling turbines up to 15 MW. However, the intermittent nature of RET calls for flexible solutions to balance supply and demand. Electric vehicles (EVs), beyond mobility, offer flexibility by storing and discharging electricity, acting as mobile batteries. Once no longer suitable for propulsion, EV batteries can be repurposed as stationary second-life batteries (SLBs). Life cycle assessment (LCA) is a useful tool to evaluate the environmental performance of these emerging energy technologies. While previous LCA studies explore onshore and offshore wind technologies and second-life batteries, they are often location-specific or lack forward-looking modeling. A pan-European model that incorporates regional differences in sea depth, transport, and infrastructure is still missing. SLB assessments rarely account for evolving background systems or their long-term impacts. Similarly, existing LCA studies on EV flexibility are limited to either a system-level view or static individual use cases, without capturing scale-specific impacts or modeling uncertainties in charging strategies. In light of Belgium’s 11.3 % RET gap under its final National Energy and Climate Plan (2024), this thesis aims to support pathways for Belgium toward the EU’s 33 % renewable energy target by 2030. It introduces improved LCA methodologies focusing on:Geospatial refinement: Enhancing wind turbine LCA models with location-specific data, floating foundations, and site-specific production to enable system-wide analysis across Europe. Short-term temporal refinement: A comparative framework combining multi-energy system modeling and LCA, including re-calculated hourly Belgian grid impact data to assess different EV charging strategies. Long-term temporal refinement:A prospective LCA (PLCA) of SLBs based on the occurrence of life cycle processes and electricity mix projections to 2050. Multi-scale integration: Applying models at micro (household), mezzo (industrial), and macro (national energy system) scales. This includes integration the TIMES energy system model for evaluating future PV–BESS–EV systems. The thesis also expands beyond deterministic environmental impact analysis, incorporating other impacts than CC, their uncertainty through Monte Carlo and perturbation analysis. It introduces a discernibility assessment and a self-sufficiency ratio for EV flexibility, while wind fleet assessments are enhanced using correlation analysis, regression, and random forest models. Offshore wind outperforms onshore in CC impacts (8 vs. 15 gCO2eq/kWh), with projections below 4 gCO2eq/kWh by 2050. Lifetime electricity production of the turbine to express impacts in the functional unit and component manufacturing are key impact drivers. Flexibility from smart EV charging and BESS shows major reductions, especially when depending on consumption of the current grid electricity mix. At the micro level, CC impacts drop by 60 % (165 to 66 gCO2eq/kWh), with over 70 % of total CC impacts steaming from grid electricity. In mezzo-level, renewable-dominant systems, impacts range from 34–41 gCO2eq/kWh. SLBs show environmental benefits in residential systems (58.7 gCO2eq/kWh) but underperform in industrial and utility applications due to housing and electronics. The decentralized rollout of PV installations supported by BESS and EV flexibility reveals a 71–72 % reduction in CC impacts (234 to 65–68 gCO2eq/kWh) by 2050, with the high-flexibility scenario also leading to lowest system costs. However, increased PV installation and storage deployment leads to higher mineral resource and ecotoxicity impacts. Offshore wind, decentralised PV, SLBs, and smart EV charging can collectively enable decarbonisation of Belgium’s electricity system. By 2050, wind energy could supply up to 30 % of national demand, with PV installations adding 5 %. These technologies are sufficient to meet the 2030 RET target. Still, achieving net-zero by 2050 requires further installation, integration of flexibility and storage, alongside attention to trade-offs in resource and toxicity impacts.