In 2023, Switzerland set a goal of producing 35 TWh per year from renewable sources by 2035 and developed three deployment scenarios to reach it through specific technology mixes of renewable energy production. However, current models do not consider the impact of the cumulative energy demand (CED) of those technologies, accounting for production, transport, installation, performance, maintenance, and disposal. This study integrates CED and its related uncertainty using a 90% confidence interval (CI) to compare net energy across three future energy scenarios. The diverse renewable sources scenario yields a median net production of 29.1 TWh (90% CI: [27.7, 30.2]) and requires 6.24 GW of additional capacity to meet the 35 TWh target. The solar PV-focused scenario, with the highest embodied energy due to battery storage demands, has the lowest net output at 25.2 TWh (90% CI: [23.7, 26.5]), necessitating an extra 11.35 GW. The productivity-maximization scenario achieves the highest net production at 30.8 TWh (90% CI: [29.5, 31.7]), requiring 3.68 GW more capacity. In all energy scenarios, increasing gross renewable targets is essential to reach the net 35 TWh/year goal. Optimizing renewable deployment by prioritizing low CED technologies, such as wind, can maximize net energy production. Policymakers should incorporate embodied energy metrics into planning to ensure sustainable and realistic energy transition strategies.

The adoption of photovoltaic panels (PV) by households is essential to meet residential Swiss energy transition targets. While previous studies have mostly focused on the factors influencing PV adoption by comparing adopters with non-adopters, few have acknowledged the heterogeneity of adopters and examined how these factors interrelate and differ among them. This study examines how factors associated with PV adoption differ among groups of adopters, and how these differences relate to socio-demographic, household and building characteristics and the geographic context. In 2022, we surveyed 1300 Swiss PV adopters. To segment the adopters, we used factor analysis and clustering methods factors associated with PV adoption. This multi-dimensional approach revealed recommendations for supporting PV adoption: (i) seize opportunity windows, such as renovations, for professionals to act as change agents, (ii) leverage attitude towards energy-conscious behaviour and perceived control over the technology, and (iii) leverage social influence for vertical diffusion.