Single junction crystalline silicon (c-Si) solar cells are reaching their practical efficiency limit whereas perovskite/c-Si tandem solar cells have achieved efficiencies above the theoretical limit of single junction c-Si solar cells. Next to low-thermal budget silicon heterojunction architecture, high-thermal budget carrier-selective passivating contacts (CSPCs) based on polycrystalline-SiO_x (poly-SiO_x) also constitute a promising architecture for high efficiency perovskite/c-Si tandem solar cells. In this work, we present the development of c-Si bottom cells based on high temperature poly-SiO_x CSPCs and demonstrate novel high efficiency four-terminal (4T) and two-terminal (2T) perovskite/c-Si tandem solar cells. First, we tuned the ultra-thin, thermally grown SiO_x. Then we optimized the passivation properties of p-type and n-type doped poly-SiO_x CSPCs. Here, we have optimized the p-type doped poly-SiO_x CSPC on textured interfaces via a two-step annealing process. Finally, we integrated such bottom solar cells in both 4T and 2T tandems, achieving 28.1% and 23.2% conversion efficiency, respectively.

In the last decade, instigated by the Paris agreement and United Nations Climate Change Conferences (COP22 and COP23), the efforts to limit temperature increase to 1.5 °C above pre-industrial levels are expanding. The required reductions in greenhouse gas emissions imply a massive decarbonization worldwide with much involvement of regions, cities, businesses, and individuals in addition to the commitments at the national levels. Improving end-use efficiency is emphasized in previous IPCC reports (IPCC 2014). Serving as the primary ‘agents of change’ in the transformative process towards green economies, households have a key role in global emission reduction. Individual actions, especially when amplified through social dynamics, shape green energy demand and affect investments in new energy technologies that collectively can curb regional and national emissions. However, most energy-economics models—usually based on equilibrium and optimization assumptions—have a very limited representation of household heterogeneity and treat households as purely rational economic actors. This paper illustrates how computational social science models can complement traditional models by addressing this limitation. We demonstrate the usefulness of behaviorally rich agent-based computational models by simulating various behavioral and climate scenarios for residential electricity demand and compare them with the business as usual (SSP2) scenario. Our results show that residential energy demand is strongly linked to personal and social norms. Empirical evidence from surveys reveals that social norms have an essential role in shaping personal norms. When assessing the cumulative impacts of these behavioral processes, we quantify individual and combined effects of social dynamics and of carbon pricing on individual energy efficiency and on the aggregated regional energy demand and emissions. The intensity of social interactions and learning plays an equally important role for the uptake of green technologies as economic considerations, and therefore in addition to carbon-price policies (top-down approach), implementing policies on education, social and cultural practices can significantly reduce residential carbon emissions.