Moritz Wussow

The diffusion of clean technologies is critical for climate goals, yet political polarization influences adoption. Here we examine how ideology affects residential solar photovoltaic (PV) and electric vehicle (EV) uptake in Germany, drawing on responses from a three-wave individual-level survey, municipality-level observations of PV and EV registrations and election results. Supporters of the far-right Alternative für Deutschland (AfD) show significantly lower willingness to adopt EVs and, to a lesser degree, PV systems compared with supporters of the pro-environmental Bündnis 90 / Die Grünen (GRÜNE) party. However, polarization is not static. The relative adoption gap between municipalities with strong AfD support and those with strong GRÜNE support declined from 76% to 31% for solar PV between 2000 and 2023, and from 84% to 60% for EVs between 2015 and 2019. Here, using an instrumental-variable strategy based on wolf attacks as a predictor of AfD vote share, we provide complementary evidence on the relationship between far-right voting and the adoption of clean technology. Counterfactual simulations indicate that, by 2023, solar PV diffusion could have been up to 1.3% higher and EV diffusion up to 2.9% higher in the absence of rising electoral support for the far right.

Climate change is increasingly impacting urban areas worldwide. Climate risks such as heat waves and other extreme weather events threaten health, productivity and urban infrastructure. However, these impacts are not equally distributed across society. Some population groups and neighborhoods are being hit harder than others, with inhabitants from disadvantaged socioeconomic backgrounds, low-income households and the elderly being disproportionally affected. In response, cities are attempting to rapidly implement various mitigation and adaptation strategies that often include nature-based solutions such as expanding urban vegetation to combat heat effects. However, the economic consequences of these interventions for urban residents remain underexplored, raising key questions about whether such strategies alleviate or exacerbate social inequalities.This study addresses these questions by analyzing the relationship between the urban heat island (UHI) effect, urban vegetation, and residential property values across 15 metropolitan regions in the United States and Germany. To do so, we adopt a data-driven approach that combines property transaction and listing data with satellite imagery of ground cover obtained from the Copernicus Sentinel-2 program from 2015 to 2025. Through geospatial analysis, we quantify a "green premium" - a markup on real estate prices - and its development over time and in regions that are heterogeneous with respect to economic activity, climate zones and urban landscapes. Using this approach, we can identify wealth impacts of urban vegetation and changes in its perceived importance for home buyers over the past 11 years. By further integrating high-resolution thermal data, we examine how local microclimates and green space coverage influence housing values at the neighborhood level. We explore how these effects vary across different socioeconomic and demographic contexts with a focus on equity implications and climate vulnerability, contributing to the growing interdisciplinary literature on climate adaptation, urban planning, and environmental justice.

Climate change is intensifying heat extremes in cities, where dense built environments amplify thermal exposure and increase risks to human health, productivity, and well-being. Urban Heat Island (UHI) effects, driven by impervious surfaces, reduced vegetation, and altered surface energy balances disproportionately burden urban populations, particularly vulnerable groups such as the elderly, low-income households, and those with limited access to cooling or green space. Nature-based solutions (NBS), including urban greening and de-sealing, offer substantial potential to mitigate urban heat by restoring local cooling processes. However, effective heat adaptation requires more than identifying locations with the largest temperature reductions: policymakers must also consider how many people are affected, which population groups are exposed, and where elevated heat coincides with social vulnerability and limited adaptive capacity.This contribution presents a prototype machine learning-based framework for modeling hyperlocal land surface temperature (LST) as a function of land cover and urban form, and for linking heat exposure to human-centered impacts, using Leipzig, Germany as a case study. At the core of the framework is a machine-learning model that predicts pixel-level LST from satellite-derived land-cover information, spectral indices, and selected indicators of urban morphology. By learning the relationship between local land-cover configurations and surface temperature under city-wide climatic conditions, the model generates high-resolution heat maps that reveal fine-grained spatial variation in thermal exposure and its underlying drivers.To move beyond purely physical measures of urban heat, predicted LST patterns are integrated with socio-economic and demographic indicators, including population density, land use, and proxies for vulnerable population groups. This coupling enables heat exposure to be assessed in relation to the distribution of people and social characteristics across the urban landscape, highlighting where high temperatures intersect with heightened risks to health and well-being. The framework thus supports an explicitly impact-oriented perspective on urban heat.Finally, the framework provides a basis for evaluating the cooling potential and distributional implications of nature-based solutions, such as targeted greening or de-sealing interventions. By linking land-cover changes to both thermal effects and population exposure, the approach can inform spatially targeted, socially aware heat mitigation strategies and support urban climate adaptation planning that prioritizes both effectiveness and equity.