Characterizing the role of socioeconomic pathways in shaping future urban heat-related challenges

Abstract

Urban dwellers worldwide are increasingly affected by more frequent and intense extreme temperature events, ongoing urbanization, and changes in socioeconomic conditions. Decades of research have shown that vulnerability is a crucial determinant of heat-related risk and mortality in cities, yet assessments of future urban heat-related challenges have largely overlooked the contribution of changes in socioeconomic conditions to future heat-related risk and mortality. The scenario framework for climate change research, made up of socioeconomic scenarios (Shared Socioeconomic Pathways – SSPs) combined with climate scenarios (Representative Concentration Pathways – RCPs), facilitates the integration of socioeconomic scenarios into climate risks assessments. In this study, we used Greater Houston (Texas) as a case study to implement the scenario framework at the intra-urban scale. Integrating locally extended SSPs along with a range of sectoral modelling approaches, we combined projections of urban extreme heat – which account for SSP-specific urban heat islands – with projections of future population and vulnerability. We then produced estimates of future heat-related risk and mortality for 2041–2060 (2050s) summers at Census tract level, for multiple combinations of climate and socioeconomic scenarios. Using a scenario matrix, we showed that the projected ~15,738—24,521 future summer excess mortalities compared to 1991–2010 are essentially driven by population growth and changes in vulnerability, with changes in climatic conditions alone being of little influence. We outline methods to apply the new scenario framework at intra-urban scale and to better characterize the contribution of socioeconomic pathways to future urban climate risks. This socio-climatic approach provides comprehensive estimates of future climate risks in urban areas, which are essential for adaptation planning under climatic and socioeconomic uncertainty.