Ecosystem service mismatches evidence inequalities in urban heat vulnerability.

Exposure to heat poses a pressing challenge in cities, with uneven health and environmental impacts across the urban fabric. To assess disparities in heat vulnerability and its environmental justice implications, we model supply-demand mismatches for the ecosystem service (ES) urban temperature regulation. We integrated remote sensing, health, and socio-demographic data with Artificial Intelligence for Environment and Sustainability (ARIES) and geographical information system tools. We computed composite indicators at the census tract level for urban cooling supply, and vulnerability to heat as a measure of demand. We do so in the context of the mid-size city of Vitoria-Gasteiz, Basque Country (Europe). We mapped relative mismatches after identifying and analysed their relationship with socio-demographic and health factors. Our findings show disparities in heat vulnerability, with increased exposure observed among socio-economically disadvantaged communities, the elderly, and people with health issues. Areas associated with higher income levels show lower ES mismatches, indicating higher temperature regulation supply and reduced heat vulnerability. The results point at the need for nature-based heat mitigation interventions that especially focus on the more socio-economically disadvantaged communities.

Scientific modelling can be accessible, interoperable and user friendly: A case study for pasture and livestock modelling in Spain.

This article describes the adaptation of a non-spatial model of pastureland dynamics, including vegetation life cycle, livestock management and nitrogen cycle, for use in a spatially explicit and modular modelling platform (k.LAB) dedicated to make data and models more interoperable. The aim is to showcase to the social-ecological modelling community the delivery of an existing, monolithic model, into a more modular, transparent and accessible approach to potential end users, regional managers, farmers and other stakeholders. This also allows better usability and adaptability of the model beyond its originally intended geographical scope (the Cantabrian Region in the North of Spain). The original code base (written in R in 1,491 lines of code divided into 13 files) combines several algorithms drawn from the literature in an opaque fashion due to lack of modularity, non-semantic variable naming and implicit assumptions. The spatiotemporal rewrite is structured around a set of 10 namespaces called PaL (Pasture and Livestock), which includes 198 interoperable and independent models. The end user chooses the spatial and temporal context of the analysis through an intuitive web-based user interface called k.Explorer. Each model can be called individually or in conjunction with the others, by querying any PaL-related concepts in a search bar. A scientific dataflow and a provenance diagram are produced in conjunction with the model results for full transparency. We argue that this work demonstrates key steps needed to create more Findable, Accessible, Interoperable and Reusable (FAIR) models beyond the selected example. This is particularly essential in environments as complex as agricultural systems, where multidisciplinary knowledge needs to be integrated across diverse spatial and temporal scales in order to understand complex and changing problems.