Abstract
Urban overheating is increasingly becoming a complex urban phenomenon. Paris agreement stressed upon limiting the urban warming to a 2 degree Celsius limit. Further, Sustainable Development Goals (SDGs) in 2015 stressed upon improving sustainability in cities, climate action and physical well-being. Extreme heat is known to cause severe mortality and morbidity, especially in hot and arid regions with temperature frequently reaching more than 45 degree Celsius. Hence, it is imperative to analyse such extreme urban events and various drivers associated with outdoor thermal comfort in hot and arid climate. A huge body of literature is available pertaining to outdoor thermal comfort in Hot and Arid regions. We analyse more than 50 peer reviewed articles to present a meta-synthesis to evaluate existing methodologies, data analysis techniques and associated lacuna or gaps in the domain of outdoor thermal comfort research in hot and arid climate. We have found that most of the cases analyse outdoor thermal comfort in hot and arid region from a data-driven human biometeorology-based methodological approach. Ordinal data has been used mostly to quantify subjective parameters. Various linear regression methods have been used to establish a relationship between subjective and objective parameters. ANCOVA and Ordinal Logistic regressions are widely employed. Ordered Probabilistic Models are mostly used to determine preferred temperature, whereas Multiple Linear Regression has been used to determine neutral temperature. We further identify various stochastic and policy related uncertainties associated with subjective and objective assessment of outdoor thermal comfort. Next, we propose a policy framework in the context of Indian hot and arid urban areas to address this gap. This policy framework will help in implementing efficient data-driven solution for outdoor thermal comfort improvement in hot and arid regions.
