Quantitative model of present and future well-being in the EU-28 : a spatial multi-criteria evaluation of socioeconomic and climatic comfort factors

Abstract: Both within the EU and within other regions/countries, there is a need to measure and monitor the well-being/Quality of Life (QoL) of people, in order to identify, anticipate, and address potential social or economic problems, at national and regional levels. This study presents a model to quantitatively estimate the spatial well-being (QoL) distribution across the EU-28 member states down to the municipal level, using tools offered by GIS. The model is weight-driven and based on Eurostat statistics on objective key QoL indicators, identified by the 2016 Eurostat Analytical report on subjective well-being and the 2013 EU-SILC ad-hoc module on well-being. Additionally, some Europe 2020 strategy targets of the European Commission, deemed to be important to a sense of personal well-being, are included, such as the risk of poverty or social exclusion and advanced educational attainment. A climatic comfort component based on 1961-1990 climatic normals is added to estimate the importance of (a static) climate to QoL. Thermal comfort levels are obtained using the Universal Thermal Climate Index (UTCI) and Predicted Mean Vote (PMV), and overall climatic comfort levels are obtained as a weighted linear combination based on the classical Tourism Climatic Index (TCI). To evaluate the performance of the model, individual mean country results for year 2014 are compared to actual country results of the 2013 EU and 2014 OECD subjective well-being surveys. The modeled spatial QoL distribution is also forecast into year 2020, using simple linear regression of the selected socioeconomic factors. The findings suggest that the model is able to estimate actual well-being levels from quantitative country statistics. Even closer agreement should be possible with careful calibration of the model weights to the prevailing attitudes and priorities of each individual region/member country and using more sophisticated regression methods for forecasting. The findings also suggest that subjective well-being components could be isolated from the objective model baselines. Furthermore, a (static) climate seems to play a less important, and perhaps even negative, role than we might think, compared to most of the selected socioeconomic factors.