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Climate change has emerged as a growing threat to the European economy, whose economic losses are relevant for global growth. Rising temperatures and worsening extreme events are expected to affect climate-vulnerable sectors. Due to the economic integration within the European Union (EU), these impacts will likely have spillover effects and feedback loops to and from other regions. This study uses spatial econometrics to account for the interdependencies between the subnational EU regions to estimate the future impacts of changes in temperature on sectoral labour productivity under the Paris Agreement. The study confirms the presence of spatial spillover effects of climate change, and finds that observations at the economy-wide level of a non-linear, concave and single-peaked relationship between temperature and productivity do not always hold true at the sectoral level.

Climate change has emerged as a growing threat to the European economy, whose economic losses are relevant for global growth. Rising temperatures and worsening extreme events are expected to affect climate-vulnerable sectors. Due to the economic integration within the European Union (EU), these impacts will likely have spillover effects and feedback loops to and from other regions. This study uses spatial econometrics to account for the interdependencies between the subnational EU regions to estimate the future impacts of changes in temperature on sectoral labour productivity under the Paris Agreement. The study confirms the presence of spatial spillover effects of climate change, and finds that observations at the economy-wide level of a non-linear, concave and single-peaked relationship between temperature and productivity do not always hold true at the sectoral level.

This work projects future residential electricity demand in Italy at the local (1 km grid) level based on popu lation, land use, socio-economic and climate scenarios for the year 2050. A two-step approach is employed. In the first step, a grid-level model is estimated to explain land use as a function of socio-economic and demographic variables. In the second step, a provincial-level model explaining residential electricity intensity (gigawatt hours [GWh] per kilometre of residential land) as a function of socio-economic and climatic information is estimated. The estimates of the two models are then combined to project downscaled residential electricity consumption. The evidence suggests not only that the residential electricity demand will increase in the future but, most importantly, that its spatial distribution and dispersion will change in the next decades mostly due to changes in population density. Policy implications are discussed in relation to efficiency measures and the design of green energy supply from local production plants to facilitate matching demand with supply.

Epidemiological analyses of health risks associated with non-optimal temperature are traditionally based on ground observations from weather stations that offer limited spatial and temporal coverage. Climate reanalysis represents an alternative option that provide complete spatio-temporal exposure coverage, and yet are to be systematically explored for their suitability in assessing temperature-related health risks at a global scale. Here we provide the first comprehensive analysis over multiple regions to assess the suitability of the most recent generation of reanalysis datasets for health impact assessments and evaluate their comparative performance against traditional station-based data. Our findings show that reanalysis temperature from the last ERA5 products generally compare well to station observations, with similar non-optimal temperature-related risk estimates. However, the analysis offers some indication of lower performance in tropical regions, with a likely underestimation of heat-related excess mortality. Reanalysis data represent a valid alternative source of exposure variables in epidemiological analyses of temperature-related risk. ; The study was primarily supported by Grants from the European Commission's Joint Research Centre Seville (Research Contract ID: JRC/SVQ/2020/MVP/1654), Medical Research Council-UK (Grant ID: MR/R013349/1), Natural Environment Research Council UK (Grant ID: NE/R009384/1), European Union's Horizon 2020 Project Exhaustion (Grant ID: 820655). The following individual Grants also supported this work: J.K and A.U were supported by the Czech Science Foundation, project 20-28560S. A.T was supported by MCIN/AEI/10.13039/501100011033, Grant CEX2018-000794-S. V.H was supported by the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant agreement No 101032087. This work was generated using Copernicus Climate Change Service (C3S) information [1985–2019]. ; Peer reviewed