Regional inequalities in air quality and health co-benefits due to climate change mitigation in the European electricity sector

Received: 27 February 2024 / Accepted: 28 December 2024 / Published online: 3 February 2025 © The Author(s) 2025 Hannah Pehle [email protected] 1 Renewable Energy Systems, Institute for Environmental Sciences (ISE), Section of Earth and Environmental Sciences, University of Geneva, Boulevard Carl-Vogt 66, 1205 Geneva, Switzerland Regional inequalities in air quality and health co-benefits due to climate change mitigation in the European electricity sector HannahPehle1· Jan-PhilippSasse1· EvelinaTrutnevyte1 Climatic Change (2025) 178:27 https://doi.org/10.1007/s10584-024-03851-x Abstract Electricity supply transition to reach carbon neutrality in Europe is expected to bring air quality and health co-benefits, but their regional distribution has not been investigated in detail. This study quantifies these co-benefits for 250 European electricity scenarios in 2035 across 296 sub-national regions. The study links a spatially-explicit electricity sector model with an air quality and health impact model and then accounts for susceptibility and vulnerability of populations to adverse health effects. In case of low-carbon transition, direct PM2.5 concentrations attributable to European electricity generation in 2035 could be reduced by 45–99% compared to a system with the generation capacities of 2018. Depending on the system’s make-up, health co-benefits can vary significantly, as does their regional distribution. Focus on the minimum system costs leads to 15 times higher continent-wide excess deaths and to higher regional inequality than the scenario with minimum air pollutant emissions, which would almost entirely eliminate PM2.5-related mortality attributable to electricity generation. The most vulnerable regions (Balkans, Northern Germany, Southeast France, and the West Midlands in England) would benefit from higher air quality co-benefits than the continental average. Keywords Low-carbon electricity supply · Air quality · Health co-benefits · Regional inequalities · Energy justice · Vulnerability framework 1 Introduction The European Commission has set the objective to reach carbon neutrality by 2050 and hence to reduce greenhouse gas (GHG) emissions by 55% compared to 1990 levels by 2030 (European Parliament 2023). The electricity sector plays a pivotal role in reaching emission 1 3 Climatic Change (2025) 178:27 reduction targets and needs to decarbonise first in order to decarbonise the other sectors through electrification (Williams et al. 2012; Davis et al. 2018). For the European electricity sector, the long-term carbon neutrality goal translates into emission cuts of at least 70% by 2035 compared to 2019 (European Commission 2020; Pietzcker et al. 2021). Ambient air pollution is another major concern as it remains the single largest environmental health risk in Europe (European Environment Agency 2022a). Exposure to particulate matter with a diameter of 2.5 microns or less (PM2.5) was responsible for an estimated 275,000 premature deaths in 2020 (European Environment Agency 2022b). With the Zero Pollution Action Plan, the European Commission has set a target for 2030 to reduce premature deaths from air pollution by more than 55% compared to 2005 levels (European Commission 2021b). In addition to the technical and cost concerns, justice implications of the low-carbon transition are increasingly acknowledged (European Commission 2019, 2021a). Reducing GHG emissions from the electricity sector will simultaneously reduce emissions of co-emitted air pollutants which will improve air quality overall, however, depending on the European system’s design, various regions and populations will benefit from cleaner air and associated health co-benefits to a different extent. Previous studies analysed mitigation co-benefits such as job creation and additional investments (Ram et al. 2020, 2022; Sasse and Trutnevyte 2020, 2023a) and negative impacts in terms of tax revenue (Morris et al. 2019; Pollin and Callaci 2019), land use conflicts (Sasse and Trutnevyte 2023a), and other factors, finding significant potential inequalities in the distribution of co-benefits and adverse effects throughout Europe (Sasse and Trutnevyte 2023a), hence pointing to the importance of foreseeing and managing justice implications in advance. Air quality and associated health co-benefits have been found to offset mitigation costs and hence serve as additional incentive for mitigation (Markandya et al. 2018; Vandyck et al. 2018; Karlsson et al. 2020). Several retrospective studies quantified the air quality and health co-benefits of renewable energy deployment in the United States (US) (Barbose et al. 2016; Millstein et al. 2017; Qiu et al. 2022) and Europe (Couvidat et al. 2021), but they considered individual technologies only without accounting for changes to the entire system. Prospective studies estimated co-benefits of specific policy interventions or long-term decarbonisation scenarios in China (Tang et al. 2022; Li et al. 2018), the US (Polonik et al. 2023; Picciano et al. 2023), California (Zhao et al. 2019; Zhu et al. 2022), the European Union (EU) (Klimont et al. 2022; Pisoni et al. 2023; European Commission 2024), the United Kingdom (UK) (Williams et al. 2018), but they did not focus on the electricity sector specifically or did not model spatial distribution of electricity generating units in the future. Other prospective studies in Europe investigated impacts on PM10 emissions (Sasse and Trutnevyte 2020, 2023a), but did not model the dispersion of pollutants in the atmosphere nor associated health impacts. In the US, numerous studies explored the air quality and health impacts of specific renewable energy deployment scenarios focusing on the national (Wiser et al. 2016; Buonocore et al. 2019) or sub-national levels (Buonocore et al. 2016b; Abel et al. 2018; Peng and Ou 2022), while Bistline et al. (2022) additionally assessed the air quality impacts of economy-wide electrification scenarios at sub-national level. Others assessed the effect of carbon pricing (Dimanchev et al. 2019; Sengupta et al. 2023) or power plant emission standards (Driscoll et al. 2015; Buonocore et al. 2016a) on air quality and health impacts. Some of the mentioned studies considered inequalities in air pollution exposure in the US (Gallagher and Holloway 2022; Goforth and Nock 2022; Polonik et al. 2023) or the UK (Williams et al. 2018), while others investigated inequalities in health co-benefits 1 3 27 Page 2 of 22 Climatic Change (2025) 178:27 between subpopulations in the US (Mayfield 2022; Qiu et al. 2022; Picciano et al. 2023) or in mortality burdens between Indian states (Sengupta et al. 2023). However, the prospective analysis of distributional justice implications of air pollution and related health impacts of future electricity supply scenarios in Europe have so far not been investigated. Adverse health effects of air pollution are the greatest for those who are more exposed, more sensitive (susceptible), and otherwise more vulnerable (European Environment Agency 2022b). Including differential susceptibility and vulnerability in a holistic assessment of health risks associated to air pollution is hence key (Stilianakis 2015). A variety of vulnerability concepts were used in literature assessing inequalities in air pollution exposure or associated health impacts, including the Carstairs Index (Williams et al. 2018), the Index of Multiple Deprivation (Tonne et al. 2008), a social vulnerability concept (European Environment Agency 2018), and others. Zhu et al. (2022) utilised the CalEnviroScreen score which considers the pollution burden and characteristics of the sensitive population, accounting for the population’s underlying health conditions and socioeconomic factors (August et al. 2021). Similarly, Huang and London (2012) developed indices for the spatial analysis of vulnerability to environmental hazards, considering cumulative hazards, the sensitivity of population to health challenges and the population’s capacity to mitigate adverse effects. The European Environmental Agency (2018) deployed a social vulnerability concept for the assessment of inequalities in exposure to and impacts of air pollution, distinguishing between individual sensitivity due to age and ability to cope with adverse health effects associated with socioeconomic factors. This social vulnerability concept, however, compares pollution exposure by sensitivity and ability to cope separately. No spatially holistic framework of both susceptibility and vulnerability to adverse health impacts of air pollution exists yet. The objective of this study is to investigate air pollution and associated health co-benefits of low-carbon electricity supply scenarios in Europe in 2035 and to quantify inequalities in their regional distribution as well as to assess to what extent air quality co-benefits accrue in regions most vulnerable to adverse health effects. The study aims to answer the following research questions: 1. How will low-carbon electricity supply scenarios in Europe in 2035, including scenarios minimizing total system costs and total air pollutant emissions, impact PM2.5 concentrations attributable to electricity generation and associated health impacts, especially mortality? 2. How will these scenarios affect (a) regional inequalities in the distribution of these health impacts and (b) relative disparities in air quality co-benefits when accounting for susceptibility and vulnerability of populations to these adverse health effects? 2 Methods In this study (Fig. 1), we use electricity scenarios generated by the technology-rich spatiallyexplicit electricity sector model EXPANSE (step 1) to calculate air pollutant emissions from power plants (step 2). This emissions data is fed as inputs into the reduced-complexity air quality model Global InMAP to model PM2.5 concentrations (step 3). These concentrations are then used in a health impact model to estimate mortality associated with exposure to 1 3 Page 3 of 22 27 Climatic Change (2025) 178:27 Fig. 1 Methods flow chart. Note: EXPANSE– EXploration of PAtterns in Near-optimal Energy ScEnarios, InMAP– Intervention Model for Air Pollution. NUTS– Nomenclature of Territorial Units for Statistics in Europe 1 3 27 Page 4 of 22 Climatic Change (2025) 178:27 PM2.5 concentrations attributable to electricity generation (step 4). A novel composite vulnerability index is afterwards developed to account for susceptibility and vulnerability of populations to adverse health effects of PM2.5 exposure (step 5). Finally, inequalities in the spatial distribution of mortality as well as relative disparities in air quality co-benefits across regions with different levels of vulnerability are investigated (step 6). 2.1 EXPANSE model and air pollutant emissions EXPANSE (Trutnevyte et al. 2012; Sasse and Trutnevyte 2019, 2020, 2023a) is a spatiallyexplicit technology-rich cost optimization model of the European electricity system in 2035. It includes 33 countries (EU without Cyprus and Malta, plus Albania, Bosnia and Herzegovina, Montenegro, North Macedonia, Norway, Serbia, Switzerland, and the UK) and considers electricity demand, generation, storage, and transmission. The model operates at a spatially-explicit level of 296 NUTS-2 regions (European Parliament 2019) for electricity generation and 128 grid nodes for demand, storage, and transmission. In the runs for this study, EXPANSE includes a greenhouse gas emissions constraint of 245 MtCO2−eq year−1, consistent with a 70% emissions reduction by 2035 compared to 2019 to reach the carbon neutrality goal in 2050 (Pietzcker et al. 2021; Sasse and Trutnevyte 2023a). EXPANSE uses the Modeling to Generate Alternatives (MGA) approach (DeCarolis 2011; Trutnevyte 2013), allowing for a cost-slack between 0% and 20% above the optimal total system costs in order to compute a diverse range of near-optimal scenarios. In total, 250 EXPANSE scenarios are analyzed (Sasse and Trutnevyte 2023a): ●248 MGA scenarios consistent with the 70% GHG emissions constraint, varying levels of cost slack and additional impact constraints, i.e., minimizing total GHG emissions, particulate matter emissions, and land use, as well as maximizing total employment; ●One minimum total system costs scenario consistent with the 70% GHG emissions constraint; ●One reference or ‘Frozen generation and storage capacity’ scenario, which assumes that the current (2018) fleet of electricity generation and storage capacities stays in Europe until 2035, while allowing transmission capacities to increase and some new technologies to be built when needed to meet higher electricity demand in 2035, but failing to meet the GHG emissions reduction target. With cost-effectiveness being a primary concern for policy makers and considering the European Commission’s air pollution-related mortality reduction target, the minimum system costs scenario and the MGA scenario causing the lowest total air pollutant emissions (hereafter referred to as minimum air pollutant emissions scenario) will be highlighted in the analysis. Additionally, given the exploratory nature of the MGA scenarios, average impacts across all 248 MGA scenarios will be assessed. The ‘Frozen generation and storage capacity’ scenario serves as a reference to compare impacts of the future low-carbon scenarios to impacts of the current electricity system in terms of generation and storage fleet, projected to 2035 and meeting the EU’s reduction target for GHG emissions. Based on the primary fuel use by generation technology and NUTS-2 region from EXPANSE, air pollutant emissions are calculated for each of the 250 scenarios using fuelspecific emission factors considered invariant over time (Chapter S1 in the Supplementary 1 3 Page 5 of 22 27 Climatic Change (2025) 178:27 Information provides the details). Air pollutant emissions here account for precursor pollutants of secondary fine particulate matter (PM2.5), including non-methane volatile organic compounds (NMVOC), nitrogen oxides (NOx), and sulphur oxides (SOx), as well as (primary) PM2.5. Emissions occurring at other stages of the fuel cycle, e.g., during fuel extraction, storage, or transport, are not considered. 2.2 Calculation of air pollutant emissions, PM2.5 concentrations, and health impacts To model PM2.5 concentrations attributable to electricity generation, the emissions calculated for the different scenarios are used as inputs into the reduced-complexity air quality model Global InMAP (Thakrar et al. 2022). Emissions from other sectors are accounted for as in the Global InMAP baseline emissions input without considering future decarbonisation targets. Emissions are entered as elevated emissions assuming a uniform stack height of 200 m, stack diameter of 5 m, exit velocity of 23 m s-1, and temperature of 416.5 K (U.S. Environmental Protection Agency 2017; Sengupta et al. 2023). Global InMAP distributes emissions at NUTS-2 level equally to the InMAP grid using area-weighting (Prener 2022), for which it then calculates population-weighted annual average total PM2.5 concentrations. The model requires baseline input data on meteorology and pollutants as well as variable grid resolution based on population density, which are taken from Thakrar et al. (2022). Meteorological input data as well as underlying baseline emissions from all sectors are kept constant. Emissions from the electricity sector under the different scenarios are accounted for as perturbations to the underlying baseline emissions. It is thus assumed that modeled concentrations represent the absolute contribution of PM2.5 from the electricity sector as has been done in existing literature (Goforth and Nock 2022; Sengupta et al. 2023). To estimate annual average total PM2.5 concentrations at NUTS-2 level, concentrations at InMAP grid level are averaged across NUTS-2 regions applying area weighting. A more detailed description of the Global InMAP can be found elsewhere (Thakrar et al. 2022). Applying the methodology used by the European Topic Centre for Human Health and the Environment (Soares et al. 2022), all-cause natural mortality associated with long-term PM2.5 exposure in ages above 30 years old is estimated for three alternative concentrationresponse functions (Tab. S2 in the Supplementary Information): from the ELAPSE project (Brunekreef et al. 2021) as the primary function, from the updated World Health Organisation WHO Global Air Quality Guidelines (2021) and the WHO Health risks of air pollution in Europe project (2013). These functions are expressed as the percentage increase in the risk of mortality (or relative risk) associated with an incremental increase in PM2.5 concentrations of 10 µg m–3. Mortality is calculated for estimated PM2.5 concentrations attributable to electricity generation only applying a counterfactual concentration of 0 µg m−3 which implies a total removal of concentrations associated with electricity generation (see Chapter S2 and S12 of the Supplementary Information). Mortality is quantified for two metrics: premature deaths and years of life lost (YLL). Premature deaths refer to the number of deaths occurring earlier than the expected lifespan for a given population. YLL account for the age at which premature death occurs, thus assigning different weights to deaths at different ages (Kienzler et al. 2022; Soares et al. 2022; World Health Organization 2023). A description of the mortality calculation and assumptions on population data in 2035, base1 3 27 Page 6 of 22 Climatic Change (2025) 178:27 line mortality rates, and life expectancy are described in the Supplementary Information (Chapter S2). 2.3 Vulnerability index For the purpose of this study, we create a new composite index to account for the differential susceptibility and vulnerability of populations to adverse health effects from air pollution. Susceptibility here refers to an increased health risk at any given level of exposure due to physiological factors. Vulnerability should be understood as a higher probability of being exposed as well as a lack of capacity to avoid or cope with air pollution exposure and associated health effects (Samet 2014; Stilianakis 2015; European Environment Agency 2018). We identify from literature three key components to be included in the index (Table 1 and Figs. S1a-c in the Supplementary Information): (i) Age: Children are particularly susceptible to adverse impacts of air pollution due to factors such as higher breathing rates, increased oral breathing, developing organs, and weaker immune systems (World Health Organization 2018; Perera 2018; U.S. Environmental Protection Agency 2019). During pregnancy, exposure to air pollution increases the risk of adverse outcomes for babies and is linked to smaller fetus size during pregnancy (Nyadanu et al. 2022), low birth weight (Johnson et al. 2021), and increased risk of pre-term birth (Ghosh et al. 2021). The elderly are also more susceptible to the negative health effects of air pollution due to a combination of age-related physiological changes (e.g., declining lung function), pre-existing health conditions (e.g., cardiovascular and respiratory diseases), and decreased resilience due to accumulated damage from previous exposure (Simoni et al. 2015). Therefore, the indictors to account for increased susceptibility related to age in this study are the share of children under the age of five, the share of population aged 65 or older in the total population, as well as the total fertility rate to account for unborn children. (ii) Pre-existing health conditions: Those with pre-existing health conditions, such as low birth weight, asthma, and heart disease, are also more susceptible to detrimental health impacts of air pollution (World Health Organization 2010; Belbasis et al. 2016; U.S. Environmental Protection Agency 2019; Manisalidis et al. 2020). The proportion of newborns with a low birth weight is an indicator of health problems during pregnancy as well as a predictor of newborn health and survival (World Health Organization 2020). Air pollution has been identified as both a cause and an aggravating factor for asthma (Meng et al. 2011; Guarnieri and Balmes 2014). Even at low concentrations, PM2.5 exposure has been associated with ischemic heart disease and stroke mortality (Hayes et al. 2020). Based on similar indicators used in literature (Huang and London 2012; August et al. 2021), the prevalence of low birth weight in newborns as well as hospital discharges per 100,000 inhabitants by asthma and acute myocardial infarction diagnosis serve, in this study, as proxies for susceptibility linked to the overall health status of a population. It is worth to note that susceptibility due to health conditions is considered here to be complementary to susceptibility related to age from point (i). For example, all newborns are physiologically more susceptible to the adverse health effects of air pollution, but those born with a low birth weight are additionally more susceptible due to this condition. 1 3 Page 7 of 22 27 Climatic Change (2025) 178:27 Table 1 Overview of the vulnerability concept, index components, and indicators. All input data have a spatial resolution of NUTS-2, with the exception of data on the prevalence of low birth weight, which is available at a country-level resolution only Component Vulnerability concept Indicator topic Indicator definition Unit Source Age Increased susceptibility due to physiological factors related to age Children Share of children under age 5 in total population % Eurostat (2022a) Elderly Share of population aged 65 or older in total population % Eurostat (2022a) Fetuses (pregnant women) Total fertility rate live birth woman−1 Eurostat (2021a) Health conditions Underlying health conditions resulting in higher susceptibility Low birth weight Low birth weight prevalence % World Health Organization (2020) Asthma Hospital discharges for asthma and status asthmaticus diagnosis in-patients 100,000 inhabitants−1 Eurostat (2020a) Cardiovascular diseases Hospital discharges for acute myocardial infarction or subsequent myocardial infarction diagnosis in-patients 100,000 inhabitants−1 Eurostat (2020a) Socioeconomic status Vulnerability to health challenges due to socioeconomic factors and resources available to mitigate negative health impacts Education Higher education deprivation in age class 25–64 years (100% - share of population with tertiary education) % Eurostat (2021b) Poverty Share of population at risk of poverty or social exclusion % Eurostat (2020b) Unemployment Long-term unemployment (12 months or more) rate in economically active population % Eurostat (2022b) Note: Detailed information on gap-filling of incomplete data is provided in Tab. S4 in the Supplementary Information. For each country, the latest available data at the time of the analysis in 2023 has been used 1 3 27 Page 8 of 22 Climatic Change (2025) 178:27 (iii) Socioeconomic status: Socioeconomic status plays a role in determining the ability to reduce levels of exposure, cope with or avoid the impacts of air pollution (e.g., ability to relocate, awareness of the health risks, access to health care (World Health Organization 2010; European Environment Agency 2018; U.S. Environmental Protection Agency 2020). For example, people of lower socio-economic status are more likely to live in less attractive neigbourhoods with higher pollution levels, or to work outdoors where they are more exposed to air pollution. Abundant research has investigated socioeconomic disparities in air pollution exposure, typically using income or poverty levels, education as well as employment metrics as proxies (Hajat et al. 2015). In line with existing literature (Huang and London 2012; European Environment Agency 2018; August et al. 2021), poverty and unemployment rates, as well as higher education deprivation are used as indicators for vulnerability related to socioeconomic factors. Using the components from Table 1, the vulnerability index V is calculated for each NUTS-2 region using weighted sum method (Belton and Stewart 2002) with equal weights of one third attributed to each of the three components of the index. Indicators, components, and the final vulnerability index are normalised across all NUTS-2 regions applying min-max normalisation with 0 for to the lowest original value and 1 for the highest (see Chapter S3 of the Supplementary Information). To assess the sensitivity of the index to the considered components, alternative vulnerability indices are calculated accounting only for the health and socioeconomic status ( V health _ SES) or the age and socioeconomic status components ( V age _SES ). 2.4 Regional inequality analysis We focus on regional inequality in terms of air pollution and associated health effects, using the Lorenz curve, Gini coefficient, and comparison to Europe-wide mean. Lorenz curves depict the cumulative share of mortality burden as a function of the cumulative share of regions. The Gini coefficient (Gini 1912) is then used to quantify patterns from the Lorenz curve as a single comparative indicator (Sasse and Trutnevyte 2020; Mayfield 2022). The Gini coefficient (η), here used to quantify inequality across regions, ranges from 0 to 1, where the value of 0 indicates perfect equality, meaning that all regions have the same mortality burden, and a value of 1 indicates perfect inequality. To quantify relative disparities in air quality co-benefits across regions with different levels of vulnerability, the percentage difference between population-weighted changes in PM2.5 concentrations for five levels of vulnerability (i.e. quantiles including an equal number of regions) is compared to Europewide mean reductions in concentrations. A similar approach has been used in a number of studies on disparities in air pollution exposure (Wang et al. 2023; Goforth and Nock 2022; Polonik et al. 2023; Picciano et al. 2023). 1 3 Page 9 of 22 27 Climatic Change (2025) 178:27 low-carbon scenarios. Only emissions from waste incineration, biogas, and woody biomass see increases in several scenarios. PM2.5 concentrations and related mortality burdens as well as their spatial distribution vary significantly throughout Europe between scenarios. The minimum system costs scenario leads to continent-wide excess deaths almost 15 times higher than the minimum air pollutant emissions scenario (among our MGA scenarios), which would almost entirely eliminate PM2.5 related mortality. At the regional level, the minimum system costs scenario leads to highest concentrations and mortality burdens in regions of Czech Republic, Poland, and the Balkans, mainly due to the continued presence of fossil fuel plants. In the minimum air pollutant emissions scenario, highest impacts are found in regions in the UK in proximity to woody biomass capacities. While the minimum air pollutant emissions scenario eliminates air pollution and associated mortality impacts attributable to electricity generation almost entirely, tradeoffs with the other impacts such as land use or costs have not been analysed in this study but are available elsewhere (Sasse and Trutnevyte 2023a). Previous analyses for Europe also quantified direct PM10 emissions from future electricity systems as well as regional inequalities but did not consider other air pollutants, their dispersion, or associated health impacts (Sasse and Trutnevyte 2020, 2023a). By providing an evaluation of regional disparities in PM2.5-associated mortality as well as of relative disparities in air quality co-benefits by level of populations’ vulnerability, this study contributes to the extended investigation of justice implications applying a prospective analysis of exposure inequalities. While all low-carbon scenarios reduce mortality impacts drastically Fig. 6 Relative air quality co-benefits by level of vulnerability accounting for age, health, and socioeconomic status: (a) Percentage difference between reductions in PM2.5 concentrations for regions with different levels of vulnerability and European-wide average reductions in various scenarios. Positive (negative) values indicate that, on average, regions benefit from higher (lower) air quality improvements compared to the overall European average. The colour legend uses a quantile classification scheme with each category including an equal number of regions. (b) PM2.5 concentrations in the frozen generation and storage scenario and changes in concentrations in other scenarios. NUTS-2 regions delimited by a red border indicate the 20% most vulnerable regions (red bars in subfigure (a)). Note: SES– socioeconomic status 1 3 27 Page 16 of 22 Climatic Change (2025) 178:27 compared to the frozen generation and storage scenario, the results show that low-carbon electricity systems in Europe can also reduce regional inequalities in air pollution-related health impacts and lead to comparatively higher air quality co-benefits in most vulnerable regions. The degree of regional inequality in the distribution of mortality impacts varies between scenarios, with the minimum air pollutant emissions scenario ranking as having one of the lowest spatial inequality. The minimum system cost scenario and several other MGA scenarios lead to an increase in inequality compared to the frozen scenario, though at much lower total mortality level. Accounting for differential susceptibility and vulnerability to adverse health effects, regions with the highest levels of vulnerability see larger relative air quality co-benefits from low-carbon electricity sector scenarios when compared to the overall European average, while less vulnerable regions benefit to a lesser extent. Robustness of the results was ensured by conducting a sensitivity analysis for the choice of the concentration-response functions and the vulnerability index composition. Results on calculated emissions and mortality were compared to other existing estimates, acknowledging substantial uncertainties on emissions factors used as well as the counterfactual PM2.5 concentration considered for the mortality estimates (see Chapters S10-S12 in the Supplementary Information). In terms of other future research directions, Global InMAP models only annual average PM2.5 concentrations. Deploying a comprehensive chemical transport model would yield more precise concentration estimates at a higher temporal and spatial resolution, including for other critical air pollutants such as nitrogen dioxide (NO2) and ground-level ozone (O3). Second, this study focused on the mortality burden associated with long-term PM2.5 exposure. Future analyses could assess the mortality and morbidity burden attributable to both long-term and short-term exposure to PM2.5 as well as to NO2 and O3, to provide a more comprehensive estimate of health impacts. Age-group specific concentration-response functions could be used to estimate age-specific health impacts directly. Third, only air pollutant emissions from fuel combustion were considered, excluding lifecycle emissions occurring at other stages of the fuel cycle. Fourth, future research could analyse the air quality and associated health impacts of emissions released from any source in NUTS-2 region to any receptor region in order to investigate local versus neighbour impacts of electricity generation. Lastly, a main limitation of the study is the exclusive focus on the electricity sector while keeping emissions from other sectors at a baseline, hence not accounting for future changes in other sectors, the effects of climate change and changing weather patterns. The study could thus be extended by using projected meteorological data and by accounting for emission changes in all sectors using integrated assessment models with high enough spatial resolution. 5 Conclusions While decarbonising electricity supply in Europe in line with the carbon neutrality target by 2050 will overall improve air quality and public health in 2035, our results suggest that, depending on the make-up of the electricity system, total air quality and mortality cobenefits can vary significantly, as does their spatial distribution across European regions. System designs almost or entirely eliminating fossil fuels not only result in the lowest total air pollutant emissions attributable to electricity generation, associated PM2.5 concentrations, and mortality impacts, but also in the lowest regional inequalities in health impacts 1 3 Page 17 of 22 27 Climatic Change (2025) 178:27 and larger air quality benefits in the most vulnerable regions. In low-carbon system designs with high biogas and woody biomass generation, local increases in air pollutant emissions can be an unintended consequence, pointing to the importance of aligning decarbonisation and air quality strategies. If cost-effectiveness is the primary policy goal, health impacts would be up to 15 times higher than in several near-cost optimal scenarios. Compared to the current system projected to 2035, inequality in the spatial distribution of mortality in the minimum cost scenario would be exacerbated too, with the lowest reductions in mortality being observed for countries in Eastern Europe and the Balkans. In all low-carbon scenarios, the distribution of air quality co-benefits favours regions with the highest levels of vulnerability to adverse health effects of air pollution. Differences in the socio-demographic situation as well as susceptibility and vulnerability to adverse health effects of air pollution will likely continue to exist for a long time. Hence, understanding how the air quality and health impacts of future low-carbon energy systems are distributed across regions and societies is fundamental to ensuring a just transition as it can help establish where targeted policy interventions might become necessary. Supplementary Information The online version contains supplementary material available at h t t p s : / / d o i . o r g / 1 0 . 1 0 0 7 / s 1 0 5 8 4 - 0 2 4 - 0 3 8 5 1 - x . Acknowledgements This work received funding from the Swiss State Secretariat for Education, Research, and Innovation (SEFRI) for the project IAM COMPACT “Expanding Integrated Assessment Modelling: Comprehensive and Comprehensible Science for Sustainable, Co-Created Climate Action” (Project no. 101056306, JPS, ET) and from the partnership between University of Geneva and Services Industriels de Genève (JPS, ET). The computations were performed at University of Geneva using Baobab HPC service. The authors thank Sumil Thakrar for answering questions about the air quality model. Author contributions Methodology, all authors; Software, HP, JPS; Investigation, HP; Data curation, HP, JPS; Conceptualization, all authors; Writing– Original Draft, HP; Writing– Review & Editing, all authors; Visualization, HP, JPS; Supervision– ET; Funding Acquisition– ET. Funding Open access funding provided by University of Geneva. Data availability All input data of this study are provided in the Supplementary Information or are openly available on public repositories that we cite. The spatially-explicit electricity scenarios are provided in a Zenodo database (Sasse and Trutnevyte 2023b). Declarations Competing interests The authors declare no competing interests. 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