Economic valuation of temperature-related mortality attributed to urban heat islands in European cities

Domain: earth_science
Published: 2023-11-17
Editorial outcome: major_revision

Abstract
As the climate warms, increasing heat-related health risks are expected, and can be exacerbated by the urban heat island (UHI) effect. UHIs can also offer protection against cold weather, but a clear quantification of their impacts on human health across diverse cities and seasons is still being explored. Here we provide a 500 m resolution assessment of mortality risks associated with UHIs for 85 European cities in 2015-2017. Acute impacts are found during heat extremes, with a 45% median increase in mortality risk associated with UHI, compared to a 7% decrease during cold extremes. However, protracted cold seasons result in greater integrated protective effects. On average, UHI-induced heat-/cold-related mortality is associated with economic impacts of €192/€−314 per adult urban inhabitant per year in Europe, comparable to air pollution and transit costs. These findings urge strategies aimed at designing healthier cities to consider the seasonality of UHI impacts, and to account for social costs, their controlling factors, and intra-urban variability.

Introduction
The majority of Europe’s population lives in cities and suburban areas, and while the overall European population is projected to decline in the future, the population in most large cities is expected to increase1, further increasing the share of urban dwellers. Globally, much more urban growths are expected particularly in Africa and South Asia2. As more people live in highly urbanized settings, greater exposure to extreme heat has been observed3, further amplifying the impact of warming due to climate change4–6. This is due to the urban heat island (UHI) effect, whereby urban environments are found to be warmer than their rural surroundings, e.g.,7,8. Urban environments are characterized by an abundance of built structures, intense human activities, and often a scarcity of vegetation. The geometry and materials of the built structures alter the urban energy balance through reduced evaporative cooling, increased radiative absorption, and enhanced heat storage, e.g., 7, 9–11. Combined with additional heat emissions from anthropogenic activities, this results in the UHI phenomenon, which varies in magnitude by time and location due to the variability in meteorological, surface, and anthropogenic conditions8,11.

Increased temperatures can have adverse impacts on human health12,13, as illustrated by the 2003 European heatwave when 70,000 excess deaths were observed across Europe14. A large number of those deaths occurred in Europe’s largest cities, such as Paris and Berlin15,16. In England, ~2000 excess deaths were estimated, with the greatest impact observed in London17. More generally, exposures to both heat and cold temperatures have adverse effects on human health and are associated with increased cardiovascular, respiratory, and cerebrovascular mortality risks12,18–20. UHIs are known to exacerbate such heat-related risks, e.g., ref. 21, but there is also evidence of protection against cold extremes22,23, a subject that is relatively understudied. The trade-off between summer risks and winter benefits of UHI-related mortality remains largely unexplored, especially across different cities and background climatic conditions. Furthermore, the climate benefits of UHI mitigation strategies are known to vary across seasons and geographic regions, for e.g., refs. 8,24, which must be considered in urban planning and building designs, e.g., refs. 25,26.

Most of the existing literature on UHI mitigation focuses on thermal comfort rather than quantifiable health risks27,28, while studies investigating temperature-related mortality tend to focus on a few selected cities or countries21,23,29–31 without the necessary granularity to account for intra-urban variations in temperature and population distribution. Additionally, a previous study has shown that UHIs can double the economic impacts of global climate change32 and, for example, it was estimated that UHI cost the city of Melbourne $300 million per year due to impacts on health, infrastructures, and energy demand33. The inclusion of economic assessments of mortality has also been shown to greatly increase the social cost of carbon for integrated assessment models34. Hence, we deem it useful to frame the debate in monetary terms in order to influence future planning and decision-making—which are largely shaped by socioeconomic rules35 and require robust scientific knowledge to balance multiple social costs and benefits. While such economic assessments are beneficial in quantifying the human cost in cost-benefit analyses, it is important to acknowledge that these are individual lives whose value cannot truly be expressed in monetary terms. Instead, stated preference surveys, on which mortality valuations are commonly based, represent people’s willingness to pay to reduce mortality risk, as is consistent with its use in cost-benefit analyses. Additionally, it should be noted that risks are generally not experienced equally across the population and can disproportionally affect more disadvantaged populations.

In this study, we provide a consistent analysis of UHI’s impact on mortality risk for 85 cities across Europe and consider the impact of both heat and cold temperatures over the entire annual cycle. Mortality risks are explored across urbanization gradients using high-resolution urban climate simulations, population density data, and city-level temperature-mortality relationships. We further monetize climate-related risks and compare the economic valuation of UHIs’ mortality impact with other urban living costs.

Results
Following the Intergovernmental Panel on Climate Change (IPCC) convention, risk in this study is defined as “the potential for adverse consequences”36, mainly in the form of human mortality. IPCC’s definition of exposure is “the presence of people... in places and settings that could be adversely affected”36. However, except where otherwise specified, risks in this study are assessed from an individual’s perspective, for each city’s average inhabitant. The same average inhabitant is always assumed to be exposed, and intra-city risk differentials are indicative of the potential difference in risk posed to this inhabitant if they lived in one part of the city compared to another. Exposure in this study therefore follows the epidemiological definition and refers to the presence of heat and cold. Under IPCC convention, this is more closely related to the presence of hazard ("the potential occurrence of a natural or human-induced physical event or trend that may cause loss of life...”36). Lastly, vulnerability is “the propensity or predisposition to be adversely affected..., including sensitivity or susceptibility to harm and lack of capacity to cope and adapt”36. Vulnerability determines the magnitude of impact of a given temperature exposure. In the current context, vulnerability may include, among others, physiological factors such as age and acclimatization to a particular climate, as well as societal and socioeconomic factors that hinder people’s ability to cope with heat or cold, such as access to air conditioning. In this study, vulnerability and exposure, jointly, determine risk.

Temperature-related mortality risk in European cities
Mortality risk generally increases towards the extreme ends of each city’s typical temperature range, with greater risks to older age groups (Fig. 1a–c). The degree of sensitivity to heat and cold differs between cities due to differences in population vulnerability, associated with local socioeconomic, infrastructural, and environmental characteristics37,38. The intensity of UHI, defined as the difference in average air temperature between urban areas and their rural surroundings, also varies between cities and across seasons (Fig. 1a–f, Supplementary Fig. S7)8,24,39. In this study, the differences in UHI intensity between cities are captured by the urban climate simulations through variations in the land surface, local climate, and human activities, while seasonality results from seasonal shifts in both climate (e.g., solar radiation) and anthropogenic heat emissions (e.g., central heating)40. The combined effects of varying both vulnerabilities and UHI (exposure) lead to variations in UHI impact on mortality risk across Europe. For instance, focusing on the heat extreme days, a greater impact is noted in Milan, which experiences more extreme heat conditions, than in London, despite the greater urban-rural temperature difference in the latter city. This is due to the strong non-linear increase in heat-related risk under Milan’s heat extreme days, which are notably warmer than in London (Fig. 1). Stockholm, on the other hand, has a relatively weak UHI and low vulnerability to heat, resulting in minimal impact of UHI on mortality risk.

On an annual basis, UHIs lead to a general shift in the temperature distribution to warmer conditions in urban environments (Supplementary Fig. S8). This results in greater mortality risk associated with heat and lower risk associated with cold compared to rural surroundings. The annual net impact, therefore, further depends on the balance of warm and cold days per year, in addition to each city’s vulnerability to heat vs. cold.

Impact of urban heat islands on mortality risk
Despite the binary classification into urban and rural environments, a range of built and natural environments can be found within each. Examined as a function of land imperviousness, we find the largest change in mortality risk per unit change of land imperviousness around the urban-rural boundary (slopes in Fig. 2a–c), from rural conditions of minimal imperviousness (Δ imperviousness=0%) to urban environments with slightly built-up areas (e.g., Δ imperviousness=5%). This is followed by a continued but more gradual increase/decrease within the urban imperviousness gradient. The general shape of this relationship largely reflects that between imperviousness and air temperature UHI (Supplementary Fig. S12)9,41. The degree of increase/decrease in risk with imperviousness is further associated with each city’s UHI intensity and population vulnerability, as discussed in the next section.

Given that highly populated areas also tend to be highly built-up (statistically significant mean Spearman’s correlation of 0.47, Supplementary section 1), and that heat emissions from human activities are enhanced in densely populated areas, UHI’s impact on mortality risk also increases with population density (Supplementary Fig. S1). At the city level, this implies a biased exposure whereby greater fractions of the population live in areas of greater UHI. Given that risks in this study are assessed from the perspective of an individual inhabitant living in different parts of the city, this bias in exposure is not captured. Should it be included, however, the bias would result in a 6.4% median (interquartile range across 85 cities, IQR: 2.9–9.4%) increase in the urban average mortality risk estimate during heat days and a 1.3% (IQR: 0.7–2.3%) reduction during cold days (Supplementary section 1). For the outlier cases of Brussels, Dublin, Paris, and Lyon, the increase during heat days can reach over 25%.

Grouping cities by the Köppen–Geiger climate classification (Supplementary Table S3) and summing the impact over all days of the year, we note that temperate cities with no dry season and hot summers tend to experience an annual net adverse effect of UHI on mortality, while for all other climate groups, the net effect is generally protective (Fig. 2a). However, variability and outlier cities can be found within each climate group, as well as overlaps between climate groups (Supplementary Fig. S9). During days of extreme heat, the impact of UHI on mortality risk tends to be stronger for cities with temperate, hot summer climates (Fig. 2b), though this difference is removed and the impacts become similar for all climate groups during heat extremes when population age is standardized between cities (Supplementary Fig. S5b). On the other hand, consistently and notably weaker UHI protective effects are found for cold climate cities during cold extremes days, regardless of assumed population age structure (Fig. 2c, Supplementary Fig. S5c). The differences between climate groups are further discussed in Supplementary section 3.

In general, UHIs have a weak protective net annual impact on human mortality risk for most (70 out of 85; 90% confidence interval, CI: 60 to 75) cities examined in this study (Fig. 3a, b). Across all 85 cities, a median of 2.8 fewer deaths per 100,000 people per year (CI of the median estimate, representing uncertainty in exposure-response relationships: 1.7–3.5; IQR across 85 cities, representing differences between cities: 0.7–4.4) are attributed to temperature in urban versus rural areas. This is due to the high likelihood of cold days in most parts of Europe, defined as cooler than the city- and age-specific optimal temperature below which mortality risk generally decreases with increasing temperature. The most protective annual net effects of UHI are found in Glasgow, Porto, and London, with 16 (CI: 14–18), 12 (CI: 11–14), and 11 (CI: 9–13) fewer deaths per year per 100,000 people, respectively (Supplementary table S4). The most adverse annual net effects are found in the urban areas of Trieste, Genoa, and Bologna, with 5 (CI: 2–7), 4 (CI: 1–8), and 3 (CI: 1–5) additional temperature-related deaths per year per 100,000 inhabitants, respectively (Supplementary Table S4). On average, UHI has a protective effect in all cities for all seasons except summer (Fig. 3a). In some cities with cooler summer climates, such as those in Nordic countries and the United Kingdom, the average impact is protective even in summer.

The daily impact of UHI on mortality risk is greater during heat extreme days than during cold extreme days for all cities. Over the 85 European cities examined, UHI increases mortality risk during heat extreme days by a median of 0.25 (CI: 0.21–0.27; IQR: 0.18–0.29) additional deaths per 100,000 population per day (Fig. 3a, c), a median increase of 45% (IQR: 30–61%) from the risk in rural areas around the cities (Supplementary Fig. S10). This is in contrast to a reduction of 0.05 (CI: 0.04–0.07; IQR: 0.02–0.11) deaths per 100,000 population per day during cold extremes (Fig. 3a, d), a 7% (IQR: 3–15%) decrease from the rural average. Within the urban space, the impact is further enhanced in more built-up parts of the city. For instance, there is a 67% (IQR: 46–91%; 0.35, IQR: 0.24–0.44, additional deaths per 100,000 per day) median risk differential between the most and least built-up (≥90% vs. ≤10% imperviousness) areas during heat extreme days (Supplementary Fig. S11).

If population age structures are standardized across the cities according to the 2013 European standard population, the risk estimates would increase by 9% on median (Supplementary section 2, Fig. S4c), likely indicative of slightly younger populations in cities (Supplementary Fig. S4a).

To additionally consider the age dependence of UHIs’ impact on mortality, years of life lost (YLL) analyses are included in the Supplementary materials (Fig. S13, Tables S6 and S10). While the overall conclusion of a weakly protective annual net impact for most cities still holds, an annual adverse impact is found for more cities with the YLL approach (20 vs. 15 with the mortality counts-based analysis). As younger age groups are weighted more strongly in YLL analyses, this finding may be reflective of younger populations’ greater vulnerability to heat, which is more similar to that of older age groups, compared to their vulnerability to cold, which tends to be lower (Fig. 1a–c).

Factors controlling UHI’s impact on mortality
The magnitude of UHI impact on human mortality in this study is determined by four factors: vulnerability of the population (as captured by the relative risk, RR, at different temperatures) and population age structure, which together determine total vulnerability, and frequency of occurrence of different temperatures and magnitude of UHI, which together determine exposure. The relative importance of each in determining how the impact of UHI compares between different cities is discussed below.

Mortality risk tends to increase toward the extreme ends of a city’s typical temperature range, resulting in U- or J-shaped temperature-mortality exposure-response functions (ERFs). Subsequently, an indicator of a population’s vulnerability to heat and cold is the magnitude of the relative risk (RR) at the warmest and coldest extremes of the observed temperature range (RR_Tmax and RR_Tmin, respectively). As expected given the approach we use for estimating mortality, statistically significant and notable rank correlations can be found between RR_Tmax and UHI’s impact on mortality during days of extreme heat, as well as between RR_Tmin and UHI’s impact during extreme cold (Fig. 4), indicating greater impact in more vulnerable cities. This vulnerability factor also partially explains the difference in UHI impact between cities of different Köppen–Geiger climate groups during days of temperature extremes (Supplementary section 3). Notably, the annual net impact of UHI on health is significantly correlated with RR_Tmin but not with RR_Tmax, indicating an overall dominant role of impact during cold days across European cities.

Not all summer days are considered warm (above the Minimum Mortality Temperature, MMT, above which UHI has an adverse effect on human health) for all cities (Supplementary Fig. S15), and during mild summer days, UHI has minimal or slightly protective effect on health. The number of warm days in a year therefore strongly determines UHI’s impact in summer, and to a lesser extent in spring, winter, and on the annual net balance (Fig. 4). The number of warm days in a year is correlated with the average temperature and anti-correlated with the latitude, but varies between cities independently of their population’s vulnerability to heat (Supplementary Fig. S14).

The magnitude of each city’s average UHI is correlated with the magnitude of its impact on health both during heat extreme days and on seasonal averages (Fig. 4). However, the magnitude of UHI is not a key factor in determining the relative ranking of each city’s annual net and cold extreme mortality impact. In those cases, the population’s vulnerability to cold plays the most important role.

Lastly, the proportion of older compared to younger adult populations in each city is a determining factor in the impact ranking between cities only during temperature extreme days and in winter (Fig. 4), though it affects the magnitude of the impact in each city, as discussed above and in Supplementary section 2.

Geographically, cities in eastern and northern parts of Europe, as well as those with colder average temperatures, tend to experience less protective effects of UHI on mortality during winter and cold days (Figs. 4, 3d, and 5b), possibly indicative of greater infrastructural and behavioral adaptation to cold weather. During summer, on the other hand, cities with warmer climates and in the southern and eastern parts of Europe tend to experience a stronger overall adverse impact, likely associated with a greater proportion of warm days. The daily impact during heat extremes, which is more correlated with population vulnerability and magnitude of UHI, does not follow this geographical pattern (Figs. 4 and 3c).

Economic assessment of UHI-related mortality
Economic assessments in this study mainly follow the value of statistical life (VSL) approach, which is commonly used in mortality risk valuations42. However, other valuation metrics may also be applied, and the value of life year (VOLY) approach is additionally assessed and discussed below at the end of this section.

Economic assessments of UHI heat- and cold-related mortalities in Europe following the VSL approach are on the order of a few tens to hundreds of Euros per adult city resident per year (Fig. 5a, b). The median economic impact of UHI heat-related mortality is €192 (IQR: €142 to 296) per adult resident per year, estimated in 2021 Euros, and that of cold-related mortality is €−314 (IQR: €−429 to −235) per adult resident per year. There is a weak correlation between the magnitudes of cities’ heat- and cold-related mortality impacts (Fig. 5d). The correlation persists even when population age is standardized (Supplementary Fig. S16d), though many cities represent exceptions. For instance, UHI yields considerable protection against cold in Glasgow, London, and Porto, while the annual heat-related mortality impact due to UHI remains low (lower left corner in Fig. 5d, Supplementary table S4). UHI-related mortality has an adverse net economic impact for 15 (CI: 10 to 25) of the 85 cities examined (circled in red in Fig. 5c, above the one-to-one line in Fig. 5d, Supplementary Table S4), with the greatest impacts for Turin and Bologna.

To put these figures in context, we compare these economic assessments with those for existing estimates of excess mortality due to air pollution43,44, which is another important human health hazard associated with urban environments. Across Europe, cities with higher economic impacts of UHI heat-related mortality tend to also bear greater impacts from air pollution-related mortality (Fig. 5e). In contrast, the correlation is weaker between UHIs’ protective effects against cold and each city’s air pollution-related risk (Supplementary Fig. S16e). On an annual basis, the median economic impact of UHI heat-related mortality is around one-fifth that of PM2.5-related mortality and ~1.2 times that of ozone-related mortality (Fig. 5f). However, it is important to note that UHI heat risk is a quantification of the urban-rural difference, while for the air pollution risks discussed above, city average pollutant concentrations are contrasted against an ideal threshold concentration that may also be exceeded in rural and peri-urban regions. This is especially the case for ozone, where higher concentrations can be observed in rural regions downwind of urban centers, e.g., ref. 45 and urban neighborhoods with lower imperviousness and more abundant vegetation46. Additionally, heat risk has a strong seasonality and is largely absent during colder seasons. Air pollution, on the other hand, is present throughout the year despite some seasonal variability. Moreover, mortality quantifications for air pollution used in this comparison43,44 are based on annual average estimates, while temperature-related risks are based on daily average estimates. The former, therefore, mainly represents the impact of chronic exposure while the latter captures acute effects (with lag periods of up to three weeks, see Methods). The variability in economic impacts of UHI heat-related mortality across Europe is relatively small compared to that of PM2.5 mortality (Fig. 5f).

Economic assessments by VSL as presented above do not explicitly consider age, therefore life expectancy at the time of death is assumed to be comparable to all causes of death. However, as can be observed from age-dependent temperature-mortality relationships (Fig. 1a–c), heat and cold exposure disproportionately affect older populations. The life expectancy at the time of death would therefore likely be shorter than the average for all-cause mortality. An approach to account for this is to consider the YLL through VOLY valuation, which is included in Supplementary Table S7. While there is a strong correlation between economic impacts as determined through VSL and that through VOLY (Supplementary Fig. S17a, b), the magnitude of the impact as assessed through VOLY is only ~14% (median of the multi-city median annual net, heat, and cold impacts) of that assessed through VSL (Supplementary Fig. S17c). This is mainly due to differences in valuation approaches which resulted in each VSL (3.91 million 2021-EUR per statistical life42) equating to 85 VOLY (46,000 2021-EUR per year47). However, there is currently no clear consensus on economic assessments by YLL, and at the higher end of the VOLY estimate (116,000 2021-EUR per year47), each VSL equates to around 34 VOLY, resulting in valuations by VOLY-based assessments to be around 35% of that using VSL (Supplementary Fig. S17d). Given limitations with the VOLY approach, including ethical concerns and lack of evidence in the assumption of a time-independent VOLY42, VSL-based assessments are the main focus of this study.

Methods
Data
Hourly near-surface air temperature (2 m above ground level) of 100 European cities are obtained from the 100-m simulations by UrbClim—an urban boundary layer climate model40—via the Copernicus Climate Change Service73. UrbClim contains a land surface scheme coupled to a 3-D atmospheric boundary layer module. The surface energy balance is modeled for three land cover types in each model grid (urban, vegetated, and bare soil, considering a total of 15 different surface classes), which are represented using a bulk approach without detailed subgrid features. Model boundary conditions include hourly synoptic meteorology from ERA5 reanalysis (relating to wind, humidity, atmospheric pressure, radiation, precipitation, temperature, and soil moisture), as well as terrain and city properties for each grid cell. Anthropogenic heat flux is prescribed as a sensible heat flux to surface air, based on a 0.0416-degree resolution dataset from the National Center for Atmospheric Research (NCAR) Climate and Global Dynamics Laboratory (CGD)74. Air is assumed to be homogeneously mixed within the urban canyon, with air temperature at roof height representative of the canyon as a whole. UrbClim has been validated against observations at various cities and has been found to perform similarly to other, including more complex, models40,75–77. For computational considerations, daily averaged temperatures re-gridded to 500 m resolution for the years 2015 to 2017 are used in this study.

For analysis of UHIs, urban/rural and land/sea surface type classifications are also obtained from the same Copernicus dataset73. The classification for each model grid is defined according to the 2012 CORINE land cover class, which considers 44 classes based on satellite images and in situ data78. Urban areas are defined as continuous regions of urban structures and transport networks with greater than 80% impermeable land cover. Commercial and industrial sites as well as ports are excluded even if the impermeability criterion is met, while urban areas with more than 20% scattered greenery are also excluded. When re-gridding to 500 m resolution, the dominant urban/rural land cover type is considered for each coarse grid, while for land/sea masking, only grids containing no water features are classified as land.

Additional data used in the analysis include an elevation map from Multi-Error-Removed Improved-Terrain Digital Elevation Model (MERIT DEM) at 3 arc-second resolution79, 2015 population density data from NASA Socioeconomic Data and Applications Center (SEDAC) at 30 arc-second resolution80, 2015 land imperviousness density data at 100-m resolution from Copernicus Land Monitoring Service81, Köppen-Geiger climate classification maps at 1 km resolution82, as well as the following data from Eurostat65 - Nomenclature of Territorial Units for Statistics (NUTS) 3 level: annual mortality by age and sex (data code: demo_r_magec3, last accessed 2021-07-23), population by age and sex at the start of the year (data code: demo_r_pjangrp3, last accessed 2021-03-11), and annual population structures by age (data code: demo_r_pjanind3, last accessed 2021-03-11). Life expectancy by age and sex at NUTS 2 level (data code: demo_r_mlifexp, last accessed 2021-06-03) from Eurostat65, averaged across the years 2015 to 2017, is used for quantifying the YLL.

Rent for a selection of cities is obtained from Eurostat65 where available, and is calculated by multiplying the average area of living accommodation in m2 per person (data code: urb_clivcon SA1022V, last accessed 2022-08-19) by the average annual rent for housing per m2 (data code: urb_clivcon SA1049V, last accessed 2022-08-19). Public transport cost is represented by the cost of a monthly public transport ticket within 5-10 km of the city center (data code: urb_ctranTT1080V, last accessed 2022-08-1965). Rent and public transport costs in the last ten years (2012-2021) are considered and averaged where available.

Age standardized mortality for the adult population (20 years and older) associated with annual mean PM2.5 concentrations above the World Health Organization (WHO) recommended level of 10 μg/m3 in 2015 cities in 2015 is obtained from Khomenko et al.43. Annual premature deaths associated with ozone exposure, averaged over the years 2015 to 2017 and for the whole population of all ages and without age standardization, is obtained from the European Environment Agency44,83. The 2021 version of WHO’s concentration-response function for ozone84 is assumed, with a counter-factual concentration of 70 μg/m3.

In the discussion of results, tourism data by country and aggregated to European levels from Eurostat (data codes: tour_dem_totot and tour_dem_toq, last accessed 2023-05-2365), as well as the 2019 English Index of Multiple Deprivation from the UK government63 are also quoted.

Epidemiology
Temperature-mortality exposure-response relationships for five age groups (20–44, 45–64, 65–74, 75–84, and 85+) in each city are obtained from, and explained in more detail in, Masselot et al.37, which follows a two-stage framework. In the first stage, quasi-Poisson regression is applied using distributed lag non-linear models (DLNM85) to model the relationship for a subset of cities in country-dependent age groups. Daily mortality time series from the multi-country multi-city (MCC) collaborative research network and 9 km resolution daily mean temperature from ERA5-Land are used. Lagged effects up to three weeks after exposure are considered in the DLNM and summed to represent the short-term cumulative impact of each exposure day. In the second stage, model coefficients describing the lag-cumulative exposure-response relationships are modeled in a meta-regression with age and city characteristic terms describing each city’s environmental and socioeconomic states. Residual spatial differences not explained by city characteristics are captured using ordinary kriging. The exposure-response model coefficients, describing the relationship in relative temperature percentiles, for all cities and age groups are subsequently predicted. The R codes for this analysis are available as provided in Masselot et al.37. Subsequently, in the current work, the models are translated to exposure-response relationships as functions of city mean temperatures using each city’s UrbClim domain average temperature from the 2008 to 2017 period, where simulated microclimate is available. These relationships quantify the mean risk for each city- and age-specific population for each daily mean temperature, irrespective of factors such as UHI that led to the observed temperature. They are assumed to identically apply to the whole population in each age group in each city, irrespective of differences in each individual’s actual exposure or vulnerability, i.e., it is the mean estimate. Uncertainties in the exposure-response relationships as represented by 1000 Monte Carlo simulations37 are used to quantify the confidence intervals of the attributable mortality for each city. These simulations are then used to quantify the confidence interval of median estimates and the estimated number of cities experiencing adverse annual net UHI impacts.

Attribution
With the above-described temperature data and exposure-response relationships, the daily mortality fraction attributable to temperature is calculated for each city and age group using the relative risk (RR) exposure-response functions. This is calculated as

AF = (RR-1)/RR (1)

where AF is the attributable fraction and RR is the relative risk86. Daily attributable numbers are calculated by multiplying the attributable fraction by the per population annual average all-cause mortality for each corresponding NUTS 3 region and age group, averaged over the years 2015 to 2017. Annual values are used due to a lack of finer-scale age-specific data.

Urban heat island impact analysis
The impact of UHI on mortality risk is computed for each day and city by taking the difference between the average attributable mortality number over urban areas and the average over rural areas within the model domain. Model grids containing water bodies and those with average elevation 100-m higher or lower than the domain population density-weighted average elevation are excluded from analysis. When computing the total impact across age groups, the NUTS 3 level local population age structure is used. Additional analysis with population age-standardized across all cities according to the 2013 European standard population87 is also provided for ease of comparison between cities and for comparison to other health impact assessments.

Within the temperature range observed for each city, the age group-dependent minimum mortality temperature (MMT) is defined as the temperature with the lowest cumulative associated mortality risk, according to each age group’s temperature-mortality ERF. Each day is then subsequently classified as either warm or cold for each age group, depending on whether it is warmer or cooler than the MMT. Mortality risk generally increases both with increasing temperature above the MMT and with decreasing temperature below the MMT. The age dependence of MMT, and therefore the age dependence of whether a condition is considered warm or cold, implies that the same temperature may be associated with heat-related mortality for one age group and cold-related mortality for another. Heat and cold extremes, on the other hand, are the warmest/coldest 2% (22) days (as defined by the UrbClim domain population-weighted daily average temperature) over the three-year period.

When classifying cities into Köppen-Geiger climate groups, the dominant climate classification within the UrbClim urban domain for each city is considered. Given that some cities belong in climate groups not otherwise represented by our sample of 85 cities, climate groups with only one city are merged with the closest matching group and labeled by their common base classification. For instance, Murcia with “Arid, desert, and hot climate” is grouped with six other cities with “Arid, steppe, and cold climate” into the Arid climate group.

Economic assessment
Economic impacts of mortality are quantified following the VSL approach. The best estimate of 3.6 million 2005-USD per life lost for EU-27 countries is obtained from the Organization for Economic Co-operation and Development (OECD) report on Mortality Risk Valuation in Environment, Health and Transport Policies42. This is derived from quality-screened samples in a meta-analysis of stated preference surveys88.

To translate this into relevant values for Europe, the VSL is first converted to 2005-EUR using the 2005 Euro area (19 countries) purchasing power parities (PPP). Inflation is then accounted for by considering the ratio of the Euro area consumer price index (CPI) in 2021 compared to that in 2005. i.e.,

VSL_2021,Euro = VSL_2005,USD * PPP_2005,Euro * (CPI_2021,Euro / CPI_2005,Euro) (2)

where PPP and CPI values are for the 19-country Euro area, obtained from OECD89,90. VSL_2005,USD is 3.6 million 2005-USD, PPP_2005,Euro is 0.853 EUR per USD, CPI_2021,Euro is 107.8, CPI_2005,Euro is 84.7, and VSL_2021,EUR, used for valuation in this study, is 3.91 million 2021-EUR.

To compare with economic impacts associated with air pollution, PM2.5- and ozone-related mortality from refs. 43 and 83 are translated into monetary terms following the same VSL valuation. Comparisons are made only for the cities where the risks associated with all exposures are available (74 cities).

Economic assessments of mortality can also additionally account for the age at the time of death by quantifying by YLL. This is calculated by multiplying the number of premature mortality in each age group by the average life expectancy in that age range. The value of the life year is estimated to be 40,000 2010-EUR per life year47, translated into 46,000 2021-EUR with a CPI of 93.189. Note, however, that there is high uncertainty in this estimate, with bounding estimates47 of 29,000–116,000 2021-EUR.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Dear Authors,\nThis are my comments:\nKey results\nThis article presents a complex study on the effects of the UHI on public health, evaluating its\npotential economic impact. Results are very consistent and adequately address the complexity of the\nTopic. This article accurately addresses the singularities of climate contexts and the contrasting effect\nof UHI during the warm and cold seasons. Most articles tend to only focus on the negative effects of\nUHI, unlike this article which identifies both its positive and negative impacts, providing an economical\nvaluation for diverse climate contexts, that should be seen as a referential for the assessment of the\nUHI.\nValidity\nThe authors use a large dataset, although its data were obtained by modeling. Despite its limitations,\nwhich are adequately addressed, results should be seen as reliable, since the Model has been\npreviously validated.\nSignificance\nResults are very significant since they adequately expand the current knowledge by accurately\naddressing the complexity of this topic, demonstrating that some of its most common assumptions,\nsuch as the negative impact of UHI, deserve a more complex interpretation such as the one presented\nin this article. Results can be used for urban planning, and to address climate adaptation strategies.\nData and methodology\nThis article uses modeled data that seemsconsistent with the reality that is addressed. Data is\nadequately presented and graphics are, in most cases, complete and easy to read.\nHowever, the Authors are asked to revise the following elements:\n- Figure 1 – Please provide the units of measurement for the y-axis density.\n- Some of the images of the supplementary materials lack the necessary quality to be adequately\nunderstood, such are the cases of figure S8 and Figure S15.\n- Tables S2 and S3, should repeat the heading lines on each page to improve their legibility.\nMethodologies are adequately presented and seem appropriate.\nOn lines 393-394, you mention “Residual spatial differences not explained by city characteristics are\ncaptured using ordinary kriging.” For the benefit of replicability, please mention which software tool\nwas used for this purpose.\nOn lines 431-434 - Please explain the phrase: “Sub-classifications with only one city are merged with\nthe closest matching classification into one climate group (e.g. Murcia with Arid, desert, and hot\nclimate is grouped with six other cities with Arid, steppe, and cold climate into the Arid climate\ngroup).\"\nAnalytical approach\nThe analytical approach is in my opinion correct as the methods used for addressing the UHI intensity\nare clear and adjusted. Statistical analysis is also adjusted to the objectives of the study.\nI don’t feel qualified to fully judge the quality of the methods behind epidemiology and cost\nestimation, however, they are clear and seem appropriate.\nSuggested improvements\nIn my opinion, this article requires little improvement as it already provides a multilayer approach to a\ncomplex phenomenon. Therefore, I will only suggest some minor changes:\nOn Lines 62 and 63, the authors mention: “g. 8, 24], but there are no quantitative guidelines on how\nurban planning can deliver potential health benefits.”. This is a rather controversial argument, since\nseveral articles and Books offer quantitative proposals for urban interventions, presenting case studies\nor modeling potential changes, here are some examples:\n- https://doi.org/10.1016/j.esd.2022.05.006\n- Erell, E., Pearlmutter, D., & Williamson, T. (2012). Urban microclimate: designing the spaces\nbetween buildings. Routledge.\nLines 170-172 – “Compared to when the standard population age structure is assumed, considering\nthe local age structures yields a median doubling of UHI’s impact estimate on all mortality risk\nmetrics” I – Not quite sure what the authors meant to say.\nClarity and context\nThis article provides clear and accessible language. Most of its results are very clear and should be\neasily understood by both experts and the general public.\nReferences\nReferences are adjusted and provide an adequate literature background to this article's contents.\nHowever, it seems out of format in the present document\nYour expertise\nMy expertise is in UHI analysis and urban climate planning. Aspects related to Epidemiology and Cost\nestimates would be evaluated in more depth by reviewers from those areas, however, from my\nknowledge it seems like a quality article.\nFor the overstated reasons I am suggesting that this article should be published after minor revision.", "reviewer_2": "This work studies the impacts of temperature changes due to urban heat island (UHI) on mortality,\nacross 85 European cities belonging to 5 background climatic conditions. It accounts for intra-urban\nvariations in temperature and population distribution and compute the health and economic impacts\nover the entire annual cycle, as well as the net effect.\nThe conclusions are original by providing results on the net balance of the benefits of UHI over the\ncomplete year, for many cities covering various climatic conditions. They can help the decision-maker\ngrasp the impact of UHI on short term mortality, and consider policies aimed at reducing them at the\ncity level. The conclusions and data interpretation are robust, valid and reliable, with many\nsupplementary analyses that support the main results (see however my comments below). The\nreferences seem adequate to me.\nI have suggestions of clarifications / additional work (see below Data and methodology (I) and\nsuggested improvements below (III)).\nI) Data & methodology\nData and methodology seem up to date, of high quality and clearly presented. However, I have some\ncomments regarding the reporting of some results and methodological aspects.\nI-1) Reporting\na) Lines 112-14: In the sentence “from rural conditions of minimal imperviousness (Δ imperviousness\n= zero) to urban environments with slightly built up areas (e.g. Δ imperviousness = 5)”, shouldn’t “Δ\nimperviousness = 5” instead be “Δ imperviousness = 5%” to be consistent with Figures 2(a)-(c)?\nb) Lines 173-77: Regarding the sentence “The median annual UHI impact during heat days decreases\nfrom 15.0 additional deaths per 100,000 population per year in urban areas to 6.8 additional deaths\nwhen using the standard population age structure (Fig. 3a,b). The median annual protective effect\nduring cold days also decreases from 18.9 to 9.1 fewer deaths per 100,000 population per year.” I\ndon’t understand where we can see this from figures 3 (a)-(b). Indeed, these figures represent the\nmean per day whereas the text provides the median per year. Furthermore, the text mentions “heat\ndays” and “cold days”, which refers to extreme heat / cold days or to summer / winter seasons? Do\nthese comments really apply to these figures? I wonder, because the next sentence in the text\ndescribes other results in these figures properly: “The highest daily UHI impact on mortality risk\ndecreases from 1.05 additional deaths per 100,000 population per day (Budapest, Hungary; Fig. 3a)\nto 0.61 additional deaths per day (Bucharest, Romania; Fig. 3b), both during heat extremes”.\nc) Line 181: Does figure 3d really show the impact on mortality risk estimation? I have the impression\nthat the word “this” applies rather to the 85+:20-44 population ratio for the city vs. for the standard\npopulation.\nI-2) Methodology\nMy main expertise is in economics, although I have been working with epidemiologists for over 25\nyears. This may explain why the epidemiological methodology did not seem to me to be detailed\nenough in the text or in section 4 Methods, subsection 4.2 Epidemiology.\nA) Epidemiological methodology issues\nIt essentially refers to an article of which Pierre Masselot (one of the co-authors of the present work)\nis also the author: Lines 384-86 “Temperature-mortality exposure-response relationships for five age\ngroups (20-44, 45-64, 65-74, 75-84, and 85+) in each city are obtained from, and explained in more\ndetail in, Masselot et al. [34]”. Yet this publication is neither correctly cited in the bibliography ([34]\nMasselot, P. et al. Excess mortality attributed to heat and cold in 801 cities in Europe), nor a priori\naccessible on the net.\nThis led me to three questions of understanding.\na) Lines72-4: “Mortality risks are explored across urbanisation gradients using high resolution urban\nclimate simulations, population density data, and city level temperature mortality relationships”. The\nauthors use population-specific relationships for each city, which provide a good level of precision.\nHowever, in doing so, these relationships already incorporate differences due to UHI in the calculation\nof mortality risks (see Figure S8), before applying them to temperature variations related to UHI and\ncalculating the frequency weighted UHI impact by temperature. What is the influence of these\ntemperature-mortality relationships on the computations?\nb) Lines 253-54: “The former therefore includes the impact of chronic exposures while the latter only\ncapture acute effects (with lag periods of up to three weeks, see Methods).” A lag of 3 weeks is\nmentioned, referring to the Methods section, but this section does not give any further details. What\nare the implications of this “up to three weeks” lag in the calculations, particularly those associated\nwith extreme heat and cold days? Are the impacts on mortality considered to be spread over three\nweeks? What happens if several consecutive days are classified as extreme? Is there a harvesting\neffect?\nc) Lines 317-21: It is suggested to “capture the true exposure experienced by the population”. In\naddition to the points rightly raised by the authors, more generally, deaths seem to be associated\nwith people's place of residence. If people move around the country or abroad during the hottest 2%\nof days (often in the summer, during holidays), how are deaths counted if they do not occur in their\nplace of residence? What proportion of people are in their main residence during heatwaves?\nB) Other methodological issues\na) The place of residence differs from the place of work (generally in UHI), which the authors\nacknowledge. This involves (on average) a higher exposure to high temperature during day w.r.t the\nplace of residence. Is there evidence in the literature of the magnitude of the discrepancies involved?\nb) Are the conditions of health care (speed of intervention, transport to a health care facility) during\nthe health event causing the death the same between UHI and non-UHI? Can this influence mortality\nbetween residents in and out of HUI?\nC) Remarks on economic issues\nI agree with the value of statistical life (VSL) chosen to assess mortality impacts, which was carefully\ncalculated by the OECD in 2012 and widely used since then. I am also satisfied with the way it is\ntranslated into a value relevant for Europe. Note, however, that some countries recommend that the\nVSL should be changed according to changes in Gross Net Product per capita instead of Consumer\nPrice Index, but the differences are limited. I have three points to make.\na) First, I am reluctant to the use of the terms “costs” and “benefits” when associated with non-\nmarket impacts, here the valuation of mortality. This can be misleading to the reader as these\namounts will not actually be gained or lost by the cities concerned, rather they are welfare losses in\ntheir population associated with premature mortality and valued economically. I prefer the\nterminology “economic impacts” or “economic assessments”, which can be negative or positive. I\nwould then be satisfied if this costs/benefits formulation was changed throughout the text. This\nincludes the abstract “UHIs in Europe cost/save €480/€677 in heat-/cold-related mortality”, as well\nas the title “Mortality costs and savings of urban heat islands in European cities”. For the latter,\nsomething like “Economic valuation of acute mortality attributed to urban heat islands in European\ncities” would be better.\nb) Second, in choosing this VSL, the economic computations assume that life expectancy at death is\nsimilar to that observed for all causes of death. Since the effects acute mortality are being assessed\nhere, it would be useful to discuss the expected reduction in life expectancy. A difference in life\nexpectancy, and the extreme case of a single year of life lost per death attributable to UHI\ntemperature conditions, would alter the economic computations.\nc) Related to the point above, I find that the authors too quickly eliminate the possibility of conducting\nan analysis based on years of life lost (lines 453-57), and a valuation that would calculate them on the\nbasis of ages of death (at least, age groups) and exposition response functions. This would indeed\ntake into account at least the loss of life expectancy associated with age (even if not with cause). This\nwas done by Khomenko et al (2021) for PM2.5 for example. It would not be necessary to go as far as\nmonetarization (even if some values of life year lost exist for Europe, such as in Desaigues et al.,\n2011), but at least to make a net balance of years of life gained/lost that would take into account the\nage distribution.\nII) Use of statistics\nIn the box-and-whisker plots (Figures 3a et b, as well as S3, S7, S10 S14 and S16), please specify\nwhat the values represent (certainly the median for the centre line, quartiles 1 and 3 for the box, 1.5\nthe interquartile range (IQR) Q3-Q1 for the whiskers (with the respective boundaries the minimum\nand maximum), and outliers for dots outside the IQR).\nIII) List of suggested improvements\nAlthough this work is already very comprehensive, I think that the benefits of implementing some of\nthe 4 directions I present below are worth considering.\na) The authors compare their results with those for mortality associated with PM2.5 exposure. The\nlatter is, in general, proportional to population density in cities, and thus acts in synergy with the\neffects of temperature for UHI, both in summer and winter. However, another pollution indicator\nappears to be related to heat wave mortality: ground-level ozone (e.g. Reid et al, 2012). In this case,\nthe relationship with UHI would be rather antagonistic in summer or even on hot spring or autumn\ndays, since in highly polluted areas, and under certain insolation conditions, high NOx concentrations\ncan destroy part of the ozone. Consequently, ozone levels are likely to be higher in peri-urban and\nrural areas downwind of UHI. To what extent can this affect the results?\nb) Men and women have a different reactivity to temperature stress. This is linked to physiological\nfactors: muscle mass, which is higher on average in men, generates heat, whereas fat mass, which is\nhigher on average in women, does not. It is also linked to different lifestyles: fewer women are\nactive, implying differences in exposure at work or when travelling to work. The authors seem to\nhave the annual mortality by age and sex. If other data are available by sex too, would it be possible\nto perform the calculations in step 4.2 separately by sex?\nc) The authors discuss the consequences of climate change on their analyses. Lines 276-81, it is\nwritten: “Additionally, climate change is expected to lead to increases in the proportion of warm days,\nand therefore heat-related risks, relative to cold days and cold-related risks in future [47]. Given the\nweak annual net protective effect of UHI for most cities examined and the greater impact per day for\nheat, temperature increases in future can quickly shift the balance and lead to an annual net adverse\nimpact”. Why not make these estimates? Gasparrini et al. (2017) did so on the basis of different\nrepresentative concentration pathways (RCPs). Why not also apply it to UHI under climate change\nscenarios? It seems to me that the authors can easily obtain all the data they need. And this will\neventually allow to determine, for each city, when the adverse effects of high temperature UHI will\noutweigh the protective effects of low temperature UHI.\nd) In Line 337, the authors mention in future work a more detailed study of vulnerabilities. Why\nwould it not be possible to make a first attempt to construct indicators of deprivation and to match\nthem at grid level? Several indexes are possible.\n- The European Deprivation Index within urban areas, based on EU – SILC (European Union –\nStatistics on Income and Living Conditions) can be exploited, or build from aggregated census data\nand tax incomes of the population by year.\n- A simpler version, originally constructed in France (Windenberger et al., 2012), is based on 4\nvariables: the median household income per consumption unit, the percentage of high-school\ngraduates in the population aged 15 years and older, the percentage of blue-collar workers in the\nactive population aged 15 to 64, and the unemployment rate in the active population aged 15 to 64.\n- The Townsend deprivation index (Townsed, 1987), originally constructed in England and Wales, is\nalso based on four variables: the rate of households without a car, the rate of non-owner\nhouseholds, the rate of households with more than one person per room, and the rate of\nunemployment in the active population.\nIV) References\nDesaigues, B., Ami, D., Bartczak, A., Braun-Kohlová, M., Chilton, S., Czajkowski, M., Farreras, V. et\nal. (2011) Economic valuation of air pollution mortality: A 9-country contingent valuation survey of\nvalue of a life year (VOLY). Ecological Indicators 11, 902–910\nGasparrini, A., Guo, Y., Sera, F., Vicedo-Cabrera, A. M., Huber, V., Tong, S., ... & Armstrong, B.\n(2017). Projections of temperature-related excess mortality under climate change scenarios. The\nLancet Planetary Health, 1(9), e360-e367\nGuillaume, E., Pornet, C., Dejardin, O., Launay, L., Lillini, R., Vercelli, M., Marí-Dell’Olmo, M., et al.\n2016. Development of a Cross-Cultural Deprivation Index in Five European Countries. Journal of\nEpidemiology and Community Health 70(5), 493–99.\nKhomenko, S., Cirach, M., Pereira-Barboza, E., Mueller, N., Barrera-Gómez, J., Rojas-Rueda, D., ... &\nNieuwenhuijsen, M. (2021). Premature mortality due to air pollution in European cities: a health\nimpact assessment. The Lancet Planetary Health, 5(3), e121-e134.\nReid, C. E., Snowden, J. M., Kontgis, C., & Tager, I. B. (2012). The role of ambient ozone in\nepidemiologic studies of heat-related mortality. Environmental health perspectives, 120(12), 1627-\n1630.\nTownsend, P. (1987). Deprivation. Journal of Social Policy, 16(2), 125-146.\nWindenberger, F., Rican, S., Jougla, E., & Rey, G. (2012). Spatiotemporal association between\ndeprivation and mortality: trends in France during the nineties. The European Journal of Public\nHealth, 22(3), 347-353.", "reviewer_3": "The article is innovative and brings a significant contribution to the research in the field of urban heat\nisland and mortality under heat and cold conditions. The study area is wide-ranging, and all the most\nadvanced methodologies have been applied to investigate the relationship between mortality and\ntemperature. The use of UrbClim makes the analyses (partially) integrated with the characteristics of\nthe built environment and the Köppen-Geiger climate classification ensures that the classifications\nused are validated and integrated with the current literature. The graphs are accurate and\nunderstandable. Finally, the inclusion of micro-particle pollution is an added value, although it would\nhave been interesting to see the association between O3 and PM10, 2.5 and heat- and cold- related\ndeaths.\nReview of the paper:\n“Mortality costs and savings of urban heat islands in European cities”\nFirst of all, I would like to congratulate the authors for the excellent work. The\nconducted analyses are in line with the up-to-date scientific literature and the\ndiscussion of the results is very rich and interesting. This manuscript brings a\nsignificant contribution to the research in the field of urban heat island and\nmortality under heat and cold conditions within EU urban areas. The most\nadvanced methodologies have been applied to investigate the relationship\nbetween mortality and temperature and the use of UrbClim makes the analyses\n(partially) integrated with the characteristics of the built environment. In addition,\nthe Köppen-Geiger climate classification ensures that the classifications used are\nvalidated and integrated with the current literature. The inclusion of micro-particle\npollution is an added value, although it would have been interesting to see the\nassociation between O (in summer) and PM (in winter). Notwithstanding,\n3 10, 2.5\ncontents and technical details are of excellent quality.\nAs minor comment, I would like to raise two points:\n1] A better clarification of the meaning of “exposure”, “vulnerability”, and “risk” in\nthe context of this research would not be a useless explanation. Especially in the\nclimate change field, many of these concepts/words blur getting sometimes\nmeanings sensibly different according to the reader’s background. For this\nreason, I believe that a clear definition of the used terms would be of help to close\nthe gaps between the environmental epidemiological and other interdisciplinary\nstudies (see IPCC Glossary, IPCC 2022).\n2] A clear identification of social inequalities in the nexus between extreme\ntemperatures and mortality would have provided a clearer understanding of the\nrisk identification across the different categories of the population (most educated\nvs least educated, most isolated vs least isolated, etc.), but we are aware that the\navailability of data at the urban level is not the same in all cities. Based on the\nresults produced by the study (e.g., Table S2 “Mortality associated with UHI, per\n100,000 urban adult population”), it might be interesting to further investigate\nthese aspects in some cities where similar data are available.\nOther punctual recommendations in the text:\nOn Results:\nLine 95: insert a “,” after “e.g.”\nLine 126: it could be interesting to see some examples “of cities which reach around the 20% of\nenhancement”.\nOn Discussion:\n• The use of the term “mitigation” here can lead to misunderstandings. I would\nsuggest using other terms in line with the IPCC and UNFCCC languages\n(i.e., “adaptation”) (IPCC 2022), when appropriate.\nReference in the text:\nLine 267: “this indicates the need for mitigation strategies”\nLine 299: “such analyses can then allow a more even comparison to the\ncosts of various mitigation plans”\n• Spatial heterogeneity could be influenced by outdoor (e.g., direction of the\nwindows front, building age, roofing construction, number of floors) as well\nas indoor (e.g., thermal isolation, walls and ceiling, fans, double-glazed\nwindows) environmental characteristics. Consider whether to quote them in\nthe text.\nOn Methods:\n• No citations to DLNM model in the text (section 4.2).\n• No citations to MMT in the text (section 4.4).\nLine 397: about “UrbClim domain average temperature from the 2008 to 2017\nperiod”. Why did you consider only 9 years? Is the available timeframe? For\nthe World Meteorological Organization, 30-year reference period reflects\nchanging climate.\nLine 313-316: In relation to “A previous study has found, for instance, that\nwithin Parisian neighbourhoods experiencing the greatest heat exposure,\nthose which are the most socioeconomically deprived have a twofold higher\nmortality risk compared to the most affluent [53]”. To provide a clearer overview\nof the state-of-the-art in relation to social inequalities - extreme temperatures –\nhealth, I suggest referring to other cities when considering the relation between\ntemperature and mortality and different health outcomes socio-economic\nvariables. One option may be to quote studies over cities with a higher mortality\nranking associated with UHI (Table S2 in Supplementary Material) (e.g., Turin,\nMadrid, Bologna, Thessaloniki, etc.)."}, "author_response": "Dear reviewers,\nThank you very much for your constructive feedback and suggestions which led to improvements of\nthe manuscript. In response to your comments, we have now included additional analyses on ozone\npollution, years of life lost, and socioeconomic deprivation. During the revision, we also fixed a bug\nin the age structure analysis. This brought the local age-based risk estimates closer to that using the\nstandard population. All manuscript text on that topic, along with all local age-based analyses and\nfigures, have now been revised and corrected. Please find below in indented blue text replies to\nindividual questions and comments. The manuscript with tracked changes is attached separately.\nThank you again for your time and consideration.\nBest wishes,\nKatty Huang, on behave of all co-authors\n\nReviewer #1 (Remarks to the Author):\nDear Authors,\nThis are my comments:\nKey results\nThis article presents a complex study on the effects of the UHI on public health, evaluating its\npotential economic impact. Results are very consistent and adequately address the complexity of the\nTopic. This article accurately addresses the singularities of climate contexts and the contrasting\neffect of UHI during the warm and cold seasons. Most articles tend to only focus on the negative\neffects of UHI, unlike this article which identifies both its positive and negative impacts, providing an\neconomical valuation for diverse climate contexts, that should be seen as a referential for the\nassessment of the UHI.\nValidity\nThe authors use a large dataset, although its data were obtained by modeling. Despite its limitations,\nwhich are adequately addressed, results should be seen as reliable, since the Model has been\npreviously validated.\nSignificance\nResults are very significant since they adequately expand the current knowledge by accurately\naddressing the complexity of this topic, demonstrating that some of its most common assumptions,\nsuch as the negative impact of UHI, deserve a more complex interpretation such as the one\npresented in this article. Results can be used for urban planning, and to address climate adaptation\nstrategies.\nData and methodology\nThis article uses modeled data that seems consistent with the reality that is addressed. Data is\nadequately presented and graphics are, in most cases, complete and easy to read.\nHowever, the Authors are asked to revise the following elements:\n- Figure 1 – Please provide the units of measurement for the y-axis density.\nThank you for pointing this out. The values and units for density is dependent on the\nhistogram bins (counts per bin width), which isn’t very meaningful, so this is now changed to\nshow the frequency instead, with the units properly labelled.\n- Some of the images of the supplementary materials lack the necessary quality to be adequately\nunderstood, such are the cases of figure S8 and Figure S15.\nThank you for the comment. We interpret the comment regarding the quality of the figures\nto reflect their resolution. In the revised version all figures have been produced in a vector\nformat with very high resolution for optimal clarity. More explanatory text has also now\nbeen added to the captions, in case this is what you meant instead.\n- Tables S2 and S3, should repeat the heading lines on each page to improve their legibility.\nThank you for the suggestion. This has now been added.\nMethodologies are adequately presented and seem appropriate.\nOn lines 393-394, you mention “Residual spatial differences not explained by city characteristics are\ncaptured using ordinary kriging.” For the benefit of replicability, please mention which software tool\nwas used for this purpose.\nThe R code for this analysis is publicly available as open source through Masselot et al. 2023.\nThe reference has now been updated and a note has now been added in this section.\nOn lines 431-434 - Please explain the phrase: “Sub-classifications with only one city are merged with\nthe closest matching classification into one climate group (e.g. Murcia with Arid, desert, and hot\nclimate is grouped with six other cities with Arid, steppe, and cold climate into the Arid climate\ngroup).\"\nSorry for the confusion. In some instances, a climate group would contain only one city in\nour sample of 85 cities, and in these cases the climate group with a singular city would be\nmerged with the closest matching climate group. This has now been reworded a bit in the\nmanuscript text as well, so hopefully the meaning is clearer.\nAnalytical approach\nThe analytical approach is in my opinion correct as the methods used for addressing the UHI\nintensity are clear and adjusted. Statistical analysis is also adjusted to the objectives of the study.\nI don’t feel qualified to fully judge the quality of the methods behind epidemiology and cost\nestimation, however, they are clear and seem appropriate.\nSuggested improvements\nIn my opinion, this article requires little improvement as it already provides a multilayer approach to\na complex phenomenon. Therefore, I will only suggest some minor changes:\nOn Lines 62 and 63, the authors mention: “g. 8, 24], but there are no quantitative guidelines on how\nurban planning can deliver potential health benefits.”. This is a rather controversial argument, since\nseveral articles and Books offer quantitative proposals for urban interventions, presenting case studies\nor modeling potential changes, here are some examples:\n- https://doi.org/10.1016/j.esd.2022.05.006\n- Erell, E., Pearlmutter, D., & Williamson, T. (2012). Urban microclimate: designing the spaces\nbetween buildings. Routledge.\nThank you for pointing this out and for the references. Indeed, this statement was too broad\nand has now been removed. The two references provided have also been added to the\nmanuscript.\nLines 170-172 – “Compared to when the standard population age structure is assumed, considering\nthe local age structures yields a median doubling of UHI’s impact estimate on all mortality risk\nmetrics” I – Not quite sure what the authors meant to say.\nThanks for pointing this out. We meant that by taking into consideration the local population\nage structure, the median UHI impact estimate across all cities is doubled for all measures.\nThis sentence has been modified in the manuscript to hopefully bring more clarity.\nClarity and context\nThis article provides clear and accessible language. Most of its results are very clear and should be\neasily understood by both experts and the general public.\nReferences\nReferences are adjusted and provide an adequate literature background to this article's contents.\nHowever, it seems out of format in the present document\nYour expertise\nMy expertise is in UHI analysis and urban climate planning. Aspects related to Epidemiology and Cost\nestimates would be evaluated in more depth by reviewers from those areas, however, from my\nknowledge it seems like a quality article.\nFor the overstated reasons I am suggesting that this article should be published after minor revision.\n\nReviewer #2 (Remarks to the Author):\nThis work studies the impacts of temperature changes due to urban heat island (UHI) on mortality,\nacross 85 European cities belonging to 5 background climatic conditions. It accounts for intra-urban\nvariations in temperature and population distribution and compute the health and economic\nimpacts over the entire annual cycle, as well as the net effect.\nThe conclusions are original by providing results on the net balance of the benefits of UHI over the\ncomplete year, for many cities covering various climatic conditions. They can help the decision-\nmaker grasp the impact of UHI on short term mortality, and consider policies aimed at reducing\nthem at the city level. The conclusions and data interpretation are robust, valid and reliable, with\nmany supplementary analyses that support the main results (see however my comments below).\nThe references seem adequate to me.\nI have suggestions of clarifications / additional work (see below Data and methodology (I) and\nsuggested improvements below (III)).\nI) Data & methodology\nData and methodology seem up to date, of high quality and clearly presented. However, I have some\ncomments regarding the reporting of some results and methodological aspects.\nI-1) Reporting\na) Lines 112-14: In the sentence “from rural conditions of minimal imperviousness (Δ imperviousness\n= zero) to urban environments with slightly built up areas (e.g. Δ imperviousness = 5)”, shouldn’t “Δ\nimperviousness = 5” instead be “Δ imperviousness = 5%” to be consistent with Figures 2(a)-(c)?\nThank you for spotting this. This has now been corrected in the manuscript.\nb) Lines 173-77: Regarding the sentence “The median annual UHI impact during heat days decreases\nfrom 15.0 additional deaths per 100,000 population per year in urban areas to 6.8 additional deaths\nwhen using the standard population age structure (Fig. 3a,b). The median annual protective effect\nduring cold days also decreases from 18.9 to 9.1 fewer deaths per 100,000 population per year.” I\ndon’t understand where we can see this from figures 3 (a)-(b). Indeed, these figures represent the\nmean per day whereas the text provides the median per year. Furthermore, the text mentions “heat\ndays” and “cold days”, which refers to extreme heat / cold days or to summer / winter seasons? Do\nthese comments really apply to these figures? I wonder, because the next sentence in the text\ndescribes other results in these figures properly: “The highest daily UHI impact on mortality risk\ndecreases from 1.05 additional deaths per 100,000 population per day (Budapest, Hungary; Fig. 3a)\nto 0.61 additional deaths per day (Bucharest, Romania; Fig. 3b), both during heat extremes”.\nApologies for the confusion. Indeed, the first sentence incorrectly referenced figure 3. The\n“heat/cold days” mentioned here refer to all days in a year warmer/colder than the optimal\ntemperature. This is the same metric as the annual estimates in Fig. 5a,b. To avoid\nconfusion, we’ve removed these two sentences.\nc) Line 181: Does figure 3d really show the impact on mortality risk estimation? I have the\nimpression that the word “this” applies rather to the 85+:20-44 population ratio for the city vs. for\nthe standard population.\nSorry for the confusion. Fig. 3d did indeed only show the difference in age structure. “This”\nreferred to both the fact that populations are older in western Europe and subsequently,\ngreater impact on mortality risk estimation is also generally found in these regions.\nHowever, this discussion has now been removed and the figure changed following\ncorrection of a bug in the analysis code which brought the local age structure closer to that\nof the standard population.\nI-2) Methodology\nMy main expertise is in economics, although I have been working with epidemiologists for over 25\nyears. This may explain why the epidemiological methodology did not seem to me to be detailed\nenough in the text or in section 4 Methods, subsection 4.2 Epidemiology.\nA) Epidemiological methodology issues\nIt essentially refers to an article of which Pierre Masselot (one of the co-authors of the present work)\nis also the author: Lines 384-86 “Temperature-mortality exposure-response relationships for five age\ngroups (20-44, 45-64, 65-74, 75-84, and 85+) in each city are obtained from, and explained in more\ndetail in, Masselot et al. [34]”. Yet this publication is neither correctly cited in the bibliography ([34]\nMasselot, P. et al. Excess mortality attributed to heat and cold in 801 cities in Europe), nor a priori\naccessible on the net.\nApologies for this. This article was under review and has recently been published. The\nbibliography has been updated and the details can now be found on the net\n(https://doi.org/10.1016/S2542-5196(23)00023-2). The associated R code is available on\nhttps://github.com/pierremasselot.\nThis led me to three questions of understanding.\na) Lines72-4: “Mortality risks are explored across urbanisation gradients using high resolution urban\nclimate simulations, population density data, and city level temperature mortality relationships”.\nThe authors use population-specific relationships for each city, which provide a good level of\nprecision. However, in doing so, these relationships already incorporate differences due to UHI in\nthe calculation of mortality risks (see Figure S8), before applying them to temperature variations\nrelated to UHI and calculating the frequency weighted UHI impact by temperature. What is the\ninfluence of these temperature-mortality relationships on the computations?\nThank you for the comment and question. Just to clarify, Figure S8 was plotted for\nillustration purpose only, mainly to show why the annual net impact may be minimal or\nprotective when the daily impact is greater during heat extremes. Other analyses in the rest\nof the study are not based on the approach shown in this figure.\nIt is true that the city-level exposure-responses already include part of the UHI effect, which\nis incorporated in the second stage of the epidemiological analysis by estimating the\nrelationships between cities with different average values of this indicator. However, these\nexposure-responses cannot capture within-city differences in UHI, which we argue is the\nmore important part and is the part evaluated by our analysis through use of UrbClim data.\nIn this context, the results of our analysis provide the correct interpretation even in the\npresence of between-city baseline differences in the UHI prevalence.\nTo further clarify, for each city, mortality in our analysis is related to the prevalent\nenvironmental temperature. Whether these temperatures are boosted or not due to UHI in\nthe city, the relationships would still be valid. Additionally, city-specific exposure-responses\nare assumed to identically apply to the whole population in the city, separated by age\ngroups, irrespective of their individual actual exposure or vulnerability, i.e., it is the mean\nestimate. Therefore in the attribution stage, the point estimate of risk for a given daily mean\ntemperature is identical regardless of where in the city it is observed.\nThe following sentences are now added to the Epidemiology section under Methods in the\nmanuscript:\n“These relationships quantify the mean risk for each city- and age-specific population\nfor each daily mean temperature, irrespective of factors such as UHI that led to the\nobserved temperature. They are assumed to identically apply to the whole\npopulation in each age group in each city, irrespective of differences in each\nindividual’s actual exposure or vulnerability, i.e., it is the mean estimate.”\nb) Lines 253-54: “The former therefore includes the impact of chronic exposures while the latter only\ncapture acute effects (with lag periods of up to three weeks, see Methods).” A lag of 3 weeks is\nmentioned, referring to the Methods section, but this section does not give any further details. What\nare the implications of this “up to three weeks” lag in the calculations, particularly those associated\nwith extreme heat and cold days? Are the impacts on mortality considered to be spread over three\nweeks? What happens if several consecutive days are classified as extreme? Is there a harvesting\neffect?\nApologies for missing this in the methods description. In the distributed lag non-linear\nmodels used to model the temperature-mortality relationship, we consider lagged effects of\neach exposure for up to three weeks after the exposure day. The shape of the lagged\nresponse is also captured by the statistical model, so for cold exposure, this tends to imply a\nprotracted period of elevated risk, while for heat, the impact is usually a strong peak in risk\nimmediately after exposure. Since we are looking at the cumulative impact of single\nexposure days, we don’t capture the compounding effect of having several consecutive days\nof temperature extreme. However, harvesting effect within the three weeks lag period is\ncaptured. A note on this has now been added to the methods section and some of the\nimplications added in the discussion section, as appended below.\nIn the methods section:\n“Lagged effects up to three weeks after exposure are considered in the DLNM and\nsummed to represent the short-term cumulative impact of each exposure day.”\nIn the discussion section:\n“Lastly, it should be noted that the impact of cold weather tends to be spread out\nover a longer period after exposure while that of heat tends to be concentrated on or\nshortly after the exposure day. Thus, despite similar cumulative impacts on mortality,\nheat and cold weather have different implications for health care.”\nc) Lines 317-21: It is suggested to “capture the true exposure experienced by the population”. In\naddition to the points rightly raised by the authors, more generally, deaths seem to be associated\nwith people's place of residence. If people move around the country or abroad during the hottest 2%\nof days (often in the summer, during holidays), how are deaths counted if they do not occur in their\nplace of residence? What proportion of people are in their main residence during heatwaves?\nDeaths are counted by people’s residential address, so indeed people being on holiday also\naffects the true exposure experienced by the population. According to Eurostat, in each\nyear, around 60% of the European population partakes in tourism away from their place of\nresidence for at least one night. This peaks in the third quarter of each year, and can vary a\nlot by country, with Sweden for instance seeing almost everyone above 15 years old\ntravelling for personal or business reasons away from their place of residence for at least\none night during peak season. While a clear indicator of the relevance of this topic, it doesn’t\ntranslate directly into how each city’s population count changes during tourism season, and\ntherefore how many people are truly exposed during heatwaves, given differing timing and\nduration of trips, as well as inbound tourists. The topic of varying exposure due to\npopulation mobility is something we’re very interested in and that we’re looking into in\nmore detail in separate studies. In the meantime, a comment, as appended below, has now\nbeen added in the manuscript noting mobility due to tourism as an additional factor\naffecting population exposure.\n“Additionally, in each year, around 60% of the European population participates in\ntourism away from their place of residence for at least one night. With peak tourist\nseason in the third quarter of the year when heat extremes also tend to occur, a\nseasonal factor can further mediate the impact of mobility on population exposure.”\nReference: Eurostat data codes: TOUR_DEM_TOTOT, TOUR_DEM_TOQ, DEMO_PJANBROAD\nB) Other methodological issues\na) The place of residence differs from the place of work (generally in UHI), which the authors\nacknowledge. This involves (on average) a higher exposure to high temperature during day w.r.t the\nplace of residence. Is there evidence in the literature of the magnitude of the discrepancies\ninvolved?\nWe are not aware of literature that quantifies the difference in temperature exposure due\nto difference between work and residential addresses. It is something we are currently\ninvestigating in a separate study by looking at the difference in daytime and nighttime\npopulations (Batista e Silva et al. 2020, https://doi.org/10.1038/s41467-020-18344-5).\nPreliminary findings indicate a seasonal cycle and tendency towards greater daytime total\npopulation in the city as people travel in to work, though with exceptions (Fig. 1 below). For\nthe case of London, it was found that people tend to travel to warmer parts of the city\nduring the day; the warmer, the greater increase in population (Fig. 2 below). This trend is\nweaker during later spring and summer months, but picks up again by autumn, when heat\nextreme events may still be observed. We decided not to include this analysis in the\nmanuscript as it’s still ongoing and will be investigated in detail separately.\nFig. 1: Seasonal variation in total nighttime and daytime population in Milan, London, and\nStockholm. The nighttime population is based on registered address of the residents and data\nfrom online booking platforms. The daytime population is derived based on the locations of\nthe primary daily activities of 16 population groups include residents, employees, students,\nnon-working population, etc. City total is the sum over each city’s domain from Batista e Silva\net al. (2020).\nFig. 2: Difference between daytime and nighttime population as function of grid monthly\naverage temperature for 2015 in London. Note that the year of reference for daytime and\nnighttime population data is 2011 (Batista e Silva et al. 2020), so the results should just be\nseen as an example of how using daytime or nighttime population changes the estimated\npopulation exposure to different temperatures.\nb) Are the conditions of health care (speed of intervention, transport to a health care facility) during\nthe health event causing the death the same between UHI and non-UHI? Can this influence mortality\nbetween residents in and out of HUI?\nThank you for highlighting this. Yes indeed, access to healthcare and other interventions may\ndiffer between urban and rural communities, which can influence health outcomes. We do\nnot consider these differences in our analysis, which focuses on the role of temperature\ndifferences due to UHI alone. However, this is now noted in the discussion section.\nC) Remarks on economic issues\nI agree with the value of statistical life (VSL) chosen to assess mortality impacts, which was carefully\ncalculated by the OECD in 2012 and widely used since then. I am also satisfied with the way it is\ntranslated into a value relevant for Europe. Note, however, that some countries recommend that\nthe VSL should be changed according to changes in Gross Net Product per capita instead of\nConsumer Price Index, but the differences are limited. I have three points to make.\na) First, I am reluctant to the use of the terms “costs” and “benefits” when associated with non-\nmarket impacts, here the valuation of mortality. This can be misleading to the reader as these\namounts will not actually be gained or lost by the cities concerned, rather they are welfare losses in\ntheir population associated with premature mortality and valued economically. I prefer the\nterminology “economic impacts” or “economic assessments”, which can be negative or positive. I\nwould then be satisfied if this costs/benefits formulation was changed throughout the text. This\nincludes the abstract “UHIs in Europe cost/save €480/€677 in heat-/cold-related mortality”, as well\nas the title “Mortality costs and savings of urban heat islands in European cities”. For the latter,\nsomething like “Economic valuation of acute mortality attributed to urban heat islands in European\ncities” would be better.\nThank you for the suggestion. We have now changed the title and amended the wording\nthroughout the manuscript. We removed the word “acute” from the suggested title\nbecause, even though we don’t capture impacts that may affect long term health, we do\nconsider mortality occurring within 3 weeks after exposure. This is to avoid\nmisunderstanding with acute mortality in the context of heat-related deaths occurring\nwithin a day or two after the event as opposed to cold-related deaths occurring over a\ncouple weeks. We specify instead “temperature-related mortality”.\nb) Second, in choosing this VSL, the economic computations assume that life expectancy at death is\nsimilar to that observed for all causes of death. Since the effects acute mortality are being assessed\nhere, it would be useful to discuss the expected reduction in life expectancy. A difference in life\nexpectancy, and the extreme case of a single year of life lost per death attributable to UHI\ntemperature conditions, would alter the economic computations.\nThank you for the comment and suggestion. We have now added an analysis with years of\nlife lost and value of life year. Discussion on this has now been added in the sections on\nimpacts of UHIs and on economic assessments, also appended below.\nIn the section on impacts of UHIs (~L206 in the tracked version):\nTo additionally consider the age dependence of UHIs' impact on mortality, years of\nlife lost (YLL) analyses are included in the Supplementary materials (Fig. S13, Tables\nS5 and S9). While the overall conclusion of a weakly protective annual net impact for\nmost cities still holds, an annual adverse impact is found for more cities with the YLL\napproach (29, vs. 18 with the mortality counts-based analysis). As younger age\ngroups are weighted more strongly in YLL analyses, this finding may be reflective of\nyounger populations' greater vulnerability to heat, which is more similar to that of\nolder age groups, compared to their vulnerability to cold, which tends to be lower\n(Fig. 1a-c).\nIn the section on economic assessments (~L326 in the tracked version):\nEconomic assessments by VSL as presented above does not explicitly consider age,\ntherefore life expectancy at time of death is assumed to be comparable to all causes\nof death. However, as can be observed from age-dependent temperature-mortality\nrelationships (Fig. 1a-c), heat and cold exposure disproportionately affect older\npopulations. The life expectancy at the time of death would therefore likely be\nshorter than the average for all-cause mortality. An approach to account for this is to\nconsider the YLL through value of life year (VOLY) valuation, which is included in\nSupplementary table S6. While there is strong correlation between economic impacts\nas determined through VSL and that through VOLY (Supplementary fig. S17a,b), the\nmagnitude of the impact as assessed through VOLY is only around 14% (median of\nthe multi-city median annual net, heat, and cold impacts) of that assessed through\nVSL (Supplementary fig. S17c). This is mainly due to differences in valuation\napproaches which resulted in each VSL (3.91 million 2021-EUR per statistical life [47])\nequating to 85 VOLY (46,000 2021-EUR per year [48]). However, there is currently no\nclear consensus on economic assessments by YLL, and at the higher end of the VOLY\nestimate (116,000 2021-EUR per year [48]), each VSL equates to around 34 VOLY,\nresulting in valuations by VOLY-based assessments to be around 35% of that using\nVSL (Supplementary fig. S17d). Given limitations with the VOLY approach, including\nethical concerns and lack of evidence in the assumption of a time-independent VOLY\n[47], VSL-based assessments are the main focus in this study.\nc) Related to the point above, I find that the authors too quickly eliminate the possibility of\nconducting an analysis based on years of life lost (lines 453-57), and a valuation that would calculate\nthem on the basis of ages of death (at least, age groups) and exposition response functions. This\nwould indeed take into account at least the loss of life expectancy associated with age (even if not\nwith cause). This was done by Khomenko et al (2021) for PM2.5 for example. It would not be\nnecessary to go as far as monetarization (even if some values of life year lost exist for Europe, such\nas in Desaigues et al., 2011), but at least to make a net balance of years of life gained/lost that would\ntake into account the age distribution.\nThank you for the suggestion. Quantification of years of life lost, as well as monetarisation\nfollowing Desaigues et al. 2011, have now been added. Discussions are included in the\nsection on impacts of UHI (lines 206-221 in the tracked changes version) and the section on\neconomic assessment (lines 326-344 in the tracked version), also included in the reply to the\nprevious comment.\nII) Use of statistics\nIn the box-and-whisker plots (Figures 3a et b, as well as S3, S7, S10 S14 and S16), please specify what\nthe values represent (certainly the median for the centre line, quartiles 1 and 3 for the box, 1.5\nthe interquartile range (IQR) Q3-Q1 for the whiskers (with the respective boundaries the minimum\nand maximum), and outliers for dots outside the IQR).\nThank you for the suggestion. Now added.\nIII) List of suggested improvements\nAlthough this work is already very comprehensive, I think that the benefits of implementing some of\nthe 4 directions I present below are worth considering.\na) The authors compare their results with those for mortality associated with PM2.5 exposure. The\nlatter is, in general, proportional to population density in cities, and thus acts in synergy with the\neffects of temperature for UHI, both in summer and winter. However, another pollution indicator\nappears to be related to heat wave mortality: ground-level ozone (e.g. Reid et al, 2012). In this case,\nthe relationship with UHI would be rather antagonistic in summer or even on hot spring or autumn\ndays, since in highly polluted areas, and under certain insolation conditions, high NOx concentrations\ncan destroy part of the ozone. Consequently, ozone levels are likely to be higher in peri-urban and\nrural areas downwind of UHI. To what extent can this affect the results?\nThank you for this comment. We indeed agree with the reviewer that ozone is generally\nlower in central urban areas and higher in the suburbs and surroundings, since ozone\nformation takes time and the reduction in VOC emission in cities means that ozone\nformation now requires some of the biogenic emissions coming from vegetation around\ncities (e.g., see analysis by one co-author in Llaguno-Munitxa and Bou-Zeid 2020,\nhttps://iopscience.iop.org/article/10.1088/1748-9326/ab8d7e). However, to deduce\nwhether ozone concentrations in cities will in fact anticorrelate or correlate with\ntemperatures during extreme heat events, one has to also account for multiple confounding\nprocesses that affect the ozone concentrations here including lower typical wind speed\nduring extreme heat (increases ozone), higher insolation (increases ozone), and altered\nvegetation activity (might reduce ozone if plants shutoff during extreme heat, or increase it\nif they transpire more to control their temperatures). As the mentioned Reid et al. 2012\npaper summarised, there is no consensus on the treatment of ozone in temperature-related\nhealth impact assessment studies.\nIn the manuscript we have now added a comparison of annual heat and ozone mortality,\nand in fact we find a positive correlation as with PM2.5, whereby cities with greater heat\nmortality risk also tend to experience greater ozone-related mortality risk. This is, however,\non an annual scale as daily air pollution-related mortality data is not available. Additionally,\nozone-related risk is quantified against thresholds set by WHO guidelines as opposed to the\nrural values as is the case for UHI. This does not contradict the fact that during periods of\nextreme heat, ozone concentrations might still be more elevated in rural terrain; therefore,\nwhen correlation in time are considered, both ozone and heat increase mortality in urban\nand rural areas during heat waves. A discussion on this is included in the manuscript along\nwith the additional ozone analysis. (paragraph starting on line 298 in the manuscript with\ntracked changes)\nb) Men and women have a different reactivity to temperature stress. This is linked to physiological\nfactors: muscle mass, which is higher on average in men, generates heat, whereas fat mass, which is\nhigher on average in women, does not. It is also linked to different lifestyles: fewer women are\nactive, implying differences in exposure at work or when travelling to work. The authors seem to\nhave the annual mortality by age and sex. If other data are available by sex too, would it be possible\nto perform the calculations in step 4.2 separately by sex?\nUnfortunately, this is not possible as it would require daily mortality counts separated by\nsex, on top of age, for all cities used in developing the exposure-response relationships, as\nwell as sex-specific observations to be used in the meta-analysis, which are not fully\navailable. We agree that some differences in risk may be present and would be worth\nexploring, though it’s not the focus of this study since we are focusing on overall city impacts\nand the risk relationships here are already representative of the sex make-up of each city.\nc) The authors discuss the consequences of climate change on their analyses. Lines 276-81, it is\nwritten: “Additionally, climate change is expected to lead to increases in the proportion of warm days,\nand therefore heat-related risks, relative to cold days and cold-related risks in future [47].\nGiven the weak annual net protective effect of UHI for most cities examined and the greater impact\nper day for heat, temperature increases in future can quickly shift the balance and lead to an annual\nnet adverse impact”. Why not make these estimates? Gasparrini et al. (2017) did so on the basis of\ndifferent representative concentration pathways (RCPs). Why not also apply it to UHI under climate\nchange scenarios? It seems to me that the authors can easily obtain all the data they need. And this\nwill eventually allow to determine, for each city, when the adverse effects of high temperature UHI\nwill outweigh the protective effects of low temperature UHI.\nThank you for the suggestion. While in principle it may be possible to provide a first order\nestimate of climate change impacts, for instance by applying a uniform shift to the\ntemperature distribution, we feel a more sophisticated approach is necessary especially in\nthe context of UHI. An important distinction between our study and for instance Gasparrini\net al. (2017) is that UHI is not a static quantity. The temperature difference between urban\nand rural areas will be strongly affected by urban growth and decline in future, and it varies\nsignificantly in time especially during heat and cold waves. Even without infrastructural\nchanges, the response of urban temperatures to climate change may not be the same as the\nresponse in rural areas (e.g. Zhang et al. 2023,\nhttps://iopscience.iop.org/article/10.1088/1748-9326/acbc90). Climate change projections\nare based on coarse-grid global climate models, which often have minimal representation of\nurban areas. Therefore, to examine UHI, urban downscaling would generally be required,\noften in time slices. In the case of our study setup, model simulations using UrbClim would\nneed to be performed under future conditions, which would need to be properly\nconstrained. On the epidemiology side, future population growth and aging will also alter\nthe quantification of impact, even if vulnerability of each age group is assumed to remain\nthe same (e.g. no advances in healthcare). Given these considerations, we feel that\nappending a first order estimate by assuming uniform shifts in temperature and UHI may be\nmore misleading than helpful. Instead, a more complete consideration of UHI impact in\nfuture under climate change would be required, which goes beyond the scope of this study.\nd) In Line 337, the authors mention in future work a more detailed study of vulnerabilities. Why\nwould it not be possible to make a first attempt to construct indicators of deprivation and to match\nthem at grid level? Several indexes are possible.\n- The European Deprivation Index within urban areas, based on EU – SILC (European Union –\nStatistics on Income and Living Conditions) can be exploited, or build from aggregated census data\nand tax incomes of the population by year.\n- A simpler version, originally constructed in France (Windenberger et al., 2012), is based on 4\nvariables: the median household income per consumption unit, the percentage of high-school\ngraduates in the population aged 15 years and older, the percentage of blue-collar workers in the\nactive population aged 15 to 64, and the unemployment rate in the active population aged 15 to 64.\n- The Townsend deprivation index (Townsed, 1987), originally constructed in England and Wales, is\nalso based on four variables: the rate of households without a car, the rate of non-owner\nhouseholds, the rate of households with more than one person per room, and the rate of\nunemployment in the active population.\nThank you for the suggestions, especially of the deprivation indices. Due to the limited\navailability of granular deprivation and associated census data at intra-urban resolution, we\nare not able to perform this analysis for many cities. However, we were able to obtain at\nhigh spatial resolution the Index of Multiple Deprivation for England, and analyses for\nLondon and Leeds as example cities are now added in the supplementary materials (Fig.\nS18), with a discussion added in the Discussion section in the main text (line 417-431 in the\ntracked version), also appended below.\nExamination with the 2019 English Index of Multiple Deprivation [64] for example\ncities of London and Leeds in the current study also shows statistically significant,\nthough moderate (Spearman's correlations of -0.4 and -0.6, respectively),\ncorrelations between socioeconomic deprivation and UHI-induced mortality risk\nduring heat extreme days (Supplementary fig. S18). More socioeconomically\ndeprived neighbourhoods tend to experience greater UHI impact on average, though\nsimilar UHI impacts are also observed in neighbourhoods with significantly different\ndeprivation levels (Supplementary fig. S18b,d). In addition to indicating a biased\nexposure for more socioeconomically deprived neighbourhoods, this also highlights a\npotential overlap between neighbourhoods with higher UHI and those with greater\nvulnerability. Populations in more socioeconomically deprived neighbourhoods may\nbe more vulnerable due to factors such as lower income, poorer health, inferior living\nenvironment, and greater barrier to housing and services, all of which are included in\nthe 2019 English Index of Multiple Deprivation [64]. The greater vulnerability on top\nof higher exposure may therefore further compound the overall impact of UHI.\nIV) References\nDesaigues, B., Ami, D., Bartczak, A., Braun-Kohlová, M., Chilton, S., Czajkowski, M., Farreras, V. et\nal. (2011) Economic valuation of air pollution mortality: A 9-country contingent valuation survey of\nvalue of a life year (VOLY). Ecological Indicators 11, 902–910\nGasparrini, A., Guo, Y., Sera, F., Vicedo-Cabrera, A. M., Huber, V., Tong, S., ... & Armstrong, B. (2017).\nProjections of temperature-related excess mortality under climate change scenarios. The Lancet\nPlanetary Health, 1(9), e360-e367\nGuillaume, E., Pornet, C., Dejardin, O., Launay, L., Lillini, R., Vercelli, M., Marí-Dell’Olmo, M., et al.\n2016. Development of a Cross-Cultural Deprivation Index in Five European Countries. Journal of\nEpidemiology and Community Health 70(5), 493–99.\nKhomenko, S., Cirach, M., Pereira-Barboza, E., Mueller, N., Barrera-Gómez, J., Rojas-Rueda, D., ... &\nNieuwenhuijsen, M. (2021). Premature mortality due to air pollution in European cities: a health\nimpact assessment. The Lancet Planetary Health, 5(3), e121-e134.\nReid, C. E., Snowden, J. M., Kontgis, C., & Tager, I. B. (2012). The role of ambient ozone in\nepidemiologic studies of heat-related mortality. Environmental health perspectives, 120(12), 1627-\n1630.\nTownsend, P. (1987). Deprivation. Journal of Social Policy, 16(2), 125-146.\nWindenberger, F., Rican, S., Jougla, E., & Rey, G. (2012). Spatiotemporal association between\ndeprivation and mortality: trends in France during the nineties. The European Journal of Public\nHealth, 22(3), 347-353.\n\nReviewer #3 (Remarks to the Author):\nThe article is innovative and brings a significant contribution to the research in the field of urban\nheat island and mortality under heat and cold conditions. The study area is wide-ranging, and all the\nmost advanced methodologies have been applied to investigate the relationship between mortality\nand temperature. The use of UrbClim makes the analyses (partially) integrated with the\ncharacteristics of the built environment and the Köppen-Geiger climate classification ensures that\nthe classifications used are validated and integrated with the current literature. The graphs are\naccurate and understandable. Finally, the inclusion of micro-particle pollution is an added value,\nalthough it would have been interesting to see the association between O3 and PM10, 2.5 and heat-\nand cold- related deaths.\nReview of the paper:\n“Mortality costs and savings of urban heat islands in European cities”\nFirst of all, I would like to congratulate the authors for the excellent work. The conducted analyses\nare in line with the up-to-date scientific literature and the discussion of the results is very rich and\ninteresting. This manuscript brings a significant contribution to the research in the field of urban\nheat island and mortality under heat and cold conditions within EU urban areas. The most advanced\nmethodologies have been applied to investigate the relationship between mortality and\ntemperature and the use of UrbClim makes the analyses (partially) integrated with the\ncharacteristics of the built environment. In addition, the Köppen-Geiger climate classification\nensures that the classifications used are validated and integrated with the current literature. The\ninclusion of micro-particle pollution is an added value, although it would have been interesting to\nsee the association between O3 (in summer) and PM10, 2.5 (in winter). Notwithstanding,\ncontents and technical details are of excellent quality.\nThank you for the positive feedback and suggestions. An additional analysis for ozone-\nrelated mortality and how it relates to temperature-related mortality has now been added in\nthe discussion about comparison to air pollution (paragraph starting on line 298 in the\nmanuscript with tracked changes). Though not the focus of the main text, associations\nbetween O3- and PM2.5-related mortality (annual total estimates) have also been added in\nthe supplementary materials (Fig. S16f).\nAs minor comment, I would like to raise two points:\n1] A better clarification of the meaning of “exposure”, “vulnerability”, and “risk” in the context of this\nresearch would not be a useless explanation. Especially in the climate change field, many of these\nconcepts/words blur getting sometimes meanings sensibly different according to the reader’s\nbackground. For this reason, I believe that a clear definition of the used terms would be of help to\nclose the gaps between the environmental epidemiological and other interdisciplinary studies (see\nIPCC Glossary, IPCC 2022).\nThank you for the suggestion. A new paragraph has now been added at the end of the\nintroduction section to define these words.\n2] A clear identification of social inequalities in the nexus between extreme temperatures and\nmortality would have provided a clearer understanding of the risk identification across the different\ncategories of the population (most educated vs least educated, most isolated vs least isolated, etc.),\nbut we are aware that the availability of data at the urban level is not the same in all cities. Based on\nthe results produced by the study (e.g., Table S2 “Mortality associated with UHI, per 100,000 urban\nadult population”), it might be interesting to further investigate these aspects in some cities where\nsimilar data are available.\nOther punctual recommendations in the text:\nOn Results:\nLine 95: insert a “,” after “e.g.”\nA comma has now been added to all instances of e.g. and i.e. in the manuscript.\nLine 126: it could be interesting to see some examples “of cities which reach around the 20% of\nenhancement”.\nOn Discussion:\n• The use of the term “mitigation” here can lead to misunderstandings. I would suggest using other\nterms in line with the IPCC and UNFCCC languages (i.e., “adaptation”) (IPCC 2022), when\nappropriate.\nReference in the text:\nLine 267: “this indicates the need for mitigation strategies”\nLine 299: “such analyses can then allow a more even comparison to the costs of various mitigation\nplans”\nThank you for pointing this out. We meant mitigation in the sense of reducing the amount of\nurban heat, instead of adapting to existing urban heat, though we agree that the word\nmitigation may be misunderstood as “mitigation of climate change,” in the way of carbon\nbudgeting. Given the importance of UHI mitigation strategies that prevent the build up of\nurban heat in the first place (e.g. urban greening), as opposed to adaptation strategies that\ntry to cope with the heat (e.g. air conditioning), we’ve kept the word “mitigation” in these\ntwo examples listed above, but now noted specifically that we’re referring to “UHI\nmitigation.” On the other hand, we’ve removed “climate mitigation” in the abstract.\n• Spatial heterogeneity could be influenced by outdoor (e.g., direction of the windows front, building\nage, roofing construction, number of floors) as well as indoor (e.g., thermal isolation, walls and\nceiling, fans, double-glazed windows) environmental characteristics. Consider whether to quote\nthem in the text.\nThank you for the suggestion. We have now expanded this point in the manuscript with\nmore examples, as appended below.\nAdditional spatial heterogeneity in other vulnerability factors, such as properties\ncontrolling the temperature of the housing stock associated with both structural\nfeatures (e.g., insulation, number of floors) and internal environmental regulation\n(e.g., ventilation, air conditioning), may compound the impact associated with\noutdoor UHI.\nOn Methods:\n• No citations to DLNM model in the text (section 4.2).\nThank you for pointing this out. Now added.\n• No citations to MMT in the text (section 4.4).\nMMT is a measure commonly used in temperature-related health assessments to indicate\nthe optimal temperature where the associated risk is at its minimal. We are not able to\nidentify the reference where this measure was first introduced as no reference is given in\nany of the published literature we found where MMT is used. However, a definition is given\nin the manuscript so hopefully its meaning is clear.\nLine 397: about “UrbClim domain average temperature from the 2008 to 2017 period”. Why did you\nconsider only 9 years? Is the available timeframe? For the World Meteorological Organization, 30-\nyear reference period reflects changing climate.\nYes, 2008 to 2017 is the full range of UrbClim simulations available. Given the biases that can\nexist between different temperature datasets, and given that UrbClim provides temperature\nacross the entire urban domain at high spatial resolution, we felt it more important to use\nthe UrbClim temperatures rather than a longer time series from another dataset.\nLine 313-316: In relation to “A previous study has found, for instance, that within Parisian\nneighbourhoods experiencing the greatest heat exposure, those which are the most\nsocioeconomically deprived have a twofold higher mortality risk compared to the most affluent [53]”.\nTo provide a clearer overview of the state-of-the-art in relation to social inequalities - extreme\ntemperatures – health, I suggest referring to other cities when considering the relation between\ntemperature and mortality and different health outcomes socio-economic variables. One option may\nbe to quote studies over cities with a higher mortality ranking associated with UHI (Table S2 in\nSupplementary Material) (e.g., Turin, Madrid, Bologna, Thessaloniki, etc.).\nThank you for the suggestion. Unfortunately, we were unable to find publications in the\nliterature looking at correlations between socioeconomic status and temperature exposure\nfor these cities. High resolution data of socioeconomic status for these cities is also not\nreadily available for us to perform the analysis ourselves. However, we were able to obtain\nthe Index of Multiple Deprivation for England at high spatial resolution, and analyses for\nLondon and Leeds as example cities have now been included in the manuscript (line 417-431\nin the tracked version). The associated text in the discussion is appended below.\nExamination with the 2019 English Index of Multiple Deprivation [64] for example\ncities of London and Leeds in the current study also shows statistically significant,\nthough moderate (Spearman's correlations of -0.4 and -0.6, respectively),\ncorrelations between socioeconomic deprivation and UHI-induced mortality risk\nduring heat extreme days (Supplementary fig. S18). More socioeconomically\ndeprived neighbourhoods tend to experience greater UHI impact on average, though\nsimilar UHI impacts are also observed in neighbourhoods with significantly different\ndeprivation levels (Supplementary fig. S18b,d). In addition to indicating a biased\nexposure for more socioeconomically deprived neighbourhoods, this also highlights a\npotential overlap between neighbourhoods with higher UHI and those with greater\nvulnerability. Populations in more socioeconomically deprived neighbourhoods may\nbe more vulnerable due to factors such as lower income, poorer health, inferior living\nenvironment, and greater barrier to housing and services, all of which are included in\nthe 2019 English Index of Multiple Deprivation [64]. The greater vulnerability on top\nof higher exposure may therefore further compound the overall impact of UHI."}, {"round": 2, "reviewer_comments": {"reviewer_2": "This revised work studies the impacts of temperature changes due to urban heat island (UHI) on\nmortality, across 85 European cities belonging to 5 background climatic conditions. It accounts for\nintra-urban variations in temperature and population distribution and compute the health and\neconomic impacts over the entire annual cycle, as well as the net effect.\nThe conclusions are original in that they provide results on the net balance of UHI benefits over the\nwhole year, for numerous cities covering a variety of climatic conditions. They can help decision-\nmakers understand the impact of UHIs on (short-term) mortality and consider policies to reduce them\nat city level. The conclusions and interpretation of the data are robust, valid and reliable, with many\nadditional analyses supporting the main results, some of them responding to my comments on the\noriginal version (see below). The references seem adequate.\nI explain below how the authors have taken my comments into account in the revised version, and\nend with three remarks.\n* The revised version adequately addresses my three comments on the reporting of the results.\n* Regarding methodology, it now provides the appropriate (and up-to-date) reference to the main\narticle that presents the epidemiological methodology used (Masselot et al., 2023).\n* Regarding epidemiological methodological issues, the authors have correctly addressed my\ncomments concerning the influence of city-level temperature-mortality relationships on calculations of\nthe impact of UHIs within the city, how lag periods between exposure and mortality effects are taken\ninto account, and the potential influence of mobility due to tourism on the population exposure during\nthe hottest days.\n* Regarding other methodological issues, I thank the authors for sharing, in their rebuttal,\npreliminary results on seasonal variation in the total nighttime and daytime population in response to my\ncomments on potential differences between place of residence and place of work. They also include in\nthe discussion section of the article my remark on potential differences in healthcare conditions\nbetween UHIs and non-UHIs.\n* With regard to economic issues, I am satisfied with the replacement of the terms \"costs\" and\n\"benefits\" by the terms \"economic impacts\", \"economic assessments\" or \"economic evaluation\"\nthroughout the article (including the title). Similarly, I am satisfied with the additional analysis in\nterms of years of life lost / life expectancy which I requested and which is now included in the main\ntext as well as in the Supplementary Appendix.\n* Regarding the use of statistics, the description of box and whisker diagrams has been clarified in\nFigures 3 and 5, as well as in the Appendix.\n* The authors have taken into account the four additional directions I suggested in my initial report,\nproviding different types of response.\n- With regard to ozone, the correlation between ozone-related deaths and heat-related deaths is\npresented in Figure 5e at city level, the comparison with the costs of heat-related deaths is\npresented in Figure 5d, and a few sentences explain the results in the main text.\n- As regards sex-specific calculations, the authors explain that they are not possible because the\ndaily mortality counts and sex-specific observations to be used in the meta-analysis are not fully\navailable.\n- As for the impact of climate change on their analyses, the authors replied that it would require\neither a very detailed analysis at the UHI level, thanks to urban downscaling which is outside the scope\nof their study, or a potentially spurious assumption of uniform temperature changes which could be more\nmisleading than helpful.\n- Regarding my suggestion to make a first attempt to match indicators of deprivation and\ntemperature-related health effects at grid level, the authors have made an initial exploration. They\nfocused on UHI-induced mortality among 65–74-year-old on the warmest 2% days in two cities,\nLondon and Leeds, based on the availability of the English Index of Multiple Deprivation. The result\nwas a negative and statistically significant correlation between UHI-induced mortality and\nsocioeconomic deprivation in both cities (see Table S18).\n* Remarks\n- As indicated above, the exploration of a possible negative relationship is currently limited to the 65-\n74 age group, which should be specified in the main text.\n- Furthermore, given that the mortality RR for this age group appears to be more sensitive to cold\nthan to high temperatures in London (see Figure 1b and Figure S8e), and probably also in Leeds, it\nwould be interesting to also calculate the correlation between UHI-induced mortality and socio-\neconomic deprivation during the coldest 2% days. Indeed, Tables S3, S5, S7 and S9 show that the\neffect per 100,000 urban adult population for these days is comparable for London, but three times\ngreater for Leeds. Moreover, if we compare cold days with heat days, the same four tables indicate\nthat the effects are 6 times greater for London and around 16 times greater for Leeds. Therefore, it\nwould be interesting to check whether the negative correlations obtained during the 2% warmest days\nare also obtained during the 2% coldest days, and/or during heat and cold days.\n- Finally, it should be noted that the economic impacts provided in Tables S4, S6, S8 and S10 are\ncalculated per adult and not per 100,000 adults, as incorrectly stated in the titles.\nOlivier Chanel\nAix Marseille Univ, CNRS, AMSE, Marseille, France"}, "author_response": "Thank you very much for the feedback. Please find below in blue responses to individual\nremarks. Additionally, we have updated the exposure-response relationships used in this study\nto be consistent with the published version associated with Masselot et al. 2023 and added\nuncertainty quantifications of the mortality estimates using the Monte Carlo simulations\ntherein. This resulted in some minor changes in the estimates and associated discussions, but\nthe overall findings remain unchanged. Please find attached documents with tracked changes\nfor both the manuscript and the supplement.\n\nReviewer #2 (Remarks to the Author):\nThis revised work studies the impacts of temperature changes due to urban heat island (UHI) on\nmortality, across 85 European cities belonging to 5 background climatic conditions. It accounts\nfor intra-urban variations in temperature and population distribution and compute the health\nand economic impacts over the entire annual cycle, as well as the net effect.\nThe conclusions are original in that they provide results on the net balance of UHI benefits over\nthe whole year, for numerous cities covering a variety of climatic conditions. They can help\ndecision-makers understand the impact of UHIs on (short-term) mortality and consider policies\nto reduce them at city level. The conclusions and interpretation of the data are robust, valid\nand reliable, with many additional analyses supporting the main results, some of them\nresponding to my comments on the original version (see below). The references seem\nadequate.\nI explain below how the authors have taken my comments into account in the revised version,\nand end with three remarks.\n* The revised version adequately addresses my three comments on the reporting of the results.\n* Regarding methodology, it now provides the appropriate (and up-to-date) reference to the\nmain article that presents the epidemiological methodology used (Masselot et al., 2023).\n* Regarding epidemiological methodological issues, the authors have correctly addressed my\ncomments concerning the influence of city-level temperature-mortality relationships on\ncalculations of the impact of UHIs within the city, how lag periods between exposure and\nmortality effects are taken into account, and the potential influence of mobility due to tourism\non the population exposure during the hottest days.\n* Regarding other methodological issues, I thank the authors for sharing, in their rebuttal,\npreliminary results on seasonal variation in the total nighttime and daytime population in\nresponse to my comments on potential differences between place of residence and place of\nwork. They also include in the discussion section of the article my remark on potential\ndifferences in healthcare conditions between UHIs and non-UHIs.\n* With regard to economic issues, I am satisfied with the replacement of the terms \"costs\" and\n\"benefits\" by the terms \"economic impacts\", \"economic assessments\" or \"economic\nevaluation\" throughout the article (including the title). Similarly, I am satisfied with the\nadditional analysis in terms of years of life lost / life expectancy which I requested and which is\nnow included in the main text as well as in the Supplementary Appendix.\n* Regarding the use of statistics, the description of box and whisker diagrams has been clarified\nin Figures 3 and 5, as well as in the Appendix.\n* The authors have taken into account the four additional directions I suggested in my initial\nreport, providing different types of response.\n- With regard to ozone, the correlation between ozone-related deaths and heat-related deaths\nis presented in Figure 5e at city level, the comparison with the costs of heat-related deaths is\npresented in Figure 5d, and a few sentences explain the results in the main text.\n- As regards sex-specific calculations, the authors explain that they are not possible because the\ndaily mortality counts and sex-specific observations to be used in the meta-analysis are not fully\navailable.\n- As for the impact of climate change on their analyses, the authors replied that it would require\neither a very detailed analysis at the UHI level, thanks to urban downscaling which is outside\nthe scope of their study, or a potentially spurious assumption of uniform temperature changes\nwhich could be more misleading than helpful.\n- Regarding my suggestion to make a first attempt to match indicators of deprivation and\ntemperature-related health effects at grid level, the authors have made an initial exploration.\nThey focused on UHI-induced mortality among 65–74-year-old on the warmest 2% days in two\ncities, London and Leeds, based on the availability of the English Index of Multiple Deprivation.\nThe result was a negative and statistically significant correlation between UHI-induced mortality\nand socioeconomic deprivation in both cities (see Table S18).\n* Remarks\n- As indicated above, the exploration of a possible negative relationship is currently limited to\nthe 65-74 age group, which should be specified in the main text.\nThank you for pointing this out. The 65-74 age group was chosen as a representative\nexample. The relationship is near identical for all other age groups (please see figures\nbelow) because the exposure-response curves follow similar monotonic shapes for all\nage groups in each city. We also do not capture potential differences in spatial\ndistribution of different aged populations. The rank relationship between deprivation\nand temperature-related mortality is therefore essentially a relationship between\ndeprivation and temperature. To avoid confusion, we have now changed the plot in the\nsupplement to show the relationship for the entire age weighted adult population.\nFigure 1: Correlation between socioeconomic deprivation and UHI's impact on mortality\nrisk for (a-e) London and (f-j) Leeds in the UK. UHI impact is shown as the difference in\nmortality risk compared to the rural average for the (a,f) 20-44, (b,g) 45-64, (c,h) 65-74,\n(d, I) 75-84, and (e,j) 85+ age groups during the warmest 2% days in 2015-2017. The\ndeprivation index ranks 32,844 LSOA areas in England from most deprived (low IMD\nrank) to least deprived (high IMD rank).\nFigure 2: As figure 1 but for the coldest 2% days.\n- Furthermore, given that the mortality RR for this age group appears to be more sensitive to\ncold than to high temperatures in London (see Figure 1b and Figure S8e), and probably also in\nLeeds, it would be interesting to also calculate the correlation between UHI-induced mortality\nand socio-economic deprivation during the coldest 2% days. Indeed, Tables S3, S5, S7 and S9\nshow that the effect per 100,000 urban adult population for these days is comparable for\nLondon, but three times greater for Leeds. Moreover, if we compare cold days with heat days,\nthe same four tables indicate that the effects are 6 times greater for London and around 16\ntimes greater for Leeds. Therefore, it would be interesting to check whether the negative\ncorrelations obtained during the 2% warmest days are also obtained during the 2% coldest\ndays, and/or during heat and cold days.\nThank you for the suggestion. Plots for the coldest 2% days have now been added in\nSupplementary figure S18. We see a positive correlation for cold temperatures, whereby\nmore deprived neighbourhoods tend to experience greater protective effects during\ncold extreme days. This is consistent with the expectation that the magnitude of UHI\nimpacts, whether protective or adverse, is correlated with socioeconomic deprivation.\nThis is now also noted in the discussion in the manuscript.\n- Finally, it should be noted that the economic impacts provided in Tables S4, S6, S8 and S10 are\ncalculated per adult and not per 100,000 adults, as incorrectly stated in the titles.\nThank you for spotting this. This has now been corrected."}, {"round": 3, "reviewer_comments": {"reviewer_2": "The second revised version adequately addresses my three comments on the reporting of\nthe results.\nOlivier Chanel, Aix Marseille Univ, CNRS, AMSE, Marseille, France"}, "author_response": "Thank you very much for the valuable and insightful feedback throughout the review\nprocess."}]

Decision letter