Cooling benefits of Urban agriculture to inhabitants—mapping cooling potential of allotments in European functional urban areas

Research article Cooling benefits of Urban agriculture to inhabitants—mapping cooling potential of allotments in European functional urban areas Grzegorz Budzik a,* , Marta Sylla b,c , Catharina J.E. Schulp d a Wrocław University of Environmental and Life Sciences, Department of Environmental Protection and Development, Grunwaldzki Sq. 24, 50-363, Wrocław, Poland b Wrocław University of Environmental and Life Sciences, Institute of Spatial Management, Grunwaldzka Str. 55, 50-357, Wrocław, Poland c Stockholm Resilience Centre, Stockholm University, Stockholm, Sweden d Vrije Universiteit Amsterdam, Institute for Environmental Studies, Environmental Geography Group, De Boelelaan 1111, 1081HV, Amsterdam, the Netherlands ABSTRACT As climate change accelerates and open urban spaces diminish, multifunctional urban planning solutions that enhance multiple ecosystem services (ES) are essential. Urban agriculture, particularly allotment gardens, plays a key role in addressing these challenges. This study assesses the cooling potential of all allotments across European Functional Urban Areas (FUAs) in terms of area cooled, maximum cooling distance and intensity, cooling effectiveness, and the population benefiting from cooling services, using a custom watershed-based tool, OpenStreetMap data, and land surface temperature from Landsat 8/9. Allotments were classified according to their proximity to blue-green infrastructure (BGI) and the types of cooled built-up areas, employing the concept of local climate zones and NDVI statistics. Results show that allotments provide cooling services to 4.1 million people within FUAs, with 1.7 million in Germany. Each square kilometre of allotments cools an average of 8,221 people, with the cooled area being, on average, 2.8 times larger than the allotment size. The most effective allotments are in Brussels, where each square kilometre cools about 100,000 people. In terms of urban morphology, urban agriculture was 23 % more efficient than peri-urban agriculture, independent allotments demonstrated effectiveness comparable to those located in proximity to larger BGI objects, and only 5 % of allotments cooled areas with the highest heat risk. This is the first large-scale assessment of the cooling ES provided by urban agriculture in Europe, quantitatively indicating that urban planning should prioritize allotment placement near dense urban areas while maintaining their optimal size to maximize urban heat island mitigation. 1. Introduction European cities are increasingly facing the direct socio-economic consequences of a warming climate (Robine et al., 2008). Urban morphology significantly contributes to the formation of urban heat islands (UHI)—a phenomenon of higher temperatures in urban areas compared with those in the surrounding rural zones (Oke, 1982; Sundborg, 1951; Ward et al., 2016). High population density, infrastructure concentration, population aging, and increasing heatwave frequency and intensity (Easterling et al., 2000; Meehl and Tebaldi, 2004) make European cities particularly vulnerable to UHI, emphasizing the urgent need for effective UHI mitigation through urban planning (Ballester et al., 2023). In this context, the role of blue-green infrastructure (BGI) management in delivering regulating ecosystem services (ES) in overheated urban environments has gained increasing attention (Emmanuel and Loconsole, 2015). BGI is crucial for urban cooling due to its unique ability to regulate local climate during heatwaves through evapotranspiration, convective heat exchange, and shading (Gunawardena et al., 2017), consequently being particularly important for reducing heat-related health impacts (McDonald et al., 2024). Beyond its thermal benefits, BGI also provides a variety of additional ES, including stormwater retention, habitat provision for biodiversity, recreational opportunities, mental and physical health benefits, and air quality regulation (Cheng et al., 2021). As such, quantifying and communicating the thermal and co-benefits of BGI to urban planners and decision makers is essential. A distinctive form of BGI is outdoor urban agriculture (UA), which provides not only the benefits listed above but also food and fiber provisioning services (Evans et al., 2022; Russo et al., 2017). Therefore, UA is perceived as a sustainable solution to current urban socio-ecological problems of cities in both high- (Aslano˘ glu et al., 2025; Kirby et al., 2021; Kirkpatrick and Davison, 2018; Passidomo, 2016) and low- (Ayambire et al., 2019; Pedzisai et al., 2014; Robineau and Dugu´ e, 2018; Smart et al., 2015) income countries. The most widely spread type of UA in Europe is allotment gardens, embracing a wide range of ES (Cabral et al., 2017; Evans et al., 2022). Cabral et al. (2017) argue that allotment development should be prioritized in urban green space planning, as such areas maximize multifunctionality in compact urban settings. This is especially important amid ongoing urbanization and increasing investment pressure in European cities. However, the heterogeneity of * Corresponding author. E-mail addresses: [email protected] (G. Budzik), [email protected] (M. Sylla), [email protected] (C.J.E. Schulp). Contents lists available at ScienceDirect Journal of Environmental Management journal homepage: www.elsevier.com/locate/jenvman https://doi.org/10.1016/j.jenvman.2025.128275 Received 10 July 2025; Received in revised form 28 October 2025; Accepted 6 December 2025 Journal of Environmental Management 397 (2026) 128275 Available online 11 December 2025 0301-4797/© 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ). allotments is significant among European countries (Poni˙ zy et al., 2021) and even greater between continents (Kirby et al., 2021). Additionally, the motivations behind the demand for allotments (Cepic et al., 2020), especially in times of disruption (Schoen et al., 2021), differ depending on location. This diversity necessitates comparative studies of cooling ES of allotments across European cities using a standardized methodological approach for spatial policy optimization. Benefits of allotments can be assessed at the site level (Dennis and James, 2017) or on a collective basis (Breuste and Artmann, 2015). Case–study assessments of allotments and community gardens are approached from the governance perspective (Fox-K¨ amper et al., 2018), such as in post-socialist countries (Blazek and ˇ Suˇ ska, 2017; Slavuj Borˇ ci´ c et al., 2016), and from the policy (White and Bunn, 2017) or ecological perspective (Hawes et al., 2024; Sieczko et al., 2023; Speak et al., 2015). However, in the context of quantitatively assessing the cooling ES of allotments, referring to methods developed for BGI is the most effective approach due to their established use and the comparable spatial scale of allotments. Various methods exist for assessing the cooling capacity of BGI, primarily categorized into three types: remote sensing, field measurements, and numerical simulations (Budzik et al., 2025b). Among these, satellite remote sensing offers the most consistent analysis of cooling capacity simultaneously across multiple BGI objects simultaneously (Zhou et al., 2019). High-resolution data from Thermal Infra-Red Sensors (TIRS) aboard Landsat 8 and 9 serve as a widely used source of information on urban thermal dynamics (Chen et al., 2017), enabling effective assessment of BGI cooling potential, including allotments, at a scale relevant to urban planning (Ermida et al., 2020). Using Landsat thermal imagery, Zhang et al. (2024) and García-Haro et al. (2023) effectively analyzed the increase in land surface temperature (LST) with distance, based on zonal means within designated buffers around BGI, defining the maximum cooling extent as the first point of LST decline. Qiu et al. (2023) applied a sampling method by drawing straight transects from BGI facilities, considering the cooling extent direction. Bao et al. (2016) employed the semivariance function, whereas Lin et al. (2015) used the field-validated watershed algorithm on LST data to assess BGI cooling potential. Extensive research using Landsat data highlights the reliability and effectiveness of LST in assessing BGI cooling capacity. Despite increasing interest in the cooling potential of UA, large-scale evaluations remain scarce (Hawes et al., 2024). Furthermore, no research has quantitatively assessed the thermal benefits of UA for local populations in the context of the UHI effect, especially in the case of vulnerable residents, as most existing studies focus on green infrastructure or BGI instead of allotments, limiting their applicability in UA policies. Additionally, these studies typically measure cooling potential based on coverage and intensity, paying limited attention to heat risk, demographic factors, and potential social benefits, which are crucial for the development of spatial policies aimed at climate adaptation. Finally, a prominent lack of homogeneous research exists in assessing the cooling efficiency of UA on a European scale. This lack of quantitative evidence on the cooling benefits of UA hinders effective and precise continental-scale urban policy development, limiting the ability to accurately allocate potential subsidies to cities most in need of UA expansion for UHI mitigation, among others, and consequently prevents cities from strengthening resilience against increasing heatwaves and UHI, resulting in higher mortality rates and reduced quality of life. To address these gaps, we aim to assess the cooling potential of allotment gardens across all European Functional Urban Areas (FUAs), including the UK, in terms of: (1) area cooled, (2) maximum cooling distance and intensity, (3) cooling effectiveness, and (4) the number of people benefiting from cooling ES, including vulnerable residents in high heat-risk areas. Additionally, we distinguish between urban and peri-urban agriculture, compare independently cooling allotments with those enhanced by proximity to other BGI, and consider urban morphology types with varying heat risk levels. Quantifying the thermal benefits of UA is invaluable for guiding its protection and strategic development. Our findings can inform urban food strategies and policies (Moragues-Faus and Battersby, 2021) and promote the transformation toward sustainable food systems and improved food self-sufficiency across FUA regions in Europe (Sylla et al., 2022). 2. Methods 2.1. Study area The study area constitutes 682 European FUAs defined during the 2020 and 2021 Urban Audit (URAU) – Fig. 1. Of the total 929 FUAs, only those containing allotments and meeting the thermal data criteria (see Section 2.3) were included. Data is sourced from the European Commission’s Geographic Information System (GISCO) and is collected at two spatial levels: cities within administrative boundaries and FUA (formerly Larger Urban Zone – LUZ), which represent urban influence zones (Eurostat, 2017). These units serve as key spatial statistical entities in European urban studies and are widely used in pan-European research on urban green infrastructure (Marando et al., 2022). The study scope includes FUAs across various climate zones (Fig. 1). According to the K¨ oppen-Geiger Climate Classification (Beck et al., 2023), the analysis covers: Hot-Summer Mediterranean, Warm-Summer Mediterranean, Cold Semi-Arid, Hot Semi-Arid, Cold Desert, Humid Continental – Dry Cool Summer, Humid Subtropical, and Oceanic Climates, and Subarctic With Cool Summers and Year-Round Rainfall, Humid Continental Mild Summer – Wet All Year, Humid Continental Hot Summers with Year-Round Precipitation, Continental Subarctic – Cold Dry Summer, and Tundra Climate. Abbreviations: AR –allotment area BGI –blue-green infrastructure CA –cooled area CA_AR –ratio of cooled area to allotment area CA_AR_CITY –ratio of the sum of cooled area to the sum of allotment area within the FUA boundaries CA_AR_COUNTRY –ratio of the sum of cooled area to the sum of allotment area within the FUA boundaries in the country FUA –Functional Urban Area HCI –high-intensity cooling intensity HCD –high-intensity cooling distance LCZ –local climate zones LST –land surface temperature NDVI –normalized difference vegetation index OSM –Open Street Map POP –number of beneficiaries of the cooling ecosystem service of a single allotment POP_AR –ratio of beneficiaries to allotment area POP_AR_CITY –ratio of the sum of beneficiaries to the sum of allotment area within the FUA boundaries POP_AR_COUNTRY –ratio of the sum of beneficiaries to the sum of allotment area within the FUA boundaries in the country UA –urban agriculture UHI –urban heat island URAU –Urban Audit statistical units WUDAPT –World Urban Database and Access Portal Tools G. Budzik et al. Journal of Environmental Management 397 (2026) 128275 2 Fig. 1. Localization of the study area on a global scale (a); study area (Functional Urban Areas) (b); urban agriculture representation of OpenStreetMap allotments class (dark green) overlaid with Koppen-Geiger climate classification (c). G. Budzik et al. Journal of Environmental Management 397 (2026) 128275 3 2.2. Research flow and data used The research flow can be divided into five main steps (Fig. 2), enabling the classification of different BGI-proximity allotment types and estimation of the number of people benefiting from their cooling effects. 1. Determining the average LST for 2019–2024 to assess the cooling potential of allotments. 2. Identifying UA within individual FUAs as allotments to create a dataset for comparison with the LST data from step 1. 3. Evaluating the ability of allotments to cool surrounding urban areas by integrating spatial analyses of LST (step 1) and allotment locations (step 2). 4. Categorizing allotments based on their integration within broader BGI structures and the morphology of the cooled urban area, including urban versus peri-urban location, to standardize the comparisons of FUA. 5. Estimating the number of people benefiting from the cooling ES of allotments by overlaying cooled areas (data from step 3) with population spatial distribution. The details of each step are described in Sections 2.3-2.7. The study utilized data presented in Table 1. Fig. 2. Research flowchart. G. Budzik et al. Journal of Environmental Management 397 (2026) 128275 4 2.3. Assessment of the thermal characteristics of the study area To assess the cooling efficiency of allotments, we utilized average LST data from April to October 2019–2024, derived from TIRS sensors on Landsat 8 and 9 satellites. LST reflects energy exchange between the land surface, atmospheric insulation, and solar radiation (Haynes et al., 2018; Zhang and Sun, 2019), influencing air temperature, humidity, and perceived temperature (Li et al., 2023). The TIRS sensor captures thermal infrared bands at 100-m resolution, resampled to 30 m, aligning with those of the urban planning needs. With a temporal resolution of 8 days (16 days per satellite) and an image size of 185 ×185 km, these data are suitable for evaluating the cooling potential of BGI (Budzik et al., 2025b), and thus, concerning the same scale, can also support studies of the allotments. Data acquisition was performed using the Google Earth Engine (GEE) platform, following the methodology of Ermida et al. (2020). Within the GEE environment, the average LST for the selected time period was calculated and exported for each FUA. This approach ensured efficient and standardized data retrieval across the entire study area. Detailed parameters are provided in Table 1. Averaging LST over the selected period minimized the impact of anomalous weather and ensured consistency, with the five-year window short enough to assume no major land cover changes in the studied FUAs. 2.4. Delineation of urban agriculture objects Urban agriculture (UA) was defined as allotments based on available data covering the study area. While allotments are a well-documented form of UA, they do not cover all of its possible types. Therefore, in some FUAs, the actual extent of UA may exceed that of the mapped allotment areas. However, the primary mechanism for cooling at the city scale stems from evapotranspiration and shading provided by larger patches of intensive vegetation. Allotments, with their dense and irrigated vegetation, represent the dominant land-use type related to UA able to provide such effects. They can thus be considered as a robust and suitable proxy for assessing the cooling potential of UA as a whole. The spatial extent of allotments was determined using Open Street Map (OSM) land cover data (Fig. 1). OSM is the largest source of allotment data in Europe, representing dispersed green spaces, primarily consisting of fruit trees, shrubs, and vegetables, managed by local communities and often featuring small structures and transportation infrastructure. Additionally, to ensure data consistency, OSM incorporates internal validation mechanisms, including user-driven data verification and automated error detection algorithms (OpenStreetMap), which help mitigate uncertainty and gradually improve data quality. Given the internal quality assurance processes embedded within OSM, no additional manual validation was needed. To adapt the high spatial resolution of OSM data to the continental scale of the study, aggregation was applied. Allotments within 100 m of each other were grouped as single entities, assuming they form unified structures. This approach reduced uncertainties in attributing collaborative cooling capabilities to fragmented allotments within larger complexes. Finally, a total of 59,865 complexes of allotments were selected for the study, and the shape area (AR) value was encoded for each complex. 2.5. Assessment of the cooling potential of allotments Cooling variables were calculated using the method from Lin et al. (2015) implemented with a watershed algorithm-based tool developed by Budzik et al. (2025a). In this approach, the interior of each allotment was a seed point grid, and LST data represented terrain morphology in watershed analysis. Therefore, the input data for the tool were the allotment geometries and a raster of averaged LST from the years 2019–2024. The threshold slope value parameter, which defines the critical rate of temperature increase with distance, and is used to delineate the boundary of the cooling area that can be attributed exclusively to the influence of the allotments, was set at 2 % as proposed by Budzik et al. (2025a). This approach was validated by Lin et al. (2015) through field studies and accepted as a feasible, simple, and computationally efficient method for delineating the cooling extents of BGI elements based on LST data, without the need for resource-intensive on-site measurements. Cooling variables were evaluated based on geometry and LST within the catchment area. 1. High-intensity cooling distance (HCD): maximum distance the allotment affects neighboring area—based on the raster statistics of the Euclidean distance inside the catchment; 2. High-intensity cooling intensity (HCI): temperature difference between the allotment boundary and max HCD—based on comparisons of LST values within the catchment; 3. Cooled area (CA): zone directly influenced by the cooling effect of the allotment—based on the catchment area; 4. CA to allotment area ratio (CA_AR): cooling efficiency. Calculations were performed in Esri ArcGIS Pro 3.2. The resulting catchments bounded the area under the exclusive influence of the cooling capacity of the allotments. Results were aggregated by FUAs, with HCD and HCI averaged, and CA summed. Additionally, to assess the Table 1 Description of data used in the study. Thermal, NIR, and VIS data: Satellite Sensor Bands Used Dataset/level of processing Spatial Resolution Temporal resolution Time period and selection criteria Steps Landsat 8 OLI Red: B4 NIR: B5 Collection 2 Level-1 30 m (16-day; 8-day combined with Landsat 9) 2019–2024 (April–October; max cloud pixel percentage: 10) 1 TIRS TIR: B10 100 m resampled to 30 m Landsat 9 OLI Red: B4 NIR: B5 30 m TIRS TIR: B10 100 m resampled to 30 m Other datasets Type Source Details Acquisition time Steps Open Street Map GEOFABRIK; volunteered geographic information (VGI) Tag: land use =allotments 06.2024 2 Urban Audit (FUAs) Geographic Information System of the Commission 2020 for the UK and 2021 for others 4 Global LCZ map WUDAPT, (Demuzere et al., 2022) 2022, 100 m resolution 4 Census population grid Geographic Information System of the Commission, (Eurostat, 2025) 2018 for the UK and 2021 for others, 1 km resolution 5 G. Budzik et al. Journal of Environmental Management 397 (2026) 128275 5 global effectiveness of allotments within each city, the ratio of total CA to total allotment area (CA_AR_CITY) was calculated, allowing for comparison across FUAs. Similarly, CA_AR_COUNTRY was calculated at the national level. 2.6. Classification of allotment types 2.6.1. Assessment of the relationships between allotments and BGI UHI mitigation in cities primarily relies on BGI (Chang et al., 2007; Spronken-Smith and Oke, 1998). Allotment gardens, as a specific type of BGI, are often embedded within broader urban ecosystems where various vegetated and non-vegetated surfaces interact thermally. Consequently, their individual cooling potential may be overestimated when the influence of adjacent BGI elements (e.g., parks or tree clusters) is not separated. To isolate the exclusive cooling effect of allotments from the amplified effect of surrounding BGI, a classification procedure was developed to group allotments according to their spatial relationship with nearby BGI structures. Each allotment was analyzed within a 100-m buffer zone, representing the immediate surrounding environment potentially affecting its microclimate. Within each buffer, two NDVI-based indicators were computed. 1. Mean NDVI, describing the general level of vegetation greenness and density around the allotment. 2. NDVI percentile range (20th–80th percentile), reflecting the internal heterogeneity of vegetation cover — higher percentile ranges indicate a mixture of vegetated and artificial surfaces, suggesting a more heterogeneous urban morphology in which cooling may result from both allotments and adjacent BGI-related vegetation. To categorize the allotments, both indicators were statistically evaluated across all buffer zones. For each parameter, thresholds were established based on the distribution of the dataset: •High values were defined as greater than those of the mean plus half the standard deviation, •Low values were defined as less than those of the mean minus half the standard deviation. This approach allowed for relative classification, accounting for variability across the study area rather than using arbitrary or absolute NDVI cutoffs. Based on the combination of NDVI mean and percentile range values, allotments were grouped into four classes. 1. Class A – UA exclusive cooling: low NDVI mean and low NDVI percentile range, representing allotments largely isolated from other BGI elements, where cooling is primarily attributable to the allotment itself. 2. Class B – BGI-boosted cooling: high NDVI percentile range, indicating allotments embedded in heterogeneous urban morphologies where cooling is likely intensified by adjacent vegetated areas. 3. Class C – UA surrounded by BGI: high NDVI mean but low NDVI range, corresponding to those of allotments located within uniformly vegetated areas. 4. Class D – Mixed UA–BGI cooling: allotments not meeting the criteria for the above categories, reflecting mixed landscape configurations. Fig. 3 illustrates representative examples of each allotment class. This classification formed the foundation for subsequent analyses of spatial cooling patterns and the interpretation of UHI mitigation potential. 2.6.2. Assessment of relationships between allotments and the morphology of the urban environment To ensure consistency in the comparison of FUAs and to accurately determine the cooling potential of allotments, specifically discerning the allotments providing cooling to areas potentially most vulnerable to UHI effects, individual allotments were classified based on the types of urban structures they cool, using the globally standardized Local Climate Zones (LCZ) concept (Stewart and Oke, 2012) – Fig. 4. LCZ divides urban spaces into homogeneous units based on morphological types, which differ in screen-height temperature (Stewart and Oke, 2012) and air humidity regimes (X. Yang et al., 2020), local-scale urban ventilation performance, and population density (Demuzere et al., 2020). This helps identify high-risk heat areas, with highly urbanized zones being the most at risk due to higher LST and population density. LCZ data for the study area were sourced from the Global LCZ Map (Demuzere et al., 2022), a raster dataset with a 100-m resolution encoding numerical equivalents for ten built zone types and seven land cover types, developed under the World Urban Database and Access Portal Tools (WUDAPT) project (Ching et al., 2018). The LCZ classification for each allotment was determined by calculating the majority LCZ value within its cooling zone (Fig. 4). LCZ 1–10 and LCZ A–G were classified as urban and peri-urban, respectively, based on their morphological characteristics (Stewart and Oke, 2012). The WUDAPT-based LCZ framework was chosen as it represents the most widely applied and internationally harmonized approach linking urban morphology with thermal behavior (Demuzere et al., 2022). This approach ensures comparability and reproducibility across different cities and climates. However, it may not fully capture mixed-morphology cooling zones. In transitional areas, where allotment cooling zones overlap multiple LCZ types, the majority-based classification may slightly overestimate the assignment of allotments to more intensely urbanized classes. This potential overestimation arises because cooling zones often extend preferentially toward densely built, Fig. 3. Examples of allotment types based on their relationship with larger BGI structures (the transparent blue polygon indicates the cooled zone). G. Budzik et al. Journal of Environmental Management 397 (2026) 128275 6 Fig. 4. Spatial distribution of LCZ types within the Functional Urban Areas (FUAs) (a); example of allotments with the LCZ types they cool (b); the total area (km 2 ) of LCZ types within the FUAs boundaries (c). G. Budzik et al. Journal of Environmental Management 397 (2026) 128275 7 heat-prone areas, where park breeze effects are more pronounced (Gunawardena et al., 2017). Consequently, some allotments may have been classified as those cooling the most urbanized LCZs, even though their influence also extends into less urban or peri-urban morphologies. Consequently, the cooling effect in less intensely urbanized LCZs may be somewhat underestimated. 2.7. Estimation of the number of beneficiaries of cooling ecosystem services provided by different types of allotments To estimate the number of people benefiting from the cooling effects of allotments (POP variable), the cooled areas were intersected with the 1 km 2 population grid from GISCO (Eurostat, 2025)—Fig. 5. The estimated number of beneficiaries for each allotment was calculated by multiplying the population in a given grid cell by the area of the cooling zone within that cell, expressed in km 2 . Additionally, we propose an efficiency indicator (POP_AR) for allotment areas defined as the ratio of the number of beneficiaries (POP) to the allotment area (AR). This indicator represents the number of beneficiaries corresponding to each unit of the allotment area. The POP_AR index was calculated at three levels: individual allotment (POP_AR), FUA (POP_AR_CITY, total beneficiaries in an FUA to total allotment area in the FUA), and country (POP_AR_COUNTRY, total beneficiaries in a country to total allotments area in a country). Furthermore, population density (total and vulnerable only: young <15 and elderly >65) and LST statistics were calculated for various LCZs to assess the heat risk of urban areas, defined by exposure, vulnerability and heat hazard, based on the Clayton Triangle framework (Xin et al., 2023). The values were calculated based on zonal statistics within LCZs limited by the extent of the FUAs. Areas at higher heat risk were assumed to be those with a combination of high LST (heat hazard), population density (exposure), and a high concentration of young and elderly residents (vulnerability) (Xin et al., 2023). 3. Results 3.1. Assessment of the cooling potential of allotments Within the studied FUAs, allotments cover 2041 km 2 , generating cooled areas excending an additional 1879 km 2 beyond their boundaries, resulting in a total cooled area of 3920 km 2 . This indicates that the total cooled area outside allotments represents 92 % of the total allotment area, with each individual cooling zone being, on average, 2.8 times larger than that of the allotment generating it (95 % CI = 2.785–2.851; SE =0.0168). The countries with the largest total allotment area are Germany (671 km 2 ), Hungary (502 km 2 ), and Poland (285 km 2 ). The hierarchy of the cooled area (CA) across countries is similar – Germany (839 km 2 ), Poland (216 km 2 ), and the UK (205 km 2 ). However, the countries with the most efficient allotments in terms of CA_AR_COUNTRY are Belgium and Croatia, followed by the UK. In these countries, each km 2 of allotments corresponds to 3.3, 3.1, and 2.2 km 2 of cooled area, respectively. This may be due to significantly smaller Fig. 5. Population grid (1 km) from GISCO within the FUAs. G. Budzik et al. Journal of Environmental Management 397 (2026) 128275 8 average allotment sizes, which, compared with those of Hungary, are 87, 132, and 31 times smaller in Belgium, Croatia, and the UK. This suggests that these countries optimally locate allotments within cities, reaching the appropriate size threshold for effective cooling (Yu et al., 2020). In their case, increasing the size of individual allotments would be inefficient, as it would increase the cooled area, but not proportionally to the increased allotment area. The spatial distribution of cooling variables in FUAs, along with statistics by countries, is shown in Fig. 6. Detailed country and FUAs statistics can be found in Appendix B and C. The results show that FUAs in Central and Eastern Europe comprise approx. 8 times more allotments compared with those in the Mediterranean. This translates into a proportionally larger total cooled area, reaching maximum values in German FUAs (Stuttgart: 60 km 2 ; Frankfurt: 55.2 km 2 ; Berlin: 40.7 km 2 ). In terms of average HCI and HCD values, the highest values for FUAs were recorded in Sweden (Uppsala: 390 m and 2.53 ◦C), Italy (Savona: 366 m and 4.04 ◦C), and Bulgaria (e. g., Vratsa: 349 m and 5.66 ◦C; Ruse: 251 m and 6.2 ◦C; Pleven: 242 m and 7.7 ◦C). Despite the dominance of Central and Eastern European FUAs in terms of stronger cooling in HCI, HCD, and CA, these cities showed 2.7 times lower cooling efficiency compared with those in Southern and Western Europe, including the UK. These differences, despite of potential impact of background climatic conditions, may arise from differences in the total allotment area between these city types and from differences in the average allotment size—the larger the total area Fig. 6. Results of the aggregation within the studied Functional Urban Areas of average high-intensity cooling distance (HCD), sum of allotment area (AR), sum of cooled zones area (CA), the mean area of allotment, and the indicator of cooled area to allotments area (CA_AR) (a); stats by country (b). G. Budzik et al. Journal of Environmental Management 397 (2026) 128275 9 Fig. 11. Spatial distribution of the total number of people living in Local Climate Zones 1–5 benefiting from the cooling services of allotments (a) and the ratio of the total number of beneficiaries to the total allotments area in Functional Urban Area (b); share of Local Climate Zones 1–5 area within Functional Urban Areas (c); share of Local Climate Zones 1–5 area within Functional Urban Areas by country (d). G. Budzik et al. Journal of Environmental Management 397 (2026) 128275 16 efficiency by limiting evapotranspiration (Khan et al., 2025). Conversely, in Central and Eastern European FUAs more continental climates may amplify absolute cooling intensity and distance from allotments due to larger diurnal temperature ranges, lower humidity, and reduced air mixing (Gunawardena et al., 2017). These climatic influences likely interact with local urban morphology and vegetation, suggesting that both biophysical and spatial contexts can shape UA cooling benefits. Future research should integrate regional climate variables—such as background air temperature, wind regime, and humidity—into models assessing UA-related cooling ES to disentangle their combined effects from purely spatial determinants. Existing literature lacks studies specifically analyzing the cooling effect of allotments within European FUAs. However, comparable research on broader BGI networks within FUAs exists, such as Marando et al. (2022). They reported an average BGI cooling effect of 1.07 ◦C across FUAs, whereas our findings indicate that allotments provide more than twice this intensity (global HCI: 2.84 ◦C). Beyond differences in the types of facilities analyzed, this discrepancy may also stem from differences in methodological approaches. We measured cooling intensity as the LST difference between each allotment boundary and its immediate surroundings, while Marando et al. (2022) compared a baseline scenario (without BGI) to that with BGI, assessing air temperature differences. While LST depends on emissivity and solar exposure, air temperature is mainly influenced by atmospheric dynamics (Good et al., 2017; Oyler et al., 2016), implying that the “cooling effect” in these studies reflects a fundamentally different phenomenon. When positioned against global LST-based studies, it becomes evident that the cooling intensity of allotments in Europe is not only high locally but also stands out globally. For instance, studies from urban areas in Asia consistently report mean cooling intensities for various BGI types ranging from 1.3 ◦C for urban parks in Nagoya, Japan (Cao et al., 2010), 1.78 ◦C for broad BGI types in Fuzhou, China (Yu et al., 2017) to 2.74 ◦C for wetlands in Changchun, China (Xue et al., 2019), with values often clustering around 2.2–2.6 ◦C (e.g., (Shah et al., 2021) in Bengaluru, India: 2.23◦C; (Zhang et al., 2024) in Xi’an, China: 2.22◦C; (Du et al., 2017), in Shanghai, China: 2.63 ◦C). Research from other continents further contextualizes these findings. In Africa, a study in Addis Ababa, Ethiopia, reported a substantial cooling effect of 3.93 ◦C for urban green spaces (Gudina Legese Feyisa et al., 2014), a value higher than that of our result, potentially driven by arid local conditions. In South America, research from Aracaju, Brazil, showed a more moderate cooling intensity for urban vegetation, ranging from 1.2 ◦C to 2.0 ◦C (Anjos and Lopes, 2017), which aligns with that of the lower end of the global spectrum. Similarly, a study in Phoenix, USA (North America), documented an average cooling effect of 2.0 ◦C (Middel et al., 2015). Our result of 2.84 ◦C aligns with that of or even exceeds the upper range of these global findings, highlighting the strong cooling potential of European allotments. This superior cooling performance is likely a direct result of the unique socio-ecological characteristics of allotments. Unlike broader BGI categories such as non-irrigated grasslands or ruderal vegetation, allotments are typically intensively managed systems, including regular irrigation by gardeners, which may enhance cooling efficiency by maintaining high vegetation health and sustaining elevated evapotranspiration rates (Spronken-Smith and Oke, 1998). Consequently, our study does more than fill a critical gap in the European context; it positions allotments as a uniquely potent form of BGI with a cooling efficacy rivalling or surpassing that of many other green spaces in cities globally. By generating high-resolution spatial data on the cooling capacity of individual allotments within European FUAs, we enable precise mapping of the cooled area and the directionality of its propagation. This methodological advancement paves the way for future research into the complex spatial relationships between allotments and the urban fabric—for instance, quantifying their impact on building energy efficiency or local real estate values. Furthermore, by controlling for interactions with urban morphology, our framework allows for objective, cross-city comparisons of cooling performance across homogenous built-environment types. Finally, the integration of population density data, with a specific focus on vulnerable age groups, moves beyond purely biophysical metrics to enhance the assessment of the contribution of allotments to social equity and heat risk mitigation for urban residents. 4.2. Limitations and uncertainties We precisely quantify the scale and intensity of cooling ES provided by allotments, considering both their physical characteristics and the number of beneficiaries. However, given the broad scope—covering hundreds of urban functional areas across multiple climate zones—and the need for methodological universality and transferability, simplifications were necessary to ensure the homogeneous use of publicly available data. Consequently, our approach has certain limitations, though these did not hinder the study from acheving the stated goals. The main limitation of this study is the reliance on LST data. While widely used for evaluating urban thermal properties and BGI cooling potential, LST represents energy exchange between the land surface, atmospheric insulation, and solar radiation (Haynes et al., 2018; Zhang and Sun, 2019), which only partially influene the air temperature experienced by residents (Li et al., 2023). Furthermore, satellite sensors capture only part of the terrain due to their viewing angle, missing areas “shaded” for sensor by urban structures (Voogt and Oke, 1997, 1998). Fig. 11. (continued). G. Budzik et al. Journal of Environmental Management 397 (2026) 128275 17 Additionally, LST measurements may be affected by calculation errors, atmospheric absorption, and reflected radiation, introducing further uncertainties (Beck et al., 2023; Mirzaei and Haghighat, 2010). A more comprehensive assessment of allotments cooling capacity would require high-resolution air temperature data and thermal comfort indices such as the physiological equivalent temperature (PET) (Lee et al., 2016). Despite these limitations, LST data were chosen for their optimal spatial and temporal resolution, global coverage, and the lack of high-resolution air temperature data. To mitigate inconsistencies, a uniform satellite image selection criterion was applied, ensuring methodological consistency. This approach enhances the universality, scalability, and transferability of our method. However, due to the use of Landsat LST data, the cooling potential and number of beneficiaries may be slightly overestimated, as mixed pixels in urban areas could unjustifiably inflate the cooled areas. Another limitation is the calculation of the cooled LCZs, based on the majority of pixels within the cooling zones. This approach does not account for the cooling of multiple LCZ types by single allotment. However, using the majority as the determining factor helps maintain a finite number of LCZ classes, leading to easier interpretation of the results. An additional limitation is the method used to calculate the number of beneficiaries. The 1 km resolution of the GISCO population grid does not guarantee that each cooling zone actually covers built-up areas, which may lead to an overestimation of beneficiaries due to misclassification of uninhabited areas as residential. Nevertheless, it is the only layer providing information on population density at the scale used here and serves as a good indicator for mapping heatwave-vulnerable groups. 4.3. Practical implications We show that smaller, dispersed allotments in densely built-up areas provide stronger cooling benefits. Eastern and Southeastern European cities, particularly in Hungary, Bulgaria, Poland, Lithuania, and Latvia, should prioritize their development. In these regions, allotments tend to be too large and located far from high-density urban areas. A strategic redistribution—converting portions of large peri-urban allotments into smaller sites within city centers—would enhance cooling efficiency while supporting sustainable urban expansion in line with the Compact City concept. It is especially important in the context of the most densely built-up urban districts, where exposure to heat-related risks is greatest. Our results indicate that LCZs characterized by the highest heat risk are concentrated in a belt spanning the UK, Benelux, southwestern Germany, and northern Italy. Particularly, UK cities, should focus on expanding allotments in high-density LCZs 1–5, where urban morphology can exacerbate UHI effects. The UK has a high proportion of LCZs 1–5 but a below-average number of people benefiting from allotment cooling. Expanding allotments in these regions would provide the highest efficiency, potentially serving up to 25 times more people. The highest heat-risk cities align with the “Blue Banana" corridor—a densely populated and economically vital European zone. This convergence highlights the urgency of developing allotments, especially in highincome FUAs. To maximize both environmental and social benefits, allotments should be designed with features such as shaded rest areas, safe and accessible pathways, and age-friendly infrastructure (Negrini and Walford, 2022). These elements are particularly beneficial for older adults, as they promote thermal comfort, social interaction, and heat-coping behaviors. Parallelly, schoolyards can be reimagined as “oasis” gardens or shaded vegetable plots, creating child-friendly cooling zones. Allotments should also encourage intergenerational engagement through the inclusion of benches, community sheds, shared workshops, and proximity to playgrounds—fostering mental health, social cohesion (Ambrose et al., 2023), and collective heat resilience (Łaszkiewicz et al., 2023). Furthermore, to ensure equitable access and impact, public funding and tax incentives for allotment development should be linked to heat-risk indicators—mainly the prevalence of LCZs 1–5 and concentrations of vulnerable populations. Support for vulnerable populations could also be strengthened through communal gardening programs offering training, tool-sharing, and community events co-designed with residents. These initiatives could not only boost participation and a sense of ownership but also promote the fair and inclusive distribution of the benefits provided by UA. Within the EU, Mediterranean countries exhibit the lowest availability of allotments, despite facing some of the most intense heat stress. Policy efforts should therefore prioritize the development of new urban gardening spaces with a high share of vegetation. Our findings indicate that in these regions, each square kilometer of allotments can deliver cooling benefits to between 5000 and 25,000 people. Supporting allotment expansion in Mediterranean cities can thus serve as a cost-effective and adaptive strategy for enhancing urban resilience on an EU scale. UA should be formally recognized as a distinct land-use category within planning systems. Allotments must be integrated into broader policy frameworks covering climate adaptation, public health, and food security, ensuring coordination across sectors and avoiding institutional fragmentation. However, beyond the results of our study, numerous other factors can also influence allotment development strategies. These include national, regional, and local planning regulations, geographical conditions and constraints (e.g., availability of space, administrative commitment, and stakeholder acceptance). Therefore, an effective allotment development policy must be shaped through locally grounded, integrated analyses that consider all these elements together. 5. Conclusions We show that allotments in European Functional Urban Areas (FUAs) are crucial for mitigating urban heat. On average, each allotment cools 3.59 ha, surpassing its mean size of 3.41 ha, with an average cooled-toallotment area ratio of 2.8, a cooling distance of 106.8 m, and a cooling intensity of 2.84 ◦C. Allotments provide cooling benefits to approx. 4.1 million people, with each km 2 benefiting an average of 8221 individuals. We developed four distinct urban agriculture cooling settings based on neighborhood BGI connections and used the Global LCZ Map for urban morphology assessment. Urban agriculture exhibits a 23 % greater cooling efficiency than that of peri-urban agriculture. Only 6.5 % of allotments function independently of BGI, and only 5 % are located in the most UHI-impacted areas. Contrastingly, 43 % of allotments are integrated into broader BGI systems, highlighting the synergistic value of combining allotments with BGI. Despite this, standalone allotments exhibit comparable cooling efficiency to those reinforced by BGI, emphasizing their relevance in urban planning. Spatial policies should therefore prioritize allotments to enhance heat resilience and multifunctionality in city centers, especially in highly developed FUAs with a high proportion of densely populated areas. While allotments are primarily concentrated in Central and Eastern Europe, with Germany leading in absolute cooling ES beneficiaries, cities in the UK, Benelux, western Germany, northern Italy, and parts of Spain rank the highest in terms of cooling efficiency due to a higher proportion of allotments near densely populated areas and smaller average allotment sizes. This indicates that key actions should focus on maintaining the optimal efficiency threshold for allotment area and ensuring strong integration with the dense built-up areas of city centers. These findings emphasize the importance of promoting allotments as a multifunctional urban agriculture strategy, not only for cooling but also for delivering additional ecosystem services such as food production, biodiversity enhancement, and stormwater management. In the face of intensifying climate change and urbanization pressures, targeted policies to develop and optimize allotment spaces could substantially enhance urban resilience against the negative impacts of the UHI. G. Budzik et al. Journal of Environmental Management 397 (2026) 128275 18 CRediT authorship contribution statement Grzegorz Budzik: Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Marta Sylla: Writing – review & editing, Writing – original draft, Supervision, Project administration, Methodology, Conceptualization. Catharina J. E. Schulp: Writing – review & editing, Supervision. Declaration of generative AI and AI-assisted technologies in the writing process During the preparation of this work, the authors used ChatGPT to compress volume of the text and Keenious to support their search for relevant literature. After using these tools, the authors reviewed and edited the content as needed and take full responsibility for the publication’s content. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements Funding for research by G.B. has been provided by the Ministry of Science and Higher Education, Poland (agreement number DWD/6/ 00178/2022). M.S. acknowledges funding provided by the Marianne and Marcus Wallenberg Foundation (grant no. MMW2023.0023). Funding for research by C.J.E.S. has been provided by the European Union’s Horizon Europe research and innovation program FOODCITYBOOST (Grant Agreement Number 101132315), funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Executive Agency. 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