Assessing European cities with the 3-30-300 rule underscores the need for enhanced urban greening efforts

Domain: neuroscience
Published: 2026-04-09
Editorial outcome: minor_revision

Abstract
Urban green spaces are fundamental to sustainable city living, providing essential temperature regulation and social well-being. However, rapid urbanization often threatens these areas, exacerbating socio-economic disparities in access . This study evaluates adherence to the 3-30-300 rule - a guideline advocating for three trees visible from every home, 30% neighborhood canopy cover, and a park within 300 meters - across 862 European cities. Here we show that less than 15% of the studied population lives in full accordance with these criteria, while 21% reside in areas that do not meet any of the three benchmarks. Our analysis reveals strong vegetation inequalities, where high levels of urban greenness are consistently associated with wealthier settlements. These findings indicate that most European cities currently fall short of providing equitable access to nature, underscoring an urgent need for a paradigm shift in urban planning to sustainably and equitably address the demands of growing populations.

Introduction
Cities are pivotal in driving societal change1. Within this context, urban green spaces significantly enhance quality of life by mitigating environmental stressors such as high temperatures2,3, noise pollution4, and poor air quality5, while simultaneously fostering health and social well-being6,7. Despite these critical benefits, ongoing urbanization frequently leads to the loss of these vital spaces, thereby exacerbating socio-economic disparities in access to urban greenery8. As the global urban population expands, prioritizing proximity to green spaces and ensuring sufficient tree coverage becomes increasingly imperative to meet UN Sustainable Development Goal 11 Target 7, which advocates for universal availability of inclusive and accessible green areas9. In the context of a rapidly urbanizing world, concepts like the biophilic cities10 or smart sustainable cities11 urge urban planners to facilitate daily interactions with nature, promoting environmental awareness and stewardship. These cities are envisioned to be sustainable and resilient, strengthening their social and physical capacity against future shocks such as climate change, natural disasters, and economic uncertainties.

The European Union, EU, has experienced considerable urbanization in recent decades, with approximately 73% of its population now living within urban areas, a figure that exceeds the global average12,13. Building upon the European Green Deal's (EC 2019/640)14 commitment to a green transition, the EU is actively promoting urban greening through key initiatives like the Green City Accord (EC 2021)15, a voluntary commitment by cities to improve environmental quality and integrate nature. The legally binding Nature Restoration Regulation (EC 2024/1991)16 further mandates that EU cities and towns prevent the loss of green spaces and tree cover by 2030 and demonstrate a continuous increase thereafter. To track progress, the EU has also established a system for ecosystem accounting17, enabling the measurement and monitoring of urban green space and the benefits they provide.

In parallel with these legislation and policy initiatives, Konijnendijk (2023)18 launched a rule of thumb for urban forestry and urban greening: the 3-30-300 rule. This rule focuses on the crucial contributions of urban trees and other urban nature to health and wellbeing, as well as climate change adaptation. The multifaceted nature of the rule implicitly recognises the need to consider many different aspects of city life to benefit from urban greenery. A key ambition is to let urban forests and other green spaces percolate into all of our living, working, and learning environments. Furthermore, the rule is easy to remember, and as we will show, relatively straightforward to implement, monitor, and evaluate. The 3-30-300 rule specifies that every dwelling, educational facility and workplace should have a view of 3 trees, 30% canopy in the neighborhood, and a park within a 300-meter walk.

The 3-30-300 rule provides a set of objectives, universal benchmarks for cities to determine how they fare on urban green space visibility, coverage and proximity, as well as where to target investments for the future. Since its introduction, the 3-30-300 rule has attracted considerable interest and numerous cities globally, including Malmo (Sweden), Zurich (Switzerland), St. Petersburg (Florida, USA), Haarlem (Netherlands), Saanich (Canada), and Viladecans (Spain), have incorporated the rule into their urban greening strategies, either formally or informally. Despite its quite rapid diffusion, the lack of comprehensive assessment of the 3-30-300 rule's implementation presents a critical gap, with only a handful of studies evaluating the rule's fulfilment in cities19,20,21,22,23. Without robust evaluation, cities risk implementing strategies that fall short of effectively addressing disparities in green space access or achieving broader environmental goals. Urban densification strategies, in particular, have sometimes resulted in uneven distribution of greening efforts24, with some instances contributing to green gentrification8. In these cases, well-intentioned greening initiatives inadvertently lead to the displacement of low-income and minority residents, highlighting the need for careful assessment and planning to ensure that urban greening benefits all community members equitably.

To address this gap, here, we evaluate the 3-30-300 rule across Europe, encompassing 862 cities with at least 50,000 inhabitants in the 27 EU countries, Iceland, Norway, Switzerland and the UK (refer to Supplementary Table 1 for details). Our approach integrates innovative spatial analytics with up-to-date data on urban green spaces from the LUISA (Land Use-based Integrated Sustainability Assessment) Base Map25, tree density from the Copernicus Land Monitoring Service26, and individual tree counts from high-resolution satellite imagery27,28 alongside population density and building height information from the Global Human Settlement Layer29,30.

Results
Nearly half of residents in the studied cities meet the three-tree visibility criterion
Konijnendijk (2023)18 suggests that every resident should have a view of three trees from their dwelling, educational facility and workplace. However, defining this visibility is challenging. Various studies employ different methods: some use surveys and window-view analyses to assess tree visibility from households31,32, others utilize computer vision to quantify street greenery33, and some define tree visibility using buffer zones with distance thresholds19,20,34. Here, to assess the visibility of three trees, we used viewshed analysis31,35,36(see Methods). First, we mapped areas visible from specific observation points, accounting for obstructions like building height and surrounding terrain features. We then integrated these maps with high-resolution (3 m) tree count data from PlanetScope27,28 (see Methods) to focus on raster cells containing at least three trees. Finally, we applied this layer of visual green exposure to the Global Human Settlement Layer Population37 (GHS-POP) data to estimate the population residing in areas with visibility of at least three trees.

We find that about 46.7% ± 18% (standard deviation, SD) of the total urban population in the analyzed cities view at least 3 trees (Figure 1a-d). In approximately 30 cities, including Lugano (Switzerland), Espoo (Finland), and Berlin (Germany), over 70% of residents view more than three trees. Conversely, in around 30 cities - such as Murcia (Spain), Ragusa (Italy), and Valletta (Malta) - fewer than 10% of the population meet this criterion. Our results reveal significant spatial variability in the adherence to this rule across European cities, the largest when compared to the other two criteria of the 3-30-300 guideline (see Supplementary Figure 1). Southern European cities - particularly those in Spain, southern Italy, and Greece - generally demonstrate lower compliance with the 3 visible trees rule.

One-quarter of the analyzed population live in areas with 30% tree cover
To examine the second rule, we estimated the share of urban population residing in neighborhoods with over 30% tree cover. Due to the lack of a standardized cross-city neighborhood definition, we adopted 1 km grid blocks to delineate neighborhoods (see Methods). This selection is consistent with prior literature that frequently utilizes a 500 m egocentric radius to define local environments38,39,40. Our analysis reveals that the 30% rule is, on average, the least fulfilled of the three rules, with about 28% of the population in the analyzed cities residing in areas with at least 30% tree cover (Figure 1b-e). At the same time, it is also the rule with the largest variability (SD = 25%), with cities located in northern and eastern Europe having a larger share of citizens living in green neighborhoods. For example, in cities like Berlin (Germany), Stockholm (Sweden) and Warsaw (Poland) more than 70% of the population resides in neighborhoods with high tree cover density. In stark contrast, one out three European cities with more than 50,000 inhabitants have less than 10% of their population residing in green neighborhoods. The layout of green areas throughout the city strongly affects the outcome of this rule. For example, cities like Savona (Italy) and Baia Mare (Romania) have high tree cover density (85% for Savona and 82% for Baia Mare), yet only a small percentage of their populations live in compliance with the 30-rule (36% for Savona and 34% for Baia Mare). This highlights that a more even distribution of green spaces across a city leads to a better compliance with urban green coverage.

Over half of residents in the analyzed cities live within 300 meters of a park
To examine the third rule, we utilized spatial buffering to estimate the proportion of the population residing within a 300-meter zone surrounding parks and other urban green areas larger than 1 ha (see Methods). This buffer serves as an indicator of the accessible green area for nearby residents. We find that 57% ± 14% (SD) of urban dwellers in the analyzed cities can access a green space (e.g., park) within 300 m distance (Figure 1c-f), which makes this rule the most fulfilled of the three rules. Compliance with this rule also shows noticeable geographical disparity. While more than 60% of European cities have over half of their population residing near green spaces, in northern cities like Stockholm (Sweden), Helsinki (Finalnd), Hastings (UK), and Zoetermeer (Netherlands) this exceeds 75%. Conversely, southern and south-eastern European cities possess smaller shares of green areas exceeding 1 ha and consequently display lower access to urban green spaces, resulting in less than 10% of the population having access to urban green areas within 300 m distance in cities such as Bagheria (Italy), Xanthi (Greece), and Arrecife (Spain).

Less than 15% of the population in the analyzed cities fulfills the 3-30-300 rule
The overall fulfillment of the 3-30-300 rule is contingent upon the interaction among its individual criteria. A city might achieve high scores in each criterion independently, yet exhibit lower overall fulfillment if the spatial regions defined by these criteria do not intersect. Overall, we find that only a small fraction, 13.5%, of the dwellers in the analyzed European cities live according to the 3-30-300 rule.

There are only two cities where more than 50% of the population satisfies the rule (Espoo in Finland and Varese in Italy), and about 20 cities where this percentage is above 40% (see Figure 2a). With few exceptions, the largest share of populations satisfying all the three criteria are located in Scandinavian regions along with Germany and Poland with cities like Berlin, Stockholm, and, Helsinki having respectively 40%, 41%, and 43% of residents fulfilling the 3-30-300 rule. However, in 177 less than 1% of the residents fulfilled the three rules.

Our analysis reveals that areas within cities meeting all three criteria of the 3-30-300 rule are scarce. Approximately 21% of the analyzed urban population live in areas that do not meet any of the 3-30-300 conditions. Furthermore, about 35% of the population resides in areas where only one condition is met, resulting in 56% of the total urban population of the 862 European cities living in locations where at most one condition is satisfied. These low percentages are primarily due to the limited proportion of the population living in neighborhoods with at least 30% tree cover, underscoring the difficulty for most cities in attaining the second rule. This finding is consistent with recent studies indicating that many cities fail to meet the 3-30-300 rule largely due to insufficient tree canopy19,20,21.

There is significant spatial variability among cities concerning this metric (Figure 2b). For example, in Berlin, the majority of the area (67%) meets at least two criteria of the 3-30-300 rule. In contrast, this proportion is lower in Vienna at 55%, resulting in only 20% of residents living in accordance with the 3-30-300 rule. Conversely, in Zaragoza, approximately 88% of the city's area does not meet any of the 3-30-300 rule's requirements leading to less than 2% of the city’s population satisfying the rule.

Wealth influences access to urban greenery
The diverse benefits of urban green spaces are often undermined by their inequitable distribution. In this context, we find that there is a clear link between access to urban green and economic conditions (Figure 3). Cities with higher per-capita GDP (GDPc) often provide more green space. This difference is especially visible in the spatial divide between central-northern European cities versus south-eastern cities. This trend is consistent with Chen et al., (2022)41 who identified a contrasting difference of greenspace exposure between Global South and North cities, where wealthier cities experience about three times the greenspace exposure level than poorer cities. Another crucial factor influencing this relationship are humidity-aridity conditions. Cities located in more humid environments tend to have a greater share of green spaces, which results in a higher adherence to the 3-30-300 rule. Notably, we showed that on average, within the same GDP per capita classes, cities with higher humidity levels provide residents with more access to green space s (Figure 3b). It could be speculated that the greater access to green in cities in central-north Europe compared to southern-east cities might relate to more humid climatic conditions that favor the growth and proliferation of plants and urban green.

One potential limitation of using GDPc as a proxy for wealth is that it may fail to capture the real living standard of households42. Another limitation is that relying on a single metric to represent an entire city, such as Madrid, Paris, Rome, or Athens, masks smaller-scale heterogeneity. To address these limitations, we conducted additional analyses using high-resolution data on disposable income. First, we compared the proportion of the population meeting the 3-30-300 rule with a recently developed downscaled income dataset available at 1x1 km resolution, generated through an innovative machine learning framework43. Our results reveal a positive correlation between disposable income and the presence of green, suggesting that across Europe city-dwellers with higher disposable income tend to reside in urban areas with greater access to green spaces (Supplementary Figure 2). Then, we performed a more granular analysis for 186 cities across France, Belgium, and Spain, using newly available high-resolution datasets on disposable income specific to these countries. The methodology is illustrated in Figure 4a for the city of Lyon (France) where disposable income data at 200 x 200 m were obtained from the National Institute of Statistics and Economic Studies, INSEE44. Examining data at such a finer scale not only corroborates broader trends but also enhances our understanding at the local level, offering a more nuanced and reliable insight into the relationship between income and urban greenness. Granular data significantly reduces aggregation bias, thereby providing an enhanced representation of the socioeconomic landscape, which is crucial for evaluating the impact of urban planning and development policies. The case of Lyon serves as a compelling example of this approach, illustrating the value of detailed analysis in capturing the complexities of urban dynamics. When assessed at the national level (Figure 4b), a clear trend emerges across all three countries, reinforcing the consistent association between higher disposable income and the likelihood of residing in greener urban neighborhoods.

Opportunities for urban green space development
The dynamic interplay between green infrastructure and urbanization presents a complex set of trade-offs that require careful consideration45. While urbanization is often seen as a positive force driving economic growth and resource efficiency, it can impact the availability and quality of urban green spaces if not managed thoughtfully. Addressing this challenge through intentional design and planning is crucial to creating sustainable and livable cities. The recent decade (2010-2020) has indeed witnessed a significant increase in urban population (+16% on average) and urban area expansion (+2.3% on average) within city boundaries in Europe (Supplementary Figure 3). This urban growth has not been accompanied by a commensurate increase in green urban areas and tree cover density (Supplementary Figure 3), with both indicators exhibiting stable or declining trends (UGA: -0.3% and TCD: -1.6%). This trend may be a contributing factor to the low percentage of the population in European cities living in satisfaction with the 3-30-300 rule. Without proper intervention, we risk further diminishing the already limited access to urban green areas.

In the current city configuration, nearly 90% of the population in the studied cities resides within 1 km of a park (Supplementary Figure 4E). By strategically integrating green spaces and parks within city planning the distance to a park can be lowered. A key solution to achieving this balance is the transformation of urban mobility46, a solution also advocated by the recent concept of the 15-minute city47,48, which contends that cities can function more effectively, equitably and environmentally if essential services and key amenities are within 15 minutes by non-polluting forms of transport, such as walking or cycling. Prioritizing such sustainable transport modes can significantly cut reliance on private vehicles, thereby lowering greenhouse gas emissions and freeing up valuable urban space typically used for roads and parking lots. This reclaimed space can be repurposed for green areas, parks, and additional housing, fostering a more efficient and environmentally friendly cityscape. Importantly, there is a significant correlation (p < 0.001, rho = 0.39; see Supplementary Figure 5) between adherence to the 3-30-300 rule and the opportunity to walk49. This correlation underscores the benefits of nature exposure in reducing stress and enhancing well-being, as walking in natural settings significantly decreases self-reported stress levels and lowers physiological stress markers50.

Cities with the highest compliance with the 3-30-300 rule tend to experience fewer effects from urban heat islands (p < 0.001, rho = -0.21; see Supplementary Figure 5). This finding underscores the significant role that urban vegetation plays in reducing the exposure of urban populations to extreme land surface temperatures51. It highlights the substantial potential of trees and green spaces to mitigate urban heat in Europe. In this context, Iungman et al., (2023)52 showed that increasing tree coverage to 30% would cool cities by a mean of 0.4°C, which could prevent up to 1.84% of summer premature deaths. We acknowledge that achieving the target of 30% tree cover in densely built-up and populated cities presents significant challenges as streetscapes alone are often insufficient. To overcome this, greening strategies must utilize other major land uses. Peri-urban forests should be prioritized for their positive impact on surface climate conditions and air quality53. Tree planting programs must also be expanded to private land and residential areas54 which collectively represent a vast, often underutilized surface area for canopy growth. Greening of buildings (e.g., green roofs, green walls, and facade planting) should be a primary focus particularly in dense urban areas where ground-level space is scarce, to add crucial vertical and horizontal canopy surfaces. Although trees offer numerous benefits, achieving 30% cover solely with trees in densely built environments may be difficult, especially in arid climates. In such scenarios, implementing more pocket-sized parks with a considerable tree component can be beneficial22. These parks, created from residual land, are accessible and appealing to local communities and easier to establish.

Methods
Selection of cities
Our analysis focused on European cities listed in the Urban Audit61 dataset 2021, which follows the city definition by the Organization for Economic Cooperation and Development - European Commission (OECD-EC). This definition is based on criteria such as population density and local administrative boundaries62. From this original database we specifically selected only the “City Core” areas, resulting in a total of 729 cities across 30 European Countries (EU27, Norway, Switzerland and Iceland). To enhance our dataset, we also included 133 cities from the United Kingdom by accessing the 2020 Urban Audit dataset. This brought the total number of European cities considered in our analysis to 862.

Rule 3
To assess the first rule, which specifies that every dwelling, educational facility and workplace should have a view of 3 trees, here we used a special type of geospatial modelling, namely viewshed analysis31,35,36, which delineates the area visible from a given observation point, taking into account the surrounding terrain features such as building height and tree cover estimates. The analysis was carried out using the QGIS 3.30.2 version and required three different datasets: distribution of building height, estimates of tree cover count and population distribution (GHSL-POP). Detailed information and the resolution of these layers are reported in Supplementary Table 1.

The spatial information about building height was retrieved from the Global Human Settlement Layers website30. The spatial raster dataset illustrates the distribution of building heights derived from global digital elevation models (DEMs) and filtered satellite imagery using linear regression. Two key DEMs were used: ALOS World 3D - 30m (from 2006-2011) and Shuttle Radar Topography Mission 30m (from 2000). The building height information was updated with shadow data from the 2018 Sentinel-2 satellite image, and the final product (GHS-BUILT-H) is provided at 100 m of resolution.

Similarly, the Global Human Settlement Layers provided the distribution of residential population, expressed as the number of people per cell37. Residential population estimates for the 2020 were disaggregated from census or administrative units to 100 m resolution grid cells, informed by the distribution, volume, and classification of built-up as mapped in the Global Human Settlement Layer (GHSL) global layer per corresponding epoch. Therefore, it can be reasonably assumed that when the value of a grid cell is greater than zero, it indicates the presence of residential buildings where people actually reside.

To determine the proportion of areas containing at least three trees, we leveraged a recent methodology developed in Liu et al., (2023)27 and Brandt et al., (2024)28. Individual tree count data were generated from PlanetScope, which is a constellation of 130+ nano-satellites with a spatial resolution of 3-meter and red, green, blue and near-infrared bands, for the year 2019. The technical approach began with image preparation, where PlanetScope imagery was processed into custom 1° x 1° mosaic tiles, each comprising numerous scenes to ensure comprehensive coverage. A histogram-matching algorithm was applied to align these scenes with Landsat and Sentinel-2 data, creating a homogeneous mosaic. Image acquisition was strategically timed based on phenological windows derived from MODIS data. For deciduous regions, images were captured after the peak productivity of herbaceous vegetation, ensuring tree crowns were prominent. In evergreen areas, images were taken when trees had full green leaves, enhancing crown visibility. Given the importance of image quality, particularly sharpness, a blur kernel was used to assess each scene's sharpness directly after download. Scenes that did not meet a sharpness threshold of 0.23 were discarded, and alternative images were used until the required sharpness was achieved. The core of the tree detection method was a heatmap-based approach using convolutional neural networks (CNNs), which produced a confidence map where peaks indicated the centers of tree crowns. The models were trained using point labels for tree crown centers, with approximately 130,000 labels for PlanetScope manually verified using high-resolution imagery from Google Earth and Bing Maps. The models were trained over 2,500 epochs with a learning rate that decreased linearly after the 2000th epoch. Multiple models were trained using different semi-random splits of the training data, ensuring robust performance across varied conditions. An ensemble of five models was then used to predict tree locations on the mosaics, averaging the results from these heatmaps. Local maxima were converted into point files representing tree crown centers, with associated confidence levels > 0.15 and minimal object spacing > 3 pixels. Starting from the identified tree crown centers, we generated a 100 m resolution raster of the number of trees for each city to ensure consistency with the resolution of the building (GHS-BUILT-H) and population (GHS_POP) raster data.

The first step of the viewshed analysis consisted in defining the number of observation points from which trees are supposed to be visible. Thus, for each city map we created a set of viewpoints randomly sampled with a minimum spacing distance, namely a point will not be added if there is an already generated point within this (Euclidean) distance from the generated location. We selected three minimum distance thresholds of 100 m, 250 m, and 500 m. Lower thresholds could not be selected due to the spatial resolution of the 'obstacle layer' (building heights from GHS-BUILT-H) and the population distribution data (GHS-POP) that are only available at a 100m grid resolution. The next step consisted in setting the radius of analysis, which is the maximum distance for visibility testing for the viewshed algorithm. Here we used two values 100 m and 250 m, with the observer height set to 1.6 m. Then, we run the viewshed analysis to define as visible those portions of landscape without obstructions (i.e., buildings or sloped terrain) from the building height layer, GHS-BUILT-H. Specifically, if an observation point exists within a cell that also contains trees, those trees are deemed "visible" based on proximity, unless the line of sight is blocked by an adjacent obstacle cell. This approach is a simplification of the "window-view" concept, but the high density of observation points ensures visibility is assessed from multiple angles, thereby reducing the likelihood of "dead zones" where trees might not be counted. Finally, visual exposure values from viewsheds generated from all sampling points are added, resulting in a continuous raster map of visual exposure. By combining the visible map obtained from our viewshed analysis with this new layer of grid cells containing at least three trees, we were able to map the pixels in each city that are visible from specified observation points and contain at least three trees. However, having a visible tree is not enough because we wanted to understand what is the amount of population that satisfies such a rule. Thus, to provide a more nuanced understanding of the rule's impact, we further analyzed the proportion of the urban population with access to these green spaces by combining these green pixels with the Global Human Settlement Layer (GHSL) to estimate the population living in visible areas (∑𝑃𝑂𝑃 ), and then we normalize by the total population in the city (𝑃𝑂𝑃 ) to allow for comparison with the other two criteria of the 3-30-300 rule:
𝑃𝑂𝑃 = ∑𝑃𝑂𝑃 ⁄𝑃𝑂𝑃 (1)
𝑅3 𝑔𝑟𝑒𝑒𝑛 𝑐𝑖𝑡𝑦
The population in urban areas with visibility of at least three trees has been calculated using four distinct parameter sets, specifically exploring three values for the minimum distance between viewpoints (d = 100m, 250m, 500m) and two values for the radius of the viewshed analysis (r = 100m, 250m). The findings of this sensitivity analysis are illustrated in Supplementary Figure 6. In the main text, we chose to present the results that offer the highest viewpoint density and the most refined spatial resolution, achieved with d = 100m and r = 100m. Further refinement of the spatial scale was limited by the resolution of the input datasets. In the viewshed analysis, the precision of the output is defined by the coarsest input layer, and to the best of our knowledge, at pan-European scale the most reliable building height data (GHS-BUILT-H) is currently restricted to a 100m resolution. Therefore, using data on tree crown centroids and observation points distributed more densely (e.g. 10m) against a 100m obstruction block would not yield more accurate results; rather, it would imply a level of precision that the building height data cannot support. In particular, even if we use a set of viewpoints with higher density (e.g. 10 m of distance) we would still be limited by the radius of analysis, whose extent could be at minimum the size of the obstacle layer, and therefore 100 m.

Rule 30
The second rule indicates that every neighborhood should have a 30% canopy cover. To examine the second rule, we utilized the Tree Cover Density database provided by PlanetScope. As for the previous rule, this dataset is obtained from individual tree count data generated from PlanetScope (spatial resolution of 3-meter) and aggregated to a 100 m resolution.

Due to the lack of a standardized definition of neighborhoods across cities, we define neighborhoods as 1 km x 1 km grid cells. This simplified definition is in line with the proxy for neighborhood defined in Konijnendijk (2023)18 where a buffer of radius 500 meters was taken to simulate people’s daily neighborhood. To ensure that our focus remains on green-rich urban areas, we apply a threshold filter, retaining only those defined neighborhoods where the tree cover density is at least 30%. For the neighborhoods that meet this green density standard, we then proceed by summing the total population living in these 30% tree cover neighborhood (∑𝑃𝑂𝑃 ) and then normalizing by the total cities’ population (𝑃𝑂𝑃 ):
𝑖𝑛_30 𝑐𝑖𝑡𝑦
𝑃𝑂𝑃 = ∑𝑃𝑂𝑃 ⁄𝑃𝑂𝑃 (2)
𝑅30 𝑖𝑛_30 𝑐𝑖𝑡𝑦
To further assess the sensitivity of our results to the neighborhood definition as a 1 x 1 km grid, we calculated overall compliance with the 30% rule using three additional grids with resolutions of 500 meters, 250 meters, and 100 meters. Our sensitivity analysis (Supplementary Figure 7) indicates that the choice of grid size does not show relevant differences in the overall compliance rates with the 30% Rule. For this reason, and in accordance with specific literature that often defines an egocentric neighborhood using a 500 m radius around each building18,38,39,40,63, which we consider comparable to our 1 x 1 km approach, we decided to present the results evaluated at the 1 km grid (Figure 1b).

Rule 300
The third rule indicates that everyone should live within 300-m of a high-quality public green space of at least 1 ha in size18,64. The first step was then delineating those urban green spaces. To accomplish this, we started from the LUISA (Land Use-based Integrated Sustainability Assessment)25 map, a spatial dataset developed by the European Commission's Joint Research Centre (JRC), which provides a detailed representation of land use and land cover across 39 countries in Europe at a high spatial resolution (50 m). From this map, we first extract the pixels corresponding to the Urban Area (pixels below 2000 as classification), then isolated areas classified as Urban Vegetation, Green Urban Areas, and Sport/Leisure Green within those urban areas. We then converted these areas into shapefile polygons using the polygonize function in QGis and kept only those that were at least 1 hectare in size, defining them as our green urban areas. We created 300-meter buffer zones around the delineated parks and green spaces to map the accessible green area for nearby residents. We then calculated the percentage of the city's population that lives within these zones to show which communities benefit from green space proximity. By examining the ratio of the population living within these buffer zones to the total population of the city, we gain insight into the proportion of residents who have ready access to such green spaces. This metric is crucial, as it informs urban planners and policymakers of the extent to which the city's population is served by accessible green spaces, a key factor in promoting urban livability, public health, and well-being. Finally, as per the other two criteria is normalized by the total city population.
𝑃𝑂𝑃 = ∑𝑃𝑂𝑃 ⁄𝑃𝑂𝑃 (3)
𝑅303 𝑖𝑛_300 𝑐𝑖𝑡𝑦
We further validated our approach by comparing it with recent work conducted by the DG REGIO of the European Commission58 which utilized the Urban Atlas to assess access to urban green spaces. Their method defined proximity as a 400-meter walking distance to open and public areas, including green urban spaces. We compared our estimates of the population share meeting the R-300 rule with the proportion of urban center populations having access to at least 1 hectare of green urban areas within a 400-meter walk, as reported in the DG REGIO study. Our findings indicate a strong correlation between the two outputs (p-value << 0.01, R² = 0.37, see Supplementary Figure 8), underscoring the robustness of our method. It is noteworthy that our results identify a smaller percentage of the population with access to green spaces. This discrepancy can be attributed to two main factors: (1) our use of a more restricted buffer distance (300 meters versus 400 meters) and (2) our focus on urban cores exclusively, whereas the DG REGIO study encompasses the entire Functional Urban Area (FUA), which extends beyond the core city center.

Sensitivity for the three rules together
Based on the various sensitivity analyses conducted for criteria R3 and R30%, we have identified different combinations that impact the overall fulfillment of the 3-30-300 rule (Supplementary Table S2). It is noteworthy that while the overall variations in rule fulfillment are relatively constrained, ranging from 9.3% to 13.5% of the urban population satisfying the criteria, variability is markedly more pronounced at the municipal scale. For instance, cities such as Essen (Germany), Gdansk (Poland), and San Sebastian (Spain) exhibit maximal differential ranges of approximately 20% (Supplementary Figure 9). In the main text, we have prioritized presenting results that maximize adherence to the 3-30-300 rule. This condition is realized under criterion R3 with parameters D = 100 m and r = 100 m, and with the spatial grid size for neighborhood delineation in R30 set at 1 km x 1 km.

Disposable Income
We use multiple sources for disposable income. The one characterized by the highest spatial resolution (200 x 200 m) is obtained from The National Institute of Statistics and Economic Studies (INSEE)44 in France. For several years, INSEE has been disseminating indicators on taxable income or declared income (i.e. income before redistribution) of French households at all geographical levels permitted by respect for statistical confidentiality, from the district to the whole of metropolitan France, including a whole series of intermediate zonings: commune, employment zone, department, region.

Once we downloaded the dataset, we overlaid these 200 x 200 grids onto our spatial data concerning the fulfillment of the 3-30-300 rule (see Figure 4a for the city of Lyon). Given the difference in spatial resolution between the two rasters, we defined the Green Space Category as the average value of compliance with the 3-30-300 rule. Therefore, when the Green Space Category is equal to 2, it indicates that all 100 m x 100 m grid cells within the 200 m x 200 m grid have an average value of 2. We repeat this for all the 72 cities in France.

The other two dataset with a good spatial resolution of disposable income are Spain and Belgium. Disposable income data for Spain were sourced from the Instituto Nacional de Estadística65. These data were available at the levels of municipalities, districts, and census sections. We downloaded the Median Income Indicator for all census districts for the year 2018. As for France, we calculated the Green Space Category as the average level of compliance with the 3-30-300 rule across the various district extents. Disposable income data from Belgium were retrieved from Mikou et al., (2025)43 since it was the country - along with France and Spain - with the highest spatial resolution for administrative units (~1 km2).

In our extended analysis, we have incorporated all other European cities from our database by utilizing the recent downscaling income product developed by Mikou et al. (2025)43. This product employs an innovative machine learning framework, providing a dataset at a 1 x 1 km resolution for the year 2015. The dataset expresses disposable income in terms of purchasing power parity (PPP) to account for variations in living costs across European countries. PPP rates are currency conversion rates that adjust for differences in price levels between countries. Where income data was missing for certain administrative units, they imputed values using the average income of neighboring units to ensure a complete and comprehensive dataset. Our approach mirrored the methodology used for France, Belgium, and Spain, where we compared income data with the Green Space Category. Nevertheless, the consistency between this product and our estimates from the three aforementioned countries is notable. Despite variations in climate, economy, and culture among cities, there is a discernible trend across our analysis: higher disposable income correlates with an increased likelihood of residing in greener neighborhoods (Figure 4B and Supplementary Figure 2). Additionally, we provide a localized comparison with the city of Lyon to highlight the similarities in trends (see Supplementary Figure 10). This analysis underscores the broader pattern observed across diverse European contexts.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This article addresses crucial issues related to quality of life in European cities, focusing on access to urban green spaces, one of the most important factors. The research was conducted based on the 3-30-300 concept, which has recently become popular due to its transparent criteria. The research was clearly described, with a fairly detailed account of the methodology and results obtained, which made the entire process transparent and comprehensible. While I appreciate the significance of the overall research presented, I have a few comments regarding the adopted methodology and a few minor technical observations. They are as follow:\n1. The chapter called 'Main' (perhaps 'Introduction' would be more appropriate) lacks references to other concepts related to the study of quality of life in cities, including those that consider access to green spaces, such as 15-minute cities and biophilic cities. The 3-30-300 concept is not the only one in this field. It would be worthwhile justifying why this particular concept was chosen for this research.\n2. Line 34-35 – there is a lack of references after “to meet UN Sustainable Development Goal 11 Target 7”.\n3. Figure 1, Figure 2 - It would be helpful if the drawings (as well as titles) could be enlarged, as the descriptions in the legend are very difficult to read.\n4. Results – views 3 trees – “Half of the European urban population views 3 trees” – according to the methodology used, I have little confidence in these conclusions. I will explain this further in the comments on the methodology.\n5. Results – 30% tree cover - According to the method used, the results of the 30% tree cover rule could be even better than presented because areas smaller than 1 km² are removed. It would be useful to add such a comment. On the other hand, calculating the population with such a low level of resolution seems to introduce a rather high degree of generalisation.\n6. Line 114 – Warszawa is Warsaw in English.\n7. Results - Line 123 and subsequent lines – “Fifty percent of European city residents enjoy proximity to green spaces” – in my opinion, this term is too strong. Green spaces are not evenly distributed in virtually any city. The essence of the green city concept is to examine this phenomenon locally in order to identify areas where green spaces have been developed well and areas where they have not. This sentence only refers to the distance from the park.\n8. Line 228 – “Opportunities and challenges of the 3-30-300 rule in urban planning” - The title does not accurately reflect the content of the chapter. It primarily refers to the opportunities and challenges of developing green areas in urban planning, rather than the 3-30-300 rule. The main challenge with this concept is how to quantify the rules and transform them into measurable parameters.\n9. Line 275-276 – “Nature Restoration Regulation (NRR)” – there is a lack of reference 13.\n10. Methods - The selection criteria for the cities is not entirely clear to me. Was the city with the highest population density chosen?\n11. Methods – Rule 3 – This part of the research is its biggest flaw. Selecting measurement points at 500-metre intervals greatly simplifies the analysis. In my opinion, the authors mistakenly applied the strictly local 3-30-300 rule concept to global analyses. The 3-30-300 concept is a simple and useful tool for examining our immediate surroundings in great detail. But the main assumption of the concept is “to bring trees and nature all the way into people’s neighbourhoods, streets, and on their doorsteps in orde rto capitalise on their many benefits”. However, simplifying to a resolution of 500 metres renders it very general, detached from the underlying ideas and assumptions. Assuming the concept that each resident should see three trees from their apartment, the study should include every house or even every floor of a building. In my opinion, a resolution of 500 metres between measurement points produces unreliable results.\nMoreover, the 3-30-300 concept does not specify the distance at which residents should plant these trees. The 250-metre rule contradicts Konijnendijk's assumption that trees in close proximity are visible. A distance of 250 metres does not allow us to see greenery from our windows.\nFurthermore, what was the height of the observation point? This significantly affects the visibility of trees. Someone on the first floor might see something completely different to someone on the fifth or tenth floor.\n12. Methods – line 336 – 100x100 grid – 100m, 100 km? What size is the grid?\n13. Methods – Rule 30 – The 1 km grid eliminates several smaller parks from the analysis. Once again, we are moving away from Konijnendijk's idea of locality.\n14. Methods - Rule 300 – line 376 – The resolution is 50 m. This part of the research is at a completely different level to the 3-trees rule (50 m versus 500/250 m).\n15. Lines 398-410 - This part is more of a discussion than a method.\n16. Methods - Disposable Income - The data being compared here have different resolutions. I understand that it is problematic to have the same data for different countries. However, the disadvantages of comparing data at different levels of detail must be clearly explained.\nIn summary, we believe that the main strengths of this article are its focus on the important social issue of quality of life in cities and its analysis of access to green spaces using the 3-30-300 concept, which has recently gained popularity due to its apparent simplicity. The main drawback of this article is that it uses the 3-30-300 concept for European-scale research, utilising relatively low-detail data, whereas the essence of this concept lies in its local application (e.g. proximity to green spaces and residences). Therefore, in my opinion, the results obtained, mainly regarding the three-tree visibility requirement, raise serious doubts. Direct tree visibility is measured using parameters that are larger than the accessibility to large parks (measuring points every 500 m and visibility of 250 m, while access to the park in a distance of 300m). These are not comparable principles and should not be analysed together.\nHowever, in order to preserve the article's value, I propose rewording it to clarify the problems and inaccuracies arising from the aforementioned flaws. At the same time, we should avoid making overly strong, unequivocal statements that are not supported by the results obtained. The description should reflect the fact that the results are not entirely precise.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_3": "This is a straightforward and well-written paper, offering what is likely the largest 3-30-300 study to be published in terms of its spatial scale, offering novelty in its coverage of the entirety of Europe’s urban regions. It conclusively supports the insights offered by past studies: urban residents have poor access to green space, and this is a consistent problem across very diverse urban regions. I appreciated the examination of wealth and aridity as mediating factors, this was a valuable progression on the base message. The datavis was also a strength.\nI really enjoyed reading this paper and had very few concerns with the Main section; these are detailed below.\nHowever, on reaching the Methods I came to understand how coarse this analysis is, and also felt that some of the care in the previous sections was missing, with a few key decisions in the method lacking justification. All three ‘tests’ for the 3-30-300 are conducted at a very coarse spatial scale, with quite coarse data. Canopy data is at 10m resolution, neighbourhoods for canopy assessment are 1km x 1km blocks and viewsheds are based on ‘observation points’ at very low density per square kilometre. Tree point locations are from 2019. Park access was also calculated as a ‘crow flies’ distance using buffers rather than walking distance along footpaths. Some simplification of methods is understandable given the scale of the study, but my concern is that the research must remain consistent with the intent of the 3-30-300 rule, which aims to localise access to urban nature and pull focus to what is accessible from buildings. The methods applied here feel more aggregated, with no stage of the method using buildings as a direct unit of testing.\nThe 3 test’s viewshed method was difficult to follow and may benefit from some interpretive visuals. Why are you measuring which 100m x 100m pixels are visible within viewsheds that are >500m apart? This does not appear to be a suitable proxy for testing sightlines from buildings. It was also unclear how viewsheds were selected, and why 100m x 100m pixels were opted for. My concern is that this does not really indicate whether the buildings have views to trees. This highlights a key challenge in the method. The 3-30-300 method really looks at buildings, not population units. It seems that use of population units as the basis of the method produces significant issues of aggregation. Buildings, by contrast, can be tested individually, as has been demonstrated in past large 3-30-300 studies.\n1km neighbourhood blocks for the 30 test are also not justified and this part of the method seems an especially severe deviation from the intent of the metric. At this scale, individual buildings could be hundreds of metres from significant patches of canopy, while still passing the ‘30’ test. The authors are correct that ‘neighbourhood’ is not clearly defined – a key issue with the method itself – but this does not necessarily imply that a highly aggregated spatial scale is appropriate. I would strongly recommend a finer scale for this step of the analysis, as well as a clear justification of the scale selected. One option is to select a distance at which thermal effects of canopy – or perhaps a selected health benefit – are detectable.\nThe 300 test’s use of buffers is less of a serious problem but must be better signposted in the Main text (e.g. at line 126,127 the term ‘buffer’ could have been inserted as a cue to the method used). A discussion of how this serves to overestimate access would also be appropriate, ideally backed by a short sample analysis demonstrating the scale of overestimation/uncertainty this introduces to the study relative to more precise network-based routing analysis methods.\nOverall I am quite concerned that the use of population has broken the method. Ideally this study would carry out building-scale tests, then link these results to population by making a statistical argument around how many people each building might represent. This avoids the need to use very aggregated methods which introduce many of the problems that 3-30-300 was conceived to avoid.\nMinor issues in the Main text:\nLine 76 – What’s LUISA?\n85 – explanation of views is unclear.\nFigure 3 – this is nice but a little confusing. Is the x-axis ‘share of population’… the share of the population passing the 3-30-300 test? Please annotate this axis fully. This confusion is partly sparked by line 190-191 which state the boxes show the IQR of income values. I also suggest you change the aridity quartiles to abcd instead of 1234 so that it’s easy to discern what ‘2-4’ means (at the moment it’s tricky because one is not sure whether aridity is first or second and this takes a minute to confirm).\nFigure 3 also does appear to show that aridity is a major driver of low 3-30-300 achievement – the box for 3-2 seems to indicate that in the third income quartile, the lowest humidity cities have much lower achievement. The text at lines 177-180 appears to deny this.\n203-210. Please justify why a more granular analysis was necessary.\n231 – I object to the use of ‘inevitably’. Surely there are areas where green access is good even in areas which have urbanised. This undermines a core message that is important in this field of research: urbanisation does not need to mean huge losses of greenery. The problem is one of design; we have agency in this. The term inevitably implies that infill development is an unquestionable force of ‘greying’. To date it mostly has been, and perhaps this is a better way to acknowledge the issue, without creating a sense of helplessness.\n241-257. These are good arguments but streets are a minority of urban space, often 30-40%. What other land uses can play a role in greening? Streets alone won’t get us to 30.\n265-269. As with my comment regarding inevitability above: the argument that 30% in urban areas is unfeasible is not supported by your evidence. 30% cover is more difficult to deliver in urbanised areas but ‘spatial and environmental limitations’ are the product of design decisions; if an urban area has no space for trees then it has been designed badly. These are not feasibility problems. Indeed, the street retrofit ideas you indicate in 241-257 are promising remedies to these more dense areas, especially as these more walkable dense areas do not need to cater to drivers as generously.\nFinally – your title is very punchy, but it uses the term ‘green space’. I think many readers will interpret this as ‘parks’, which was my initial impression too. A less ambiguous term may help avoid this misconstrual of the study’s focus."}, "author_response": "Response to reviewers for the manuscript\nLess than fifteen percent of European city residents meet the 3-30-300 rule for\ngreen space visibility, canopy, and proximity\nL.E. Bertassello 1*, M. van der Velde 1, J. Maes 2, S. Liu 3, M. Brandt 3, L. Feyen 1\n1. European Commission, Joint Research Centre, Ispra, Italy\n2. European Commission, Directorate-General for Environment, Brussels, Belgium\n3. Department of Geosciences and Natural Resource Management, University of\nCopenhagen, Copenhagen, Denmark\n* Corresponding Author: Leonardo Enrico Bertassello, leonardo.bertassello@ec.europa.eu,\nORCID: https://orcid.org/0000-0001-5168-2142\nNote: the answer to reviewers’ comments are highlighted in red, and the references to the\ntext refers to the updated and clean version of the manuscript.\nReviewer #1 (Remarks to the Author):\nThis article addresses crucial issues related to quality of life in European cities, focusing on\naccess to urban green spaces, one of the most important factors. The research was\nconducted based on the 3-30-300 concept, which has recently become popular due to its\ntransparent criteria. The research was clearly described, with a fairly detailed account of the\nmethodology and results obtained, which made the entire process transparent and\ncomprehensible. While I appreciate the significance of the overall research presented, I have\na few comments regarding the adopted methodology and a few minor technical\nobservations. They are as follow:\n1. The chapter called 'Main' (perhaps 'Introduction' would be more appropriate) lacks\nreferences to other concepts related to the study of quality of life in cities, including those\nthat consider access to green spaces, such as 15-minute cities and biophilic cities. The 3-\n30-300 concept is not the only one in this field. It would be worthwhile justifying why this\nparticular concept was chosen for this research.\nWe appreciate the reviewer's suggestions and have incorporated additional concepts related\nto urban quality of life, as indicated in Lines 36-41 and 266-270 of the revised manuscript:\n“In the context of a rapidly urbanizing world, concepts like the biophilic cities10 or smart\nsustainable cities11 urge urban planners to facilitate daily interactions with nature, promoting\nenvironmental awareness and stewardship. These cities are envisioned to be sustainable\nand resilient, strengthening their social and physical capacity against future shocks such as\nclimate change, natural disasters, and economic uncertainties.”\nAnd\n“A key solution to achieving this balance is the transformation of urban mobility46, a solution\nalso advocated by the recent concept of the 15-minute city47,48, which contends that cities\ncan function more effectively, equitably and environmentally if essential services and key\namenities are within 15 minutes by non-polluting forms of transport, such as walking or\ncycling.”\nThe decision to focus on the 3-30-300 concept in our research stems from its provision of a\nquantifiable, straightforward guideline that is universally applicable and measurable (Line 58:\n“the rule is easy to remember, and as we will show, relatively straightforward to implement,\nmonitor, and evaluate.”).\nFurthermore, similar to the concepts mentioned, adopting the 3-30-300 rule enables urban\nplanners to systematically work towards creating healthier, more sustainable, and resilient\ncities (see Line 62):\n“The 3-30-300 rule provides a set of objectives, universal benchmarks for cities to determine\nhow they fare on urban green space visibility, coverage and proximity, as well as where to\ntarget investments for the future.”\nAdditionally, it facilitates monitoring the green status of cities as more data becomes\navailable (Lines 332-340):\n“The 3-30-300 rule offers a valuable framework that provides quantifiable metrics related to\nthe visibility, coverage, and proximity of urban green spaces, enabling effective tracking of\nprogress over time. This is particularly crucial in the context of the European Union’s Nature\nRestoration Regulation (NRR), which mandates an increase in urban green spaces and tree\ncanopy cover through innovative strategies, such as integrating green spaces into buildings\nand infrastructure. These strategies must prioritize accelerating canopy development by\ncreating dense, diverse urban forests even in small plots, ensuring newly planted street trees\nhave ample, uncompacted soil to mature fully, and transforming transport routes into\nshaded, continuous ecological corridors.”\nThese considerations underscore the choice of the 3-30-300 concept due to its clear,\nactionable metrics and its potential to significantly enhance urban living standards.\n2. Line 34-35 – there is a lack of references after “to meet UN Sustainable Development\nGoal 11 Target 7”.\nThanks for noticing, we added the appropriate reference [9]: “Assembly, U. G. (2015).\nTransforming our world: the 2030 Agenda for Sustainable Development”\n3. Figure 1, Figure 2 - It would be helpful if the drawings (as well as titles) could be enlarged,\nas the descriptions in the legend are very difficult to read.\nThanks for the suggestion, we hope the revised Figures 1 and 2 provide enhanced clarity. In\nFigure 1, we have removed the previous PDF plots, as they were not discussed in the main\ntext, and directed readers to Extended Figure 1, which serves as a surrogate for this\ninformation at the city scale.\nFigure 1: (a)-(b)-(c) Spatial representation of the fulfillment of each single rule of the 3-30-300\nguideline for 862 European cities. The circle size is proportional to the total city population, while the\ndiscrete color coding represents the proportion of the city's population that meets each rule's\nrequirement. The lower panels.\nFigure 2: (a) Spatial representation of the fulfillment of the 3-30-300 guideline for 862 European cities.\nThe circle size is proportional to the total city population, while the discrete color coding represents\nthe proportion of the city's population that meets each rule's requirement. (b) High-resolution analysis\nfor a sample of 6 cities, showing the number of fulfilled rules at a 100x100 m grid cell level. This finer-\nscale representation reveals the intra-urban variability in guideline fulfillment, highlighting areas where\nmultiple rules are met or not.\n4. Results – views 3 trees – “Half of the European urban population views 3 trees” –\naccording to the methodology used, I have little confidence in these conclusions. I will\nexplain this further in the comments on the methodology.\nWe appreciate the reviewer's feedback regarding the section on viewing three trees. In\nresponse to comment R1C11 (that is referred to here by R1), we have substantially revised\nthis section to enhance clarity and confidence in our methodology. We hope that the updated\ncontent effectively addresses such concerns and provides a more robust understanding of\nour approach.\n5. Results – 30% tree cover - According to the method used, the results of the 30% tree\ncover rule could be even better than presented because areas smaller than 1 km² are\nremoved. It would be useful to add such a comment. On the other hand, calculating the\npopulation with such a low level of resolution seems to introduce a rather high degree of\ngeneralisation.\nWe appreciate the reviewer's feedback highlighting the limitations and potential\nimprovements in our methodology regarding the 30% tree cover rule. In response, we have\nrevised the methods section (see Lines 466-473 and Figure S7) to include a sensitivity\nanalysis using three additional grid sizes as proxy for our neighbourhood definition: 100m,\n200m, and 500m.\n“To further assess the sensitivity of our results to the neighborhood definition as a 1 x 1 km\ngrid, we calculated overall compliance with the 30% rule using three additional grids with\nresolutions of 500 meters, 250 meters, and 100 meters. Our sensitivity analysis (Figure S7)\nindicates that the choice of grid size does not show relevant differences in the overall\ncompliance rates with the 30% Rule. For this reason, and in accordance with specific\nliterature that often defines an egocentric neighborhood using a 500 m radius around each\nbuilding18,38,39,40,63, which we consider comparable to our 1 x 1 km approach, we decided to\npresent the results evaluated at the 1 km grid (Figure 1b).”\nWe have also added a dedicated paragraph to the Limitations section (Lines 305-313)\ndetailing the methodological considerations of the 30% rule.\n“Regarding Rule 30%, it is important to note the absence of a standardized neighborhood\ndefinition; therefore, we conducted a sensitivity analysis across various neighbourhood sizes\nto evaluate the effect on the number of people in accordance with the 30% Rule (see\nMethods and Figure S7). Additionally, some studies incorporate all vegetated areas,\nincluding parks, whereas our analysis does not. Furthermore, it is crucial to acknowledge the\nrule's omission of blue spaces, which have been demonstrated to provide similar benefits to\ngreen spaces, including restoration, facilitating social interactions, and lowering\npsychological distress55,56,57.”\nThese adjustments aim to provide a more comprehensive and reliable assessment of our\nfindings.\nFigure S7: Sensitivity analysis of the proportion of the population in cities complying with the 30% rule\nacross different grid sizes (i.e., varying neighborhood sizes). Each point represents a city in our\ndatabase, with black horizontal dashed lines indicating the average compliance with the rule. At the\ntop, we provide an example from the city of Thessaloniki, Greece, where black pixels denote\n\"neighborhoods\" achieving 30% tree cover.\n6. Line 114 – Warszawa is Warsaw in English.\nThank you for noticing, we edited accordingly in Line 124.\n7. Results - Line 123 and subsequent lines – “Fifty percent of European city residents enjoy\nproximity to green spaces” – in my opinion, this term is too strong. Green spaces are not\nevenly distributed in virtually any city. The essence of the green city concept is to examine\nthis phenomenon locally in order to identify areas where green spaces have been developed\nwell and areas where they have not. This sentence only refers to the distance from the park.\nWe thank the reviewer for the suggestion and have changed the subtitle to “Over half of\nEuropean city residents live within 300 meters of a park”, which more correctly represents\nwhat we assess.\n8. Line 228 – “Opportunities and challenges of the 3-30-300 rule in urban planning” - The\ntitle does not accurately reflect the content of the chapter. It primarily refers to the\nopportunities and challenges of developing green areas in urban planning, rather than the 3-\n30-300 rule. The main challenge with this concept is how to quantify the rules and transform\nthem into measurable parameters.\nWe thank the reviewer for the suggestion. In the revised manuscript, we have divided the\nsection into two parts: \"Opportunities for urban green space development\" and \"Challenges\nand limitations.\" We believe that this new structure more accurately reflects the content of\nthe chapter.\n9. Line 275-276 – “Nature Restoration Regulation (NRR)” – there is a lack of reference 13.\nThanks for noticing, we added the appropriate reference [16]:“Regulation (EU) 2024/1991 of\nthe European Parliament and of the Council on nature restoration and amending Regulation\n(EU) 2022/869 (Text with EEArelevance) (Official Journal of the European Union, 2024).”\n10. Methods - The selection criteria for the cities is not entirely clear to me. Was the city with\nthe highest population density chosen?\nWe apologize for the lack of clarity. We did not choose cities based on having the highest\npopulation density. Instead, the selection was guided by the predefined designation within\nthe Urban Audit dataset, which encompasses central urban areas as defined by the OECD-\nEC guidelines. This approach ensures a comprehensive representation of urban centers\nacross the specified European countries, rather than focusing solely on population density\nmetrics. We address this in Lines 348-355 of the revised manuscript:\n“Our analysis focused on European cities listed in the Urban Audit61 dataset 2021, which\nfollows the city definition by the Organization for Economic Cooperation and Development -\nEuropean Commission (OECD-EC). This definition is based on criteria such as population\ndensity and local administrative boundaries62. From this original database we specifically\nselected only the “City Core” areas, resulting in a total of 729 cities across 30 European\nCountries (EU27, Norway, Switzerland and Iceland). To enhance our dataset, we also\nincluded 133 cities from the United Kingdom by accessing the 2020 Urban Audit dataset.\nThis brought the total number of European cities considered in our analysis to 862.”\n11. Methods – Rule 3 – This part of the research is its biggest flaw. Selecting measurement\npoints at 500-metre intervals greatly simplifies the analysis. In my opinion, the authors\nmistakenly applied the strictly local 3-30-300 rule concept to global analyses. The 3-30-300\nconcept is a simple and useful tool for examining our immediate surroundings in great detail.\nBut the main assumption of the concept is “to bring trees and nature all the way into people’s\nneighbourhoods, streets, and on their doorsteps in order to capitalise on their many\nbenefits”. However, simplifying to a resolution of 500 metres renders it very general,\ndetached from the underlying ideas and assumptions. Assuming the concept that each\nresident should see three trees from their apartment, the study should include every house\nor even every floor of a building. In my opinion, a resolution of 500 metres between\nmeasurement points produces unreliable results.\nMoreover, the 3-30-300 concept does not specify the distance at which residents should\nplant these trees. The 250-metre rule contradicts Konijnendijk's assumption that trees in\nclose proximity are visible. A distance of 250 metres does not allow us to see greenery from\nour windows. Furthermore, what was the height of the observation point? This significantly\naffects the visibility of trees. Someone on the first floor might see something completely\ndifferent to someone on the fifth or tenth floor.\nWe appreciate the reviewers for highlighting these points and apologize if our previous\nmethodological description lacked clarity and caused confusion.\nFirstly, we utilized a resolution of 100 meters for the three primary raster layers in our\nanalysis: building height (GHS-BUILT-H), population distribution (GHS_POP), and tree\ncount. As specified in Lines 381-385 and 406-409, the tree count was initially generated\nusing 3 x 3 meter grid cells and then aggregated for consistency with the other geospatial\ndata layers:\n“To determine the proportion of areas containing at least three trees, we leveraged a recent\nmethodology developed in Liu et al., (2023)24 and Brandt et al., (2024)25. Individual tree\ncount data were generated from PlanetScope, which is a constellation of 130+ nano-\nsatellites with a spatial resolution of 3-meter and red, green, blue and near-infrared bands,\nfor the year 2019.”\n“Starting from the identified tree crown centers, we generated a 100 m resolution raster of\nthe number of trees for each city to ensure consistency with the resolution of the building\n(GHS-BUILT-H) and population (GHS_POP) raster data”.\nSecondly, we conducted a sensitivity analysis on two parameters of the viewshed analysis:\nthe minimum spacing distance (100 m, 250 m, and 500 m) and the visibility analysis radius\n(100 m and 250 m) (see Lines 412-424, 435-443, and Figure S6).\n“The first step of the viewshed analysis consisted in defining the number of observation\npoints from which trees are supposed to be visible. Thus, for each city map we created a set\nof viewpoints randomly sampled with a minimum spacing distance, namely a point will not be\nadded if there is already a generated point within this (Euclidean) distance from the\ngenerated location. We selected three minimum distance thresholds of 100 m, 250 m, and\n500 m. Lower thresholds could not be selected due to the spatial resolution of the analyzed\nraster layers (i.e., 100 m). The next step consisted in setting the radius of analysis, which is\nthe maximum distance for visibility testing for the viewshed algorithm. Here we used two\nvalues 100 m and 250 m, with the observer height set to 1.6 m. Then, we run the viewshed\nanalysis to define as visible those portions of landscape without obstructions (i.e.,\nbuildings or sloped terrain) from the building height layer, GHS-BUILT-H. Finally, visual\nexposure values from viewsheds generated from all sampling points are added, resulting in\na continuous raster map of visual exposure.”\n“The population in urban areas with visibility of at least three trees has been calculated using\nfour distinct parameter sets, specifically exploring three values for the minimum distance\nbetween viewpoints (d = 100m, 250m, 500m) and two values for the radius of the viewshed\nanalysis (r = 100m, 250m). The findings of this sensitivity analysis are illustrated in Figure\nSX2. In the main text, we chose to present the results that offer the highest viewpoint density\nand the most refined spatial resolution, achieved with d = 100m and r = 100m. Further\nrefinement of the spatial scale was constrained by the limitations in the resolution of the\ninput datasets. With this parameter combination, 46.7% of the population meets the criteria\nspecified by rule R3.”\nFigure S6. Sensitivity Analysis of Population Visibility of Trees (Rule R3). The scatter plot\nillustrates the range of estimated population shares meeting the Rule R3 criteria (visibility of at least 3\ntrees) across 862 European cities, based on four distinct parameter sets for the viewshed analysis.\nThe analysis varied the minimum distance between viewpoints (d: 100m, 250m, 500m) and the\nviewshed radius (r: 100m, 250m). The final parameter set chosen for the main text results—d = 100m\n(highest viewpoint density) and r = 100m (most refined spatial resolution)—yielded the maximum\npopulation share meeting the criteria, with an estimated 46.7% of the population having visibility of at\nleast three trees. This selection offers the highest feasible spatial resolution, constrained by the input\ndataset limitations.\nWhile we acknowledge that the \"3-30-300 concept does not specify the distance at which\nresidents should plant these trees,\" various studies have made assumptions about visibility\ndistance when calculating R3. For example, Cimburova and Blumentrath (2022) conducted\nviewshed modeling for exposure distances ranging from 50 to 300 meters. Other\nresearchers define tree visibility as the presence of trees within a buffer with varying\ndistance thresholds, from 15 m to 100 m (Nieuwenhuijsen et al. 2022; Battisti et al. 2023;\nDaland, 2023; Owen et al., 2024). We added such information in L. xxx of the revised\nmanuscript:\n“Konijnendijk (2023)18 suggests that every resident should have a view of three trees from\ntheir dwelling, educational facility and workplace. However, defining this visibility is\nchallenging. Various studies employ different methods: some use surveys and window-view\nanalyses to assess tree visibility from households31,32, others utilize computer vision to\nquantify street greenery33, and some define tree visibility using buffer zones with distance\nthresholds19,20,34. Here, to assess the visibility of three trees, we used viewshed\nanalysis31,35,36(see Methods). First, we mapped areas visible from specific observation\npoints, accounting for obstructions like building height and surrounding terrain features. We\nthen integrated these maps with high-resolution (3 m) tree count data from PlanetScope27,28\n(see Methods) to focus on raster cells containing at least three trees. Finally, we applied this\nlayer of visual green exposure to the Global Human Settlement Layer Population37 (GHS-\nPOP) data to estimate the population residing in areas with visibility of at least three trees.”\nGiven the scope of our analysis—which is to map the 3-30-300 rule consistently across more\nthan 800 cities on a continental European scale—we are confident that the 100-meter\nthreshold aligns with limits established in the literature. Due to the limitations inherent in the\ndata layers we used, we were unable to conduct this analysis at a finer scale. These\nconstraints are documented in both the methods section (see Section Rule 3) and the main\ntext (see Section Challenges and limitations). Nonetheless, this remains a limitation that\nfuture research and advancements in satellite datasets could address.\nFinally, we selected an observation point height of 1.6 meters. We recognize that residents\non different floors may have varying visibility, but unfortunately, there is no available\ndatabase that maps population distribution across different floors at a pan-European scale\nwith a 100-meter resolution.\n12. Methods – line 336 – 100x100 grid – 100m, 100 km? What size is the grid?\nThanks for noticing, we edited in Lines 406-409 as:\n“Starting from the identified tree crown centers, we generated a 100 m resolution raster of\nthe number of trees for each city to ensure consistency with the resolution of the building\n(GHS-BUILT-H) and population (GHS_POP) raster data”.\n13. Methods – Rule 30 – The 1 km grid eliminates several smaller parks from the analysis.\nOnce again, we are moving away from Konijnendijk's idea of locality.\nWe thank the reviewer for this observation, which relates to the methodology concerns\nraised in R1C5. As detailed in our response to R1C5 and now incorporated into the revised\nMethods section (Lines 463-470), we performed a sensitivity analysis using three additional,\nfiner grid resolutions (500 m, 250 m, and 100 m) to evaluate the effect of neighborhood\ndefinition on compliance rates. This analysis (Figure S7) demonstrated no significant\ndifferences in our overall findings, supporting our use of the 1 km grid.\nFurthermore, we respectfully clarify that the 30% rule is specific to tree canopy coverage\n(\"30% tree canopy in every neighborhood\"). Therefore, our analysis focuses on the trees\nwithin parks and public spaces, rather than the total park area itself. We believe this focus\nmaintains fidelity to the specific definition of the 3-30-300 framework. We have also clarified\nthis strict focus on tree canopy, and the subsequent exclusion of total vegetated areas, in the\nLimitations section (Lines xxx) of the revised manuscript.\n14. Methods - Rule 300 – line 376 – The resolution is 50 m. This part of the research is at a\ncompletely different level to the 3-trees rule (50 m versus 500/250 m).\nThe reviewer is correct in noting that the layer used for defining our green urban areas has a\nresolution of 50 meters. Our approach involved first identifying the 50-meter pixels classified\nas Urban Vegetation, Green Urban Areas, and Sport and Leisure Green. These areas were\nthen converted from raster to polygons using the appropriate QGIS function. We only\nconsidered areas of at least 1 hectare in size as our urban green areas. The resulting\nshapefile was used for the Rule 300 analysis (Lines 479-483):\n“From this map, we first extract the pixels corresponding to the Urban Area (pixels below\n2000 as classification), then isolated areas classified as Urban Vegetation, Green Urban\nAreas, and Sport/Leisure Green within those urban areas. We then converted these areas\ninto shapefile polygons using the polygonize function in QGis and kept only those that were\nat least 1 hectare in size, defining them as our green urban areas.”\nSwitching to shapefiles and their 300-meter buffers resolves problems caused by different\ndata resolutions. This doughnut-shaped area was used to calculate the number of people\nliving within a 300-meter buffer around urban green areas, utilizing the 100-meter resolution\nGHS-POP data (Lines 483-486):\n“We created 300-meter buffer zones around the delineated parks and green spaces to map\nthe accessible green area for nearby residents. We then calculated the percentage of the\ncity's population that lives within these zones to show which communities benefit from green\nspace proximity.”\n15. Lines 398-410 - This part is more of a discussion than a method.\nWe thank the reviewer for the suggestion, we have added some of this information in the\nnew section on challenges and limitation (Lines 313-320):\n“Finally, distances in the Rule 300 were calculated using the \"as the crow flies\" method,\nbased on Euclidean measurements. While this approach is straightforward and efficient,\nit may not accurately account for physical barriers like buildings or roads without pedestrian\ncrossings. To validate our method, we compared it with recent research by DG REGIO of the\nEuropean Commission58, which investigated access to urban green spaces within a 10-\nminute walking time along the street network. Our findings demonstrate a strong correlation\nbetween the two approaches (p-value << 0.01, R² = 0.37, see Methods and Figure S6),\nhighlighting the robustness of our methodology.”\n16. Methods - Disposable Income - The data being compared here have different\nresolutions. I understand that it is problematic to have the same data for different countries.\nHowever, the disadvantages of comparing data at different levels of detail must be clearly\nexplained.\nWe appreciate the reviewer's concern regarding the comparison of disposable income data\nat varying resolutions. We acknowledge that the direct comparison of data with differing\nlevels of detail can introduce methodological challenges, particularly concerning potential\naggregation bias or misinterpretation of scale effects.\nHowever, our approach is not a simple comparison but a deliberate multi-scalar strategy\ndesigned to test the robustness and scale-invariance of the relationship between wealth and\naccess to green space. We respectfully contend that this method is a strength of the study,\nenabling a more comprehensive understanding of the observed trends:\n● Pan-European Scale (1 x 1 km resolution): We established the broad relationship\nusing the highest resolution available data for a continental assessment (Mikou et al.,\n2025 and Figure S2).\n● Granular Analysis (Country/City Levels): We then utilized available higher-resolution\ndata for select countries and cities (e.g., 200 m x 200 m in France) to demonstrate\nthat the socioeconomic disparities persist across finer scales.\nThis layered examination of the data is crucial for several reasons. Firstly, it enables the\nexamination of socioeconomic patterns at a micro-level, revealing disparities that broader\nanalyses might overlook and identifying neighborhoods with significant variations in income\nlevels and access to green spaces. Secondly, granular analysis uncovers local trends and\ncorrelations, providing detailed insights into unique patterns within specific cities or regions\nthat larger-scale studies might miss. Additionally, granular data minimizes aggregation bias,\noffering a more accurate representation of the socioeconomic landscape, which is essential\nfor evaluating the impact of urban planning and development policies. This information has\nbeen incorporated into Lines 227-236 of the revised manuscript:\n“Examining data at such a finer scale not only corroborates broader trends but also\nenhances our understanding at the local level, offering a more nuanced and reliable\ninsight into the relationship between income and urban greenness. Granular data\nsignificantly reduces aggregation bias, thereby providing an enhanced representation\nof the socioeconomic landscape, which is crucial for evaluating the impact of urban\nplanning and development policies. The case of Lyon serves as a compelling example\nof this approach, illustrating the value of detailed analysis in capturing the complexities\nof urban dynamics. When assessed at the national level (Figure 4b), a clear trend\nemerges across all three countries, reinforcing the consistent association between\nhigher disposable income and the likelihood of residing in greener urban\nneighborhoods.”\nBy demonstrating consistent findings across these varied and nested resolutions, we\nmitigate the perceived disadvantages of mixed resolutions and significantly enhance the\nreliability and generality of our conclusions.\nIn summary, we believe that the main strengths of this article are its focus on the important\nsocial issue of quality of life in cities and its analysis of access to green spaces using the 3-\n30-300 concept, which has recently gained popularity due to its apparent simplicity. The\nmain drawback of this article is that it uses the 3-30-300 concept for European-scale\nresearch, utilising relatively low-detail data, whereas the essence of this concept lies in its\nlocal application (e.g. proximity to green spaces and residences). Therefore, in my opinion,\nthe results obtained, mainly regarding the three-tree visibility requirement, raise serious\ndoubts. Direct tree visibility is measured using parameters that are larger than the\naccessibility to large parks (measuring points every 500 m and visibility of 250 m, while\naccess to the park in a distance of 300m). These are not comparable principles and should\nnot be analysed together. However, in order to preserve the article's value, I propose\nrewording it to clarify the problems and inaccuracies arising from the aforementioned flaws.\nAt the same time, we should avoid making overly strong, unequivocal statements that are\nnot supported by the results obtained. The description should reflect the fact that the results\nare not entirely precise.\nWe appreciate the reviewers for their insightful comments and suggestions, which have\nbeen instrumental in refining our manuscript, as detailed in our point-by-point response.\nIn particular, we appreciate the reviewer's acknowledgement of the study's strengths regarding\nits focus on quality of life and the crucial application of the 3-30-300 framework.\nWe agree that the concept is inherently local, but we respectfully contend that adapting the\nframework for European-scale research is a primary strength and novelty of our work, not a\ndrawback. Our goal was to test the applicability of these principles for large-scale urban\npolicy, which necessitates data that is consistently available across diverse national\ncontexts.\nAs detailed in our response R1C16, our analysis is structured as a multi-scalar study. We\nutilize the highest available consistent resolution for the pan-European analysis (1 km grid)\nbut validate the stability of our findings using higher-resolution data at the country and city\nlevels (down to 200 m in some cases). This strategy demonstrates that the underlying trends\nare scale-invariant, thereby strengthening the reliability of the aggregated results.\nAs outlined in our response to Comment R1C11 (and answer to comment R3C5), we refined\nour analysis on Rule 3 by implementing a sensitivity analysis on the viewshed parameters.\nThis enhanced the resolution of the methodology to 100 m}, which represents the highest\npossible spatial resolution achievable with the 100 m input data grid. By using these most\nrefined parameters (d=100 m, r=100 m), we ensure our analysis maintains the local fidelity\nrequired for the Rule 3 while contributing to the overall, multi-dimensional assessment of\nurban nature as intended by the 3-30-300 framework.\nAs outlined in our response to Comment R3C5, our focus is intentionally on population\nunits—aligning with the 3-30-300's goal of assessing social equity and human exposure.\nWe mitigate aggregation issues by using the 100 m resolution GHSL-POP data, a method\ndesigned to provide a more refined view of residential distribution than is typical in large-\nscale studies.\nIn line with the proposal to avoid overly strong statements and ensure precision, we have\ncarefully reviewed the discussion and results sections. We have incorporated specific\nlanguage in the revised manuscript (Section Challenges and limitations) to reflect the\ninherent complexities and assumptions that arise when adapting a local framework like 3-30-\n300 to the continental scale.\nWe hope that our revisions and responses have effectively clarified our methodology,\nallowing us to draw robust and contextually defined results and conclusions.\nReviewer #2 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This\nis part of the Nature Communications initiative to facilitate training in peer review and to\nprovide appropriate recognition for Early Career Researchers who co-review manuscripts.\nReviewer #3 (Remarks to the Author):\nR3C1: This is a straightforward and well-written paper, offering what is likely the largest 3-\n30-300 study to be published in terms of its spatial scale, offering novelty in its coverage of\nthe entirety of Europe’s urban regions. It conclusively supports the insights offered by past\nstudies: urban residents have poor access to green space, and this is a consistent problem\nacross very diverse urban regions. I appreciated the examination of wealth and aridity as\nmediating factors, this was a valuable progression on the base message. The datavis was\nalso a strength.I really enjoyed reading this paper and had very few concerns with the Main\nsection; these are detailed below.\nWe sincerely appreciate the reviewer's positive feedback on our manuscript. We're delighted\nthat the paper's extensive scale and novel coverage of Europe's urban regions, along with\nour analysis of wealth and aridity, were well-received. Thank you for recognizing the quality\nof our data visualization and for your thoughtful insights.\nR3C2: However, on reaching the Methods I came to understand how coarse this analysis is,\nand also felt that some of the care in the previous sections was missing, with a few key\ndecisions in the method lacking justification. All three ‘tests’ for the 3-30-300 are conducted\nat a very coarse spatial scale, with quite coarse data. Canopy data is at 10m resolution,\nneighbourhoods for canopy assessment are 1km x 1km blocks and viewsheds are based on\n‘observation points’ at very low density per square kilometre. Tree point locations are from\n2019. Park access was also calculated as a ‘crow flies’ distance using buffers rather than\nwalking distance along footpaths. Some simplification of methods is understandable given\nthe scale of the study, but my concern is that the research must remain consistent with the\nintent of the 3-30-300 rule, which aims to localise access to urban nature and pull focus to\nwhat is accessible from buildings. The methods applied here feel more aggregated, with no\nstage of the method using buildings as a direct unit of testing.\nWe appreciate the reviewer for highlighting these valid points regarding our methodology.\nThese feedbacks have been invaluable in helping us articulate arguments that were\npreviously underrepresented and in enhancing the robustness and consistency across the\nvarious methodologies and datasets used. We hope that the revised Methods section more\nclearly explains our reasoning and assumptions. We have addressed the issues raised in the\nresponses to the comments below.\nR3C3: The 3 test’s viewshed method was difficult to follow and may benefit from some\ninterpretive visuals.\nWe substantially reframe the Rule 3 section of the methods providing more details about our\nmethodology and choices in the main text (see L. 88-99) as:\n“Konijnendijk (2023)18 suggests that every resident should have a view of three trees from\ntheir dwelling, educational facility and workplace. However, defining this visibility is\nchallenging. Various studies employ different methods: some use surveys and window-view\nanalyses to assess tree visibility from households31,32, others utilize computer vision to\nquantify street greenery33, and some define tree visibility using buffer zones with distance\nthresholds19,20,34. Here, to assess the visibility of three trees, we used viewshed\nanalysis31,35,36(see Methods). First, we mapped areas visible from specific observation\npoints, accounting for obstructions like building height and surrounding terrain features. We\nthen integrated these maps with high-resolution (3 m) tree count data from PlanetScope27,28\n(see Methods) to focus on raster cells containing at least three trees. Finally, we applied this\nlayer of visual green exposure to the Global Human Settlement Layer Population37 (GHS-\nPOP) data to estimate the population residing in areas with visibility of at least three trees. “\nFurthermore, we have also added Figure S11 to facilitate interpretation:\nFigure S11: Example of results extraction for Rule 3 with a minimum distance between points of 100\nmeters and an analysis radius of 100 meters. Panel (A) shows the binary viewshed analysis, where\nblack pixels indicate visible areas from the yellow viewpoints. White areas are not visible due to\nobstacles provided in the Built-up height layer (Panel D). Panel (B) displays the number of trees\nidentified per pixel. In this panel, we isolated pixels with at least three trees and combined this new\nbinary layer with Panel (A). From these two rasters, we retained only the pixels with a value of 2,\nindicating that they contain at least three trees and are within visible areas (Panel C). This raster is\nthen intersected with the population distribution obtained from the Global Human Settlement Layer\n(GHS-POP, Panel E), to obtain the final dataset of population seeing at least three visible trees (Panel\nF). All the analyses are conducted on rasters with 100 m of spatial resolution.\nR3C4: Why are you measuring which 100m x 100m pixels are visible within viewsheds that\nare >500m apart? This does not appear to be a suitable proxy for testing sightlines from\nbuildings. It was also unclear how viewsheds were selected, and why 100m x 100m pixels\nwere opted for. My concern is that this does not really indicate whether the buildings have\nviews to trees. This highlights a key challenge in the method.\nOur analysis was conducted at 100 m of resolution as common bases for all the three-key\ndataset: building height, population distribution and tree count (aggregated from 3 m\nresolution). We specified this in the revised section of Methods as follows (Lines 363-380):\n“The analysis was carried out using the QGIS 3.30.2 version and required three different\ndatasets: distribution of building height, estimates of tree cover count and population\ndistribution (GHSL-POP). Detailed information and the resolution of these layers are\nreported in Extended Data Table 1.\nThe spatial information about building height was retrieved from the Global Human\nSettlement Layers website (citation). The spatial raster dataset illustrates the distribution of\nbuilding heights derived from global digital elevation models (DEMs) and filtered satellite\nimagery using linear regression. Two key DEMs were used: ALOS World 3D - 30m (from\n2006-2011) and Shuttle Radar Topography Mission 30m (from 2000). The building height\ninformation was updated with shadow data from the 2018 Sentinel-2 satellite image, and the\nfinal product (GHS-BUILT-H) is provided at 100 m of resolution.\nSimilarly, the Global Human Settlement Layers provided the distribution of residential\npopulation, expressed as the number of people per cell. Residential population estimates for\nthe year 2020 were disaggregated from census or administrative units to 100 m resolution\ngrid cells, informed by the distribution, volume, and classification of built-up as mapped in\nthe Global Human Settlement Layer (GHSL) global layer per corresponding epoch.\nTherefore, it can be reasonably assumed that when the value of a grid cell is greater than\nzero, it indicates the presence of residential buildings where people actually reside.”\nTo improve the robustness of our results we have conducted a sensitivity analysis of the\nminimum spacing between viewpoints and their respective radius of analysis (see L. 412-\n424 and 435-443):\n“The first step of the viewshed analysis consisted in defining the number of observation\npoints from which trees are supposed to be visible. Thus, for each city we created a set of\nviewpoints randomly sampled with a minimum spacing distance, namely a point will not be\nadded if there is an already generated point within this (Euclidean) distance from the\ngenerated location. We selected three minimum distance thresholds of 100 m, 250 m, and\n500 m. Lower thresholds could not not be selected due to the spatial resolution of the\nanalyzed raster layers (i.e., 100 m). The next step consisted in setting the radius of analysis,\nwhich is the maximum distance for visibility testing for the viewshed algorithm. Here we used\ntwo values 100 m and 250 m, with the observer height set to 1.6 m. Then, we run the\nviewshed analysis to define as visible those portions of landscape without obstructions (i.e.,\nbuildings or sloped terrain) from the building height layer, GHS-BUILT-H. Finally, visual\nexposure values from viewsheds generated from all sampling points are added, resulting in\na continuous raster map of visual exposure”.\n“The population in urban areas with visibility of at least three trees has been calculated using\nfour distinct parameter sets, specifically exploring three values for the minimum distance\nbetween viewpoints (d = 100m, 250m, 500m) and two values for the radius of the viewshed\nanalysis (r = 100m, 250m). The findings of this sensitivity analysis are illustrated in Figure\nSX2. In the main text, we chose to present the results that offer the highest viewpoint density\nand the most refined spatial resolution, achieved with d = 100m and r = 100m. Further\nrefinement of the spatial scale was constrained by the limitations in the resolution of the\ninput datasets. With this parameter combination, 46.7% of the population meets the criteria\nspecified by rule R3.”\nFigure S6. Sensitivity Analysis of Population Visibility of Trees (Rule R3). The scatter plot\nillustrates the range of estimated population shares meeting the Rule R3 criteria (visibility of at least 3\ntrees) across 862 European cities, based on four distinct parameter sets for the viewshed analysis.\nThe analysis varied the minimum distance between viewpoints (d: 100m, 250m, 500m) and the\nviewshed radius (r: 100m, 250m). The final parameter set chosen for the main text results—d = 100m\n(highest viewpoint density) and r = 100m (most refined spatial resolution)—yielded the maximum\npopulation share meeting the criteria, with an estimated 46.7% of the population having visibility of at\nleast three trees. This selection offers the highest feasible spatial resolution, constrained by the input\ndataset limitations.\nAs detailed in the new paragraph on method limitations, we were unable to achieve a\nresolution finer than 100 meters due to the constraints of the data layers employed.\nHowever, this limitation could be addressed by future research and advancements in\nsatellite datasets. While finer resolution data and local information are available for some\ncities, allowing for more detailed mapping of the 3-30-300 rule, our analysis focuses on\nconsistently applying this rule across more than 800 cities on a continental European scale\nfor the first time.\nR3C5: The 3-30-300 method really looks at buildings, not population units. It seems that use\nof population units as the basis of the method produces significant issues of aggregation.\nBuildings, by contrast, can be tested individually, as has been demonstrated in past large 3-\n30-300 studies.\nWe appreciate the reviewer's insight regarding the unit of analysis. We respectfully clarify\nthat the 3-30-300 rule is fundamentally centered on ensuring social equity and public health\nbenefits for people. Therefore, our focus on population units aligns with the core goal of the\nframework: investigating how residents experience tree visibility, coverage, and proximity.\nWhile many prior studies have focused on buildings for methodological convenience, we\nsought to overcome this limitation and provide a more direct assessment of human exposure\nto urban greenness.\nTo mitigate the aggregation issues associated with population data, we utilized the Global\nHuman Settlement Layers Population (GHSL-POP) dataset, which disaggregates population\nestimates to a fine 100 m resolution grid informed by the built-up areas mapped in the Global\nHuman Settlement Layer (GHSL). This detailed spatial resolution helps bridge the gap\nbetween population units and building-level analysis by providing a more granular view of\nresidential distribution. The underlying GHSL data considers the presence and density of\nbuilt-up areas and buildings within each 100 m grid cell, providing a crucial link between\nwhere people reside and the urban structures they inhabit. While GHSL-POP may not\ndirectly indicate views to trees for individual buildings, it offers a refined approach to\nunderstanding how the population is distributed in the context of urban structures.\nBy focusing on people, our study addresses a crucial gap, as few 3-30-300 studies have had\nthis explicit, population-centric focus. Therefore, while GHSL-POP may not eliminate all\nchallenges associated with the method, this robust data resolution minimizes aggregation\nbias and provides a valuable layer of detail that enhances the assessment of population\nexposure to urban green spaces. We believe this methodological choice strengthens the\npolicy relevance and social impact assessment of our findings.\nR3C6: 1km neighbourhood blocks for the 30 test are also not justified and this part of the\nmethod seems an especially severe deviation from the intent of the metric. At this scale,\nindividual buildings could be hundreds of metres from significant patches of canopy, while\nstill passing the ‘30’ test. The authors are correct that ‘neighbourhood’ is not clearly defined\n– a key issue with the method itself – but this does not necessarily imply that a highly\naggregated spatial scale is appropriate. I would strongly recommend a finer scale for this\nstep of the analysis, as well as a clear justification of the scale selected. One option is to\nselect a distance at which thermal effects of canopy – or perhaps a selected health benefit –\nare detectable.\nWe appreciate the reviewer's feedback highlighting the limitations and potential\nimprovements in our methodology regarding the 30% tree cover rule. In response, we have\nrevised the methods section (see Lines 463-470) to include a sensitivity analysis using three\nadditional grid sizes as proxy for our neighbourhood definition: 100m, 200m, and 500m.\n“To further assess the sensitivity of our results to the neighborhood definition as a 1 x 1 km\ngrid, we calculated overall compliance with the 30% rule using three additional grids with\nresolutions of 500 meters, 250 meters, and 100 meters. Our sensitivity analysis (Figure S7)\nindicates that the choice of grid size does not show relevant differences in the overall\ncompliance rates with the 30% Rule. For this reason, and in accordance with specific\nliterature that often defines an egocentric neighborhood using a 500 m radius around each\nbuilding18,38,39,40,63, which we consider comparable to our 1 x 1 km approach, we decided to\npresent the results evaluated at the 1 km grid (Figure 1b).”\nWe have also added a dedicated paragraph to the Limitations section (Lines 305-313)\ndetailing the methodological considerations of the 30% rule.\n“Regarding Rule 30%, it is important to note the absence of a standardized neighborhood\ndefinition; therefore, we conducted a sensitivity analysis across various neighbourhood sizes\nto evaluate the effect on the number of people in accordance with the 30% Rule(see\nMethods and Figure S7). Additionally, some studies incorporate all vegetated areas,\nincluding parks, whereas our analysis does not. Furthermore, it is crucial to acknowledge the\nrule's omission of blue spaces, which have been demonstrated to provide similar benefits to\ngreen spaces, including restoration, facilitating social interactions, and lowering\npsychological distres55,56,57.”\nThese adjustments aim to provide a more comprehensive and reliable assessment of our\nfindings.\nFigure S7: Sensitivity analysis of the proportion of the population in cities complying with the\n30% rule across different grid sizes (i.e., varying neighborhood sizes). Each point represents\na city in our database, with black horizontal dashed lines indicating the average compliance\nwith the rule. At the top, we provide an example from the city of Thessaloniki, Greece, where\nblack pixels denote \"neighborhoods\" achieving 30% tree cover.\nR3C7: The 300 test’s use of buffers is less of a serious problem but must be better\nsignposted in the Main text (e.g. at line 126,127 the term ‘buffer’ could have been inserted as\na cue to the method used). A discussion of how this serves to overestimate access would\nalso be appropriate, ideally backed by a short sample analysis demonstrating the scale of\noverestimation/uncertainty this introduces to the study relative to more precise network-\nbased routing analysis methods.\nWe thank the reviewer for the recommendation. First, we have edited the specific part in\nLines 134-137 as:\n“To examine the third rule, we utilized spatial buffering to estimate the proportion of the\npopulation residing within a 300-meter zone surrounding parks and other urban green areas\nlarger than 1 ha (see Methods). This buffer serves as an indicator of the accessible green\narea for nearby residents.”\nFurthermore, in the new section on limitations and challenges we report more information on\nthe methodology used for Rule 300, and we discuss the limit of our approach and we also\nshow a comparison with a more refined criteria for defining distance to urban green areas\n(see Lines 309-320 and Lines 495-508 in Methods):\n“Additionally, some studies incorporate all vegetated areas, including parks, whereas our\nanalysis does not. Furthermore, it is crucial to acknowledge the rule's omission of blue\nspaces, which have been demonstrated to provide similar benefits to green spaces,\nincluding restoration, facilitating social interactions, and lowering psychological\ndistress55,56,57. Finally, distances in the Rule 300 were calculated using the \"as the crow flies\"\nmethod, based on Euclidean measurements. While this approach is straightforward and\nefficient, it may not accurately account for physical barriers like buildings or roads without\npedestrian crossings. To validate our method, we compared it with recent research by DG\nREGIO of the European Commission58, which investigated access to urban green spaces\nwithin a 10-minute walking time along the street network. Our findings demonstrate a strong\ncorrelation between the two approaches (p-value << 0.01, R² = 0.37, see Methods and\nFigure S8), highlighting the robustness of our methodology.”\nFigure S8: Comparison between the share of European cities' population meeting the R-300\nrule with the proportion of urban center populations having access to at least 1 hectare of\ngreen urban areas within a 400-meter walk, as reported in the DG REGIO study.\nR3C8: Overall I am quite concerned that the use of population has broken the method.\nIdeally this study would carry out building-scale tests, then link these results to population by\nmaking a statistical argument around how many people each building might represent. This\navoids the need to use very aggregated methods which introduce many of the problems that\n3-30-300 was conceived to avoid.\nWe thank the reviewer for the recommendation, we have address similar issue in our answer\nto comment R3C5 above.\nMinor issues in the Main text:\nR3C9: Line 76 – What’s LUISA?\nWe spelled out the acronym LUISA in the main text as “Land Use-based Integrated\nSustainability Assessment”. In particular, the LUISA Base Map 2018 is a high-resolution land\nuse/land cover map developed and produced by the Joint Research Centre of the European\nCommission (see Citation 25). It corresponds to a modified and improved version of the\nCORINE Land Cover 2018 map. Compared to CORINE the LUISA Base Map delivers a\nhigher overall spatial detail and finer thematic breakdown of artificial land use/cover\ncategories (17 categories instead of 11 in CORINE).\nR3C10: 85 – explanation of views is unclear.\nWe apologize for any confusion caused by the initial wording. We have revised the first\nparagraph (Lines 88-99) of the section titled \"Half of the European urban population views 3\ntrees\" to enhance clarity:\n“Konijnendijk (2023)18 suggests that every resident should have a view of three trees from\ntheir dwelling, educational facility and workplace. However, defining this visibility is\nchallenging. Various studies employ different methods: some use surveys and window-view\nanalyses to assess tree visibility from households31,32, others utilize computer vision to\nquantify street greenery33, and some define tree visibility using buffer zones with distance\nthresholds19,20,34. Here, to assess the visibility of three trees, we used viewshed\nanalysis31,35,36(see Methods). First, we mapped areas visible from specific observation\npoints, accounting for obstructions like building height and surrounding terrain features. We\nthen integrated these maps with high-resolution (3 m) tree count data from PlanetScope27,28\n(see Methods) to focus on raster cells containing at least three trees. Finally, we applied this\nlayer of visual green exposure to the Global Human Settlement Layer Population37 (GHS-\nPOP) data to estimate the population residing in areas with visibility of at least three trees.”\nR3C11: Figure 3 – this is nice but a little confusing. Is the x-axis ‘share of population’… the\nshare of the population passing the 3-30-300 test? Please annotate this axis fully. This\nconfusion is partly sparked by line 190-191 which state the boxes show the IQR of income\nvalues. I also suggest you change the aridity quartiles to abcd instead of 1234 so that it’s\neasy to discern what ‘2-4’ means (at the moment it’s tricky because one is not sure whether\naridity is first or second and this takes a minute to confirm).\nThank you for the suggestions and for noticing the mistake in the caption for description of\nthe IQR values. Yes, in the x-axis we are reporting the share of the population fulfilling the 3-\n30-300 rule. We incorporated the edits in the updated version of Figure 3.\nFigure 3: Relationship between economic development, climate, and urban greenness in 862\nEuropean cities. (a) Spatial bivariate representation of per capita GDP (GDPc) and aridity index,\nwhere circle size represents total city population and discrete color indicates the 4th quantile of each\ndistribution. (b) Boxplot analysis of the 16 resulting groups, showing the correlation between GDPc,\naridity index, and the percentage of the city's population fulfilling the 3-30-300 rule. The horizontal\ndashed lines separate the four quantiles of GDPc, while the sub-groups reveal the impact of\nincreasing humidity on urban greenness. Notably, cities with higher GDPc tend to be located in areas\nwith low aridity and a higher proportion of their population living in greener areas, suggesting a\npositive relationship between economic development and urban environmental quality. Each box\ndepicts the interquartile range (IQR) of the percentage of the city's population fulfilling the 3-30-300\nrule with the median marked by a line inside the box. The whiskers extend to the most extreme data\npoints within 1.5 times the IQR, highlighting the spread of the data. Outliers beyond the whiskers,\nif\nany, are plotted individually.\nR3C12: Figure 3 also does appear to show that aridity is a major driver of low 3-30-300\nachievement – the box for 3-2 seems to indicate that in the third income quartile, the lowest\nhumidity cities have much lower achievement. The text at lines 177-180 appears to deny\nthis.\nThe reviewer rightly highlights that aridity significantly contributes to low achievement levels.\nAcross all four income classes, it's evident that more arid cities tend to have a lower\npercentage of their population meeting the 3-30-300 rule. We have revised the relevant\nsection to emphasize and clarify this point (see L. 191-195):\n“Another crucial factor influencing this relationship are humidity-aridity conditions. Cities\nlocated in more humid environments tend to have a greater share of green spaces, which\nresults in a higher adherence to the 3-30-300 rule. Notably, we showed that on average,\nwithin the same GDP per capita classes, cities with higher humidity levels provide residents\nwith more access to green spaces (Figure 3b).”\nR3C13: 203-210. Please justify why a more granular analysis was necessary.\nThank you for your feedback, which has allowed us to clarify the importance of enhanced\ngranularity in our analysis. This approach is crucial for several reasons. Firstly, it enables the\nexamination of socioeconomic patterns at a micro-level, revealing disparities that broader\nanalyses might overlook and identifying neighborhoods with significant variations in income\nlevels and access to green spaces. Secondly, granular analysis uncovers local trends and\ncorrelations, providing detailed insights into unique patterns within specific cities or regions\nthat larger-scale studies might miss. Additionally, granular data minimizes aggregation bias,\noffering a more accurate representation of the socioeconomic landscape, which is essential\nfor evaluating the impact of urban planning and development policies. This information has\nbeen incorporated into Lines 227-236 of the revised manuscript:\n“Examining data at such a finer scale not only corroborates broader trends but also\nenhances our understanding at the local level, offering a more nuanced and reliable\ninsight into the relationship between income and urban greenness. Granular data\nsignificantly reduces aggregation bias, thereby providing an enhanced representation\nof the socioeconomic landscape, which is crucial for evaluating the impact of urban\nplanning and development policies. The case of Lyon serves as a compelling example\nof this approach, illustrating the value of detailed analysis in capturing the complexities\nof urban dynamics. When assessed at the national level (Figure 4b), a clear trend\nemerges across all three countries, reinforcing the consistent association between\nhigher disposable income and the likelihood of residing in greener urban\nneighborhoods.”\nBy demonstrating consistent findings across these varied and nested resolutions, we\nmitigate the perceived disadvantages of mixed resolutions and significantly enhance the\nreliability and generality of our conclusions.\nR3C14: 231 – I object to the use of ‘inevitably’. Surely there are areas where green access\nis good even in areas which have urbanised. This undermines a core message that is\nimportant in this field of research: urbanisation does not need to mean huge losses of\ngreenery. The problem is one of design; we have agency in this. The term inevitably implies\nthat infill development is an unquestionable force of ‘greying’. To date it mostly has been,\nand perhaps this is a better way to acknowledge the issue, without creating a sense of\nhelplessness.\nThank you for the suggestion, we rephrased the sentence in Lines 252-257 as: “While\nurbanization is often seen as a positive force driving economic growth and resource\nefficiency, it can impact the availability and quality of urban green spaces if not managed\nthoughtfully. Addressing this challenge through intentional design and planning is crucial to\ncreating sustainable and livable cities”\nR3C15: 241-257. These are good arguments but streets are a minority of urban space, often\n30-40%. What other land uses can play a role in greening? Streets alone won’t get us to 30.\nWe thank the reviewer for the suggestion. We have revised the section to offer more insights\non how cities can overcome these challenges to achieve the 30% cover target. Our\nrecommendations include prioritizing peri-urban forests, expanding tree planting initiatives to\ninclude private spaces, aiming for 30% overall vegetation with a substantial tree component\nin densely built and arid areas, and creating accessible pocket-sized parks in highly\nurbanized settings. We species this in Lines 283-298 as:\n“In this context, Iungman et al., (2023)52 showed that increasing tree coverage to 30% would\ncool cities by a mean of 0.4°C, which could prevent up to 1.84% of summer premature\ndeaths. We acknowledge that achieving the target of 30% tree cover in densely built-up and\npopulated cities presents significant challenges as streetscapes alone are often insufficient.\nTo overcome this, greening strategies must utilize other major land uses. Peri-urban forests\nshould be prioritized for their positive impact on surface climate conditions and air quality53.\nTree planting programs must also be expanded to private land and residential areas54 which\ncollectively represent a vast, often underutilized surface area for canopy growth. Greening of\nbuildings (e.g., green roofs, green walls, and facade planting) should be a primary focus\nparticularly in dense urban areas where ground-level space is scarce, to add crucial vertical\nand horizontal canopy surfaces. Although trees offer numerous benefits, achieving 30%\ncover solely with trees in densely built environments may be difficult, especially in arid\nclimates. In such scenarios, implementing more pocket-sized parks with a considerable tree\ncomponent can be beneficial22. These parks, created from residual land, are accessible and\nappealing to local communities and easier to establish.”\nR3C16: 265-269. As with my comment regarding inevitability above: the argument that 30%\nin urban areas is unfeasible is not supported by your evidence. 30% cover is more difficult to\ndeliver in urbanised areas but ‘spatial and environmental limitations’ are the product of\ndesign decisions; if an urban area has no space for trees then it has been designed badly.\nThese are not feasibility problems. Indeed, the street retrofit ideas you indicate in 241-257\nare promising remedies to these more dense areas, especially as these more walkable\ndense areas do not need to cater to drivers as generously.\nWe thank the reviewer for the recommendation, we have address similar issue in our answer\nto comment R3C15 above.\nR3C17: Finally – your title is very punchy, but it uses the term ‘green space’. I think many\nreaders will interpret this as ‘parks’, which was my initial impression too. A less ambiguous\nterm may help avoid this misconstrual of the study’s focus.\nWe thank the reviewer for the recommendation, we have changed our title in:\n“Less than fifteen percent of European city residents meet the 3-30-300 rule for green\nspace visibility, canopy, and proximity”\nto better highlight the focus on the 3-30-300 rule and avoid misunderstanding."}, {"round": 2, "reviewer_comments": {"reviewer_1": "Thank you to the authors for addressing the proposed revisions and carefully responding to the comments raised. The changes introduced in many places have clearly improved the clarity of the research and its presentation. Numerous parameters have been explained, formulations have been refined, the argumentation has been better structured and key aspects of the methodology have been clarified. Several previously ambiguous sections have been significantly clarified. Consequently, I now have a much better understanding of the research, and the article is more coherent and easier to follow.\nWhile the explanations and arguments provided by the authors are sufficient for most of the comments I raised, two issues still require further refinement in my opinion.\nThe first issue concerns Rule 1 (the visibility of three trees). The additional explanations provided by the authors greatly enhance the reader's understanding of the research process, which appears logical and interesting. However, I remain concerned about the level of detail in the analysis and the conclusions drawn from it. For instance, given the tree centroids and observation points, I do not understand why the authors first generalised the tree information by counting trees within a 100 m grid cell and only then determined their visibility. Performing this operation prior to generalising the tree data would have yielded more precise results.\nMoreover, I do not understand how grid cells containing obstacles that block the visibility of trees were taken into account within the adopted workflow. In particular, in the case of 100 m axis, in particular, the cells obstructing visibility may be the same as those containing the three trees. What, then, is the outcome of the analysis in such cases? What happens when the cell that blocks visibility is the same cell where the observation point falls?\nIn my opinion, the results obtained in this rule are highly generalised (and thus unreliable) for another reason. The spacing of observation points at 100-metre intervals is too coarse; within a radius of 100 metres, several buildings and other obstacles may be present, leading to very different local visibility conditions. Using such large distances to assess tree visibility moves the analysis away from the core principle of the 3-30-300 concept: that greenery should be in residents' immediate surroundings. Using data on tree crown centroids and observation points distributed more densely (e.g. 10m) would make it possible to obtain fairly precise results that capture local conditions in a very straightforward manner, which is the essence of Konijnendijk’s concept. Moreover, these assumptions strongly influence the general results of the three-tree criterion assessment, yet they are addressed with only a single sentence of commentary in the limitations section.\nThe second issue relates to the language used to describe the conclusions. I raised this point in my first review. Many of the formulations, especially given the high level of generality of the analyses, are overstated. In other words, the conclusions drawn are too strong and radical given the level of detail in the data used and the results obtained. The authors use overly strong wording which is often not fully justified. Such statements might be appropriate if the level of detail in the data corresponded to the assumptions of the concept. However, since this is not the case, the research results require a more critical and cautious interpretation.\nE.g.:\n“Less than 15% of the population in European cities…”\n“A quarter of city-dwellers in Europe…”\n“Half of the European urban population…”\nIf the study only analyses 800+ cities in Europe, whereas there are around 2,000 cities in Germany alone, then it still only covers a portion of the population in European cities. While it does represent the populations of the largest and most urbanised cities, it does not represent the entirety of the urban population.\nIn summary, while acknowledging the amount of work involved, I recommend recalculating the three-tree criterion using observation points that are more densely distributed. This would substantially increase the value of the article and ensure that the conclusions drawn are well-founded.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_3": "Thanks for appropriately acknowledging the limitations of this study, and producing the sensitivity analyses that show relatively low median differences. I feel this is now ready for publication, and will likely cite it shortly. Thanks for an important piece of work."}, "author_response": "Response to reviewers for the manuscript\nAssessing European Cities with the 3-30-300 rule underscores the need for\nenhanced urban greening efforts\nL.E. Bertassello 1*, M. van der Velde 1, J. Maes 2, S. Liu 3, M. Brandt 3, L. Feyen 1\n1. European Commission, Joint Research Centre (JRC), Ispra, Italy\n2. European Commission, Directorate-General for Environment (DG-ENV), Brussels,\nBelgium\n3. Department of Geosciences and Natural Resource Management, University of\nCopenhagen, Copenhagen, Denmark\n* Corresponding Author: Leonardo Enrico Bertassello, leonardo.bertassello@ec.europa.eu,\nORCID: https://orcid.org/0000-0001-5168-2142\nNote: the answer to reviewers’ comments are highlighted in red, and the references to the\ntext refers to the tracked changes version of the manuscript.\nReviewer #1 (Remarks to the Author):\nR1C1: Thank you to the authors for addressing the proposed revisions and carefully\nresponding to the comments raised. The changes introduced in many places have clearly\nimproved the clarity of the research and its presentation. Numerous parameters have been\nexplained, formulations have been refined, the argumentation has been better structured\nand key aspects of the methodology have been clarified. Several previously ambiguous\nsections have been significantly clarified. Consequently, I now have a much better\nunderstanding of the research, and the article is more coherent and easier to follow.\nWe sincerely appreciate the reviewer's positive feedback on our revised manuscript.\nWhile the explanations and arguments provided by the authors are sufficient for most of the\ncomments I raised, two issues still require further refinement in my opinion.\nR1C2: The first issue concerns Rule 1 (the visibility of three trees). The additional\nexplanations provided by the authors greatly enhance the reader's understanding of the\nresearch process, which appears logical and interesting. However, I remain concerned about\nthe level of detail in the analysis and the conclusions drawn from it. For instance, given the\ntree centroids and observation points, I do not understand why the authors first generalised\nthe tree information by counting trees within a 100 m grid cell and only then determined their\nvisibility. Performing this operation prior to generalising the tree data would have yielded\nmore precise results.\nWe thank the reviewer for these critical observations regarding the spatial resolution of our\nanalysis. We apologize for the confusion but the decision to generalize tree data into 100m\ncells prior to the visibility analysis was a deliberate methodological choice necessitated by\nthe resolution of our auxiliary datasets.\nWhile tree centroids were available at a higher resolution, the 'obstacle layer' (building\nheights from GHS-BUILT-H) and the population distribution data (GHS-POP) are only\navailable at a 100m grid resolution (see updated Lines 314-316). In a viewshed analysis, the\nprecision of the output is constrained by the coarsest input layer, and to the best of our\nknowledge, at pan-European scale, the most reliable building height data (GHS-BUILT-H) is\ncurrently restricted to a 100m resolution. We added this limitation in L. 459-470 of the\nmethod section as:\n“Further refinement of the spatial scale was limited by the resolution of the input datasets. In\nthe viewshed analysis, the precision of the output is defined by the coarsest input layer, and\nto the best of our knowledge, at pan-European scale the most reliable building height data\n(GHS-BUILT-H) is currently restricted to a 100m resolution. Therefore, using data on tree\ncrown centroids and observation points distributed more densely (e.g. 10m) against a 100m\nobstruction block would not yield more accurate results; rather, it would imply a level of\nprecision that the building height data cannot support. In particular, even if we use a set of\nviewpoints with higher density (e.g. 10 m of distance) we would still be limited by the radius\nof analysis, whose extent could be at minimum the size of the obstacle layer, and therefore\n100 m.”\nR1C3: Moreover, I do not understand how grid cells containing obstacles that block the\nvisibility of trees were taken into account within the adopted workflow. In particular, in the\ncase of 100 m axis, in particular, the cells obstructing visibility may be the same as those\ncontaining the three trees. What, then, is the outcome of the analysis in such cases? What\nhappens when the cell that blocks visibility is the same cell where the observation point\nfalls?\nOur approach considers the entire 100m cell as a single unit of analysis. If an observation\npoint exists within a cell that also contains trees, those trees are deemed \"visible\" based on\nproximity, unless the line of sight is blocked by an adjacent obstacle cell. This approach is a\nsimplification of the \"window-view\" concept, but the high density of observation points\nensures visibility is assessed from multiple angles, thereby reducing the likelihood of \"dead\nzones\" where trees might not be counted.\nIn cases where a cell obstructs visibility but is still visible from other observation points, it will\nbe marked as \"visible\" (illustrated as the black region in Figure S11(a)). Subsequently, we\nassess whether this visible cell contains three trees (as shown in Figure S11(b)). If it does, it\nwill be classified as \"visible with three trees\" (as depicted in Figure S11(c)); otherwise, it will\nnot be included in the final count. This methodology ensures that even when obstacles and\nobservation points coincide, visibility can be reliably assessed across the urban landscape.\nR1C4: In my opinion the results obtained in this rule are highly generalised (and thus\nunreliable) for another reason. The spacing of observation points at 100-metre intervals is\ntoo coarse; within a radius of 100 metres, several buildings and other obstacles may be\npresent, leading to very different local visibility conditions. Using such large distances to\nassess tree visibility moves the analysis away from the core principle of the 3-30-300\nconcept: that greenery should be in residents' immediate surroundings. Using data on tree\ncrown centroids and observation points distributed more densely (e.g. 10m) would make it\npossible to obtain fairly precise results that capture local conditions in a very straightforward\nmanner, which is the essence of Konijnendijk’s concept. Moreover, these assumptions\nstrongly influence the general results of the three-tree criterion assessment, yet they are\naddressed with only a single sentence of commentary in the limitations section.\nWe acknowledge the reviewer's concern regarding the coarse resolution, which indeed\nposes a significant limitation to the output of the three-tree criterion. Although our tree count\ndataset allows for analysis at a finer 3-meter resolution concerning tree visibility, we are\nhindered by the coarse resolution of the \"obstacle layer\" (specifically, the building height\nmap, GHS-BUILT-H) and the population distribution (GHS-POP), both of which are available\nonly at a 100-meter resolution. Enhancements in these datasets are essential for conducting\na more precise analysis. While local studies have achieved greater precision with more\ndetailed data for individual cities (Nieuwenhuijsen et al., 2022; Coreser et al., 2024; ,Antonio\nLopez et al., 2025; Battisti et al., 2024), a comprehensive obstacle dataset at the European\nscale is currently unavailable. Finally, to evaluate the impact of our 100-meter resolution on\nthe reliability of the results, we benchmarked our findings against several recent high-\nresolution case studies (see Table below reported also in Supporting Information as Table\nS2). These local studies utilized high-precision datasets and building-level vantage points.\nWhile our continental-scale approach necessarily generalizes local conditions, the resulting\nestimates for Rule 1 remain broadly consistent with these high-resolution assessments. The\nobserved variations are largely attributable to two factors. First, our study measures the\nshare of the population fulfilling the rule, whereas most local studies calculate the share of\nbuildings. Second, while a 100-meter grain may average out specific micro-scale visibility\n\"gaps,\" it effectively captures the neighborhood-level green character that underpins the 3-\n30-300 concept. The alignment between our pan-European results and these localized\nstudies suggests that the 100-meter resolution serves as a robust proxy for identifying broad\nspatial trends across the continent.\nWe have included this observation in line 312-325 of the limitations section as:\n\"A primary limitation of our assessment of Rule 1 (the ‘three-tree’ criterion) is the 100m\nspatial grain of the GHS-BUILT-H and GHS-POP datasets. While the 3-30-300 rule is\nideally evaluated from the micro-scale perspective of residential windows, the current\nlack of uniform, high-resolution pan-European datasets for building geometry and\npopulation distribution necessitates a coarser approach. This 100m generalization\nmay not capture micro-scale visibility nuances, such as individual trees visible through\nnarrow gaps between buildings. To evaluate the impact of this generalization, we\nbenchmarked our results against several high-resolution local studies19,20,21,22. Despite\nthe differences in spatial grain, our findings are broadly consistent with these localized\nassessments. This alignment suggests that while our results represent a more\ngeneralized scale, they effectively capture the overarching spatial trends of urban\nforest visibility. This trade-off is essential for maintaining methodological consistency\nacross the European continent; while local studies offer superior precision for\nindividual cities, our approach provides the first harmonized, comparable baseline at a\ncontinental scale.\"\nLocal Study This Study Methodological\nCity Reference\n(High-Res) (100m) Difference\nLocal study used\nNieuwenhuijsen et\nBarcelona (ES) 43% 31% individual\nal. (2022)\nsurveys/perception.\nLocal study: % of\nAmsterdam Coreser et al.\n50% 46% Buildings; Our study:\n(NL) (2024)\n% of Population.\nLocal study: % of\nAix-en- Antonio Lopez et al.\n68% 62% Buildings; Our study:\nProvence (FR) (2025)\n% of Population.\nLocal study: % of\nAntonio Lopez et al.\nFlorence (IT) 38% 59% Buildings; Our study:\n(2025)\n% of Population.\nLocal study:\nTurin (IT) Battisti et al. (2024) 40% 48% Neighborhood-level\naverage.\nR1C5: The second issue relates to the language used to describe the conclusions. I raised\nthis point in my first review. Many of the formulations, especially given the high level of\ngenerality of the analyses, are overstated. In other words, the conclusions drawn are too\nstrong and radical given the level of detail in the data used and the results obtained. The\nauthors use overly strong wording which is often not fully justified. Such statements might be\nappropriate if the level of detail in the data corresponded to the assumptions of the concept.\nHowever, since this is not the case, the research results require a more critical and cautious\ninterpretation.\nE.g.:\n“Less than 15% of the population in European cities…”\n“A quarter of city-dwellers in Europe…”\n“Half of the European urban population…”\nIf the study only analyses 800+ cities in Europe, whereas there are around 2,000 cities in\nGermany alone, then it still only covers a portion of the population in European cities. While\nit does represent the populations of the largest and most urbanised cities, it does not\nrepresent the entirety of the urban population.\nWe thank the reviewers for the comments. We have revised some parts of the manuscript\nwhere we believe the tone was overly strong. For example:\nTitle: Assessing European Cities with the 3-30-300 rule underscores the need for enhanced\nurban greening efforts\nSubtitles in the Results and Discussion sections:\nL 89: Nearly half of residents in the studied cities meet the three-tree visibility criterion\nL 117: One-quarter of the analyzed population live in areas with 30% tree cover\nL 138: Over half of residents in the analyzed cities meet the 300-meter proximity target\nL 152: Less than 15% of the population in the analyzed cities fulfills the 3-30-300 rule\nWe further edited other sentences in the main text as in L. 19, 24, 102, 124, 143, 156, 166,\n169, 270.\nR1C6: In summary, while acknowledging the amount of work involved, I recommend\nrecalculating the three-tree criterion using observation points that are more densely\ndistributed. This would substantially increase the value of the article and ensure that the\nconclusions drawn are well-founded.\nWe thank the reviewers for the comments and suggestions that have helped make our work\nclearer and more well-founded. We hope we clarified the issue regarding the three-tree\ncriterion and the reason why with the current dataset limitation (building height and\npopulation layer at 100 m resolution) we cannot enhance the granularity of our analysis as\n100 m represent the highest resolution of the obstacle layer which is necessary to first run\nthe viewshed algorithm and then compute the population living within this visible area.\nReviewer #2 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This\nis part of the Nature Communications initiative to facilitate training in peer review and to\nprovide appropriate recognition for Early Career Researchers who co-review manuscripts.\nReviewer #3 (Remarks to the Author):\nThanks for appropriately acknowledging the limitations of this study, and producing the\nsensitivity analyses that show relatively low median differences. I feel this is now ready for\npublication, and will likely cite it shortly. Thanks for an important piece of work.\nWe are grateful to the reviewer for their encouraging final remarks and for recognizing the\nimportance of this study. We appreciate the thorough review process, which significantly\nimproved the clarity and foundation of our work"}]

Decision letter
I have carefully reviewed the revised paper currently introduced by the Authors. I am pleased to accept most of these changes, particularly as they significantly refine the terminology used to describe the research and its findings. This indicates a shared understanding regarding a crucial aspect: the appropriate mode of scientific discourse—specifically, using language that accurately reflects the factual basis of the study. In my assessment, this provides necessary clarity to the results section and establishes a sound framework for their empirical interpretation.
Furthermore, I appreciate the additional details concerning the study’s limitations, specifically those regarding the visibility of the three trees. These explanations are pertinent and essential, as they address the deficiencies and imperfections within both the conducted work and the datasets employed. Consequently, they facilitate a more accurate interpretation of the results obtained. Maintaining a critical perspective on one's own research is vital for ensuring scientific objectivity. Research is not required to be flawless, and researchers frequently encounter data constraints. Such constraints do not constitute errors; rather, they represent common methodological limitations. An actual error lies in drawing overreaching conclusions that are not supported by the data.
I maintain the position that local-scale studies, given their granular nature, should not be based on excessively generalized data, as this introduces significant margins of error. The argument that one must utilize available data in the absence of more appropriate sources is unconvincing; the principle of data adequacy should always be the primary consideration.
Nevertheless, considering the substantive value of this research, its compelling methodological approach, and its significance for both the quality-of-life research community and those advancing spatial analysis methods in this field, I recommend this paper for publication. I would only further suggest incorporating the explanations provided in the responses to my previous comments—specifically regarding grid cells containing obstacles that block the visibility of trees (R1C3)—into the data description or methodology section of the manuscript.

Reviewer #2
(Remarks to the Author)
Editor's note: Translated comments.
I co-reviewed this manuscript with one of the reviewers who prepared the reports mentioned. This is part of a Nature Communications initiative to facilitate training in peer review and ensure appropriate recognition for young researchers who co-review articles.
Współrecenzowałem ten rękopis z jednym z recenzentów, którzy przygotowali wymienione raporty. Jest to część inicjatywy Nature Communications, mającej na celu ułatwienie szkoleń w zakresie recenzji naukowej oraz zapewnienie odpowiedniego uznania dla młodych badaczy, którzy współrecenzują artykuły.