Integrated Assessment of Health Benefits and Burdens of Urban Greenspace Designs

Abstract

Urban greening is a major goal in policies for sustainable cities, and spatial planners are nowadays strongly interested in the benefits of greenspace for the well-being of urban residents. We present a novel, model-based approach to support the development of effective greening strategies. The approach is quantitative and spatially explicit and accounts for multiple health benefits as well as burdens. In our study, we applied this generic approach to the city of Maastricht (The Netherlands) and conducted an integrated, city-scale assessment of the health benefits and burdens of four urban greenspace designs. These included: ‘No greenspace’, ‘Current greenspace’, ‘Green parking lots and squares’, and ‘Optimized greenspace locations’. For each greenspace design, indicator values were calculated for five determinants of health and well-being: heat stress, air pollution, perceived unsafety, unattractive views, and tick-bite risk. To assess the health contribution of urban greenspace in a given design, these indicator values were compared with the values in the ‘No greenspace’ design. The study produced clear, quantitative conclusions about the health benefits and burdens of the urban greenspace designs for the case of Maastricht but also generated novel, more general insights relevant to the planning of urban greenspace for health and well-being. These insights concern the importance of translating health policy objectives into specific target values or thresholds and the importance of ‘smart’ choices in greenspace type and location that can effectively reduce trade-offs between health benefits and burdens, as well as the insights that adding more greenery not always improves urban health and that urban greenspace alone cannot solve major air pollution problems. The priorities for future research, which will address the limitations of the presented approach, concern a further expansion of the range of health benefits and burdens covered by the model and the development of a common metric for the entire range of health benefits and burdens to optimize greenspace design and maximize its overall net health benefit.

1. Introduction

Urban greening is a major goal in policies for sustainable cities [1,2], and the use of urban green space (UGS) to address health and well-being issues related to urban spatial design, such as heat stress and mental stress, is increasingly advocated for [3,4,5,6,7]. Hence, urban policy makers and spatial planners have developed a strong interest in using urban greening for the benefit of resident well-being and urban sustainability [8,9]. Currently, most science-based guidelines and urban policies deal with the various health benefits and burdens of UGS separately [10,11,12]. For example, the impacts of UGS on heat stress in the context of climate adaptation are addressed in isolation from other relevant UGS impacts on urban health [13,14,15]. Moreover, guidelines are often unclear about the quantitative dimension, e.g., how much UGS is needed in a city or neighborhood to achieve a desired level of health benefits. Additionally, urban planners are confronted with contradictory messages because UGS can also have negative consequences for human health through impacts on, for example, pollen concentration and social safety, and the issue- and situation-specific nature of available guidelines and support tools further add to the complexity of UGS planning to promote residents’ health and well-being [16].

A spatial model named Urban-EcoMATCH (Urban Ecosystems Mapping and Assessment Tool of Costs and Benefits for Health), presented in Oosterbroek et al. [17], however, offers the possibility to support the development of ‘healthy’ urban greening strategies in an integrated, quantitative, and context-specific way, accounting for multiple health benefits as well as burdens. In this study, the model is applied to assess the performance of alternative UGS design scenarios as compared to the current situation. This explorative strategic scenario study [18] aims to answer ‘what-if’ questions regarding UGS design for a concrete, specific case (the city of Maastricht, the Netherlands) but also to generate broader insights concerning effective urban greening strategies with respect to health and climate resilience. With its integrated approach, the study fills a gap left by recent scenario-based assessments of UGS design [19,20,21,22,23], which focused on certain specific benefits of UGS, such as promoting active transport [24] or adapting to climate change by reducing heat stress [25] or the risks of urban flooding [26]. The study also covers several health issues that, thus far, have not been addressed in other UGS scenario-based assessments, namely, the effects of UGS on mental stress from visually unattractive cityscapes, social safety, and tick-borne infectious diseases.

In the next sections, we first present the study approach and introduce the UGS design scenarios, which will be assessed with respect to human health and well-being. Then, we describe the results of this integrated scenario assessment. In the final section, we discuss the major findings and their implications for urban greening strategies, as well as the limitations of the study in this regard. We end with a conclusion on the value of the presented approach and on priorities for further research.

2. Methods

In this section, we first briefly describe the scenario study approach and input data. Then, we explain in more detail the different UGS design scenarios that were assessed with the model and the thresholds used in this assessment.

2.1. Scenario Study Approach and Input Data

For each of the four UGS design scenarios, including two alternative greening strategy scenarios, the indicator values of five health determinants were estimated per street segment, with the spatial model described in Oosterbroek et al. [17]. These five health determinants concerned ‘Unattractive views’, ‘Heat stress’, ‘Air pollution’, ‘Perceived unsafety’, and ‘Tick-bite risk’. The UGS contributions to these determinants were calculated by comparing the determinant values in the ‘Current greenspace’, ‘Green parking lots and squares’, and ‘Optimized greenspace locations’ scenarios with the values in the ‘No greenspace’ scenario. The performance of the two alternative greening strategy scenarios (‘Green parking lots and squares’, ‘Optimized greenspace locations’) was assessed relative to the current situation (‘Current greenspace’) and relative to each other.

For the scenario assessment, overall, city-scale indicator values for the five health determinants were calculated as the area-weighted average of the indicator values for all street segments. Furthermore, for each street segment, the model-estimated indicator values of the health determinants were compared with a pre-set threshold value. With these thresholds, an additional indicator of the health effects of each scenario was calculated: the number (and proportion) of street segments where the indicator value of a health determinant exceeded the threshold. This may be a more policy-relevant indicator to compare the UGS design scenarios because locations where one or more thresholds are exceeded are likely to attract the attention of urban policy makers and planners. The value of this indicator will not differ between scenarios if differences in UGS impacts, as reflected in different average indicator values, do not result in more or less exceedance of the threshold.

To generate the UGS design for the ‘Optimized greenspace locations’ scenario, a module was added to the original model (Figure 1). Further information on the model, as well as a detailed description of the five health determinants included, can be found in Oosterbroek et al. [17]. To run the spatial model, we used several input datasets that are all publicly available for the full Dutch territory and that are often high in resolution (<1 m). See Oosterbroek et al. [17] for a more elaborate description of the input data.

The case study area for this scenario study is the built-up area of Maastricht, The Netherlands. It is a city in the south of the Netherlands, with 122,000 inhabitants [27]. Within the Netherlands, Maastricht is an interesting case in which to apply the model, primarily because it has a heterogeneous urban morphology. We used data from the year 2018 for all datasets. For a further description of reasons to focus on Maastricht and its health situation related to the assessed health benefits and burdens, see Oosterbroek et al. [17]. Figure 2 presents a map of the study area, including the borders of the built-up area and the buffer area for geo-processing, as well as the location of the example areas that we use to visualize the results.

2.2. UGS Design Scenarios

This section introduces the UGS design scenarios for which health impacts were assessed with the model. For all scenarios, privately-owned UGSs were included, but only UGSs in public areas and areas of private owners accessible to the public (pedestrians) were changed in the alternative UGS design scenarios. As such, these scenarios only include design options that are—in principle—within the power of urban spatial planners to implement. For a visual impression of the four UGS design scenarios at street level, see Figure 3.

2.2.1. Scenario ‘No Greenspace’

This scenario assumes that Maastricht does not have any public UGS at all. Trees are removed, and currently (year 2018) existing shrubs, herbs, and grass are replaced by sealed soil. This scenario represents the so-called reference scenario, which is used to estimate the UGS contributions to health in the other scenarios. This is conducted by calculating the difference between the health determinant indicator values estimated in this reference scenario and those in the scenarios with greenspace.

2.2.2. ‘Current Greenspace’ Scenario

In this scenario, aerial imagery and topographic maps were combined to reconstruct the UGS situation for the built-up area of Maastricht in the year 2018 (see Appendix B in Oosterbroek et al. [17]). Other elements of the spatial design of Maastricht that influence health determinants, for example, those related to buildings, roads, and cars, were also extracted from the 2018 datasets.

2.2.3. ‘Green Parking Lots and Squares’ Scenario

Maastricht’s urban planners have the ambition to green part of the less used parking lots, as well as part of the city’s squares, and they are interested in what effects this may have on health benefits and burdens [28]. As we expected that the city-scale effects on health determinants of the original plans would be very small, we developed a scenario that represents a more extreme version of this ambition, in which all parking lots are replaced by grass fields with shrubs, and all squares are replaced by grass fields with trees. The purpose of this more extreme version of the planners’ ambition is to determine the potential of this greening strategy. The greening of all parking lots and squares would add 4% additional UGS cover, expressed as a percentage of Maastricht’s built-up area in 2018.

Anticipating a preference of residents for an open landscape, shrubs added in this scenario were of a modest size (1.2 m in height). Moreover, in line with the municipality’s ambition to increase urban biodiversity, larger trees (20 m in diameter, 12 m in height) were added where space allowed this, reasoning that one large tree increases biodiversity more than two smaller trees.

2.2.4. ‘Optimized Greenspace Locations’ Scenario

This scenario uses the UGS volume (i.e., number and size of UGS elements) from the ‘Current greenspace’ scenario but employs the computer model to redistribute the current UGS over the built-up area of Maastricht to optimize health benefits. All health determinants have the same priority in this scenario: UGS will be located (‘planted’) in areas with an equally high suitability for each of the UGS benefits and an equally low suitability for each of the UGS burdens (see Box 1 for further explanation). In contrast to the scenario ‘Greening of parking lots and squares’, this scenario does not add any UGS elements compared to the 2018 situation. Hence, the purpose of this scenario is to show the potential benefits of the same amount of UGS when locations are chosen such that health benefits are maximized and burdens minimized.

Box 1. Processing steps to create the urban green space design scenario, ‘Optimized greenspace locations’.

Step 1: Firstly, we identified areas in Maastricht’s built-up area that may have potential for UGS (re)design. We only selected public areas and areas of private owners that are accessible to the public (pedestrians). Within these areas, we selected areas that were not covered by water, buildings, or roads (including footpaths), such as squares, lawns, herbaceous areas, hedges, areas planted with trees, parking lots, squares, and paved areas around buildings. See Figure 4A for an example of the location, number, and size of these areas in this ‘potential UGS areas map’.

Step 2: Secondly, we constructed a separate ‘UGS location suitability map’ for each of the five health determinants. This location suitability map was created by running the model for all locations within the potential UGS areas map from Step 1 and calculating the UGS contribution. Subsequently, (detrimental) health determinant indicator values for the UGS contribution were inverted and normalized to range 0–1 by setting the highest value in the study area to zero (the most detrimental health effects and lowest suitability) and the lowest value to one (least detrimental health effects, highest suitability). In this ‘location suitability map’, the lowest suitability values result from no/lowest beneficial UGS effects or highest detrimental UGS effects in the case of health-related burdens and vice versa. For example, for the UGS location suitability map for health determinant ‘Unattractive views’, the lowest suitability scores were assigned to all locations that would not at all be visible by pedestrians. See Figure 4B,C for examples of tree location suitability maps for the health determinants ‘Unattractive views’ and ‘Air pollution’.

Step 3: Next, we calculated the total area covered by tree crowns within UGS design scenario, ‘Current green space’ (representing the situation in 2018). Subsequently, an area with the highest suitability scores for all health determinants was selected, with the size of this area being just sufficiently large enough to accommodate the total tree crown area. The result was a ‘highest benefits and lowest burdens map’ with sufficient space to accommodate all trees from the current UGS scenario (Figure 4D). Hence, this map indicates the potential UGS areas that are most suitable to accommodate the total area covered by trees in the most ‘healthy’ way. A similar procedure was implemented to identify the most suitable potential UGS areas to accommodate the other UGS types: shrubs, herbs, and grass (see Appendix A). Finally, the model was run for all UGS types at these ‘optimal’ locations together to take the effect of possible UGS interactions into account.

Figure 4. Examples illustrating, for example, area 1 with the stepwise method to identify areas highly suitable for trees in view of multiple health determinants. (A) ‘Potential UGS areas map’. (B) ‘Location suitability map’ for health determinant ‘Unattractive views’. (C) ‘Location suitability map’ for health determinant ‘Air pollution’. (D) ‘High benefits and low burdens map’ for health determinant ‘Unattractive views’ and ‘Air pollution’.

Figure 4. Examples illustrating, for example, area 1 with the stepwise method to identify areas highly suitable for trees in view of multiple health determinants. (A) ‘Potential UGS areas map’. (B) ‘Location suitability map’ for health determinant ‘Unattractive views’. (C) ‘Location suitability map’ for health determinant ‘Air pollution’. (D) ‘High benefits and low burdens map’ for health determinant ‘Unattractive views’ and ‘Air pollution’.

In Maastricht’s built-up area, the space available for optimizing UGS in support of residents’ health is rather limited, especially in the city center. In 2018, grass, herbs, and shrubs covered 30% of the built-up area (including both public and private UGS), whilst tree crowns covered 15%. As tree stems do not take up much space, about 30–31% of the surface cover of the built-up area was covered by UGS. Only 8% of the built-up area’s surface is covered by areas that have potential for implementing UGS (re)design. This is mainly because 41% of Maastricht’s built-up area consists of roads, buildings, and water. Moreover, 21% of the built-up area consists of private areas without UGS that are not accessible to the public. The surface of such private areas mainly consists of paved area (roads, parking lots) or storage area (e.g., for sand and building materials). We assume that the space available for optimizing UGS in other cities in the Netherlands will not differ greatly from the Maastricht case.

The ‘Optimized greenspace locations’ scenario was generated with module 9 (Figure 1). The process of determining optimal UGS locations can be divided into three distinct steps, which are described in Box 1 and Figure 4. We will explain Steps 2 and 3 using specific examples of location optimization for trees and for the health determinants ‘Unattractive views’ and ‘Air pollution’. Appendix A explains the geoprocessing methods applied in these steps in detail, including all parameter values and sources used.

2.3. Threshold Values to Assess and Compare UGS Design Scenarios

To assess the health impacts of the UGS design scenarios, the model-estimated indicator values of the health determinants were compared with threshold values for each health determinant (see

Table 1. Threshold values used per health determinant.

Health Determinant	Threshold Value	Unit	Description
Unattractive views	0	m2/m	A score larger than this threshold value means that unattractive objects are more dominant within the pedestrian’s field of view than attractive objects.
Heat stress	46	°C PET	Physiologically equivalent temperature (PET) at ‘Extreme Heat Stress Level 2’ [29]. It refers to the mean PET between 12:00 and 18:00 local time for the hottest day in 2018 during a national heatwave (July 26).
Air pollution	20	μg/m3 NO2	This threshold value is twice the WHO guideline value for the annual mean concentration of 10 μg/m3 NO2 [30]. The value is chosen because the WHO guideline value is exceeded for all street segments in all scenarios. (The lowest value per street segment is 15 μg/m3.)
Perceived unsafety	50	-	This threshold value is reached when, for example, the location is over 50% concealed, supervision from the most nearby home is 25 m away, and an area with at least 50 houses per ha is 50 m away.
Tick-bite risk	1	%	This threshold value is reached when, for example, all conditions are optimal for survival and activity of ticks and tick-host animals, but only 1% of the area directly adjacent to footpaths contains shrubs and herbs.

). The chosen thresholds have different origins. The heat stress and air pollution thresholds were based on peer-reviewed literature and official WHO guidelines, respectively [29,30]. However, it should be noted that these choices are still pragmatic to a certain extent. Regarding the threshold for air pollution, the NO₂ concentration, below which health effects do not occur, is much lower and often below background values [31]. Regarding the threshold for heat stress, temperatures above 35 °C PET are already categorized as ‘strong heat stress’ [29], and this level is easily met for every street segment during a heatwave day in Maastricht. For example, air temperature at the hottest heatwave day in 2018 reached 37.5 °C [32], and the model-estimated values for all street segments of Maastricht were above 35 °C PET. ‘Extreme Heat Stress Level 2’ (and not Level 1 or 3) was chosen to make differences between UGS design scenarios visible: it is a threshold value for which the frequency of exceedance varies between 0 and 100 percent for all scenarios. The thresholds for unattractive views, perceived (social) unsafety, and tick-bite risk are based on intuitive cut-off values. Unattractive view indicator values above zero mean that unattractive objects are more dominant within the pedestrian’s field of view than attractive objects, so these areas are scored as ‘net unattractive’. A perceived social unsafety score of 50 is right in between the minimum and maximum scores, so a street segment with this score would be perceived as neither really safe nor really unsafe. Finally, a threshold value of 1% for a tick-bite risk may intuitively make sense, as people seem to perceive chances larger than one percent as being much more significant in general [33]. This indicator can be considered the chance of being bitten during May to September, the period during which ticks are most active [34].

3. Results

3.1. Greenspace Design Per Scenario

To show how the different scenarios work in greenspace design, example area 1 is used to illustrate the differences. Example area 1, located in the city center, can, on a more general level, be regarded as representative of the entire built-up area of Maastricht. It contains types of footpath surroundings that are dominant in the city, such as residential roads, more central local roads with a high traffic intensity, and squares. However, example area 1 does not include a park, whilst the city contains several parks. This also explains why the fraction of open area (the area without buildings/total area) is lower than that in the entire built-up area of Maastricht (70% vs. 82%). However, the differences in UGS design that we discuss below are illustrative for the entire built-up area of Maastricht and apply to other areas in the city as well.

Figure 5 shows the green space design in example area 1 for the reference scenario (Figure 5A), as well as for the three UGS design scenarios (Figure 5B–D). The ‘Green parking lots and squares’ scenario (Figure 5C) shows the effect of replacing all parking lots with grass fields with shrubs and all squares with grass fields with trees. Compared with the ‘Current greenspace’ scenario (Figure 5B), these areas are added as greenspace areas. However, for the entire built-up area of Maastricht, the part taken up by parking lots and squares is small (4%), so health effects can be expected to be relatively small as well. Moreover, most parking lots are individual (dispersed) parking lots alongside and mostly parallel to the road. In these areas, there is often only space to add just one type of UGS. In these cases, we added only shrubs (hedgerows), as their volume is higher than that of mown grass. This potentially yields more benefits than mown grass in terms of reducing heat stress, unattractive views, and air pollution. Comparing the ‘Current greenspace’ (Figure 5B) and ‘Optimized greenspace locations’ scenarios (Figure 5D) shows that in the optimized scenario, fewer trees and more hedgerows are located next to roads that are more traffic intensive. The opposite is true for less busy roads, such as residential streets, which now contain more trees and less hedgerows next to roads. Furthermore, in the scenario ‘Optimized greenspace locations’, squares are populated with more trees and filled with herbs and grass around these trees, while in parks, shrubs and herbs are removed from below trees. Trees further away from footpaths are partially removed as well and moved closer to footpaths, for example, in residential areas. Finally, in all types of footpath surroundings, trees, shrubs, and grass were relocated somewhat closer to footpaths.

3.2. Health Benefits and Burdens Per Scenario

Table 2. Effects of four UGS design scenarios for Maastricht on five health determinants. For each health determinant, both the ‘UGS contribution’ (left) and the ‘threshold exceedance’ (right) are presented. ‘UGS contribution’ represents the difference in indicator value with the ‘No greenspace’ scenario. ‘Threshold exceedance’ represents the percentage of the 3721 street segments where the health determinant threshold was exceeded. Note: Positive UGS contribution values represent health burdens; negative values represent health benefits.

Scenario	Unattractive Views (m2/m)		Heat Stress (°C PET)		Air Pollution (μg/m3 NO2)		Perceived Unsafety (0–100)		Tick-Bite Risk (%)
No greenspace	-	87%	-	35%	-	14%	-	7%	-	0.0%
Current greenspace	−49.1	24%	−2.2	18%	+0.1	14%	6.2	11%	+0.04	0.5%
Green parking lots and squares	−75.7	8%	−2.7	13%	−0.1	14%	7.2	11%	+0.07	0.5%
Optimized greenspace locations	−85.6	16%	−3.0	12%	−0.2	13%	6.6	11%	+0.00	0.4%

presents the results regarding health benefits and burdens and the associated contributions of UGS for the reference scenario (‘No greenspace’) and the three UGS design scenarios (‘Current green space’; ‘Green parking lots and squares’; ‘Optimized greenspace locations’). Figure 6, Figure 7, Figure 8, Figure 9 and Figure 10 show these results at a neighborhood scale (within the example areas) to give an impression of local variation in the values of health determinants and UGS contributions. In these figures and table, the UGS contributions are expressed such that negative values should be interpreted as beneficial for human health because they reduce detrimental health determinant values. Vice versa, positive UGS contribution values should be interpreted as detrimental for human health. Values for health determinants, such as unattractive views, can also be negative, which should be interpreted as beneficial for health as well, as negative scores mean that attractive objects are more dominant for a certain footpath segment.

Overall, Table 2 shows that with the current UGS design of Maastricht’s built-up area, numerous street segments exceed the threshold values (provided in Table 1). Yet, for heat stress and unattractive views, the average indicator value and, in particular, the exceedance frequency is reduced compared to the ‘No greenspace’ scenario, indicating substantial health benefits of the current UGS. For perceived unsafety and tick-bite risk, and, to a lesser extent, for air pollution, the average indicator value and exceedance frequency are higher, indicating that current UGS has a detrimental effect on health with respect to these determinants. Compared to the ‘Current greenspace’ scenario, implementation of the ‘Green parking lots and squares’ scenario is estimated to result in a further decrease in heat stress and unattractive views and a slight increase in perceived unsafety and tick-bite risk. The UGS contribution to air pollution remains relatively small but changes from a burden into a benefit. The implementation of the ‘Optimized greenspace locations’ scenario is estimated to only slightly increase the perceived unsafety whilst (beneficially) decreasing the indicator values of the four other health determinants. This translates into even stronger reductions in the number of street segments that exceed threshold levels, especially for heat stress, unattractive views, and tick-bite risk. However, for unattractive views, the exceedance frequency did not decrease as much as in the ‘Green parking lots and squares’ scenario. This is because street segments that scored lower (i.e., were considered more attractive) in the ‘Optimized greenspace locations’ scenario were mainly those that already scored below the unattractive views threshold in the ‘Current greenspace’ scenario.

The following paragraphs detail the changes in UGS design and their associated effects on health determinants for the ‘Green parking lots and squares’ and ‘Optimized greenspace locations’ scenarios in both cases in comparison to the ‘Current greenspace’ scenario.

3.2.1. Detailed Results for the ‘Green Parking Lots and Squares’ Scenario

The strategy of greening all parking lots and squares adds 4% additional UGS cover, expressed as a percentage of Maastricht’s built-up area in 2018. In this UGS design scenario, pedestrians are better shielded from air pollution through hedgerows that replaced parking lots, especially close to roads with high traffic intensity. Since the hedgerows and shrubs were not too tall, the perceived unsafety did not increase much, as the view and sightline are only slightly blocked by the hedgerows (only directly behind the hedgerow). Some neighborhoods, however, have shaded parking lots in areas that qualify as potential tick habitat. Adding shrubs to these parking lots increased the estimated tick-bite risk in these areas. The greening of squares and larger parking lots notably decreased the score for unattractive views. The number of larger squares in Maastricht that were filled with grass and trees was small, but where they were present, this also led to a local reduction in heat stress due to the combined effect of tree shading and reduced thermal radiation from the grass.

3.2.2. Detailed Results for the ‘Optimized Greenspace Locations’ Scenario

In the ‘Optimized greenspace locations’ scenario, the optimization method moved trees out of street canyons with traffic-intensive roads. This resulted in a smaller local street canyon effect, reducing local pedestrian-level air pollution (see Figure 8). Although the absence of trees at these locations resulted in increased local heat stress, it did not result in an increase in the average heat stress at the neighborhood and city scale since a large part of these trees was relocated to nearby streets with less car emissions, such as residential roads. At these locations, those trees often had a slightly lower (because such streets are often already slightly more shaded through buildings) but still substantial effect on decreasing heat stress. The optimization method also moved trees that were located further away from footpaths to less traffic-intensive streets, where they were more effective in reducing unattractive views and heat stress. Similar to the ‘Green parking lots and squares’ scenario, shrubs were located alongside busy roads instead of, for example, solitary shrubs alongside less busy roads or further away from footpaths. This mainly decreased air pollution but also (to a lesser extent) decreased heat stress and unattractive views. Where possible, trees and shrubs located further away from footpaths, as well as trees from street canyons, were relocated adjacent to busy roads with an open road situation. As such, they formed vegetation barriers, reducing air pollution levels. However, this form of location optimization was not often implemented, because in most busy streets the distance between road and footpath was often too small to do this. Relocating shrubs and herbs, mainly from park-like areas to squares, reduced both unattractive views and heat stress. Overall, relocating UGS in the ‘Optimized greenspace locations’ scenario especially had an effect on unattractive views and heat stress: threshold exceedance for unattractive views was reduced from 87% to 16%, whereas heat stress was reduced by about 3 °C PET.

Similar to the ‘Green parking lots and squares’ scenario, perceived unsafety only slightly increased. This can be explained by two factors: (1) relocated hedgerows only slightly blocked the view and sightline of pedestrians, and (2) only trees with high crowns were moved closer to pedestrian areas. Another reason why perceived unsafety only showed a slight increase is that the optimization method replaced trees closer to the footpath with low tree crowns as well as tall shrubs with trees with high tree crowns. In contrast with the UGS design scenario, ‘Green parking lots and squares’, the optimization method did not add shrubs in areas with overarching trees, very nearby footpaths, or in tick host habitats. As a result, tick-bite risk did not increase. For street segments with a tick-bite risk larger than zero in the ‘Current greenspace’ scenario and with only publicly accessible UGS affecting footpaths, the risk was reduced to zero in the ‘Optimized greenspace locations’ scenario. This was due to the fact that all shrubs and herbs were removed from these places and replaced by mown grass where possible.

4. Discussion and Conclusions

This section discusses the major findings and their implications for urban greening strategy, as well as the limitations of this scenario study. We end with a conclusion on the key contributions of the study and the priorities for further research.

4.1. Major Findings and Implications for Urban Greenspace Planning

The results of this scenario study of urban greening strategies can be summarized into four major findings with important implications for urban greenspace planning. First, when the health effects of UGS designs are assessed by the average indicator values of health determinants or when assessed by the frequencies of exceedance of pre-set thresholds, the outcomes may differ. As urban decision makers are most probably more concerned about the exceedance of thresholds than about average indicator values, this finding stresses the importance of translating general urban health policy objectives and priorities into specific threshold or target values and using these values when assessing the effectiveness of proposed greening strategies. Second, there is a real risk that a greening strategy not only results in greater health benefits of UGS but increases health burdens as well. This means that ‘more green’ is not always an improvement from a health perspective and that greening strategies should be carefully considered. This leads to the third major finding, which is that—within certain limits—‘location’ matters more than ‘area’ of UGS and that well-considered positioning of the various UGS types can reduce trade-offs between beneficial and detrimental effects of UGS. For urban greenspace planning, this implies that it is important to consider UGS location already in an early stage of urban development, as relocating well-developed trees and shrubs is costly and often not even feasible. The analysis of the ‘Optimized greenspace locations’ scenario suggests that a strong reduction in trade-offs between benefits and burdens can already be achieved with only a limited set of UGS design principles. Using statistical analyses of large datasets of greenspace morphology and disease prevalence, Wang and Tassinary [35] also concluded that the location, shape, and connectivity of urban greenspace have a significant effect on health above and beyond the sheer amount of greenspace. However, consideration of the underlying mechanisms, as in our model, is essential to inform context-specific greenspace design [36]. The fourth and final major finding concerns air pollution. The results show that UGS can have an overall detrimental or beneficial effect and that a current net burden can be changed into a net benefit by adding more UGS area or changing UGS location. However, the results also show that in both cases, the UGS contribution is relatively small, which means that urban spatial planners should realize that greenspace is unlikely to be a major solution to serious air pollution problems.

4.2. Limitations

The approach followed in this assessment of alternative UGS designs has various limitations, which means that, in particular, the method to determine the optimal location of UGS cannot be implemented in practice without further consideration. First of all, other factors than health were not included in this method. An important factor to consider when choosing a UGS location is the cost of planting and maintenance. It might be much more expensive to keep vegetation alive at the ‘optimal’ location (e.g., due to watering requirements) or to prevent it from being a nuisance to the people living or working nearby (e.g., due to pruning requirements or the need to remove leaf litter). Additionally, the conservation of urban biodiversity or cultural heritage can be important factors to consider in UGS design, just as social factors (e.g., optimizing UGS to promote interaction between certain social groups). With respect to the latter, when health benefits are considered that involve the active use of urban greenspace by residents, such as ‘meeting’ or ‘physical activity’, we recommend combining an expert- or model-based assessment with a resident-based assessment of greenspace designs [37,38]. Such a participatory approach is recommended, in particular, when implementing a city-level greenspace strategy at the neighborhood level [16].

Another limitation of the approach is that there are other health benefits and burdens of UGS that have not been taken into account but that might change optimal locations. For example, trees (e.g., birches) also produce pollen, resulting in sometimes very serious hay fever symptoms. A related limitation is that the approach does consider the UGS type (trees, shrubs, etc.) but does not consider the choice of plant species. Tree species, however, can differ greatly in the production of allergenic pollen and their effect on air quality [39], as well as in their shading capacity [40].

The presented approach to assess urban greenspace strategies and designs at a city scale was applied to Maastricht but is, in principle, generic and not limited to this specific case. To run the spatial model underlying the approach, input datasets were used that are publicly available in high resolution. These input data are currently available country-wide for the Netherlands, Belgium, Germany, and the UK [17]. This means that the approach can be applied to any city in these countries. Moreover, the availability of this type of input data is rapidly increasing for many other countries as well, and hence, the application domain of the approach in terms of geographic coverage is large and growing. Finally, the model does not provide an estimate of the overall net health outcome of a given UGS design, which also limits its optimization capacity. A first step would be to include actual health outcomes, such as disease. Examples are the calculation of a reduction in cardiovascular diseases based on an estimated decrease in air pollution or an epidemiological exposure–response curve to link heat stress (PET) to mortality. Not only would this improvement result in indicators that are more tangible for local decision makers, spatial planners, and residents, but it would also be easier to perform a monetary valuation of the health benefits of greenspace, for example, in the form of avoided medical or sick leave costs. Integrating these actual health outcomes with a common metric would have even more advantages. Firstly, it would offer the ability to fully optimize greenspaces for human health. Secondly, the total health (outcome) burden, as well as the total UGS contribution to the total health burden, could be expressed in a single comparable figure for each design scenario. Modeling the actual health effects of UGS design in terms of disease burden, therefore, requires two additional model development steps: (1) translation of the current health determinant indicators into specific disease or disability indicators, for example, via quantitative epidemiological exposure–response models, and (2) translation of these disease or disability indicators into an aggregate health indicator or common metric.

4.3. Conclusions

Urban greening is a major goal in policies for sustainable cities, and we presented a novel approach to support the development of urban greening strategies aimed at promoting residents’ health. The approach involves a model-based assessment of the effectiveness of alternative city-scale UGS design scenarios that is integrated, quantitative, and spatially explicit and account for multiple health benefits as well as burdens. This generic approach was applied to a specific city (Maastricht) and led to clear conclusions about the health benefits and burdens of concrete UGS designs for this city, but it also generated novel, more general insights relevant to the planning of UGS for health. These insights concern the importance of translating health policy objectives into specific target values or thresholds and the importance of ‘smart’ choices in UGS locations that can effectively reduce trade-offs between the health benefits and burdens of urban greening, as well as the insights that adding more greenery not always improves urban health and that urban greenspace alone cannot solve major air pollution problems. Priorities for future research, addressing limitations of the current approach, concern the expansion of the range of health benefits and burdens of UGS covered by the model, and the development of a common metric for the entire range of health benefits and burdens, allowing the optimization of UGS design to maximize its overall net health benefit.