Next Article in Journal
The Effects of Flood Damage on Urban Road Networks in Italy: The Critical Function of Underpasses
Next Article in Special Issue
Exploring the Impact of Urban Amenities on Business Circle Vitality Using Multi-Source Big Data
Previous Article in Journal
Measuring Change in Urban Land Consumption: A Global Analysis
Previous Article in Special Issue
Nature-Based Solutions Scenario Planning for Climate Change Adaptation in Arid and Semi-Arid Regions
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Land
Volume 13
Issue 9
10.3390/land13091492
land-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Eco-Spatial Indices as an Effective Tool for Climate Change Adaptation in Residential Neighbourhoods—Comparative Study

by
Renata Giedych
,
Gabriela Maksymiuk
* and
Agata Cieszewska
Department of Landscape Architecture, Institute of Environmental Engineering, Warsaw University of Life Sciences, 159 Nowoursynowska Street, 02-776 Warsaw, Poland
*
Author to whom correspondence should be addressed.
Land 2024, 13(9), 1492; https://doi.org/10.3390/land13091492
Submission received: 10 August 2024 / Revised: 10 September 2024 / Accepted: 13 September 2024 / Published: 14 September 2024
(This article belongs to the Special Issue Strategic Planning for Urban Sustainability)
Browse Figures
Versions Notes

Abstract

:
Eco-spatial indices are commonly used tools to improve the quality of the environment in cities. Initially modelled on the Berlin BAF, indices have evolved to address current challenges, particularly climate change adaptation. The Ratio of Biologically Vital Areas (RBVA), introduced in Poland in the mid-1990s, is an early planning tool for implementing Nature-based Solutions (NbSs) at the site level. This research aimed to assess the effectiveness of the RBVA in Poland compared to its counterparts in Oslo and Malmö. The study employed a serious simulation game developed under the Norwegian-funded CoAdapt project, testing six development scenarios, varied in terms of applied NbSs, for a typical multi-family housing estate. The adaptive potential of the tested scenarios was assessed based on the values of five environmental parameters calculated in the game; that is, air temperature, oxygen production, CO2 sequestration, rainwater harvesting, and biodiversity. The findings revealed that the RBVA, in its current form, has limited effectiveness in supporting climate adaptation. Its two-dimensional nature makes it less effective than the more comprehensive Green Factors used in Oslo and Malmö. The research presented in the article proves that better-constructed indices result in the efficiency of applied NbSs and consequently better adaptation to climate change.

1. Introduction

The year 2023 has been identified as the warmest on record since observations began in 1850, according to the Copernicus Climate Change Service [1]. In urban areas, which house the majority of the global population, the effects of climate change are particularly severe. Numerous studies highlight the impact of changes in radiation balance due to the high proportion of impervious surfaces [2], reduced vegetation, and limited water resources, which in turn affect oxygen production and exacerbate air pollution [3,4]. Additionally, there has been an increased frequency of extreme events, including recurring droughts and floods. These factors not only threaten the health of residents but also degrade the overall quality of daily life [5]. The above reasons clearly indicate the need to implement climate change adaptation solutions in cities to mitigate the impacts of climate change on the economy, society, and the environment. Examples of adaptation measures include spatial planning tools aimed to implement various forms of green infrastructure, ranging from regional-scale ecological networks, through urban systems of parks and green spaces, to specific Nature-based Solutions (NbSs) like green roofs or sustainable drainage systems. Basic planning tools for implementing NbSs are green space factors [6], which are also known as eco-spatial indices [7] or green area indicators [8]. These measures are designed to introduce various types of green and blue elements in densely urbanised areas [9]. The first of its kind in the world was the Biotope Area Factor (BAF), introduced in Berlin in the early 1990s [7,10,11]. It is expressed by the share of the area occupied by different eco-friendly elements (from vegetation growing on the ground, green roofs and green walls, to various types of permeable surfaces) to the total land area. The adopted system of weighting factors adequate to the ecological effectiveness of each element allows for a very flexible and site-specific design [7,12].
Over the past thirty years, eco-spatial indices have been implemented in many places around the world, e.g., in Poland [7], Malmö [10], Stockholm [13], Helsinki [13], London [14], Vienna [15], Seattle [16], Melbourne [17], Seoul [16], and São Paulo [18]. Most of them originally were modelled on the Berlin BAF [7,16], but consequently, they have been creatively developed by adding new components to meet today’s challenges. Initially, the priority was to improve the functioning of urban ecosystems, and now more attention is focused on issues related to adaptation to climate change, e.g., flash floods [19] and microclimate regulation [12]. It should also be added that over time, qualitative elements began to be added in addition to quantitative ones related solely to the area occupied by each element. For example, in Helsinki, it is related to ensuring habitat connectivity and the sustainable management of rainwater, and, e.g., in the case of Malmö and Seattle, the vertical structure of vegetation is considered [16,19]. As a result, the scope of NbSs used in urban planning is constantly expanding, evolving with advances in eco-spatial indices.
Nature-based Solutions (NbSs), as defined by Bulkeley et al. [20], are approaches that draw inspiration from and are supported by natural processes. These solutions are characterised by their economic cost-efficiency, the range of environmental, economic, and social benefits they provide, and their role in aiding climate change adaptation [21]. According to the International Union for Conservation of Nature (IUCN) [22], NbSs are efforts to conserve, sustainably manage, and restore natural and modified ecosystems in ways that effectively and adaptively address societal challenges to ensure both human well-being and biodiversity benefits. Thus, NbSs can be treated as an overarching concept encompassing other significant ideas and discussions, such as ecosystem-based adaptation, green infrastructure, and nature’s contributions to people [23,24]. NbSs are now broadly recognised as a crucial foundation for creating resilient and liveable cities. These include green infrastructure interventions that can be implemented at various scales, from site-specific solutions like green roofs and green walls to citywide measures such as parks and urban forests [25]. At the neighbourhood scale, the NbSs refer to diverse vegetation, social gardens, rain gardens, bioswales, green roofs, etc.
As Chausson et al. [12] pointed out in their review paper on the effectiveness of Nature-based Solutions in adapting to climate change and minimising hydrometeorological risks to humans, most authors described positive impacts of NbS use on climate change adaptation. Moreover, NbS measures are equally or even more effective than traditional grey infrastructure [26,27]. Many articles detailing specific efficiency results regarding NbS functionality focus on extensive green roofs. These studies highlight the roofs’ ability to retain rainwater, mitigate the effects of heavy rains (such as reducing peak flow), and lower air and indoor temperatures, which subsequently reduces energy costs for cooling or heating. Green roofs reflect 20–30% of solar radiation and absorb up to 60% through photosynthesis, transferring less than 20% of heat to the ground. Depending on the climate zone, they reduce heat flow to the ground by 70–90% in summer and 10–30% in winter. The higher albedo of green roofs compared to bituminous roofing (0.7–0.85 vs. 0.1–0.2) helps mitigate the urban heat island effect [28]. Green roofs can lower surface temperatures by 7.3 °C and air temperatures above them by 0.5 °C [29] and reduce mortality during heat waves [30]. The large-scale, citywide use of green roofs can reduce air temperature by 0.3 °C to 3 °C [28]. According to research by Liu et al. [31] average annual roof retention is higher in dry continental climates (73.8%, Lanzhou) than in humid climates (48.9%, Beijing). On the other hand, empirical studies of the assessment of the retention capacity of a 6 cm roof in a Mediterranean climate have shown that the retention ranges from 32 to 54% of precipitation, and interestingly, no significant improvement in retention was observed when increasing the substrate layer (up to 15 cm), which leads to the conclusion that a roof of 6 cm is sufficient in this climate [32].
Studies on green roofs as a strategic solution, like Zölch et al. [33] for Munich, show that covering 50% of the city’s land with green roofs could reduce surface runoff by 80.9%. The effectiveness of NbSs is influenced by city-specific factors, as shown in comprehensive research like the study by Cortinovis et al. [34], which modelled various NbS scenarios and their climate impacts in cities such as Barcelona, Utrecht, and Malmö. For example, densely built Barcelona has a high potential for green roofs, while Malmö, with more open spaces, benefits more from street and park plantings. The best adaptation results from a combination of NbSs, with green roofs reducing surface runoff and tall vegetation in parks mitigating heat waves. However, unsealing car parks showed relatively small benefits. These findings align with Majidi et al. [35], who tested NbSs in Bangkok to reduce flood risk and improve thermal comfort. Among the five scenarios, green roofs were most effective at reducing surface runoff and precipitation peak, while rain gardens with trees best improved thermal comfort. In turn, Vojinovic et al. [36] found that large-scale solutions like retention reservoirs and canal reconstruction, when combined with traditional grey infrastructure, offer the highest level of safety during heavy rains. Single NbSs, such as green roofs or bioretention basins, are less effective on their own. The best flood protection results from integrating both small-scale and large-scale NbSs with traditional flood protection measures. American research [37] on green infrastructure solutions to mitigate flooding in residential areas confirms the effectiveness of comprehensive implementations. Using a system of combined bioretention basins, artificial wetlands, and green roofs, surface runoff was reduced by approximately 36.4% over a 2-year period. Additionally, the size of the rainfall event was the strongest predictor of retention for all systems.
Another frequently studied issue is the impact of green and blue infrastructure (GBI) on lowering city air temperature. A notable study by Zuvela-Aloise et al. [38] simulated the effects of GBI on urban cooling in Vienna. Adding about 350 hectares of parks, either centrally or on the outskirts, was analysed, and parks in central areas had a more significant cooling effect, impacting over 8000 hectares around them, while parks on the outskirts had a lesser impact on surrounding temperatures. Additionally, water reservoirs (at 18 °C) were found to cool the environment more effectively than parks of the same size and location, with green infrastructure being more efficient in windless conditions. These results are consistent with the results of studies on the effect of vegetation cover on the temperature of the ground surface, in built-up areas and on the outskirts of the city, in conditions of extreme summer heat, obtained by Ossola et al. [39]. In the city of Adelaide (AU), tree canopies, and to a lesser extent grass and low vegetation, reduced ground temperatures by 6 °C during the day, though not at night. In built-up areas away from the coast, even small patches of vegetation in courtyards and gardens lowered ground temperatures within 30 m. Also, Marando et al. [40] highlight the benefits of tree canopy coverage in reducing urban temperatures, with an average decrease of 1.07 °C and up to 2.9 °C in some cases. Their study, involving spatial, mathematical, and statistical modelling for 601 European cities with populations over 50,000, found that at least 16% tree canopy coverage is needed to achieve a 1 °C reduction in temperature. In contrast, empirical studies on the effect of linear parks on thermal comfort found a temperature reduction of only 0.5 °C compared to nearby built-up areas, with a minimal impact on the urban heat island effect. However, user surveys revealed a significantly higher level of satisfaction, with respondents reporting a noticeable improvement in thermal comfort within the park [41].
The examples indicated above show the many benefits that can be derived from using NbSs. And while individual NBSs are being studied, there is an observed scarcity of studies that evaluate them in the context of eco-spatial index effectiveness. Although the literature on comparisons of the indices themselves is quite rich [12,42,43,44,45,46], the studies conducted usually involve a comparison of the selection of components that make up the index and the weighting factors assigned to each component.
To fill this knowledge gap in our research, we focus on comparing the effectiveness of three eco-spatial indices: the Ratio of Biologically Vital Area (RBVA) used in Poland [7,47] and two indices used in Scandinavian cities, i.e., Malmö Green Factor (MGF) [48] and Oslo Green Factor (OGF) [49].
The eco-spatial index that was introduced in Poland during the mid-1990s is one of the first of its kind used in the world just after Berlin’s Biotope Area Factor (BAF) [7]. In contrast to the abovementioned Scandinavian eco-spatial indices, RBVA is used in the whole of Poland and is defined in national law [47]. It should also be emphasised that of the three indices examined, it is the least complex index, considering only the quantitative aspects of NbSs and not the qualitative ones.
The research presented here aims to assess the environmental effectiveness of RBVA in comparison with its counterparts applied in Oslo and Malmö. This comparison will make it possible to assess to what extent the current index in Poland effectively enforces planning provisions favourable to residential areas from the point of view of their climate change adaptation. This assessment is of great practical importance since it seems that the existing index in Poland is less effective due to its quantitative character than the Scandinavian ones.
To evaluate the environmental effectiveness of studied eco-spatial indices, we applied a serious simulation game, “Neighbourhood with Climate”, developed as part of a Norwegian-funded CoAdapt project. Using a typical multi-family housing estate as an example study area, we tested different development scenarios, each based on the feasible NbS specific to each of the indices analysed.

2. Materials and Methods

2.1. Research Background

The research on eco-spatial indices was performed as a part of the Norwegian-funded project CoAdapt–Communities for Climate Change Action that dealt with the possibilities of the adaptation of residential areas to climate change with the application of NbSs. Therefore, the selection of indices, the tool for evaluating adaptation potential and the test area are linked to this project. To evaluate the effectiveness of eco-spatial indices as a measure for developing the adaptive potential of multi-family housing estates, three indices were selected—one quantitative and two qualitative, discussed in detail in Section 2.2. To determine the adaptation potential of the spatial solutions (including various NbSs) resulting from the application of each index, the serious computer game “Neighbourhood with Climate” (described in Section 2.3) was used. To fulfil the research aim, we selected a multi-family housing estate in Warsaw (characterised in Section 2.5), which served as the tested area.

2.2. Eco-Spatial Indices under Consideration

A key objective of eco-spatial indices (ESIs) is to set a minimum standard for the proportion of blue and green elements that must be incorporated into a developed area [8]. Although ESIs may differ from each other, the way they are calculated is similar and is expressed as a ratio of the area covered by greenery, open water, permeable paving, stormwater infiltration, etc., in relation to the total site area [43]. Therefore, the following general formula for their calculation can be given:
Eco − spatial index value = areas of green and blue elements × weighting factors total site area
What makes them different is the number of elements taken into account and the size of the weighting factor attributed to each element (see Table 1).
RBVA was introduced into Polish planning practice in the mid-1990s and Polish law in 2002 as a mandatory element of local spatial development plans [7]. From 2023 onwards, national standards for a minimum value of the index were introduced [50]. The calculation of the index considers areas covered by vegetation, water, and roof gardens, giving the first two a weighting of 1, and for roof gardens half the value of vegetation on the ground, i.e., 0.5.
In Malmö, the green factor was introduced in 2001 in connection with the Bo01, the international housing exhibition fair. The positive results led to the instrument now being used in the city’s new construction projects, primarily via its inclusion in the green building programme [51]. Principles for calculating MGF are set out in the municipal guidelines [48]. MGF takes into account vegetation on the ground, vegetation on underground constructions (where the weighting factor depends on the depth of the soil), areas covered with climbing plants, drainage from sealed surfaces to green areas, and permeable and semipermeable pavements. In addition, the variation in vegetation cover is considered by adding an additional area for each tree and shrub.
The most complex of the indices studied is the OGF. The OGF was first introduced in 2014 and was designed as a tool to facilitate the integration of blue and green structures in building and landscape architectural design at the property level [52]. The latest guidance for calculating the Oslo index from 2023 [49] besides components of the index calculated by area occupied and by area per piece, takes into consideration additional fixed variables related to vegetation and water management. This last component concerns the physical expansion of the existing blue–green structure, the restoration or establishment of new habitats for biological diversity, the collection of stormwater for irrigation and other reuse, the coordination of measures with adjacent areas and/or owners of neighbouring lands, and the reopening of closed waterways, streams, and rivers in pipes. In the case of the OGF, it is noteworthy that existing vegetation is given prominence in the calculation of the index; this applies to existing vegetation on the ground and existing trees. An additional characteristic of the OGF is the division of trees into small, large, and medium-sized and considering retention basins where stormwater can be diverted and infiltrated through a permeable surface.
With regard to the target minimum value of the index in the case of RBVA and MGF, it depends on the land use, and in the case of OGF, on the zone in which the site is located. For residential areas, the index values are RBVA—0.3 and MGF—0.6. For Oslo—in the central part—0.7, and in the peripheral part of the city, 0.8.

2.3. Simulation Tool

Serious computer games are increasingly being used as tools to test new, often experimental solutions in land use as well [53,54]. Such a game is the computer game “Neighbourhood with Climate” (https://coadapt.pl/en/game/, accessed on 9 August 2024) [55]. It is a strategy simulation game in which players adapt a selected neighbourhood to climate change by introducing Nature-based Solutions (NbSs). The game is based on the Open Street Map combined with an elevation model. The menu of the game includes 37 diverse NbSs that have been parameterised, e.g., trees, shrubs, flower meadows, orchards, green roofs and walls, permeable pavements, and raingardens, and create different adaptation scenarios.
Parameterisation algorithms were based on literature indicators [56,57,58,59,60] including published environmental calculators [61] and research conducted within the CoAdapt project. The game allows to learn about the effectiveness of NbSs and the benefits of adaptation to climate change, which is included in the information layer and the post-game summary.
Parameterisation also allows to estimate the overall benefits that result from the NbS introduced in the game: environmental (climate, hydrological, biodiversity), social, and economic.
In the conducted study, the game was used as a tool to assess the potential of the settlement to adapt to climate change by introducing various NbSs with reference to eco-spatial indices. Five environmental parameters were analysed: air temperature, oxygen production, CO2 sequestration, rainwater harvesting, and biodiversity. These parameters indicate how the tested area is prepared for heat waves or heavy rains, which are the most crucial problems related to climate threats in Poland.

2.4. Research Framework

2.4.1. Calculations of Eco-Spatial Indices

The first step of the research was calculating the values of three eco-spatial indices, i.e., RBVA, MGF, and OGF, for the existing state of the studied housing estate, according to the rules specific to each of them. All data necessary for the studied index calculations were compiled on the basis of topographic data retrieved from the National Database of Topographic Objects (in Polish, abbreviated as BDOT) [62], orthophotos [63], and fieldwork performed in July 2024. The inventory comprised all development elements, including architectural objects and vegetation, i.e., buildings, pavements, existing trees, hedgerows, groups of shrubs, and lawns. The calculations of the area occupied by buildings, pavements, and vegetation were made in ArcGIS Pro 3.3.1 software. Next, we determined whether the calculated RBVA met the minimum amount of biologically active area required for residential development in Warsaw and whether it would meet the required ratio for such development in Malmö (MGF) and Oslo (OGF). When calculating MGF and OGF values, we use conversion factors for existing inventoried development elements in agreement with the practice for the specific index [48,49]. According to the planning guidelines for Malmö and Oslo, the assumed minimum value of the indices was 0.6 for MGF (in regards to residential areas) and 0.8 for OGF (in regards to peripheral urban zones).

2.4.2. Scenarios Building

The second step of the research was the development of variant versions of scenarios for the spatial development of the test housing estate in order to meet the required minimum level of MGF and OGF. Altogether, six different scenarios (that is, three for each index) were developed, characterised by using a different array of NbSs (e.g., vegetation elements on the ground only, solutions on architectural surfaces, and a more diverse combination of NbSs). For the correctness of calculations, the area of climbing plants or vertical gardens was counted only up to the 3rd floor (up to 9 m of facade) and reduced by the area of both windows and doors, and the area of green roofs accounted for 0.7 of the total roof area, due to existing chimneys and structural elements.

2.4.3. Simulation Phase

The third step was a simulation phase, in which the “Neighbourhood with Climate” game was applied with the purpose of assessing the adaptation potential of the test housing estate, both in the existing way it is developed and for the 6 studied scenarios. The adaptive potential was assessed based on the results of the values of the 5 environmental parameters calculated in the game; that is, air temperature, oxygen production, CO2 sequestration, rainwater harvesting, and biodiversity. The application of the “Neighbourhood with Climate” game as a simulation tool first required, from the NbS menu, the selection of the elements that are variables of the analysed eco-spatial indices. Table 2 shows a relationship between the NbSs used in the game and the variables applied in the indices studied. It is worth noting that MGF takes into account trees with a stem girth of more than 35 cm, calculated for the maximum area of 25 m2 for each tree, which is compatible with a “medium tree” in the “Neighbourhood with Climate” game. Also treated differently are shrubs, which are only considered for MGF if they exceed 3 m in height, while when calculating OGF, shrubs up to 2 m are relevant.

2.5. Case Study

The area of analysis is the Klobucka housing estate, located in a residential district in the southern part of Warsaw. The rationale for choosing this particular housing estate is that it is a typical example of contemporary multi-family housing in Warsaw, where the vast majority (88 per cent) of residents live in multi-family buildings [64]. In addition, it is a social housing development that needs to improve the quality of space and adapt to climate change. The case study represents a relatively new social housing estate that was built in 2011, covering an area of approximately 1.99 ha. The estate comprises 9 buildings ranging from 4 to 7 floors with 265 apartments. The number of inhabitants, as approximately estimated based on the calculation of the average number of people per apartment according to the indicators of the Ministry of Development and Technology for 2022 [65], is 640 people (2.42 people/apartment). The development layout is radial, and all buildings are arranged in a fan shape (see Figure 1). A good pedestrian communication system of concrete sidewalks connects the buildings, and parking lots and access roads are located at the edge of the estate. The housing estate has poor vegetation structure, mown lawns, locally planted hedgerows, and predominant young trees.
As it concerns the environmental conditions, the estate is located in an area rated as moderately exposed to the urban heat island (UHI) [67,68]. Flooding has not been reported here, although in the vicinity, in the valley of a small stream called Potok Sluzewski, a problem with rainwater pouring out of the riverbed and flooding one of Warsaw’s main streets has been observed for years [69].

3. Results

3.1. Calculations of Eco-Spatial Indices of Klobucka Housing Estate

The inventory included those development elements and surfaces that are necessary for calculating the studied eco-spatial indices. The share of individual land cover categories is shown in Table 3.
As it concerns greenery, almost the entire housing estate area, besides buildings, two playgrounds, and sidewalks, is covered with mown lawns, making the greenery cover equal to 8387.65m2. Reinforced lawns have been used at the entrances to the buildings, but their area is very insignificant at about 88m2. Within the estate, there are 38 very small trees (mainly lindens and globe maples, which, even as adults, would not reach crown sizes of more than 2 m). In addition, there are a few low hedges and individual shrubs in the estate, with a total area of about 1413 m2. Due to the predominance of very young trees, a tree canopy cover (TCC) in the Klobucka estate, is only about 4.23%.
To calculate the RBVA, the proportion of land covered by impervious surfaces, i.e., under buildings, paved sidewalks and playgrounds, was subtracted from the total area of the housing estate. In total, impervious surfaces occupy 57.78%, so the RBVA is 42.22%. The Klobucka neighbourhood does not have any areas arranged as green roofs or any vegetation placed on other architectural surfaces, so this did not affect the value of this indicator.
Among the variables affecting the MGF score in relation to the studied housing estate, only the biologically active area was applicable, characterised in MGF guidelines as an “area where the plant roots have direct contact with deeper soil layers, and water can freely percolate to groundwater level”. Other variables, such as existing trees, did not affect the value of the index, as their size is too negligible and relevant to MGF are trees with a trunk girth of more than 35 cm. The value of the MGF for the estate’s existing state is 0.42.
Regarding the OGF, more variables could be taken into account due to other methods of index calculations, i.e., permeable ground covered by grass, moss or lichen, layer of shrubs up to 2 metres high, and also reinforced lawn (that is, a lawn prepared for higher intensity of use). The OGF for the existing state of the studied housing estate is 0.6, based on the proportion of permeable surfaces covered with grass, including reinforced lawns and shrubs. The results of the three calculated indices for the Klobucka housing estate’s existing state are presented in Table 4.
The obtained values of the examined indices were compared with planning requirements, which are political decisions in each country. According to the spatial planning regulations in force in Poland, each residential development must have at least 30% of the biologically active area, hence the obtained RBVA result of 42.22% is sufficient from the point of view of the regulations. However, as for the other two indices are concerned, they are not sufficient. According to current regulations in Malmö, residential areas must have an MGF of at least 0.6. Also in Oslo, residential areas located on the urban periphery (that is, like the Klobucka estate in the context of Warsaw) must meet an OGF of at least 0.8.

3.2. Scenarios for MGF and OGF

Since the analysed housing estate does not meet the minimum values of the MGF and OGF indices, a series of six development scenarios was elaborated (three for each index), which, using different NbSs scored in the examined indices, would allow them to achieve their minimum values. The scenarios tested were differentiated in terms of the solutions used, as schematically presented in Figure 2 and described in Table 5. The MGF1 and OGF1 scenarios were based on the widest possible selection of NbSs, so they are called miscellaneous scenarios. In the second version, the scenarios were supposed to comprise only vegetation on the ground, i.e., trees and shrubs as in OGF2, while permeable pavement was added because it was not possible to achieve the desired minimum index value with the introduced vegetation elements only in MGF2. In the third version of scenarios, the choice of NbSs was limited only to those that can be applied to architectural surfaces; that is, roofs and building walls. All the developed scenarios meet the assumed minimum values of the eco-spatial indices studied.
In the MGF1 scenario, an additional 12 medium-sized trees (i.e., those with a trunk circumference of at least 35 cm and a canopy cover of 25 m2), a 30 m2 open retention pond, and climbers on the facades of four buildings were introduced in the studied estate. On the other hand, in the MGF2 scenario, only vegetation elements, i.e., trees and shrubs, were used as a base, but the addition of as many as 89 trees and 1000 m2 of shrubs (which is the maximum value in the Klobucka neighbourhood due to area shortage) did not allow the achievement of the minimum MGF index. Therefore, in this scenario, 4000 m2 of concrete pavements were also replaced with permeable sidewalks. This solution made it possible to achieve the required minimum MGF index value of 0.6. In the third scenario developed for Malmö (MGF3), only NbSs on architectural surfaces were used; that is, green facades on three buildings and extensive green roofs on eight buildings.
As the OGF is also partly based on qualitative variables, such as those related to restoring connectivity between green spaces or collecting rainwater for reuse, 20 rain barrels could possibly be incorporated into the OGF1 scenario. In addition, this scenario introduced eight more large trees, climbing plants (i.e., green facades on four buildings), and vertical gardens on another four buildings.
On the other hand, in the OGF2 scenario, only plant solutions were used, i.e., 89 large trees were introduced, which was already enough to achieve the required value of the index. In the last scenario OGF3, as in the case of MGF3, NbSs were introduced only on the facades and roofs of buildings. In order to achieve the index value of 0.8, it was necessary to introduce green facades on eight buildings and green extensive roofs on all nine buildings located in the studied neighbourhood.
Data on the current development of the studied neighbourhood and input data describing all six scenarios were entered into the serious game “Neighbourhood with Climate”. Sample visualisations of the existing state and individual scenarios are shown in Figure 3.

3.3. Adaptive Potential of Studied Scenarios

The adaptation potential for the estate’s current land-use state and the six scenarios studied was calculated using the “Neighbourhood with Climate” simulation game. The results obtained for environmental parameters such as temperature, oxygen production, carbon sequestration, rainwater harvesting, and biodiversity are shown in Table 6.
The air temperature was reduced the most in the OGF2 scenario (a decrease of 1.07 °C), which confirms the very clear impact of large trees, of which as many as 89 were used in this setting (see Figure 4). In the other scenarios, the temperature reduction was less significant but noticeable. A similar level of temperature reduction was observed in the OGF3 and OGF1 scenarios (0.39 °C and 0.35 °C reduction, respectively). In the case of the OGF3 scenario, green roofs placed on all nine buildings and vertical gardens (green walls) placed on the facades of eight buildings of the test estate are significant in reducing the temperature. On the other hand, a similar result obtained in the OGF1 scenario is due to the use of a combination of climbing plants, vertical gardens, green roofs, and several large trees. Among the scenarios developed for the MGF index, the most favourable result in terms of temperature reduction was obtained in the MGF3 scenario (−0.31 °C), in which NbSs were introduced only on architectural surfaces. This shows that a significant share of green roofs (here located on as many as eight buildings) and climbing plants (placed on the facades of three buildings) in terms of lowering air temperature is a more effective solution than the use of a significant number of trees, but smaller ones. In addition, the applied water reservoir in the MGF1 scenario did not substantially affect the temperature reduction, which is due to its insignificant area (only 30 m2). At the same time, it should be noted that the possibility of introducing specific NbSs is always dictated by the field possibilities in a given space. When comparing temperature reduction results, all three OGF scenarios outperformed MGF, indicating that the higher index value required in Oslo (0.8) compared to Malmö (0.6) has a positive impact.
Oxygen production and carbon sequestration are processes closely related to plant photosynthesis, and their value is influenced by the amount of biomass. The results for oxygen production and carbon sequestration are shown in Figure 5.
The highest values in this regard were obtained in the OGF2 scenario, where the increase in oxygen production is +272.69% and carbon sequestration is +137.55%. Slightly lower, but still significant results were obtained for the MGF2 scenario, where the increase was +171.91% and +81.86% for oxygen production and CO2 sequestration, respectively. In both scenarios, such a significant increase in the studied parameters is due to the introduction of a large number of large or medium-sized trees in the estate. In the other analysed scenarios, the increase in these two parameters was at a similar level, with a slight advantage for the OGF variants.
As for rainwater harvesting, there are no solutions for doing so in the current state of development of the Klobucka housing estate. The largest amount of rainwater is allowed to be collected by the OGF1 scenario (that is, 2860.35 L), which uses 20 barrels at the gutters and extensive green roofs on four buildings (see Figure 6). The application of extensive green roofs alone on all buildings of the studied housing estate, as proposed in the OGF3 scenario, allows for the collection of 1007.46 L of rainwater. The lower value of this parameter obtained in the MGF3 scenario (i.e., 793.93 L) is due to the use of extensive green roofs not on all buildings. Scenarios based only on the introduction of vegetation, i.e., MGF2 and OGF2, do not provide any benefit in terms of water harvesting.
The last parameter examined was the effect of the applied land-use scenarios and the introduced NbSs on the level of biodiversity. All analysed scenarios increase the level of biodiversity to a moderate degree, and the most favourable in this regard is the OGF2 variant, in which this parameter increased to a value of 1.88, which is 23.7%.

4. Discussion

Our research is embedded in the trend of research on evaluating the effectiveness of eco-spatial indices (in various approaches, from improving microclimatic conditions, in the context of the impact of the use of these indicators to ensuring a certain degree of ecosystem service delivery or improving the quality of space from the point of view of landscape issues). Over the past several years, the use of various indices has been gaining popularity, precisely because of the convenience of application, flexibility, and transferability potential.
Articles comparing eco-spatial indices have appeared since the introduction of the Berlin Biotope Area Factor (BAF). In many cities, BAF was later adapted and implemented, e.g., in Malmö, Helsinki and Padua in Europe, as well as in Seattle in the USA [43,70]. These indices have also been tested in other countries, including Greece [12], Turkey [42] and Finland [43]. Among the articles comparing indices, the study by Song and Kil [16] stands out, where they compared a modified index used in Korea with the original index from Berlin, Malmö, and Seattle. The comparison primarily focused on guidelines available on their respective homepages, particularly on the categories and their associated values. These studies were preceded by others aimed at improving the index used in Korea [44,45,71], which focused on costs, evaluations, and weights used as criteria for certifying green buildings and ecological environments.
A different approach was presented in an article by Silva et al. [46], comparing four Environmental Quotas (EQs) from São Paulo with factors from Berlin, Malmö, and Seattle. The study aimed to contribute to a better understanding of environmental quality issues, ultimately improving the EQ index in São Paulo. The research compared model solutions for the indexes on a hypothetical plot of 500 m2 with a single building, analysing selected Nature-based Solutions (NbSs) such as small trees, green roofs, green walls, and bioretention areas. This study mainly focused on the types of solutions and the area they covered, without considering the environmental impacts of their implementation. However, it provides insight into the varying effects that the application of these indexes can have.
The discussed articles, however, did not concern applying research in real spaces; they did not take into account the entire neighbourhood area but only a single building. Specific environmental values achieved through the application of the tested indexes were also not provided. Additionally, an important contribution to the discussion on comparative studies of eco-spatial indexes is the work by Ring et al. [15], which highlighted the limitations of these indexes, including an excessive focus on environmental aspects without considering socio-cultural factors, as well as the lack of consideration for floor area ratio. This omission results in a focus on the relationship between built-up area and plot size, without considering the size of the building.
This last element is particularly emphasised in the article, where a comparison of the indices from Malmö and Oslo with the Polish index, based solely on the relationship between built-up areas and areas covered with vegetation and water, shows how different spatial, environmental, and socio-cultural impacts can result from the use of indexes based on various components. One of the major limitations in using eco-spatial indices lies in how they are calculated. For instance, the Polish RBVA indicator considers only the area covered by vegetation or water, without accounting for vegetation structure features that are included in the indices for Oslo and Malmö, such as variations in tree weight depending on whether they are planted or naturally occurring, or the circumference of the trunk. The RBVA is limited by its narrow selection of NbS elements and the omission of these important qualitative factors. The application of different scenarios for the OGF and MGF indicators illustrates how varying the complexity of an index can lead to significantly different adaptation outcomes for the same area. It is crucial to note that the weighting of individual elements ultimately determines the results achieved.
The method by which each indicator is calculated is a strategic and political choice, which in turn influences the type of NbS that is promoted. For example, in Oslo, the high weighting of large trees significantly impacts developers’ decisions regarding the NbS they implement, as they align their projects with these scoring priorities.

5. Conclusions

In summary, the conducted analyses underscore that the structure of the applied Nature-based Solutions (NbSs) is a critical aspect when comparing different spatial indices and working on improving urban planning tools aimed at enhancing the quality of life in cities. The characteristics of the NbS structure, such as the type of solutions used and their assigned weight, can lead to significantly different environmental outcomes. This is particularly evident in the rather flat, two-dimensional index used in Poland, where an appropriate proportion of lawn often suffices to meet environmental requirements. In contrast, comparisons with more complex indices like those in Malmö or Oslo clearly show that environmental benefits, including improved thermal conditions and rainwater harvesting, are achieved when a more sophisticated NbS structure is considered. This is highlighted by the scenarios applied, such as focusing exclusively on elements integrated into the architectural structures of buildings (as preferred in some indices like Malmö) or placing greater emphasis on preserving large trees (as in Oslo). The research presented in the article demonstrates that well-constructed indices lead to more efficient Nature-based Solutions (NbSs), thereby enhancing adaptation to climate change. A review of only three eco-spatial indices reveals significant differences in the outcomes of their application. Expanding the analysis to include the effectiveness of other eco-spatial indices would undoubtedly be of great interest, particularly in the context of improving urban environments and, consequently, the quality of life—a key objective of the Sustainable Development Goals 2030.

Author Contributions

Conceptualisation, R.G., G.M. and A.C.; methodology, G.M.; software, G.M.; validation, G.M., R.G. and A.C.; formal analysis, G.M., R.G. and A.C.; investigation, R.G., G.M. and A.C.; resources, G.M., R.G. and A.C.; data curation, G.M.; writing—original draft preparation, G.M., A.C. and R.G.; writing—review and editing, G.M., A.C. and R.G.; visualisation, A.C. and G.M.; funding acquisition, A.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by CoAdapt: Communities for Climate Change Action, grant number Adapt/0002/2020-00-Communities for Climate Change Action.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Acknowledgments

The authors would like to thank all members of the CoAdapt team.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Copernicus: 2023 Is the Hottest Year on Record, with Global Temperatures close to the 1.5 °C Limit. Copernicus. Available online: https://climate.copernicus.eu/copernicus-2023-hottest-year-record (accessed on 9 August 2024).
  2. Liu, R.; Han, Z.; Wu, J.; Hu, Y.; Li, J. The impacts of urban surface characteristics on radiation balance and meteorological variables in the boundary layer around Beijing in summertime. Atmos. Res. 2017, 197, 167–176. [Google Scholar] [CrossRef]
  3. Junior, D.P.M.; Bueno, C.; da Silva, C.M. The Effect of Urban Green Spaces on Reduction of Particulate Matter Concentration. Bull. Environ. Contam. Toxicol. 2022, 108, 1104–1110. [Google Scholar] [CrossRef] [PubMed]
  4. Ramyar, R.; Ackerman, A.; Johnston, D.M. Adapting cities for climate change through urban green infrastructure planning. Cities 2021, 117, 103316. [Google Scholar] [CrossRef]
  5. Girard, L.F.; Nocca, F. Climate Change and Health Impacts in Urban Areas: Towards Hybrid Evaluation Tools for New Governance. Atmosphere 2020, 11, 1344. [Google Scholar] [CrossRef]
  6. Perera, A.; Chathurika, S.; Davies, P.; Graham, P. A global review of urban blue-green planning tools. Land Use Policy 2024, 140, 107093. [Google Scholar] [CrossRef]
  7. Szulczewska, B.; Giedych, R.; Borowski, J.; Kuchcik, M.; Sikorski, P.; Mazurkiewicz, A.; Stańczyk, T. How much green is needed for a vital neighbourhood? In search for empirical evidence. Land Use Policy 2014, 38, 330–345. [Google Scholar] [CrossRef]
  8. Stange, E.E.; Barton, D.N.; Andersson, E.; Haase, D. Comparing the implicit valuation of ecosystem services from nature-based solutions in performance-based green area indicators across three European cities. Landsc. Urban Plan. 2022, 219, 104310. [Google Scholar] [CrossRef]
  9. Reinwald, F.; Brandenburg, C.; Gabor, A.; Hinterkörner, P.; Kainz, A.; Kraus, F.; Ring, Z.; Scharf, B.; Tötzer, T.; Damyanovic, D. Multi-Level Toolset for Steering Urban Green Infrastructure to Support the Development of Climate-Proofed Cities. Sustainability 2021, 13, 12111. [Google Scholar] [CrossRef]
  10. Pristeri, G.; Peroni, F.; Pappalardo, S.E.; Codato, D.; Castaldo, A.G.; Masi, A.; De Marchi, M. Mapping and Assessing Soil Sealing in Padua Municipality through Biotope Area Factor Index. Sustainability 2020, 12, 5167. [Google Scholar] [CrossRef]
  11. Slätmo, E.; Nilsson, K.; Turunen, E. Implementing Green Infrastructure in Spatial Planning in Europe. Land 2019, 8, 62. [Google Scholar] [CrossRef]
  12. Vartholomaios, A.; Kalogirou, N.; Athanassiou, E.; Papadopoulou, M. The green space factor as a tool for regulating the urban microclimate in vegetation-deprived Greek cities. In Proceedings of the International Conference on “Changing Cities“: Spatial, Morphological, Formal & Socio-Economic Dimensions, Skiathos, Greece, 18–21 June 2013. [Google Scholar]
  13. Khoshkar, S.; Hammer, M.; Borgström, S.; Balfors, B. Ways Forward for Advancing Ecosystem Services in Municipal Planning—Experiences from Stockholm County. Land 2020, 9, 296. [Google Scholar] [CrossRef]
  14. Schrade, S.N.; Collomb, N.; Coleman, A. Making GI Count—Tackling Climate Justice and Improving Delivery Using Urban Green Space Factors; Town and Country Planning Association: London, UK, 2021; pp. 385–391. [Google Scholar]
  15. Ring, Z.; Damyanovic, D.; Reinwald, F. Green and open space factor Vienna: A steering and evaluation tool for urban green infrastructure. Urban For. Urban Green. 2021, 62, 127131. [Google Scholar] [CrossRef]
  16. Song, B.-H.; Kil, S.-H. Research Trends for Improvement of Biotope Area Index. J. People Plants Environ. 2018, 21, 103–116. [Google Scholar] [CrossRef]
  17. Bush, J.; Ashley, G.; Foster, B.; Hall, G. Integrating Green Infrastructure into Urban Planning: Developing Melbourne’s Green Factor Tool. Urban Plan. 2021, 6, 20–31. [Google Scholar] [CrossRef]
  18. Caetano, P.M.D.; Pereira, H.M.S.B.; Figueiredo, L.C.R.; Sepe, P.M.; Giatti, L.L. The City of São Paulo’s Environmental Quota: A Policy to Embrace Urban Environmental Services and Green Infrastructure Inequalities in the Global South. Front. Sustain. Cities 2021, 3, 685875. [Google Scholar] [CrossRef]
  19. Podhajska, E.; Burszta-Adamiak, E.; Drzeniecka-Osiadacz, A.; Zienowicz, M.; Podhajski, B.; Sawiński, T.; Jasińska, A. Sustainability as a Function of an Area: Application of Multi-Criteria Evaluation in Assessing the Effectiveness of Nature-Based Solutions. Atmosphere 2021, 12, 1464. [Google Scholar] [CrossRef]
  20. Bulkeley, H.; Naumann, S.; Vojinovic, Z.; Calfapietra, C.; Whiteoak, K.; Freitas, T.; Vandewoestijne, S.; Wild, T. Nature-Based Solutions: State of the Art in EU Funded Projects; Publications Office of the European Union: Luxembourg, 2020; ISBN 978-92-76-17334-2. Available online: https://data.europa.eu/doi/10.2777/236007 (accessed on 9 June 2023).
  21. Seddon, N.; Chausson, A.; Berry, P.; Girardin, C.A.J.; Smith, A.; Turner, B. Understanding the value and limits of nature-based solutions to climate change and other global challenges. Philos. Trans. R. Soc. B Biol. Sci. 2020, 375, 20190120. [Google Scholar] [CrossRef]
  22. Nature-Based Solutions. IUCN. Available online: https://iucn.org/our-work/nature-based-solutions (accessed on 9 August 2024).
  23. Nesshöver, C.; Assmuth, T.; Irvine, K.N.; Rusch, G.M.; Waylen, K.A.; Delbaere, B.; Haase, D.; Jones-Walters, L.; Keune, H.; Kovacs, E.; et al. The science, policy and practice of nature-based solutions: An interdisciplinary perspective. Sci. Total Environ. 2017, 579, 1215–1227. [Google Scholar] [CrossRef]
  24. Pauleit, S.; Zölch, T.; Hansen, R.; Randrup, T.; Konijnendijk, C. Nature-Based Solutions and Climate Change—Four Shades of Green. In Nature-Based Solutions to Climate Change Adaptation in Urban Areas. Theory and Practice of Urban Sustainability Transitions; Springer: Cham, Switzerland, 2017; pp. 29–49. ISBN 978-3-319-56091-5. [Google Scholar]
  25. McPhearson, T.; Andersson, E.; Elmqvist, T.; Frantzeskaki, N. Resilience of and through urban ecosystem services. Ecosyst. Serv. 2015, 12, 152–156. [Google Scholar] [CrossRef]
  26. Chausson, A.; Turner, B.; Seddon, D.; Chabaneix, N.; Girardin, C.A.J.; Kapos, V.; Key, I.; Roe, D.; Smith, A.; Woroniecki, S.; et al. Mapping the effectiveness of nature-based solutions for climate change adaptation. Glob. Chang. Biol. 2020, 26, 6134–6155. [Google Scholar] [CrossRef]
  27. Pugliese, F.; Caroppi, G.; Zingraff-Hamed, A.; Lupp, G.; Gerundo, C. Assessment of NBSs effectiveness for flood risk management: The Isar River case study. AQUA J. Water Supply Res. Technol. 2021, 71, 42–61. [Google Scholar] [CrossRef]
  28. Berardi, U.; GhaffarianHoseini, A.; GhaffarianHoseini, A. State-of-the-art analysis of the environmental benefits of green roofs. Appl. Energy 2014, 115, 411–428. [Google Scholar] [CrossRef]
  29. Qin, X. A Green Roof Test Bed for Stormwater Management and Reduction of Urban Heat Island Effect in Singapore. Br. J. Environ. Clim. Change 2013, 2, 410–420. [Google Scholar] [CrossRef] [PubMed]
  30. Marvuglia, A.; Koppelaar, R.; Rugani, B. The effect of green roofs on the reduction of mortality due to heatwaves: Results from the application of a spatial microsimulation model to four European cities. Ecol. Model. 2020, 438, 109351. [Google Scholar] [CrossRef]
  31. Liu, W.; Engel, B.A.; Feng, Q.; Li, R. Simulating annual runoff retention performance of extensive green roofs: A comparison of four climatic regions in China. J. Hydrol. 2022, 610, 127871. [Google Scholar] [CrossRef]
  32. Palermo, S.A.; Turco, M.; Principato, F.; Piro, P. Hydrological Effectiveness of an Extensive Green Roof in Mediterranean Climate. Water 2019, 11, 1378. [Google Scholar] [CrossRef]
  33. Zölch, T.; Henze, L.; Keilholz, P.; Pauleit, S. Regulating urban surface runoff through nature-based solutions—An assessment at the micro-scale. Environ. Res. 2017, 157, 135–144. [Google Scholar] [CrossRef]
  34. Cortinovis, C.; Olsson, P.; Boke-Olén, N.; Hedlund, K. Scaling up nature-based solutions for climate-change adaptation: Potential and benefits in three European cities. Urban For. Urban Green. 2022, 67, 127450. [Google Scholar] [CrossRef]
  35. Majidi, A.N.; Vojinovic, Z.; Alves, A.; Weesakul, S.; Sanchez, A.; Boogaard, F.; Kluck, J. Planning Nature-Based Solutions for Urban Flood Reduction and Thermal Comfort Enhancement. Sustainability 2019, 11, 6361. [Google Scholar] [CrossRef]
  36. Vojinovic, Z.; Alves, A.; Gómez, J.P.; Weesakul, S.; Keerakamolchai, W.; Meesuk, V.; Sanchez, A. Effectiveness of small- and large-scale Nature-Based Solutions for flood mitigation: The case of Ayutthaya, Thailand. Sci. Total Environ. 2021, 789, 147725. [Google Scholar] [CrossRef]
  37. Thompson, R.; Tjaden, S.; Tilley, D. Stormwater Retention of an In-Series System Composed of a Green Roof, Constructed Wetland, and Bioretention Cell for a Single-Family Home. J. Sustain. Water Built Environ. 2022, 8, 04021023. [Google Scholar] [CrossRef]
  38. Zuvela-Aloise, M.; Koch, R.; Buchholz, S.; Früh, B. Modelling the potential of green and blue infrastructure to reduce urban heat load in the city of Vienna. Clim. Chang. 2016, 135, 425–438. [Google Scholar] [CrossRef]
  39. Ossola, A.; Jenerette, G.D.; McGrath, A.; Chow, W.; Hughes, L.; Leishman, M.R. Small vegetated patches greatly reduce urban surface temperature during a summer heatwave in Adelaide, Australia. Landsc. Urban Plan. 2021, 209, 104046. [Google Scholar] [CrossRef]
  40. Marando, F.; Heris, M.P.; Zulian, G.; Udías, A.; Mentaschi, L.; Chrysoulakis, N.; Parastatidis, D.; Maes, J. Urban heat island mitigation by green infrastructure in European Functional Urban Areas. Sustain. Cities Soc. 2022, 77, 103564. [Google Scholar] [CrossRef]
  41. Giannakis, E.; Bruggeman, A.; Poulou, D.; Zoumides, C.; Eliades, M. Linear Parks along Urban Rivers: Perceptions of Thermal Comfort and Climate Change Adaptation in Cyprus. Sustainability 2016, 8, 1023. [Google Scholar] [CrossRef]
  42. Kacmaz, G. The Biotope Area Factor Method for Sustainable Urban Landscapes: The Cases of the Bornova and Bayraklı Districts, İzmir. J. Int. Environ. Appl. Sci. 2021, 16, 82–90. [Google Scholar]
  43. Juhola, S. Planning for a green city: The Green Factor tool. Urban For. Urban Green. 2018, 34, 254–258. [Google Scholar] [CrossRef]
  44. Jang, D.; Kim, H.; Kang, B.K. A basic study on Improvement for Biotops Area Ratio through the Post Evaluation Plan for Outdoor Space of Apartment Housings. KIEAE J. 2010, 10, 91–96. [Google Scholar]
  45. Yeom, D.; Lee, K.-I. Study on the Improvement of G-SEED Management System. J. Archit. Inst. Korea Plan. Des. 2015, 31, 13–22. [Google Scholar] [CrossRef]
  46. Da Silva, P.W.S.; Benites, H.S.; Monteiro, L.M.; Duarte, D.H.S. INSTRUMENTOS URBANÍSTICOS PARA INCREMENTO DE VEGETAÇÃO EM ÁREAS URBANAS: Análise comparada a partir da quota ambiental do município de São Paulo. Cad. Zygmunt Bauman 2019, 8, 167–187. Available online: https://periodicoseletronicos.ufma.br/index.php/bauman/article/view/10227 (accessed on 9 August 2024).
  47. Szulczewska, B.; Giedych, R.; Blaszke, M.; Wójcik-Gront, E.; Nowak, M.; Legutko-Kobus, P. Ratio of Biologically Vital Area in Local Spatial Plans as an Instrument of Green Infrastructure Creation in Single—And Multi-Family Residential in Small and Medium-Sized Towns in Poland. Teka Kom. Urban. Archit. Oddziału Pol. Akad. Nauk Krakowie 2022, L, 189–201. [Google Scholar]
  48. Riktlinjer för Grönytefaktor (Guidelines for the Green Space Factor), Malmö Stad, 2014, Stadsbyggnadskontoret Godkänd av Stadsbyggnadsnämnden 2014. Available online: https://malmo.se/download/18.2b036ae717c5447e582895b/1637596358956/Gr%C3%B6nytefaktordec+2014.pdf (accessed on 15 July 2024).
  49. Norm for Vegetasjon og Vannhåndtering—Blågrønn Factor (Norm for Vegetation and Water Management—Blue-Green Factor), Oslo, 2023; Plan-og Bygningsetaten: Oslo, Norway, 2023.
  50. Rozporządzenie Ministra Rozwoju i Technologii z dnia 8 Grudnia 2023 r. w Sprawie Projektu Planu Ogólnego Gminy, Dokumentowania Prac Planistycznych w Zakresie tego Planu oraz Wydawania z Niego Wypisów i Wyrysów (Ordinance of the Minister of Development and Technology of 8 December 2023 on the Draft General Plan of a Municipality, the Documentation of Planning Work for This Plan and the Issuing of Extracts from It). 2023. Available online: https://isap.sejm.gov.pl/isap.nsf/download.xsp/WDU20230002758/O/D20232758.pdf (accessed on 15 July 2024).
  51. Lenhart, J.; Fitzgerald, J. Eco-Districts as a Transition Pathway to Low-Carbon Cities. In Creating Low Carbon Cities; Dhakal, S., Ruth, M., Eds.; Springer International Publishing: Cham, Switzerland, 2017; pp. 187–199. ISBN 978-3-319-49730-3. [Google Scholar]
  52. Horvath, P.; Barton, D.N.; Hauglin, E.A.; Ellefsen, H.W. Blue-Green Factor (BGF) Mapping in QGIS. User Guide and Documentation; Norsk Institutt for Naturforskning (NINA): Trondheim, Norway, 2017; ISBN 978-82-426-3176-3. [Google Scholar]
  53. Irsalina, N.; Gamal, A. Urban Planning Simulation Game and the Development of Spatial Competence Urban Planning Simulation Game and the Development of Spatial Competence. IOP Conf. Ser. Earth Environ. Sci. 2019, 396, 012016. [Google Scholar]
  54. Sousa, M.; Antunes, A.P.; Pinto, N.; Zagalo, N. Serious Games in Spatial Planning: Strengths, Limitations and Support Frameworks. Int. J. Serious Games 2022, 9, 115–133. [Google Scholar] [CrossRef]
  55. Game|Projekt CoAdapt—Communities for Climate Change Action. Available online: https://coadapt.pl/en/game/ (accessed on 9 August 2024).
  56. Kántor, N.; Kovács, A.; Takács, Á. Small-scale human-biometeorological impacts of shading by a large tree. Open Geosci. 2016, 8, 231–245. [Google Scholar] [CrossRef]
  57. Srivanit, M.; Hokao, K. Evaluating the cooling effects of greening for improving the outdoor thermal environment at an institutional campus in the summer. Build. Environ. 2013, 66, 158–172. [Google Scholar] [CrossRef]
  58. Gillner, S.; Vogt, J.; Tharang, A.; Dettmann, S.; Roloff, A. Role of street trees in mitigating effects of heat and drought at highly sealed urban sites. Landsc. Urban Plan. 2015, 143, 33–42. [Google Scholar] [CrossRef]
  59. Armson, D.; Stringer, P.; Ennos, A.R. The effect of tree shade and grass on surface and globe temperatures in an urban area. Urban For. Urban Green. 2012, 11, 245–255. [Google Scholar] [CrossRef]
  60. Edmondson, J.L.; Stott, I.; Davies, Z.G.; Gaston, K.J.; Leake, J.R. Soil surface temperatures reveal moderation of the urban heat island effect by trees and shrubs. Sci. Rep. 2016, 6, 33708. [Google Scholar] [CrossRef]
  61. Kalkulator Korzyści z Drzew (Trees Benefits Calculator). Available online: https://www.omnicalculator.com/pl/ekologia/korzysci-z-drzew (accessed on 10 June 2024).
  62. National Database of Topographic Objects. Available online: https://bdot10k.geoportal.gov.pl/ (accessed on 15 June 2024).
  63. Warsaw City Map Service. Available online: https://mapa.um.warszawa.pl/en/index.html (accessed on 15 June 2024).
  64. Ilu Nas Jest? A Ilu Będzie? Raporty z Planowania WARSZAWY (How Many of Us Are There, and How Many Will There Be? Warsaw Planning Reports). Available online: https://architektura.um.warszawa.pl/documents/12025039/21022710/RAPORT_Z_PROJEKTOWANIA_WARSZAWY_CZ1_Ilu_nas_jest_a_Ilu_nas_b%C4%99dzie.pdf/68b51821-af6f-ec1c-586e-279eea2fc9ce?t=1619802121397 (accessed on 15 July 2024).
  65. Podstawowe Informacje o Mieszkalnictwie w Polsce—Ministerstwo Rozwoju i Technologii—Portal Gov.pl. Available online: https://www.gov.pl/web/rozwoj-technologia/podstawowe-informacje-o-mieszkalnictwie-w-polsce (accessed on 9 August 2024).
  66. Warszawa Ukośna. Available online: https://ukosne.um.warszawa.pl/ (accessed on 9 August 2024).
  67. Błażejczyk, A.; Błażejczyk, K.; Degórska, B.; Dudek, W.; Kręcisz, B.; Kuchcik, M.; Milewski, P.; Pałczyński, C.; Szmyd, J. (Eds.) Miejska Wyspa Ciepła w Warszawie: Uwarunkowania Klimatyczne i Urbanistyczne; Wydawnictwo Akademickie Sedno: Instytut Geografii i Przestrzennego Zagospodarowania PAN: Warszawa, Poland, 2014; ISBN 978-83-7963-018-9. [Google Scholar]
  68. Kuchcik, M.; Milewski, P. Miejska wyspa ciepła w Warszawie—Próba oceny z wykorzystaniem Local Climate Zones. Acta Geogr. Lodz. 2016, 104, 21–33. [Google Scholar]
  69. Barszcz, M. Zastosowanie modelu SWMM do prognozy przepływów prawdopodobnych w zlewni miejskiej. Przegląd Nauk. Inż. Kształt. Śr. 2015, 2015, 209–223. [Google Scholar]
  70. Peroni, F.; Pristeri, G.; Codato, D.; Pappalardo, S.E.; De Marchi, M. Biotope Area Factor: An Ecological Urban Index to Geovisualize Soil Sealing in Padua, Italy. Sustainability 2020, 12, 150. [Google Scholar] [CrossRef]
  71. Son, D.P. The preliminary study for the introduction of biotope area ratio in renewal project area: Focused on cost-effectiveness analysis. J. Korea Plan. Assoc. 2015, 50, 109–122. [Google Scholar] [CrossRef]
Land 13 01492 g001
Figure 1. Existing condition of the Klobucka housing estate showing the spatial layout and distribution of buildings, permeable surfaces, and vegetation structure. Diagonal aerial photo up to date for 2022: [66].
Figure 1. Existing condition of the Klobucka housing estate showing the spatial layout and distribution of buildings, permeable surfaces, and vegetation structure. Diagonal aerial photo up to date for 2022: [66].
Land 13 01492 g001
Land 13 01492 g002
Figure 2. Idea diagrams of the tested scenarios.
Figure 2. Idea diagrams of the tested scenarios.
Land 13 01492 g002
Land 13 01492 g003
Figure 3. The Klobucka housing estate as visualised in the “Neighbourhood with Climate” serious game: (a) the existing state of the Klobucka housing estate; (b) OGF1 scenario visualisation; (c) OGF2 scenario visualisation; (d) MGF3 scenario visualisation.
Figure 3. The Klobucka housing estate as visualised in the “Neighbourhood with Climate” serious game: (a) the existing state of the Klobucka housing estate; (b) OGF1 scenario visualisation; (c) OGF2 scenario visualisation; (d) MGF3 scenario visualisation.
Land 13 01492 g003
Land 13 01492 g004
Figure 4. Impact of scenarios on the air temperature reduction.
Figure 4. Impact of scenarios on the air temperature reduction.
Land 13 01492 g004
Land 13 01492 g005
Figure 5. Impact of the scenarios on oxygen production and CO2 sequestration.
Figure 5. Impact of the scenarios on oxygen production and CO2 sequestration.
Land 13 01492 g005
Land 13 01492 g006
Figure 6. Impact of scenarios on water harvesting.
Figure 6. Impact of scenarios on water harvesting.
Land 13 01492 g006
Table 1. Comparison of studied eco-spatial indices.
Table 1. Comparison of studied eco-spatial indices.
CharacteristicsRBVAMGFOGF
Weighting Factors
Components of the index calculated by area occupied
Vegetation on the ground1.01.01.0–1.4
Vegetation on underground constructions-0.6–0.8-
Green roofs0.50.80.3–0.9
Green walls-0.70.3–0.6
Open water1.01.03.0
Retention basins--1.0
Sealed surfaces runoff to green areas-0.20.2
Permeable pavements-0.2–0.40.2–0.4
Components of the index calculated in area per piece
Trees-0.4 11.0 2
Shrubs-0.2 3-
Constant variable counted, if any, for the area
Vegetation and water management--0.05–0.15
Minimal target value0.2–0.50.5–0.60.7–0.8
1 The maximum area of 25 m2 for each tree; 2 the maximum area of 20–70 m2 for each tree (depending on existing and planned trees); 3 the maximum area of 5 m2 for each shrub.
Table 2. Relationship between NbSs used in the game and the variables applied in the indices studied.
Table 2. Relationship between NbSs used in the game and the variables applied in the indices studied.
NbS Categories“Neighbourhood with Climate” Game MenuRBVAMGFOGF
Areas covered by vegetation on the ground ☐☐☐
Large trees☐ ☐
Medium trees☐ ☐ 1☐
Small trees☐ ☐
Shrubs☐ ☐ 2☐ 3
Unmown lawn☐ ☐
Mown lawn☐ ☐
Groundcovers☐ ☐
Retention pond☐☐☐☐
Infiltration basin☐ ☐
Rainwater harvesting☐ ☐
Green facades☐ ☐☐
Green walls☐ ☐
Green roofs 4☐☐☐☐
Permeable pavement 5☐ ☐☐
Reinforced lawn☐ ☐
1 Taking into account the tree parameters indicated in the calculation of the index in Malmö, trees were classified as medium. 2 Shrubs higher than three meters: calculated for the maximum area of 5 m2 for each shrub. 3 Shrubs up to 2 m. 4 In the case of Oslo, the weighting factor for extensive green roofs is taken into account (due to the actual feasibility of such a solution in the test housing estate). 5 For Malmö and Oslo, surfaces with the highest weighting factor are taken into account.
Table 3. Share of various surface types in the studied housing estate.
Table 3. Share of various surface types in the studied housing estate.
Land Cover CategoriesArea (m2)Share in a Total Housing Estate Area (%)
Buildings 4441.5722.37
Impervious surfaces7026.3435.39
Greenery8387.6542.24
Total area19,855.56100.00
Table 4. Values of studied indices for the existing state of Klobucka housing estate.
Table 4. Values of studied indices for the existing state of Klobucka housing estate.
RBVAMGFOGF
Spatial planning requirementsMin. 30%Min. 0.6Min. 0.8
Existing state42.24%0.420.6
Table 5. Characteristics of MGF and OGF eco-spatial index scenarios developed for the Klobucka housing estate as compared to the existing state.
Table 5. Characteristics of MGF and OGF eco-spatial index scenarios developed for the Klobucka housing estate as compared to the existing state.
“Neighbourhood with Climate” Game MenuExisting StateMGF1
(Miscellaneous)		MGF2
(Vegetation on the Ground, Pavements)		MGF3
(Vegetation on
Buildings)		OGF1
(Miscellaneous)		OGF2
(Vegetation on Ground)		OGF3
(Vegetation on Buildings)
Large trees-NANANA889-
Medium trees-1289----
Small trees38NANANA383838
Shrubs1413 m2-1000 m2-1413 m22413 m2 1413 m2
Mown lawn8300 m2NANANA8300 m28300 m28300 m2
Retention pond-30 m2-----
Rainwater harvesting-NANANA20 barrels--
Green facades-4896 m2 (on 8 buildings)-1836 m2 (on 3 buildings)2448 m2 (on 4 buildings)--
Green walls-NANANA2448 m2 (on 4 buildings)-4896 m2 (on 8 buildings)
Green roofs-- 3200 m2 (on 8 buildings)1200 m2 (on 3 buildings)-3900 m2 (on 9 buildings)
Permeable pavement--4000 m2----
Reinforced lawn88 m2NANANA88 m288 m288 m2
Index value 0.60.60.610.80.80.8
Table 6. Environmental parameters of tested scenarios (based on the “Neighbourhood with Climate” game).
Table 6. Environmental parameters of tested scenarios (based on the “Neighbourhood with Climate” game).
Existing StateMGF1MGF2MGF3OGF1OGF2OGF3
temperature (°C)20.1520.0419.9219.8419.819.0819.76
oxygen production (kg)770,480.5843,024.42,094,981964,531.71,009,8582,871,4811,045,389
CO2 sequestration (kg)91,649.7993,588.55166,669.899,139.92102,353217,709.8100,828
water harvesting (l)0267.840793.932860.3501007.46
biodiversity1.521.81.741.851.831.881.83
The four-degree green shading scale—the darker the colour, the better the result achieved.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Giedych, R.; Maksymiuk, G.; Cieszewska, A. Eco-Spatial Indices as an Effective Tool for Climate Change Adaptation in Residential Neighbourhoods—Comparative Study. Land 2024, 13, 1492. https://doi.org/10.3390/land13091492

AMA Style

Giedych R, Maksymiuk G, Cieszewska A. Eco-Spatial Indices as an Effective Tool for Climate Change Adaptation in Residential Neighbourhoods—Comparative Study. Land. 2024; 13(9):1492. https://doi.org/10.3390/land13091492

Chicago/Turabian Style

Giedych, Renata, Gabriela Maksymiuk, and Agata Cieszewska. 2024. "Eco-Spatial Indices as an Effective Tool for Climate Change Adaptation in Residential Neighbourhoods—Comparative Study" Land 13, no. 9: 1492. https://doi.org/10.3390/land13091492

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop