Applying the 3-30-300 Guideline to Evaluate Green Equality in a Medium-Sized Brazilian City

  • Arboriculture & Urban Forestry (AUF)
  • October 2025,
  • jauf.2025.031;
  • DOI: https://doi.org/10.48044/jauf.2025.031

Abstract

Background It is essential to provide higher quality environments for the development of human activities in urbanized areas, such as better adapted urban centers. Regarding concerns related to climate change, there is a discussion about joint efforts to establish an ideal standard to be followed worldwide, significantly increasing the ecosystem services provided by urban forests, which directly affects the community’s quality of life.

Methods This research aimed to characterize neighborhoods with different per capita incomes according to the 3-30-300 guideline in Cachoeiro de Itapemirim, a medium-sized Brazilian municipality. An inventory of the road component of urban forests was carried out for 9 neighborhoods from 3 different economic strata based on average per capita income.

Results No neighborhood considered in the different per capita incomes fully complied with the 3-30-300 guideline. There was no difference in applying the guideline about the socioeconomic distinctions of the neighborhoods evaluated. Only 1 neighborhood in 9 evaluated (Neighborhood H) presented canopy coverage values above 30% because its urbanization process had not fully occurred, thus presenting a high percentage of the remaining natural forest matrix. Only one neighborhood evaluated (Neighborhood F) had all residences no more than 300 m from a public green area.

Conclusions There is a need for public authorities to assess the implementation and maintenance of urban forests in the municipality, aiming to increase them significantly. It is worth noting that when the urban environment is not planned considering the precepts of 3-30-300, its parameters can be measured using it.

Keywords

Introduction

The advancement of urbanization, especially over the last century, has promoted a significant increase in the size and density of cities (Santos 2008). In Brazil this phenomenon became more evident from the 1960s onwards with the rural exodus and the transformation of living standards concentrated mainly in the Southeast region, where 94.44% of the population lives in urban areas (IBGE 2022). This process has generated significant inequalities in access to essential services, including green areas (Ferreira et al. 2019). In this context, the need to create more resilient urban environments to the impacts of climate change becomes evident (Herzog and Rosa 2010; IPCC 2023), with urban vegetation, especially in the form of urban forests, being fundamental in mitigating these problems on a local scale (Mendes et al. 2019).

Urban forests, defined by Konijnendijk et al. (2006) as all vegetation cover located within the urban perimeter, encompass different vegetation layers, both vertical and horizontal, offering multiple local benefits. Among the main ecosystem services provided, the following stand out: thermal regulation (Nowak and McPherson 1993; Pauleit and Duhme 2000; Mendes et al. 2019); noise reduction and blocking (Santos et al. 2001; Yang et al. 2010); interception of particulates in the air (Firkowski 1990; Nowak et al. 2014; Martins et al. 2021); reduction of surface runoff and increase of water infiltration into the soil (Zhang et al. 2015; Song et al. 2020); and benefits to psychological health (Wolf et al. 2020; Moreira et al. 2022).

However, the distribution of these benefits is uneven, reflecting significant socioeconomic disparities between neighboring neighborhoods. Studies indicate marked differences in parameters such as canopy cover (Zhou et al. 2021); species richness (de Lima Neto et al. 2021); and supply of green areas (Shiraishi 2022) in neighborhoods with higher per capita income presenting better urban conditions and greater access to these services compared to those with lower income, perpetuating discrepancies in quality of life and access to ecosystem services provided by urban forests (de Lima Neto et al. 2021).

The 3-30-300 concept, proposed by Konijnendijk (2023) and based on the Nature-Based Solutions (NBS) guidelines (IUCN 2020), represents an approach to sustainable management and planning of urban forests. This strategy establishes that all residences must have visibility to at least 3 trees, neighborhoods should have at least 30% canopy cover, and a green area should be located no more than 300 m from any residence. This concept aims to improve the health and resilience of urban forests in urbanized environments, in addition to promoting human wellbeing and biodiversity (Konijnendijk 2023).

Scientific studies have explored application, linking it to mental health benefits (Nieuwenhuijsen et al. 2022) as well as to the assessment of access and preferences regarding urban forests (Koeser et al. 2024). However, the lack of scientific research on relationships with socioeconomic inequalities within cities represents a significant gap in understanding urban dynamics. Implementing the concept has the potential to enhance ecological and urban planning, promoting outcomes that benefit both human communities and natural ecosystems. Furthermore, the rule’s simplicity facilitates communication and engagement of public managers, residents, and other stakeholders, making it a viable tool for expanding urban forests and improving community management in different urban contexts (Konijnendijk 2023).

In this sense, the objective of this research was to determine whether neighborhoods with different per capita incomes in Cachoeiro de Itapemirim, a medium sized municipality in Southeastern Brazil, met the 3-30-300 guideline. We sought to test the hypothesis that higher per capita income neighborhoods would be more likely to meet the rule, meaning families in these neighborhoods are privileged with a greater quantity and quality of urban forests than families in middle or lower income neighborhoods.

Methodology

The study was carried out in the municipality of Cachoeiro de Itapemirim, in the south of the state of Espírito Santo, the Southeast region of Brazil. Located at coordinates 20°50′58′′S, 41°6′48′′W, the municipality has a total area of 864.58 km2, with the urban center spanning 40 km2. Its population, according to the Brazilian Institute of Geography and Statistics (IBGE), is 185,786 (IBGE 2022), classifying it as a municipality of medium urban concentration (IBGE 2016) with a population density of 216.23 inhabitants/km2 and an average per capita income of $616.37 USD per month (IBGE 2010).

For this study, only the urban area of the municipality was considered; that is, 40 km2, since the rural area contains fragments of native and productive forests that provide ecosystem services benefiting the quality of life of rural families. However, for this research, fragments of peri-urban natural forests in rural areas will not be considered so that this research can be replicated in all municipalities, regardless of the country.

For data collection and 100% inventory completion, 3 neighborhoods were selected (i.e., treated as experimental repetitions) from 3 different economic strata (i.e., treatments), totaling 9 neighborhoods in which the per capita income of residents over 10 years old was the parameter to differentiate the treatments and select the repetitions, that is, 3 neighborhoods with similar per capita incomes.

In this sense, the neighborhoods of the municipality were divided into 3 different classes: 3 neighborhoods with the highest per capita income of $2,103.63 USD defined in this study as A, B, C; neighborhoods with an average per capita income of $622.13 USD defined in this study as D, E, F; and neighborhoods with the lowest average per capita income of $373.93 defined in this study as G, H, I. To select the neighborhoods, the data provided by IBGE in the last census (IBGE 2010) were taken into consideration, where the demographic data was organized by neighborhood. Dollar conversion rates were applied using the exchange rate at the time of the IBGE data collection. To follow the statistical assumption of repetition, 3 neighborhoods were selected from each predefined class; the 3 neighborhoods with the highest income compared to the others were selected as high income, and 3 neighborhoods with the lowest income compared to the others were selected as low income. For the neighborhoods with average income, the average per capita income of the municipality was used as the central value, and 3 other neighborhoods with central monetary values from the total set of incomes of all neighborhoods (i.e., median) were selected to compose this class.

The neighborhoods, with different per capita incomes, selected for the experimental sampling in the municipality of Cachoeiro de Itapemirim are shown in Figure 1.

Location of the study area
Figure 1.

Location of the study area

Analysis of the 3-30-300 Theory

After establishing the data collection process, it was compared to the 3-30-300 theory (Konijnendijk 2023) guiding this work. The following are the details of the methodology used:

“3”—The methodology used consists of evaluating the number of tree individuals inventoried in the field between February and May 2023 in public areas, considering the ideal classification of 3 trees per residential lot (Konijnendijk 2023) and a maximum distance of 18 m from each lot. This metric was defined based on the Snellen chart (Hussain et al. 2006), using visual acuity of 20/60 as a parameter. Visual acuity of 20/60 refers to a person’s ability to see at 20 ft (6.09 m) or that an individual with normal vision can see at 60 ft (18.28 m), decreasing a level of visual loss or vision close to normal. This methodological choice ensures the inclusion of individuals with different visual conditions, increasing the universality of the analysis. For practical application, an 18-m buffer was generated around each lot in the neighborhoods analyzed, allowing the number of trees located within this delimitation to be counted.

The lot was used as a unit of analysis to encompass possible multiple residences in the same physical space, ensuring that all residents share the same visual perspective on tree individuals in public areas. This choice reflects the recognition of the diffuse nature of the right to a balanced environment, considered a transindividual right of an indivisible nature as defined in article 81 of Law No. 8,078/1990 (Consumer Rights Law 1990). In this way, the methodology aims to guarantee equitable access to this environmental benefit, promoting the universality of the right to trees as an essential element of urban space.

“30”—To quantify the canopy coverage of road trees and green areas in relation to the total area of the neighborhoods analyzed, two complementary procedures were implemented. The first involved the georeferencing of tree individuals located on public roads, with the precise measurement of their crowns, expressed in m2. The second consisted of the photointerpretation of satellite images acquired in 2023 using the open source software QGIS 3.22 (Free Software Foundation, Inc.; Boston, MA, USA) to identify and delimit public and private green areas. The images were processed, and from them, polygons representing the green areas were created, the dimensions of which were calculated directly in the software, also in m2.

The final analysis was based on the sum of the total area of the crowns of the tree individuals sampled on public roads and the green areas identified in each neighborhood, expressed in m2, according to the approach recommended by Konijnendijk (2023). This methodology prioritized tree canopy coverage, regardless of ownership (public or private). The ecosystem services provided by trees, such as climate regulation, improved air quality, and aesthetic and psychological benefits, transcend ownership concerns. Thus, the approach sought to capture the impact of these canopies in the urban context, reflecting their role in promoting the quality of life of residents.

“300”—To assess the distance from homes to the nearest public green area, the guideline that establishes a radius of 300 m as the ideal reference was used (Konijnendijk 2023). The analysis was carried out based on the precise delimitation of public green areas in the neighborhoods, following the precepts of the diffuse right of access to a balanced environment (Consumer Rights Law 1990). Then, a buffer with a radius of 300 m was created around the green area polygons, representing the scope of access.

Based on this delimitation, the number of private lots located within and outside the limits established by the 300-m buffer were counted, allowing the evaluation of the spatial distribution of access to public green areas in relation to residential occupation in the neighborhoods analyzed.

Results and Discussion

Trees per Household (3)

There was no difference between the neighborhoods considering the average per capita income. Although neighborhoods with higher per capita income had a higher percentage of lots with 3 nearby trees (42.1%), this did not differ statistically from the other two treatments, with 35.16% for middle income neighborhoods and 40.07% for low income neighborhoods. The values found do not show any influence of the average income of the neighborhood residents on the presence of trees near their residence, as can be seen in Figure 2, which shows the percentage of lots with 3 nearby trees for each neighborhood evaluated.

Three trees nearby (18 m) for each residence in neighborhoods with different per capita income.(ABC) High income neighborhood.(DEF)medium income neighborhood.(GHI) Low income neighborhood.
Three trees nearby (18 m) for each residence in neighborhoods with different per capita income.(ABC) High income neighborhood.(DEF)medium income neighborhood.(GHI) Low income neighborhood.
Figure 2.

Three trees nearby (18 m) for each residence in neighborhoods with different per capita income.(ABC) High income neighborhood.(DEF)medium income neighborhood.(GHI) Low income neighborhood.

It is clear that the number of trees required to meet the directive is not being met, as several lots do not have the required coverage of individuals in their vicinity. It is also possible to notice the nonsymmetry in the distribution of trees across the territory, since neighborhoods with similar numbers of individuals have different values for lots with 3 nearby trees, such as Neighborhood E (25.76%) and Neighborhood H (34.62%), with a percentage difference of 9 points.

It is worth highlighting the difficulty of uniform distribution throughout the territory of the neighborhoods. Considering that tree individuals ideally require specific sidewalk conditions for their implementation (North et al. 2017) and for the municipality in question, with notable urbanization without prior planning, it is not always possible to allocate tree individuals. However, it is the role of the public authorities to verify the places possible for implementation, and thus to do so with the aim of increasing the forest population of the neighborhood.

Canopy Cover (30%)

The public canopy coverage (or public pantry coverage) (PPC), private area canopy coverage (or private green area)(PGA), and their combined total (PPC + PGA) values are shown in Table 1. There is no statistical difference when comparing the 3 treatments (P-value > 0.05) with a coefficient of variation of 43.77%.

View this table:
Table 1.

Parameters evaluated to attest compliance with the 3-30-300 guideline for effective quality of life and provision of ecosystem services in neighborhoods with different per capita incomes in Cachoeiro de Itapemirim, Espírito Santo, Brazil. PPC (Public Pantry Coverage); PGA (Private Green Areas); ns (not significant).

It is noted that for neighborhoods with high per capita income, presented in Figure 3, we have statistically equal values ranging from 13.8% to 20.9%. The same pattern was found for neighborhoods with average per capita income, with values ranging from 11.38% to 18.22%, as can be seen in Figure 3. Although lower than those recommended by the guideline, they can be considered acceptable values. Neighborhood D is a particular case because it has a large rural area, representing a horizontal physical limit for future expansion. However, as it does not yet have any construction or apparatus that characterizes it as an urbanized environment, it was not considered when calculating the total area of the neighborhood.

Canopy coverage of neighborhoods and scope of the zone of influence of green areas for the 3 treatments.(ABC) High income neighborhood.(DEF) Medium income neighborhood.(GHI) Low income neighborhood.
Canopy coverage of neighborhoods and scope of the zone of influence of green areas for the 3 treatments.(ABC) High income neighborhood.(DEF) Medium income neighborhood.(GHI) Low income neighborhood.
Figure 3.

Canopy coverage of neighborhoods and scope of the zone of influence of green areas for the 3 treatments.(ABC) High income neighborhood.(DEF) Medium income neighborhood.(GHI) Low income neighborhood.

However, the neighborhoods with the lowest per capita income have the largest canopy area, with Neighborhood H leading with 169,238.55 m2, greater than the combined total of the other two treatments. This is because these neighborhoods have more open spaces covered by the region’s original forest matrix.

Considering the total area of the neighborhoods, adopting the use of percentage values for analysis, we found that Neighborhood H is the only one that exceeds the minimum limit imposed by the guideline of 30% of the total area with canopy coverage, with 42.68%. Another neighborhood with a percentage of tree canopy coverage close to the values determined is Neighborhood I, also belonging to the lowest per capita income treatment, with a value of 23.5% of its area covered by tree canopy. The reason for its significant area of tree canopy coverage is the same as that mentioned above for Neighborhood H.

When evaluating only the canopy coverage component present in public areas, the trend is inversely proportional, where the highest absolute numbers of canopy coverage are located in neighborhoods with higher per capita income, being 27,181.85 m2 and 22,714.66 m2 for Neighborhood A and Neighborhood B, respectively. These values represent 5.66% and 6.49% of the total area of the neighborhoods, being the highest percentage values found for the neighborhoods evaluated. While the average PPC in neighborhoods with the highest per capita income is 5.61%, in neighborhoods with medium and low per capita income, the values are 2.88% and 2.60%, respectively. This pattern presents a socio-environmental advantage of the public road component of urban forests in the treatment of higher per capita income in Cachoeiro de Itapemirim, which can be translated according to de Lima Neto et al. (2021) in the term “green privilege”: in areas with greater vegetation cover, there will be better conditions of wellbeing, which brings a focus on socioeconomic characteristics where there is the objectification of afforestation and full privileges of its implementation for the wealthiest classes.

Furthermore, this green privilege is also found in other countries in the South American region. For example, Shirashi (2022), when analyzing Cali in Colombia, also found a more significant presence of residential canopy coverage in neighborhoods with higher per capita income compared to those with lower per capita income.

As stated by Harvey (2014) and explained by de Lima Neto et al. (2021), specific public policies can deliberate arrangements susceptible to the adoption of measures that benefit more influential social classes, in this case, resulting in the implementation of the road component of the urban forest in Cachoeiro de Itapemirim. Analyzing socioeconomic and sociospatial segregation separately is unthinkable, as both explain two facets of the same problem (de Lima Neto et al. 2021).

Trees offer numerous benefits to local citizens, but reaching the target of 30% canopy coverage can be difficult in densely built areas (Konijnendijk 2023). This effect can be aggravated by a policy led by a high number of suppressions to the detriment of plantings, which harms the balance of individuals that make up the urban forests of a given location.

Meters to a Public Green Area (300)

As shown in Table 1, there is a discrepancy in the distribution of public squares in the city under study, with a concentration in neighborhoods with higher per capita income. However, statistical analysis showed no difference between the averages of neighborhoods with different per capita incomes (P-value > 0.05), with a coefficient of variation of 68.24%.

In the neighborhoods with the highest per capita income, there were 11 public green areas of varying sizes. In total, 88.94% of the lots in the territorial limits of the neighborhoods with the highest per capita income were located 300 m from a public square.

The only neighborhood in the high per capita income treatment that does not have a public square within its territorial limits is Neighborhood C. However, it is almost completely covered by a square in Neighborhood F (belonging to the medium per capita income treatment). However, this coverage was not taken into account to stratify the neighborhoods into different classes.

In neighborhoods with a medium per capita income, at least one square was found in each neighborhood. In one of them, Neighborhood F, all residences were located at a maximum distance of 300 m from a public green area. In these neighborhoods, 73.84% of the lots present are located at the distance recommended by the guideline, which can be considered a satisfactory result but with room for improvement since green areas have outstanding importance for the quality of human life, performing a considerable ecological function and mainly subsidizing activities related to coexistence, highlighting their social function (da Silva et al. 2016).

Only one public green area was found for the neighborhoods with the lowest per capita income. This square is located in Neighborhood H, covering 47.74% of the residences in the neighborhood. The other 2 neighborhoods had no public squares within their territorial limits. Therefore, for the neighborhoods with the lowest per capita income, we had the lowest average number of residents within the maximum limit of 300 m from a public green area, with the quantity being 15.91%. The results found for neighborhoods with a medium per capita income are similar to those of Wüstemann and Kalisch (2016), who found that 74% of the lots were located within a maximum radius of 300 m of a green area; this number increased to 93% when considering an area of 500 m of the green area in Germany.

It is essential to pay attention to the results of this research. According to Kingsley and EcoHealth Ontario (2019), the absence of an adequate public green area may negatively influence the health of the population in terms of environmental quality, since these green areas must be of high quality, offering the most varied services and fulfilling their social function as a meeting place and as a place for children’s entertainment (possible due to the shade provided by the vegetation)(Iojă et al. 2014; Konijnendijk 2023).

The result is in line with that found by Shiraishi (2022), who, when analyzing the urban forests of Cali, Colombia, concluded that low income communities also have less access to public parks. It is worth noting that for places where it is not feasible to implement the 3-30-300 precepts, progress can be measured using the same parameter (Konijnendijk 2023). This is valid for places that did not have planned urbanization, as is the case of the study area adopted.

Conclusion

In conclusion, this study analyzed how different neighborhoods in Cachoeiro de Itapemirim align with the 3-30-300 rule. None of the neighborhoods— regardless of per capita income—fully met all 3 criteria. For instance, no area achieved the target of 3 individual trees per lot, and only Neighborhood H surpassed 30% canopy coverage, likely due to its partial urbanization and remaining forest fragments. Neighborhood F, with a medium income level, was the only one where all residences were located within 300 m of a public green space. In contrast, Neighborhoods C, G, and I had no public green areas within their limits.

These findings partially support the hypothesis that higher income neighborhoods benefit from greater access to urban green spaces compared to lower income areas. While no substantial differences were observed in the quality of vegetation, disparities in quantity—such as canopy coverage and green space proximity—were evident.

These results underscore the need for municipal authorities to develop equitable strategies for expanding and maintaining urban green areas, ensuring that access to nature is not determined by income level.

Nonetheless, this study has limitations. One major constraint is the difficulty of adapting international guidelines—such as the 3-30-300 rule—to the Brazilian urban context, which differs in socio-environmental conditions, urban planning practices, and governance structures. Future research should consider localized benchmarks and integrate community specific indicators to enhance the accuracy and relevance of assessments.

In light of these considerations, it is essential that public policies promote inclusive and context sensitive urban forestry initiatives, guaranteeing that all residents, regardless of socioeconomic status, have equitable access to healthy and functional green infrastructure.

Conflicts of Interest

The authors reported no conflicts of interest.

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In this Issue
Arboriculture & Urban Forestry (AUF)
Arboriculture & Urban Forestry (AUF)

Vol. 51, Issue 6

November 2025

Table of Contents Table of Contents (PDF) Index by author
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Keywords

Canopy Cover, City Trees, Green Areas, Injustice