Quantifying potential contributions of green facades to environmental justice: a case study of a quarter in Berlin [original]

Vol.:(0123456789)

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https://doi.org/10.1007/s11252-022-01235-6

Quantifying potential contributions ofgreen facades toenvironmental

justice: acase study ofaquarter inBerlin

EstherS.Felgentreff1 · DavidCochius1· ThomasNehls2· Jan‑HinrichW.Quandt1· EmilJ.Roesch1

Accepted: 12 April 2022

Abstract

The potential of green facades (GFs) to enhance environmental justice (EJ) has not been quantified so far. EJ in Berlin,

Germany is assessed by the core indicators (1) noise pollution, (2) air pollution, (3) bioclimatic stress, (4) provision of green

space and (5) social status. Most of the inner city is rated “poorly” in one or multiple indicators. Based on literature and

spatial data, status quo and target values are determined for indicators (1)-(4) for an exemplary, highly burdened quarter in

Berlin. It is assessed if and how much GFs could potentially improve current EJ levels. The improvements due to GFs to reach

target values are assessed in % for day/night and indoor/outdoor settings. It can be shown that installing GFs would improve

statuses of the four indicators to different extents, with the biggest enhancement found regarding indicator (3) for indoors

at daytime: 52%. Determining factors for the EJ improvement potential of GFs need to be further assessed. This feasible

method for increasing the amount of urban green can be helpful for improving life in highly burdened quarters. Therefore,

from the point of view of EJ, large-scale implementation of GFs in urban areas is recommended.

Keywords Environmental justice· Green facades· Noise pollution· Air pollution· Heat stress· Urban greenspace

Introduction

Anthropogenic activities are altering ecosystems on earth to

an unprecedented extent (Foley 2005; Steffen etal. 2015),

generating environmental burdens that are unequally dis-

tributed across society and especially counteracting the

well-being of vulnerable groups (MEA 2005; UNDP 2014;

UNEP 2019).

Developing as a grassroot movement in the USA in the

1980ies (Bullard and Johnson 2000), the concept of envi-

ronmental justice (EJ) now serves as a framework for under-

standing and addressing the issue of unequally distributed

environmental burdens. The US’ Environmental Protection

Agency (EPA 1998, p. 2) defines it as “[t]he fair treatment

and meaningful involvement of all people regardless of race,

color, national origin, or income with respect to the develop-

ment, implementation, and enforcement of environmental

laws, regulations, and policies. Fair treatment means that no

group of people […] should bear a disproportionate share

of the negative environmental consequences resulting from

[human activities]”. Especially in urban areas, people with

low income and a low social status index experience dis-

proportionate exposures to environmental stressors by often

being constrained in the choice of their living environment,

leading to adverse health effects and lower quality of life

(Corburn 2017; Allen etal. 2019).

This topic is also gaining recognition in Germany (UBA

2009), where in recent years the state of Berlin developed

an approach to map EJ by assessing the following five core

indicators, categorizing the status of each indicator as low,

medium, or high: (1) noise pollution, (2) air pollution, (3)

bioclimatic stress, (4) provision of green space and (5) social

status (SenStadtUm 2015a). Acknowledging that it is also

possible to assess EJ differently than through these indica-

tors, we adopted Berlin’s concept for this paper. It is not the

aim of this paper to question, discuss and improve this EJ

concept.

An approach to assess patterns of EJ more differentiated

than the three classes used by Berlin’s senate is proposed

by Lakes etal. (2013), who use the example of Berlin to

develop an index to quantify the relative degree of EJ of

* Esther S. Felgentreff

esther.felgentreff@posteo.de

1 Institute ofEcology, Technische Universität Berlin, Berlin,

Germany

2 Institute ofEcology, Chair forEcohydrology andLandscape

Evaluation, Technische Universität Berlin, Berlin, Germany

/ Published online: 4 May 2022

Urban Ecosystems (2022) 25:1417–1430

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residential areas on the level of planning units. We however

decided to use the official calculations of the Berlin Senate

as a baseline here.

In line with this, the World Health Organization (WHO)

(2011, 2018b) outlines environmental noise, and in par-

ticular road traffic noise, as one of the top environmental

risks to health, linking it to sleep disturbance, cognitive

impairment, or cardiovascular disease. In Germany, over

20% of the inhabitants within urban areas are exposed to

noise levels harmful to health (EEA 2020). Further, air

pollution is estimated to be related to 4.2 million prema-

ture deaths worldwide in 2016, even though the emitted

amount has been declining for about two decades (WHO

2018b). In this regard, particulate matter ≤ 2.5µm (PM2.5)

and nitrogen dioxide (NO2) emitted by energy production,

traffic, or industry are two of the main health threaten-

ing substances, being associated with increased bronchi-

tis symptoms, reduced lung function, cardiovascular and

respiratory diseases, or different kinds of cancer (WHO

2018b). The third indicator, bioclimatic stress, comprises

the sum of all climatic factors that influence the thermo-

physiological condition of humans, including air tem-

perature, wind velocity, air humidity, and the incoming

solar radiation, referring to a state of thermal discomfort.

This can lead to so-called “heat stress” or “cold stress”

(Matzarakis and Mayer 1996; Matzarakis and Amelung

2008; Mayer etal. 2008; SenStadtUm 2011, 2015a),

whereby here and in the following, only heat stress will

be considered. Due to the urban heat island effect, this is

a problem especially in urban areas (Dimoudi etal. 2013).

Heat stress has several adverse health effects and causes

numerous deaths (Matzarakis and Mayer 1996; Robine

etal. 2008; Gabriel and Endlicher 2011). Particularly the

lack of nightly indoor cooling leading to partial sleep dis-

order is thought to play a major role (Nicholls etal. 2008;

Kenny etal. 2019). Additionally, the provision of green

space comes into focus as green urban spaces are highly

valued for their recreational purposes, allowing a certain

compensation of the environmental burdens experienced

in densely built areas or serving for environmental educa-

tion. Today, in the context of urbanization and climate

and demographic change, there is even more pressure

on greenspaces within cities to fulfil multiple purposes

with as little cost as possible (Garske 2011). While ver-

tical green spaces are obviously different to horizontal

green spaces, it can be expected that vertical ones pro-

vide important ecosystem services especially in densely

populated areas with deficient green space provision. As

in these quarters, new parks are unlikely to be built, it

is worthwhile to assess the potential of green facades

(GFs) as an implementable option to improve the living

conditions.

Eventually, the fifth indicator social status is not a

direct environmental burden and cannot be thought to be

improved by a change in nearby green space. However,

it demonstrates high vulnerability when coming together

with one or more of the other four indicators and puts them

in the context of EJ.

In the assessment of EJ in Berlin, most of the inner

city is rated “poorly” in one or multiple of the indica-

tors, emphasizing the need for structural improvement

measures. Research suggests that vertical greenery sys-

tems (VGSs) can positively influence indicators (1)-(4)

– whereby indicator (4) needs to be discussed in further

detail (Ottelé etal. 2010; Wong etal. 2010a; Hoelscher

etal. 2016; Radić etal. 2019) –but not the indicator (5),

social status.

This study aims at quantifying the maximum potential

of green facades as one type of VGSs to enhance EJ as this

has not been done so far. Contrary to living walls (LWs)

which consist of vertical panels or geotextile felts with a

growing medium for low growing plants which typically

develop on a horizontal base, GFs usually apply climber

plants planted at the base of a building, using its facade or

trellises or ropes as growing support (Pérez etal. 2011;

Perini etal. 2013). There exist different nomenclatures

for facade greening techniques, but this study follows

the nomenclature where “VGS” is the generic term for

all types, and “GFs” and “LWs” are the two subtypes as

described by Radić etal. (2019).

LWs, however, require considerably higher installation

and maintenance costs than GFs, making their large-scale

implementation in low-income areas infeasible (Perini and

Rosasco 2013). Regarding the intention of improving EJ,

this study focuses on GFs, due to their easier implemen-

tation, higher data availability and lower structural com-

plexity, which allows for better comparability between the

investigated factors. Fear of facade damage due to VGS,

which accounts especially for GFs, is a misconception, as

plants growing and rooting directly at a facade only pose

a risk if walls are already damaged (Ottelé 2011).

No matter which greening system is used, it should be

noted that greening the whole facade area is not realistic.

This is not only due to architectonic features such as win-

dows, balconies, etc. but also because of sustainability

concerns such as limited water availability (Pearlmutter

etal. 2021).

In this study, based on literature and spatial data, status

quo and target values to reach EJ are determined for the

indicators (1)-(4) for a highly burdened exemplary quar-

ter in Berlin. We purposely exclude indicator (5), social

status, from the calculations, as installing a GF will obvi-

ously not affect the social status. However, we choose an

exemplary quarter with a low social status index, as it is

1418 Urban Ecosystems (2022) 25:1417–1430

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the limited adaptability capacities of these communities

that require discussions of EJ but also proactive reductions

of environmental burdens.

Subsequently, the maximum potential contribution of GFs

to achieve the set target values is assessed and quantified for

the examined indicators.

Materials andmethods

An exemplary street in the district Berlin-Gesundbrunnen

was chosen as the case study site. In the Environmental Jus-

tice Atlas, this site is classified as highly burdened, as it is

categorized as medium burdened by noise pollution, highly

burdened by air pollution and heat stress, has low access to

green space, and a low social status index (SenSW 2015).

In this area, residential buildings from the Wilhelminian

period built in perimeter block type are prevalent. This type

is the most common in Berlin, and roughly a third of Berlin’s

residents live in such buildings (SenS 2011). The area and

these buildings can therefore be considered representative of

a highly environmentally burdened, however typical Berlin

housing situation.

The example buildings presumed for this study are 22m

high, 10m wide and have four storeys. We analyzed a street

canyon with two rows of such buildings with the ratio of

height to width = 1, meaning that the street width is 22m.

A 40m long section of this canyon is looked at, i.e., four

houses as described above on either side of the street.

The ‘status quo’ was investigated by collecting status quo

values for the four indicators based on available information

from the municipality of Berlin. We then assume a poten-

tial ‘greened case’ in which this canyon section is greened

hypothetically. 70% of the street-facing walls of the example

buildings are assumed to be eligible for greening, while the

remaining 30% are windows and doors (Loga etal. 2011).

The area of the greened facades for the two sides of the street

is therefore 40mx22mx0.7 × 2 = 1232 m2. The utilized

plant Parthenocissus tricuspidata (Boston ivy) is commonly

used on facades (Preiss 2013). This study refers to a period

in which the plant bears leaves. It is assumed that no street

trees are present in the section to avoid mixing the effects of

trees and the GF. For each indicator, values for this predicted

‘greened case’ were determined.

Then, ‘target values’ which describe a desirable target

state of these indicators were researched. It is assumed that

if the status quo value equals or exceeds the target value,

EJ would be “achieved”. The differences between status

quo and target values were determined as the "gaps" which

must be closed to “achieve” EJ. The difference between the

status quo value and the predicted greened case contribu-

tion is referred to as the ‘EJ-improving potential of GFs’,

calculated as contribution in % of the full "gap", described

above. Wherever possible, a distinction was made between

day and night as well as between indoor and outdoor.

Noise pollution

The evaluation of the EJ improving potential of GFs regard-

ing the indicator noise pollution is based on a literature

review. Regarding attenuation effects, the focus in this study

lies on urban road traffic noise with relatively low speeds

and a main frequency spectrum at low to middle frequen-

cies around 500–1000hertz (Hz) (Feldmann and Volz 2000;

Nilsson and Forssén 2013), as this is the most dominant

source of urban noise and its associated adverse health

effects (van Kempen and Babisch 2012; EEA 2020). The sta-

tus quo values for the exemplary quarter were derived from

the maps 07.05.1Strategische Lärmkarte LDEN Straßen-

verkehr (SenSW 2017) and 07.05.2 Strategische Lärmkarte

LNight Straßenverkehr (SenSW 2017) of the geodata portal

FIS-Broker. The dominant outdoor sound pressure levels at

the facades are 75 A-weighted decibel (dB(A)) at day and

65dB(A) at night (data for 2017). For indoor sound pres-

sure levels, a default attenuation of -21dB(A) compared to

outdoors was assumed, as described in the Good Practice

Guide on Noise Exposure and Potential Health Effects (EEA

2010). This results in status quo indoor sound pressure val-

ues of 54dB(A) at day and 44dB(A) at night.

These values were compared with target values for envi-

ronmental noise set by the WHO to prevent adverse health

effects, recommending outdoor sound levels not exceeding

55dB(A) at day and 45dB(A) at night. For indoor sound

levels, target values of 35dB(A) at day and 30dB(A) inside

bedrooms at night are provided for continuous background

noise (WHO 1999, 2009, 2018a). For the noise reduction

potential of GFs, existing research on noise attenuation

effects of VGSs was examined to derive precise values in

db(A). These values relate to the attenuation in sound pres-

sure due to installing GFs.

The value for outside noise reduction by GFs found in

literature with best comparability to the depicted exemplary

conditions in this work was modelled for a 400m long street

canyon with 16m width and 20m high fully vegetated build-

ing facades on both sides. The noise immission was deter-

mined at a height of 2m. Outdoor sound pressure levels

are stated to be around 1.5dB(A) lower with this type of

VGS (Feldmann and Volz 2000). The indoor noise attenua-

tion potential of GFs refers to an in-situ measurement of its

acoustic insulation capacity according to the UNE-EN ISO

140-5 standard, conducted at a cubicle of 3m width, length,

and height. A double-skin GF was installed by attaching a

simple wire mesh covered with a 0.2–0.3m thick layer of

vegetation with P. tricuspidata parallel to the wall of the

cubicle in a distance of 0.25m. For the measurements inside

1419Urban Ecosystems (2022) 25:1417–1430

1 3

the cubicle, noise was emitted by a speaker 2.3m in front

of the facade at a height of 1.2m. In this study, an increased

sound insulation of 1dB(A) for traffic noise indoors was

found (Pérez etal. 2016).

Air pollution

Status quo values for the key indicator air pollution were

taken from the FIS-Broker map Umweltgerechtigkeit:

Kernindikator Luftbelastung (Umweltatlas) on EJ (annual

means for 2009) (SenStadtUm 2015b). Therefore, con-

centrations of 32.11µg/m3 NO2 and 23.44µg/m3 PM2.5,

respectively, are used for both day and night. To determine

target values for an ‘environmental just’ status, threshold

values set by the European Union and the WHO were com-

pared to the target values in the EJ monitoring of Berlin

(WHO 2018b; SenUVK 2019; UBA 2019). The lowest

set values found in one of the three previously mentioned

sources were taken as target values for this study. These

are 17.1µg/m3 NO2 from the EJ report and 10µg/m3 for

PM2.5 from the WHO (2018a).

Pugh etal. (2012) calculated specific values for the

reduction of NO2 and PM10 concentrations by green walls

using a model simulation. The simulation took place in

central London, UK which has a similar climate to Berlin

(Pfadenhauer and Klötzli 2014). Reduction potentials as

shown in the graphs are 2.7µg/m3 for NO2 and 3.3µg/m3

for PM2.5. Pugh etal. (2012) do not consider the reduction

of PM2.5, but as other research suggests, it can be expected

to even exceed PM10 reduction (Vardoulakis etal. 2003;

Litschke and Kuttler 2008; Ottelé etal. 2010). We how-

ever assumed that PM2.5 and PM10 do not differ, so that the

calculation of Pugh etal. (2012) could be used. Indoor air

pollution reduction by GFs has hardly been researched so

far, hence no values are calculated for this part of the study.

Heat stress

Buchin etal. (2016) developed an equation to calculate the

indoor temperature at time t + Δt of a building:

Tin(t) = indoor temperature at time t; Tout = outdoor tem-

perature;

λ = solar temperature elevation constant;

I = incoming horizontal radiation; τ = time constant

They adjusted Eq.(1) to be specifically for the sec-

ond floor of a west exposed residential Wilhelminian

building, being partly shaded by other buildings (i.e., not

(1)

in(t+Δt)=Tout +𝜆I+(Tin(t)−Tout −𝜆I)exp(

−Δt

𝜏)

free-standing). They modelled the building in the simu-

lation program EnergyPlus, entered climate data, and

simulated indoor temperatures. With these, Eq.(1) was

parameterized and calibrated, yielding values for the two

parameters τ and λ (τ = 4.115*105s, a measure for the

thermal inertia of the building, and λ = .025 m2K/W,

representing the temperature elevation due to solar gains)

(Buchin etal. 2016).

Buchin etal. (2016) shared the weather dataset they

used for their calculations with us, consisting of hourly

measured temperatures in Potsdam from the Deutsche Wet-

terdienst (DWD) and global horizontal short-wave radia-

tion data from a weather station at Technische Universität

Berlin in Berlin-Steglitz (Buchin etal. 2016). Here, data

from summer 2003 was used, which was a record hot sum-

mer in many parts of Europe (Robine etal. 2008) and dur-

ing which Berlin also experienced several consecutive hot

days. Indoor temperatures were calculated using Eq.(1).

The heating period during which the indoor temperatures

are determined by a heating system was set to end April 30

and begin October 1 (Mieterschutzbund Berlin e.V. n.d.).

The observed period was set to May 1–September 30, and it

was assumed that there was no air conditioning or other

cooling measure. Indoor temperatures during the heating

period were assumed to be 22°C during day and 18°C

during night, meaning that at midnight on April30, 18°C

was the present indoor temperature when calculations of

the indoor temperatures began on May 1, 1:00 am. The

temperatures were calculated in hourly resolution. Then,

daily and nightly mean values were calculated for each

day from hourly data, from 6 am to 9pm and 10pm to 5

am, respectively. The temperature profiles are visualized

in graphs. The highest of these mean values was taken as

the status quo value.

For daytime, the WHO’s Guidelines on Healthy Living

recommend 25°C maximum in temperate regions (WHO

2018c). Different approaches to setting a target value

for night-time temperature exist. Here, it is set to 24°C

(CIBSE 2006 as cited in Kenny etal. 2019).

No effect of the GF on the outdoor temperatures is

assumed (Hoelscher etal. 2016). Hoelscher etal. (2016)

found over 80% of the cooling effect on a building to be

caused by shading, especially during hot summer days.

As the evapotranspiration cooling effect mainly depends

on water supply, only shading is considered. It should be

noted that the actual effect can be assumed to be even

higher than calculated here. This shading effect of the

plants is approximated by lowering the incoming solar

radiation I by an additional factor β (2).

(2)

in(t+Δt)=Tout +𝛽𝜆I+(Tin(t)−Tout −𝛽𝜆I)exp(

−Δt

𝜏)

1420 Urban Ecosystems (2022) 25:1417–1430

1 3

Tin(t) = indoor temperature at time t; Tout = outdoor tem-

perature;

ß = additional factor to account for facade greening;

λ = solar temperature elevation constant;

I = incoming horizontal radiation; τ = time constant

Through the 30% of the facade which is uncovered, the

solar radiation arrives unfiltered, while on the remaining 70%

it is reduced by the plants. Transmissivity of P. tricuspidata

was calculated to be 0.386 based on Monsi (2004) and own

measurements forleaf area index (leaf surface per wall surfa-

cae, LAI) (1.9) and the attenuation coefficientk (0.5). For that,

photos of the facade greening were taken in front of a reference

plate mounted between the plants and the wall. Leaves were

cut and its areas determined using a xerox and image analysis.

The leaf areas were divided by the reference plate area to cal-

culate vertical LAIs. The attenuation coefficients were derived

from analyses of images regarding the leaf orientations.

The radiation still arriving at the facade is described with

the factor β:

With this, the indoor temperatures for the greened case

were calculated. The EJ-improving potential of GFs was

determined as described in 2.1. As the objective was to

study the full potential of GFs, the maximum improve-

ment was used.

Provision ofgreenspace

The data on greenspace and population density in the

exemplary quarter was downloaded from the Berlin geo-

data portal FIS-Broker. It includes the maps Grünan-

lagenbestand Berlin (einschließlich der öffentlichen

Spielplätze) (SenSW 2020) and Einwohnerdichte 2018

(Umweltatlas) (SenSW 2019) as well as the encoded

attribute data on the area extent of greenspaces, popula-

tion density in each block.

A guideline for sufficient greenspace supply and a defi-

nition of the desired proximity of greenspace to people’s

homes is provided by the Senate Administration for Urban

development and housing of Berlin. It was published in 1995

as Versorgung mit öffentlichen, wohnungsnahen Grünan-

lagen and still acts as a measure of greenspace supply in

Berlin, e.g. for its targets for EJ (SenStadtUm 2015a) and

provides the values for Table1. It should be noted that the

guideline differentiates between two types of accessibility,

smaller greenspaces close to residents’ homes and larger

greenspaces within the area. Vertical greenspaces are not

explicitly excluded in this guideline. The different functions

fulfilled by horizontal versus vertical green spaces are com-

pared in the discussion.

(3)

𝛽=0.3 ∗1+0.7 ∗0.368 =0.5702

The data was downloaded and processed using the

software QGIS (v.3.12.2-București). As preparation

for analysis, an exemplary block was chosen and a line

shapefile created, representing the modelled GF. Then,

buffers of 500m and 1000m were created to delineate

the catchment areas as described in Table1. The mini-

mum size requirements for the area of greenspaces to

be counted were not applied, as except for one big park,

they would have concluded that there was no greenspace

in the study area and made it impossible to measure

anything. As the GF covers an area smaller than the pro-

posed minimum values and as in this study, small-scale

effects are investigated, these minimum values were

neglected.

Consequently, the number of inhabitants and the area

of greenspace within 500m and 1000m were calculated

from the aforementioned attribute data. Equations(4)–(6)

were applied to calculate EJ-improving potential of GFs.

G = Area of greenspace within a 500m radius; I = Number

of inhabitants within a 500m radius;

S = Status quo greenspace per capita ratio in m

person

G = Area of greenspace within a 500m radius; F = Area

of GF in greened case; I = Number of inhabitants within

a 500m radius;

R = Greenspace per capita ratio in m

person

in greened case

R = Greenspace per capita ratio in m

person

in greened case;

S = Status quo greenspace per capita ratio in m

person

;

T = Target value in m

person

; EJIP = EJ-improving potential

of GFs

(4)

(5)

G+F

(6)

R−S

T−S

EJIP

Table 1 Provision of green space rate per capita for open spaces in

the catchment areas, a planning basis according to the Senate Admin-

istration for Urban development and housing of Berlin

Greenspace close

to home Greenspace close

to residential area

Minimum size 0.5ha 10ha

Reference value 6m

resident

Maximum distance 500m 1000m

1421Urban Ecosystems (2022) 25:1417–1430

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