64
supply, resulting in a normalized score ranging from 0 to 10. The final access score
also incorporates the assessment of ES supply conducted in the previous step, hence
the access score could also be interpreted as a ‘reduced’ supply score that accounts
for issues related to the spatial distribution of service benefitting areas and their
accessibility, when relevant to the specific ES under investigation.
The results of the access analysis vary depending on the ES. For carbon storage
and air pollution removal, which are assumed to be equally distributed over the
sample areas, no further spatial analysis is needed. For carbon storage, the same
score is assigned to all areas, while for air pollution removal a score equal to the
supply score is used to characterise the different sample areas. For the ES that produce local benefitting areas characterised by proximity to the providing areas, the
results of the access analysis are shown in Fig. 6.3. For both micro-climate regulation and noise reduction, the ranking of the sample areas based on the access score
is different than the ranking based on the supply score
6.3.3 Assessing ES Demand
According to Wolff et al. (2015), depending on the type of ES, the demand can be
assessed either based on direct use, or based on preferences for a desirable level of
ES supply. In the case of regulating services, many assessments adopts the latter
method, defining the desirable level through indicators of vulnerability (Wolff et al.
2015). Here we refer to Kazmierczak (2012), who identified four main vulnerable
groups based on criteria of poverty, diversity (presence of foreigners), and age, specifically distinguishing children (0–4 years old) and elderly (above 65 years old). In
the case study, the poverty indicator was not considered due to the lack of disaggregated spatial data. The other three criteria were assessed based on census data
provided by the local administration. The number of residents, children, elderly, and
Fig. 6.3 Assessment of access to two proximity-dependent ES in the four sample areas
6 Towards Equity in the Distribution of Ecosystem Services in Cities
supply, resulting in a normalized score ranging from 0 to 10. The final access score
also incorporates the assessment of ES supply conducted in the previous step, hence
the access score could also be interpreted as a ‘reduced’ supply score that accounts
for issues related to the spatial distribution of service benefitting areas and their
accessibility, when relevant to the specific ES under investigation.
The results of the access analysis vary depending on the ES. For carbon storage
and air pollution removal, which are assumed to be equally distributed over the
sample areas, no further spatial analysis is needed. For carbon storage, the same
score is assigned to all areas, while for air pollution removal a score equal to the
supply score is used to characterise the different sample areas. For the ES that produce local benefitting areas characterised by proximity to the providing areas, the
results of the access analysis are shown in Fig. 6.3. For both micro-climate regulation and noise reduction, the ranking of the sample areas based on the access score
is different than the ranking based on the supply score
6.3.3 Assessing ES Demand
According to Wolff et al. (2015), depending on the type of ES, the demand can be
assessed either based on direct use, or based on preferences for a desirable level of
ES supply. In the case of regulating services, many assessments adopts the latter
method, defining the desirable level through indicators of vulnerability (Wolff et al.
2015). Here we refer to Kazmierczak (2012), who identified four main vulnerable
groups based on criteria of poverty, diversity (presence of foreigners), and age, specifically distinguishing children (0–4 years old) and elderly (above 65 years old). In
the case study, the poverty indicator was not considered due to the lack of disaggregated spatial data. The other three criteria were assessed based on census data
provided by the local administration. The number of residents, children, elderly, and
Fig. 6.3 Assessment of access to two proximity-dependent ES in the four sample areas
6 Towards Equity in the Distribution of Ecosystem Services in Cities