55
Our analysis focused on the expected benefits of brownfield regeneration scenarios in terms of improved cooling effect by vegetation and enhanced opportunities
for nature-based recreation: two of the most critical issues for citizens’ wellbeing in
Trento. The results of the two ES assessments were expressed through beneficiarybased indicators. Several authors have highlighted limitations and potential drawbacks associated to the use of both biophysical measures and monetary valuations
of ES in real-life decision-making contexts (Bagstad et al. 2014; Ruckelshaus et al.
2015; Saarikoski et al. 2016; Olander et al. 2018). To this respect, beneficiary-based
indicators that explicitly link the provision of ES with changes in human wellbeing
are considered a promising way to integrate ES knowledge into decision-making
processes (Geneletti et al. 2016; Olander et al. 2018) and to communicate ecological knowledge to planners and politicians primarily focused on social and economic
objectives (von Haaren and Albert 2011; Schleyer et al. 2015), as is often the case
in urban planning.
Multi-criteria analysis proved to be an effective tool to make the results of multiple ES assessments usable by decision-makers. On the one hand, it allows multiple
sources of information and value dimensions to be combined, disregarding the indicators that are used to express them (Saarikoski et al. 2016). On the other hand, it
offers a structured way to explore different stakeholder perspectives and related
objectives, balancing diverse and sometimes competing interests in a rational and
transparent way (Adem Esmail and Geneletti 2018). In the analysed case, all scenarios had a positive effect on the provision of both ES, but potential trade-offs
could be related to competing uses of the brownfields (Kain et al. 2016) or to the
costs of intervention (Koschke et al. 2012). Starting from the described multicriteria analysis, a more complete decision support system could be built by integrating other relevant criteria about costs and benefits of planning scenarios.
As already demonstrated in other applications (Sanon et al. 2012; Grêt-Regamey
et al. 2013; Kremer and Hamstead 2016), the results of the analysis show how priorities may shift with changing policy goals. The ranking is sensitive to the relative
weights assigned to the two ES and related categories of beneficiaries and, eventually, the best scenario depends on decision-makers’ orientations about specific
planning objectives. This highlights the need for a strategic approach to ES, still
mostly lacking in current planning documents (Cortinovis and Geneletti 2018b).
Clear objectives and targets for ES provision would help to identify the values
against which the effectiveness of planning actions should be measured, and to
highlight the relevance of ES knowledge within the planning process. Information
about ES beneficiaries allows understanding the social implications of planning
decisions, particularly in terms of equity and environmental justice, as discussed in
the next chapter.
5.5 Lesson Learned and Conclusions
Our analysis focused on the expected benefits of brownfield regeneration scenarios in terms of improved cooling effect by vegetation and enhanced opportunities
for nature-based recreation: two of the most critical issues for citizens’ wellbeing in
Trento. The results of the two ES assessments were expressed through beneficiarybased indicators. Several authors have highlighted limitations and potential drawbacks associated to the use of both biophysical measures and monetary valuations
of ES in real-life decision-making contexts (Bagstad et al. 2014; Ruckelshaus et al.
2015; Saarikoski et al. 2016; Olander et al. 2018). To this respect, beneficiary-based
indicators that explicitly link the provision of ES with changes in human wellbeing
are considered a promising way to integrate ES knowledge into decision-making
processes (Geneletti et al. 2016; Olander et al. 2018) and to communicate ecological knowledge to planners and politicians primarily focused on social and economic
objectives (von Haaren and Albert 2011; Schleyer et al. 2015), as is often the case
in urban planning.
Multi-criteria analysis proved to be an effective tool to make the results of multiple ES assessments usable by decision-makers. On the one hand, it allows multiple
sources of information and value dimensions to be combined, disregarding the indicators that are used to express them (Saarikoski et al. 2016). On the other hand, it
offers a structured way to explore different stakeholder perspectives and related
objectives, balancing diverse and sometimes competing interests in a rational and
transparent way (Adem Esmail and Geneletti 2018). In the analysed case, all scenarios had a positive effect on the provision of both ES, but potential trade-offs
could be related to competing uses of the brownfields (Kain et al. 2016) or to the
costs of intervention (Koschke et al. 2012). Starting from the described multicriteria analysis, a more complete decision support system could be built by integrating other relevant criteria about costs and benefits of planning scenarios.
As already demonstrated in other applications (Sanon et al. 2012; Grêt-Regamey
et al. 2013; Kremer and Hamstead 2016), the results of the analysis show how priorities may shift with changing policy goals. The ranking is sensitive to the relative
weights assigned to the two ES and related categories of beneficiaries and, eventually, the best scenario depends on decision-makers’ orientations about specific
planning objectives. This highlights the need for a strategic approach to ES, still
mostly lacking in current planning documents (Cortinovis and Geneletti 2018b).
Clear objectives and targets for ES provision would help to identify the values
against which the effectiveness of planning actions should be measured, and to
highlight the relevance of ES knowledge within the planning process. Information
about ES beneficiaries allows understanding the social implications of planning
decisions, particularly in terms of equity and environmental justice, as discussed in
the next chapter.
5.5 Lesson Learned and Conclusions