68
was found in both types of plan, and the analysis of spatial plans revealed a wide
variety of tools for implementation.
However, the proposal of ES-related measures is rarely supported by an adequate
knowledge base and analysis, which may eventually undermine their effectiveness.
The general idea that “more green will do some good” seems to guide the inclusion
of ES-based actions in current plans, where critical decisions about the design and
the location of interventions are seldom justified by the analysis of the expected
outcomes and the distribution and vulnerability of the potential beneficiaries. As
such, the most common measure found in climate adaptation plan was the enhancement of green areas, a typical strategy that planners put in place for a variety of
purposes that go beyond climate change adaptation. Moreover, while a set of ES,
including regulating ES related to climate change adaptation, are widely acknowledged also in comprehensive spatial plans, others are hardly considered. This may
lead to trade-offs unconsciously generated by planning decisions and, ultimately, to
a loss of important but underestimated ES.
Chapter 4 illustrated how an ES model can be developed with the aim of supporting urban planning decisions. Specifically, it describes the development of a
spatially- explicit model to map and assess micro-climate regulation provided by
different typologies of urban green infrastructures. The model is based on an extensive review of the scientific literature, and summarises the main findings in a way
that is accessible and usable by planners. Tree canopy coverage, soil cover, and size
are identified as the most relevant variables determining the cooling potential of
urban green infrastructure. By combining the three variables, 50 typologies of urban
green infrastructure are defined, and each of them is assigned a score depending on
the climatic region of interest. Planners can directly refer to these “archetype”
typologies to assess the existing condition of a city based on commonly available
data (as demonstrated by the application to Amsterdam), as well as to measure the
expected benefits of planning interventions (as exemplified in the case study in the
city of Trento presented in Chap. 5).
Chapter 5 moved a step further in the operationalisation of ES knowledge, showing how the information produced by ES mapping and assessment can be used to
support real-life planning decisions. The case study in Trento (Italy) demonstrated
that the assessment of ES and related benefits can be adopted to prioritise planning
scenarios, as for the example of brownfield regeneration. The case study considered
two illustrative but critical ES for the context, i.e. micro-climate regulation and
nature-based recreation, applying models specifically developed (as the one
described in Chap. 4) or adapted (as is the case of ESTIMAP-recreation) for urban
planning purposes. Combining the assessment of ES supply with spatial information on the potential beneficiaries and their different levels of vulnerability proved
to be an effective way to build a common ground between ES assessments and
urban planning. A multi-criteria analysis offered a structured way to combine multiple indicators in a synthetic and usable outcome for decision-makers, while exploring and balancing different stakeholder perspectives and potentially competing
interests in a rational and transparent way.
7 Conclusions
was found in both types of plan, and the analysis of spatial plans revealed a wide
variety of tools for implementation.
However, the proposal of ES-related measures is rarely supported by an adequate
knowledge base and analysis, which may eventually undermine their effectiveness.
The general idea that “more green will do some good” seems to guide the inclusion
of ES-based actions in current plans, where critical decisions about the design and
the location of interventions are seldom justified by the analysis of the expected
outcomes and the distribution and vulnerability of the potential beneficiaries. As
such, the most common measure found in climate adaptation plan was the enhancement of green areas, a typical strategy that planners put in place for a variety of
purposes that go beyond climate change adaptation. Moreover, while a set of ES,
including regulating ES related to climate change adaptation, are widely acknowledged also in comprehensive spatial plans, others are hardly considered. This may
lead to trade-offs unconsciously generated by planning decisions and, ultimately, to
a loss of important but underestimated ES.
Chapter 4 illustrated how an ES model can be developed with the aim of supporting urban planning decisions. Specifically, it describes the development of a
spatially- explicit model to map and assess micro-climate regulation provided by
different typologies of urban green infrastructures. The model is based on an extensive review of the scientific literature, and summarises the main findings in a way
that is accessible and usable by planners. Tree canopy coverage, soil cover, and size
are identified as the most relevant variables determining the cooling potential of
urban green infrastructure. By combining the three variables, 50 typologies of urban
green infrastructure are defined, and each of them is assigned a score depending on
the climatic region of interest. Planners can directly refer to these “archetype”
typologies to assess the existing condition of a city based on commonly available
data (as demonstrated by the application to Amsterdam), as well as to measure the
expected benefits of planning interventions (as exemplified in the case study in the
city of Trento presented in Chap. 5).
Chapter 5 moved a step further in the operationalisation of ES knowledge, showing how the information produced by ES mapping and assessment can be used to
support real-life planning decisions. The case study in Trento (Italy) demonstrated
that the assessment of ES and related benefits can be adopted to prioritise planning
scenarios, as for the example of brownfield regeneration. The case study considered
two illustrative but critical ES for the context, i.e. micro-climate regulation and
nature-based recreation, applying models specifically developed (as the one
described in Chap. 4) or adapted (as is the case of ESTIMAP-recreation) for urban
planning purposes. Combining the assessment of ES supply with spatial information on the potential beneficiaries and their different levels of vulnerability proved
to be an effective way to build a common ground between ES assessments and
urban planning. A multi-criteria analysis offered a structured way to combine multiple indicators in a synthetic and usable outcome for decision-makers, while exploring and balancing different stakeholder perspectives and potentially competing
interests in a rational and transparent way.
7 Conclusions