MainDBNew: Global Indicator of Climate Change Adaptation in Catalonia

Title: Global Indicator of Climate Change Adaptation in Catalonia
NWPWeblink: http://canviclimatic.gencat.cat/en/index.html
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NWPGeographicRegion: Europe
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Good practices and lessons learned: - As a result of applying the principal component analysis, a synthetic adaptation indicator was obtained. This will enable us to monitor the development of Catalonia's capacity to adapt to the impacts of climate change. This synthetic adaptation indicator is determined by two factors that explain 100% of the variability of the original information contained in 29 indicators. Each of these factors corresponds to a different aspect: (1) use of resources and (2) environmental quality.
The synthetic adaptation indicator, expressed as the result of both factors, shows a medium level in terms of the capacity to adapt to climate change impacts; just a pass. The evolution of this capacity has been decreasing slightly in recent years (2011 versus 2005). It is important to bear in mind that in order to monitor the synthetic indicators properly, rapid access to the information relating to the original indicators is required. These indicators should be reviewed every five or ten years based on new information available (in order to include more aspects in the synthetic indicator).
- It should be noted that biodiversity is the primary source of environmental services, so its effective or poor adaptation to climate change impacts will directly affect the other natural systems and many, if not all, economic systems. Biodiversity has thus far not been included in this quantitative analysis of the adaptation, but its key importance means that a more qualitative evaluation is also needed.
The fishing industry was also omitted from the analysis, but for a different reason. In this case, there were initially three indicators, but they were rejected during the first selection process because they were largely indirect, since the adaptation measures were highly general. It is necessary to wait until more basic knowledge of the impacts and the most effective measures for combating climate change in this sector is available.
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Date of submission: 13/09/2017
Abbreviation: OCCC
Activities:
Adaptation element: Monitoring and evaluation/M＆E
Adaptation sector/theme: Agriculture; Ecosystems; Energy; Health; Heavy industry; Infrastructure; Services; Tourism; Urban resilience; Water resources
Climate hazard: Drought; Extreme heat; Increasing temperatures; Wildfire
Country: Spain
NWPDataSource: Partner portal
Description: In Catalonia, there is a strategic framework for planning climate change policies (Catalan Strategy for Adapting to Climate Change 2013-2020, ESCACC) and a demonstration project at Mediterranean Europe level (LIFE MEDACC) that call for the establishment of a tool to assess the effectiveness of the measures to adapt to climate change impacts. The preliminary work carried out within the framework of both the ESCACC and MEDACC projects has made it possible to reach a sufficiently advanced stage such that the creation of a global indicator of adaptation to climate change impacts in Catalonia.
The adaptation evaluation, i.e. the analysis of whether or not Catalonia is making progress in its adaptation to climate change impacts, requires the creation of an indicator with three different levels of integration: (1) for the measure, whenever possible; (2) for each sector and system; (3) and lastly, for the whole of Catalonia.
Four basic criteria must be taken into account when the indicators are created: (1) they must be easy to achieve, i.e. the information should be easily available; (2) there must be historical data on what is measured; (3) the indicator must be easy to interpret; and (4) the information and data must be specific to the Catalan region.
A preliminary task to search and select data resulted in a proposal that grouped together a total of 83 potential indicators to evaluate the effectiveness of the adaptation measures. The information included in each indicator was organized in a data-sheet format.
The diversity of the indicators and, at the same time, the differences between qualitative and quantitative information for some of these indicators or the lack of time-based consistency of the data meant that it was impossible to respond to the key question: Is Catalonia adapting well to the impacts of climate change?
Therefore, a preselection process was conducted. This second selection process was based primarily on the potential capacity of the indicator to quantify the outcome of adaptation actions implemented or in progress (and, therefore, on the effectiveness of the indicator to evaluate the measures). In other words, only indicators that directly measured the outcome of the application of the measure were included while indicators that measured a sector or system's sensitivity or degree of exposure were rejected. Indicators that were more qualitative in nature, such as planning tools that incorporate climate change impacts and adaptation (forestry plan, tourism plan, etc.), were also retained in the preselection process. During this process, the initial 83 indicators were reduced to a set of 50.
The ultimate aim of the work was to make it possible to determine, in measurable terms, the extent to which Catalonia is adapting to the impacts of climate change. This work entailed a third selection process: only those indicators with a series of historical data based on at least 10 consecutive years were chosen. This process reduced the number of indicators to a total of 29.
In order to achieve the objective mentioned above, the most appropriate statistical technique was found to be principal component analysis (PCA), a procedure related to factor analysis. The purpose of factor analysis is to analyse the structure of interrelations between a number of variables (indicators, in our case) and define common dimensions, thus producing a lower dimensional space. Principal component analysis, in particular, aims to reduce the dimensionality of the data matrix in order to obtain a lower number of new variables (Zj) or principal components.
Thus, the calculation of the first component (or factor) is performed as a linear combination of the original variables that retains the maximum amount of total variance. In the calculation of the second component (or factor), the same procedure is performed (linear combination of the original variables to retain the maximum amount of total variance of the part not included in the first), and so on.
Interpreting the components (or factors) is easy in theory, but is usually quite difficult in practice. Each variable (indicator) has a relative contribution to each factor. This contribution expresses the correlation between this variable (indicator) and the factor. A high relative contribution of the variable tells us that there is a strong correlation between this variable and the factor. In other words, it means that this variable is important for the interpretation of the factor. This contribution can be positive or negative, depending on whether that variable increases or reduces the value of the factor.
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NWPGeographicScope: Regional
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NWPInformationType: Case study
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NWPPartner: Catalan Office for Climate Change
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Target group: Academics and scientists; Policy makers
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NWPOutcome: In order to standardize the information, the values of all variables were converted to values of 0 to 1. Using the statistical program Stata, two factors that explained 100% of the variability of the original information were obtained. The first factor explained 61% of the variability and the second factor 39%. The significance of the two factors was interpreted as follows: the first factor evaluates the use of resources (primarily water and energy), while the second factor evaluates environmental quality (primarily atmospheric emissions). Values below -0.8 or above 0.8 were considered to be strong contributions.
Finally, to avoid overweighting groups with a greater number of indicators, the influence of each of the 10 groups (systems and sectors) was evaluated. Thus, the weighting of natural systems and socio-economic sectors based on their vulnerability to the impacts of climate change (and in accordance with the ESCACC diagnosis) resulted in the indicators being divided into the following five groups, from most to least importance:
1. Water management (35%)
2. Agriculture and livestock; Forest management; Health (30%, i.e. 10% each)
3. Energy (8%)
4. Industry, services and trade; Tourism; Urban planning and housing; Mobility and transport infrastructure (24%, i.e. 6% each)
5. Research, development and innovation (3%)
Lastly, within each factor, the weighted value of the indicator is multiplied by the indicator's contribution to the factor and by the value (between 0 and 1) of the indicator during the selected time period (years). By performing this calculation for both factors and for 2005 and 2011, the results are obtained: both factors have a medium value (around 5). In both cases, there was a slight decrease in the year 2011 compared with 2005.
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NWPReferences: http://canviclimatic.gencat.cat/web/.content/home/actualitat/docs/Doc-Index-complet_ENG.pdf
 
A Global Indicator of Climate Change Adaptation in Catalonia. E. Agell et al.
© Springer International Publishing Switzerland 2016
W. Leal Filho et al. (eds.), Implementing Climate Change Adaptation in Cities and Communities, Climate Change Management, DOI 10.1007/978-3-319-28591-7_10 (p.191-202)
Implementing partners: - Catalan Office for Climate Change (OCCC)
- Catalan Institute of Public Policy Evaluation (IVÀLUA)
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Created at 10/10/2018 14:30 by Serkant Samurkas
Last modified at 10/10/2018 14:30 by Serkant Samurkas
 
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