NWPStaging

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.