Leiria (Portugal, 2013)

Background

To apply the UMAn model, a significant amount of work is allocated to the data gathering process, which includes the compilation of twenty-three datasets across multiple years and spatial scales. Around 80% of the data required to proceed with the modelling was initially gathered. This means that some of the data sets lacked some spatial scales or years, or that the statistics were not as disaggregated as necessary to apply the model, or that the data was simply not available.

From the data available, the set with the least needs for proxy data corresponded to the geographical unit of Centro region (NUTS II) for the year 2013. Although the final goal was to model Leiria, defining Centro region and 2013 as the initial study boundaries allowed to speed up the process. The data that were still missing for Centro region in 2013 were collected or produced by expanding the parameters of the initial search and/or by applying different extrapolation techniques. Extrapolations were a solution when the characteristics of the data did not fit the boundaries. Often extrapolations implied using highly disaggregated data from a different year, area or nomenclature than the desired, to apply its ratios to aggregated data from the desired year, area or nomenclature. D2.2. explains how such extrapolations were used.

MFA indicators for Leiria are calculated using data obtained from the model and raw data from municipal statistics, but also through adaptations of Centro region data.

Leiria’s population in 2013 is estimated at 125 977 people. For the municipality (Figure 3), extraction of resources are significantly higher than the imports; constituting 13,79 out of the 21,90 ton per capita of material input.

In this page you will find for Leiria the following information:

Graphs

  • for aggregated material categories (1 digit) and for the top ten CN2 sections of products you may find the visual representation of: DE (Domestic Extraction), IMP (Imports); EXP (Exports), NAS (Net Addition to Stocks)

  • Graphs for product, per CN section (please see the key for CN sections in the Introductory page of the Database):

  • the following indicators are also represented in graphics for the aggregated data of products (CN2, 28 sections) and material categories (21 categories): Demand of Resources (DMI, Domestic material input, which is the sum of DE and IMP) and DEP (Dependency – that shows the weight of imports in total DMI);

  • Throughput indicates the expected waste of a product in the 50-year span after its consumption. This considers materials of interest within every product section. This means that the throughput shown is not for the total consumption of the sections, but for the portion of a specific material within them, e.g. throughput for the content of plastic (material) within electrical appliances (product section). The European Union’s priority areas in the context of circular economy (European Comission, 2015) were a guideline to choose the materials used in the calculations.

Non-ferrous heavy metals (MM3) throughput in Leiria in ton (2014-2042)

Throughput of cement (NM2) in Leiria in ton (2046-2063)

Throughput of stone (NM4) in Leiria in ton (2046-2063)

Throughput of wood (BM6) in Leiria in ton (2014-2063)

Throughput of paper and board (BM7) in Leiria in ton (2014-2063)

Products data (in absolute and per capita values)

  • Disaggregated data at CN4 level;
  • Aggregated data at CN2 level

 

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Material data (in absolute and per capita values)

 

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Data fitness to the UMAn model for Leiria

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