Cremona (Italy, 2013)

Background

MFA indicators for Cremona are calculated using data obtained from the model and raw data from province statistics, but also through adaptations of Lombardian data.

Cremona’s population in 2013 is estimated at 362 141 people. For the province (Figure 3), imports are slightly lower than the extraction of resources; constituting 12,39 out of the 27,32 ton per capita of material input.

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. Little 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 Lombardia (NUTS II) for the year 2013. Although the final goal was to model Cremona, defining Lombardia and 2013 as the initial study boundaries allowed to speed up the process. The data that were still missing for Lombardia 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.

Another step taken to address the lack of data was to adopt the necessary information from other countries involved in the project. That was the case for international trade (IT) data, a piece that is essential in the UMAn model to allocate the available resources. In the model, the available resources are assigned to different economic activities in different percentages, and ideally, the selected activities and percentages would mirror the industrial processes of the region modelled. A way to estimate this is through IT data, where it might be indicated which economic activities receive the products that are imported. However, only the Portuguese data had this type of information readily available, thus the information was not accessible for Lombardia or Italy. It was therefore assumed that economic activities in Lazio would utilize the same type of resources as indicated in the IT of Portugal. Proportions, i.e. in what percentages products go to each activity, would rely on Italy’s own industrial production values.

To ensure that this approach would render acceptable values, the model for Centro (also NUTS II) in Portugal was adjusted to reproduce the proposed approach for Lombardia. The comparison between original values for Centro and values obtained applying the new method resulted in admissible error percentages, which supported the use of this approach to model Lombardia. Other than this, the model was applied as described in D2.1 and D2.2.

Having results of the urban metabolism of Lombardia, more data was required to downscale the results to the province of Cremona (NUTS III). Here too additional IT data were necessary. Data was once more supplemented and the model modified, resulting in that the most acceptable values were obtained through the use of Italian supply and use tables from national accounts. The only downsides were higher uncertainty in the UMAn model results, and more aggregated economic activity information. Some lacks in the data also had to be accepted, such as confidential values in the industrial production.

In this page you will find for Cremona 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, Direct 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.

Top 3 sections with plastic (FF4) throughput in Cremona, in ton (2014-2037)

Non-ferrous heavy metals (MM3) throughput in Cremona in ton (2014-2062)

Throughput of cement (NM2) in Cremona in ton (2044-2062)

Throughput of stone (NM4) in Cremona in ton (2042-2062)

Throughput of wood (BM6) in Cremona in ton (2014-2062)

Throughput of paper and board (BM7) in Cremona in ton (2014-2062)

Throughput of textile biomass (BM3) in Cremona in ton (2014-2032)

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 Cremona

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