Torino (Italy, 2013)
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 recovered. This means that some of the data sets recovered lacked some spatial scales or years, 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 supplementation corresponded to the geographical unit of Piemonte (NUTS II) for the year 2013. Although the final goal was to model Torino, defining Piemonte and 2013 as the initial study boundaries allowed to speed up the process. The data that were still missing for Piemonte in 2013 were collected or produced by expanding the parameters of the initial search, by establishing direct contact with statistical institutions, and/or by applying different extrapolation techniques. Extrapolations were a solution when, after further search, the characteristics of the data did not subscribe to 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 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 Piemonte 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 Piemonte. 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 Piemonte. Other than this, the model was applied as described in D2.1 and D2.2.
Having results of the urban metabolism of Piemonte, more data was required to scale the results to the province of Torino (NUTS III). Here too were necessary additional IT data, though utilizing Portugal’s values was no longer viable. 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 the national accounts. The only downsides were higher error degrees in the data, and more aggregated economic activity information. Some lacks in the data also had to be accepted, such as confidential values in the industrial production.
MFA indicators for Torino are calculated using data obtained from the model and raw data from province statistics, but also through adaptations of Piemonte’s data. Torino’s population in 2013 is estimated at 2 297 917 people.
In this page you may find information on:
- 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 5 sections with plastic (FF4) throughput in Torino in ton (2014-2038)
Non-ferrous heavy metals (MM3) throughput in Torino in ton (2014-2062)
Throughput of cement (NM2) in Torino in ton (2044-2062)
Throughput of stone (NM4) in Torino in ton (2042-2062)
Throughput of wood (BM6) in Torino in ton (2014-2062)
Throughput of paper and board (BM7) in Torino in ton (2014-2031)
Throughput of textile biomass (BM3) in Torino in ton (2014-2032)
Material data (in absolute and per capita values)
- Disaggregated data at 4 digits level (28 material categories MATCAT);
- Aggregated data 1 digit level (5 material categories MATCAT
Data fitness to the UMAn model for Torino