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Estimating food waste under the FUSIONS definition: What are the driving factors of food waste in the Italian provinces?

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Abstract

Following the definition provided by the EU-funded FUSIONS project (food use for social innovation by optimising waste prevention strategies), this work estimates food waste at the provincial level (NUTS-3) in Italy, using official records on separate waste collection over the 2004–2014 time span. Moreover, it sheds some light on how economic and socio-demographic characteristics affect food waste levels and composition. We run a fixed effects panel regression taking into account a number of socio-economic factors. Our results show that food waste is positively affected by population density and consumption levels, while it is negatively affected by the share of women, elderly people, immigrants and unemployed in the population. We also analyse the components of food waste—the one resulting from unsorted waste and the one resulting from separated collection—and we show how they are affected by socio-economic drivers. Overall, our analysis highlights the role of consumption levels. Locally targeted awareness campaigns aiming to reduce food consumption need to be devised in order to tackle the food waste challenge.

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Notes

  1. Though technically a region of Spain, the autonomous community of Catalonia is defined by the Spanish constitution as a “nationality”, endowed with a rather wide degree of autonomy. This is why we include the study conducted by the ARC among the national-level works.

  2. The weights were assigned following Azzurro (2015).

  3. Due to space constraints, we do not report the calculations for the UFW, but interested readers can request them to the authors.

  4. http://dati.istat.it/, consulted in September 2016.

  5. http://osservatoriocommercio.sviluppoeconomico.gov.it/indice_economici.htm, consulted in September 2016.

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Acknowledgements

The current research was funded by the University of Naples Parthenope within the Research Project’ Sustainability, Externalities and Efficient Use of Environmental Resources’.

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Authors and Affiliations

  1. Department of Economic and Legal Studies, University of Naples Parthenope, Room 529C, Palazzo Pacanowski, Via Generale Parisi, 13, 80132, Naples, Italy

    Massimiliano Cerciello

  2. Department of Economic and Legal Studies, University of Naples Parthenope, Room 507C, Palazzo Pacanowski, Via Generale Parisi, 13, 80132, Naples, Italy

    Massimiliano Agovino

  3. Department of Economic and Legal Studies, University of Naples Parthenope, Room 502, Palazzo Pacanowski, Via Generale Parisi, 13, 80132, Naples, Italy

    Antonio Garofalo

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  1. Massimiliano Cerciello
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Correspondence to Massimiliano Cerciello.

Appendix: Some clarifications on the estimation of urban food waste

Appendix: Some clarifications on the estimation of urban food waste

The two components of UFW are determined in this way:

$${\text{OWCS }} = {\text{OWCP}} + {\text{OWADP}} + {\text{OWSWC}}$$

where OWCP = amount of organic waste effectively present in the organic waste (wet + green) treated in composting plants. The calculation considers a percentage of 4.5% of impurities in the organic waste (wet + green) treated in composting plants (Centemero et al. 2013). Therefore, OWCP is 95.5% of the organic fraction in the organic waste (wet + green) treated in composting plants; OWADP = amount of organic waste effectively present in the organic waste (wet + green) treated in anaerobic digestion plants. The calculation considers that the percentage of green + the percentage of extraneous fractions in the organic waste (wet + green) treated in anaerobic digestion plants are equal to 10% (Centemero et al. 2013). Therefore, OWADP is 90% of organic waste (wet + green) treated in anaerobic digestion plants; OWSWC = amount of organic waste effectively present in the organic waste from separate waste collection subjected to different treatments from composting and anaerobic digestion. The calculation considers that the percentage of organic waste from separate waste collection is equal to 50% (see Azzurro 2015). This percentage corresponds in terms of weight to about 59% of organic waste from selective collection of municipal waste and

$${\text{OWNCS}} = X\% \;{\text{of}}\;{\text{the}}\;{\text{waste}}\;{\text{not}}\;{\text{collected}}\;{\text{separately}}.$$

The ISPRA data (2013) relating to the fractions produced by mechanical biological treatment plants are considered to be representative of the product breakdown of urban waste not collected separately. We assume that the organic waste within these merchandise fractions is represented (by weight) by the following items: non-composted organic fraction; biostabilised; biodried; organic fraction; scraps; and leachates. So, considering the amount of these items over the total, we obtain a percentage of the organic waste produced within the undifferentiated fraction that enters as input in the mechanical biological treatment plants (e.g. equal to Z %). In addition, Azzurro (2015) assumes a division between the green and organic fraction similar to the one previously estimated for the organic waste from separate waste collection of municipal waste (59% of wet and 41% of green). Assuming that these percentages are applicable to the total of the waste not collected separately, this percentage is equal to X% = Z% *59% (where 59% is the percentage corresponding to OWSWC).

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Cerciello, M., Agovino, M. & Garofalo, A. Estimating food waste under the FUSIONS definition: What are the driving factors of food waste in the Italian provinces?. Environ Dev Sustain 21, 1139–1152 (2019). https://doi.org/10.1007/s10668-017-0080-0

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