Abstract
Although the recent empirical literature provides a satisfactory range of estimates of food waste at the national and global level, little attention has been devoted to lower units of aggregation. This article tackles the phenomenon of urban food waste (UFW), proposing an analysis of consumer behaviour at the local level. Using institutional data for Italian provinces, over an 11-year time span (2004–2014), we estimate the amounts of UFW and subsequently investigate the extent of persistence and spatial spillovers using the local Moran transition probability matrix. Our results suggest that the good and bad practices in food consumption that determine the levels of UFW are persistent over time. Moreover, they produce a (though limited) spatial spillover, affecting consumption practices in the neighbouring areas. Two province clusters emerge, one in Northern and Central Italy, featuring negative behaviours and the other one in South-Central and Southern Italy, displaying virtuous behaviours. This situation calls for public policies aimed at promoting convergence in the levels of UFW.
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Notes
In the US for example, although no federal tax on food disposal currently exists, counties and localities have introduced disposal taxes: this is the case of Seattle, Washington (Katare et al. 2017; Kravitz 2015). Elsewhere in the world, national level taxes have been imposed, e.g. in South Korea (Mazzoni 2013). Fiscal benefits on the other hand have recently been introduced in the US (Food Recovery Act, 2016) as well as in Italy (law 166/2016), for companies that reduce waste by donating surplus food to charities.
The weights were taken from Azzurro (2015).
Due to space constraints, we do not report the calculations lying behind our UFW estimates, but all the information is available upon request.
We thank our referees for pointing out this problem. Further research should try to take this issue into account.
A key parameter in the box map representation is the hinge value. The hinge determines the spread of acceptable values displayed as horizontal lines above and below the Inter Quartile Range (IQR) box. We chose a hinge value of 1.5, meaning the acceptable data extends (1.5 × IQR) away from the median line. Any values outside the hinge spread are considered outliers (De Smith et al. 2007).
The Moran scatterplot provides a tool for visual exploration of spatial autocorrelation (Anselin 1996, 2002a). The four different quadrants of the scatterplot identify four types of local spatial association between a province and its neighbors:
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(HH) a province with a high UFW surrounded by neighbors with high UFW (quadrant I);
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(LH) a province with a low UFW surrounded by neighbors with high UFW (quadrant II);
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(LL) a province with a low UFW surrounded by neighbors with low UFW (quadrant III);
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(HL) a province with a high UFW surrounded by neighbors with low UFW (quadrant IV).
Quadrants I and III pertain to positive forms of spatial dependence while quadrants II and IV represent negative spatial dependence (Rey and Montouri 1999).
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The Moran’s I test, implemented on UFW for each year analysed, always rejects the null hypothesis of spatial independence. We do not show for brevity the results, but they are available upon request.
We thank our referees for pointing out this issue. Further research should take into account difference in the extent of separate waste collection.
Apart from the already mentioned ‘Love Food, Hate Waste’ programme promoted by the British government, similar initiatives took place in other EU member states: ‘Qui jette un oeuf jette un boeuf’ in France, ‘Zu gut Fur die Tonne’ in Germany, ‘Stop Spild af Mad’ in Denmark, ‘De menjar, no en llencem ni mica’ in the autonomous region of Catalonia, in Spain, ‘Movimento Zero Desperdicìo’ in Portugal.
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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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Appendix
1.1 Some clarifications on the estimation of urban food waste
The two components of UFW are determined in this way:
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 plus the percentage of extraneous fractions in the organic waste (wet + green) treated in anaerobic digestion plants is 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 deriving from separate waste collection subjected to treatments different 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 OWNCS = X% of the waste not collected 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).
1.2 Separate waste collection by region
Table 4 shows the percentage of waste that ends up being collected separately in each of the 20 Italian regions. The overall trend is positive, largely thanks to governmental measures. However, the within-country variation is still remarkable. North-eastern regions (Trentino Alto Adige, Veneto and Friuli Venezia Giulia) boast the most impressive results, while the majority of the Southern regions (especially Sicily, Calabria and Molise) lag behind. The differences in the extent of separate waste collection depend on a number of factors, including the availability of separate collection facilities and the different local policy bundles. Much variation is attested in local recycling policies, which have been recognised as more fruitful in the North. Veneto represents a positive benchmark, featuring an incentive system that proved to be particularly effective (Bucciol et al. 2013, 2014, 2015). Given our estimation strategy, which puts weights on the OWCS and OWNCS components regardless of the regions, we may overestimate UFW in Northern provinces.
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Cerciello, M., Agovino, M. & Garofalo, A. Estimating urban food waste at the local level: are good practices in food consumption persistent?. Econ Polit 36, 863–886 (2019). https://doi.org/10.1007/s40888-017-0089-8
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DOI: https://doi.org/10.1007/s40888-017-0089-8