How to critically analyse the data on put-out in pay as you throw collection

In pay as you throw collection of wastes, critically analysing the put-out by users allows municipalities to get a set of statistical information regarding the collection – like how often organic waste is consigned or the increase in wastes consigned in holiday locations – in its area. In order to manage the complexity of the put-out index, the reading system for the pay as you throw system must not be rigid, but efficient and scalable.

The put-out index of the pay as you throw collection

The put-out index is the ratio between the actual daily consignments – the number of bags or bins put out by the user – and the total of registered users. The municipal database includes residential and non-residential users – trading, craft, industrial, professional businesses, production in general and communities – whilst the domestic users are in turn distinguished by single (independent residences) and group (blocks of flats) users. If a small municipality has 1000 users and 936 put out a bag or bin on a Monday, the put-out index is 93.6%.


Seasonality and peaks

The put-out index is dynamic; it is not a unique fixed value: if you check the user consignments from year to year, with reference to a specific day or period – e.g. the summer season from June to August – you can calculate an average, but you will never get two equal values. Apart from the number of users and actual consignments, it is, in fact, influenced by several factors:


♦  Seasonality

The seasons, like the climate, have an influence on the number of assignments. For example, in the summer organic waste is collected more frequently to prevent bad smells developing with the high temperatures. It is also a fact that user put-outs, such as paper which deteriorates with the rain and is consigned less frequently, reduce in the winter. The seasons also influence consumption and, consequently, the quantity of wastes produced and consigned: in the hot months, people drink more water and sugared drinks as well as eat more fruit and vegetables, two factors that make the consignments of plastic and organic waste increase.

♦  Tourist peaks

In holiday locations, the pay as you throw collection for wastes is subject to changes in the periods when there is the greatest influx of tourists – the winter season for mountain resorts and the summer one for seaside resorts. Naturally, the increase in the persons staying in the town makes the quantity of wastes consigned increase.

♦  Social habits

Social habits determine peaks in the consignment of wastes that are not linked to tourism: e.g. the consumption of wine and beer increases in the weekend, making Monday a “peak” in the glass put-out.

♦  Number of members in the family nucleus

Another determining factor in the frequency of put-outs is the number of people living in a house: a family with four members will fill the various bags or bins more quickly than a person living alone.

A critical analysis of the consignments must necessarily take into consideration that the put-out index is:

♦  Dynamic: it changes and is influenced by factors such as seasonality, tourist peaks and social habits;

♦  Recurring: it shows similar trends in certain periods or days (in cases like the put-out of glass on Mondays, the increase in the consignment of organic waste in summer, etc.);

♦  Predictive: the fact that it is recurring allows predictions to be made on its trend in specific periods.

Critically analysing the put-out index allows the authorities and collection companies to have a series of discussions aimed at applying the pay as you throw collection optimally: if people produce a greater quantity of organic waste in the summer, one can arrange to distribute larger bags than the ones used in the rest of the year, if they drink more water and fizzy drinks, the frequency of the plastic collection can be increased and so on.


The importance of the reading system for the pay as you throw collection 

In order to manage the complexity of the put-out index, it is fundamental that the data reading system for the pay as you throw tariff system:

♦  provides accurate readings, reading an RFID tag number that is exactly the same as the one on the bags or bins consigned and returns clean data, i.e. not influenced by the other tags in the environment;

♦  is efficient, flexible and scalable (capable of being enlarged without losing its efficiency), taking into account the peaks, seasonality and other elements that influence the put-out index.


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