Correlation between WEEE Collection Performance and Collection Center Distribution

In order to verify the existence of a relationship between WEEE collection and CC distribution national, we have analyzed the correlation between the quantity (kg) of e-waste collected and the number of WEEE's CCs for the period 2009–2017 for the Italian provinces.

The analysis confirms that a correlation exists being the average correlation coefficient equal to 0.67. Moreover, analyzing the time series of the distribution of the correlation coefficient by territorial areas, it emerges that it decreases for all of the areas (North: −9.5%, Central: −6.3%; South: −8.7%.). Thus, we can suppose that, even though the weight of the role of the collecting infrastructure is becoming less crucial, it still remains a critical aspect of the Italian WEEE management system, which requires further analysis.

Table 10 is a representation at a glance of the comparison of the two dimensions considered: positive, negative or null variation (+, −, =) of WEEE collection performance and CCs. The matrix considers each year of the period 2009–2017 and the cells represent the number of times in which the provinces have registered a positive (+), negative (−) and null (=) variation. For example, the cell "++" shows the times the provinces have incremented both WEEE's collection and CCs (37.6% for all the Italian provinces). This matrix permits acquiring useful information to compare the behavior of provinces by territorial area in the considered period. In particular, we can distinguish both the presence/absence of the correlation and to obtain information of the trends of specific areas and provinces (this last aspect is not considered in this paper).

Table 10. WEEE Collection and Collection Centers by variation correlation groups.

OLLECTION + Area 2 - Group (+ −) Area 2 - Group (+ =) Area 1 - Group (+ +)
Central - (1.4%) Central - (4%) Central - (10.5%)
North - (6.3%) North - (9.4%) North - (12.6%)
South - (2.3%) South - (4%) South - (14.5%)
Italy - (9.9%) Italy - (17.4%) Italy - (37.6%)
- Area 1 - Group (−−) Area 3 - Group (− =) Area 3 - Group (− +)
Central - (1.5%) Central - (2.7%) Central - (3.6%)
North - (3.1%) North - (5.3%) North - (6%)
South - (2.4%) South - (3.3%) South - (7.2%)
Italy - (6.9%) Italy - (11.4%) Italy - (16.8%)
− = +
COLLECTION CENTERS

From the analysis about the two dimensions considered, we can draw three different areas featured by different relationships between the two dimensions considered:

  • AREA 1-Groups (++) and (−−). These groups have a positive even though opposite relation. The Group (++) is the highest (37.6%). The Group (−−) which represents a contemporary reduction of the two dimensions is the lowest (6.9%). Both of these results are in line with the high correlation coefficient mentioned above. We can guess that the first group have carried out structural investments; meanwhile, the second have reduced the number of CCs. The Southern provinces are the most represented in the Group (++) (14.5%)
  • AREA 2-Groups (+−) and (+=). The provinces in these groups (9.9% + 17.4%) are not in line with the correlation hypothesis. They have showed an increase in the WEEE collection together with a reduced or stable number of CCs. These results are probably due to the investment in the promotion (information and education campaigns, media awareness initiatives, etc.) or to the improvement of e-waste management processes since the CC investments were made previously. The Northern provinces are the most represented (total 15.7%)
  • AREA 3-Groups (−=) and (−+). These groups show a worsening of the collection result even though have increased or unchanged the number of CCs, Thus, they are not coherent with the correlation hypothesis. They both represent the 28.2% of the total and denote an area of ineffectiveness since investment in CCs do not result in collection improvements. We can guess that different problems affect the WEEE management system for these provinces. The Northern provinces show the higher presence (11.3%) even though Southern provinces are the highest in the group (−+), which is the most effective.

A further deepening of the correlation about WEEE's collection and CCs in Italian provinces is realized by observing the CC transition matrix behavior of those provinces that have obtained the best and worst performances in the transition matrix of the WEEE collection (Table 6). In this view, Table 11 shows a comparison between the three groups of provinces as definite previously.

Table 11. Best and worst provinces in terms of WEEE Collection Centers: Italian provinces, 2009–2017.

Groups by WEEE Collected Transition Matrix Province (Area) Population in 2017 Transition Behavior of CC State
Best performing provinces(6) Aosta (N) 126,883 MPC to HPC (+1)
Bologna (N) 1,009,210 MPC to MPC (-)
Como (N) 600,190 MPC to MPC (-)
Gorizia(N) 139,673 LPC to MPC (+1)
Isernia (S) 85,805 MPC to HPC (+1)
Nuoro(S) 156,096 MPC to HPC (+1)
Second-best performing provinces(2) Sassari (S) 333,116 MPC to HPC (+1)
Trento (N) 538,604 HPC to HPC (+1)
Worst performing provinces ** (11) Agrigento (S) 442,049 LPC to LPC (-)
Barletta-Andria-Trani (S) 392,546 LPC to LPC (-)
Caltanissetta (S) 269,710 LPC to LPC (-)
Cosenza (S) 711,739 LPC to LPC (-)
Crotone(S) 175,566 LPC to LPC (-)
Enna (S) 168,052 LPC to LPC (-)
Foggia (S) 628,556 LPC to MPC (+1)
Pescara (C) 321,309 LPC to LPC (-)
Siracusa (S) 402,822 LPC to LPC (-)
Taranto (S) 583,479 LPC to LPC (-)
ViboValentia (S) 161,619 LPC to LPC (-)

**: The three Sardinia province s Carbonia-Iglesias, Medio Campidano and Ogliastra were in this group, but have been deleted because they were reassigned in 2017.

Furthermore, the related population is indicated with the aim to acquire information about the influence of the size of the provinces on the correlation.

In "Best performing provinces" group, it is possible to find those provinces (4 out of 6) that have invested and increased the number of CCs (+1). These provinces are also the less populated of the group. The two more populated best performing provinces show a stable condition in terms of CCs. Thus, while the localization and the population do not seem to matter, it seems to confirm the presence of correlation between the two dimensions.

The "second-best performing provinces" group, improved their state (+1) in the CC transition matrix. Both are intermediate in terms of population when compared with those of the best performing group. The correlation between WEEE collection and CCs seems to matter.

The "worst performing provinces" group is featured by two elements: (a) it is made up almost entirely of provinces that remain in LPC state (less than 2.7 centers every 50,000 inhabitants) except the province of Foggia, which is also the more populated of the group; (b) all the provinces are in Southern Italy, except one. In this view, we can say that, for this group, the correlation is strongly confirmed and that the localization does matter. Nevertheless, different sizes of population are present in this group, it does not seem to matter.