The Reverse Supply Chain of E-Waste Management Processes
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Date: | Saturday, September 7, 2024, 7:42 PM |
Description
Read this article. The authors propose that reverse supply chains can achieve economic as well as environmental and social benefits. Regarding your electronic devices, do you know how you can recycle and reverse supply them back to a vendor?
Table of contents
- Abstract
- Introduction
- The Theoretical Background
- The EU and National Law Basics of E-Waste Management
- Methodological Approach
- The Field Analysis: WEEE Collection Targets
- The Field Analysis: WEEE Collection Center Analysis
- Correlation between WEEE Collection Performance and Collection Center Distribution
- Discussion of the Results
- Conclusions
Abstract
In the last several decades, Waste Electrical and Electronic Equipment (WEEE) reverse supply chain management has increasingly gained more attention due to the development of an environmental awareness, the rapid raise of e-wasted products and the EU regulations. In particular, although the new EU WEEE collection target has not been reached by many EU countries, several studies show that an optimized WEEE wastes management processes could represent a relevant way to achieve economic, environmental and social benefits expected by the adoption of circular economy approaches. According to this, the paper aims to evaluate the extent to which the current Italian organization of the WEEE management system and the related legislation are able to support the achievement of the targets defined by EU with a specific focus on the collection centers (CCs) which play a key role being the initial point of the WEEE reverse logistic cycle. An illustrative analysis based on the transition probability matrix regarding both the e-waste collecting performance and the distribution of collecting centers in the Italian provinces is illustrated. Furthermore, we have analyzed the presence of a correlation between the WEEE collection rate and the presence of the CCs in different provinces in order to better comprehend the role that can play both the investments in CC system and other soft measures in achieving the WEEE collection targets. Results show that the current Italian organization of the WEEE management system and the related legislations are not so effective in supporting the achievement of EU WEEE collection targets at the national level, although some geographical areas and provinces outperform the EU targets.
Keywords: waste electrical and electronic equipment; e-waste; WEEE; circular economy; reverse logistics; Italian provinces
Source: Raffaele Isernia, Renato Passaro, Ivana Quinto, and Antonio Thomas, https://www.mdpi.com/2071-1050/11/8/2430/html
This work is licensed under a Creative Commons Attribution 4.0 License.
Introduction
In the last two decades, an increasing environmental awareness has strongly influenced the relationship between production-consumption of products, environment protection and sustainability. Based on the diffusion of green management approaches, great attention was paid by producers to the environmental consequences of production processes as well as waste prevention, recycling, reuse and minimization of final disposal of end-of-life products. By providing products and services able to reduce their environmental impact, manufacturers discovered further opportunities for strengthening their competitive advantage according to the circular economy (CE) approaches. CE is considered as a new business model able to support a more sustainable development by retaining, as much possible, products, resources, energy and materials. This could be achieved by reusing, refurbishing, remanufacturing and recycling products, materials and waste.
Specifically, several studies show that optimized waste management processes could represent a relevant way to achieve economic, environmental and social benefits expected by the adoption of CE approaches. In fact, the diffusion of the processes of recycling and reuse of end-of-life products, supported by specific government's regulations, has generated new economic markets and new entrepreneurial activities that are growing in many developed and developing countries.
In such a context, the Reverse logistic processes of collection, recycling and reuse of Waste of Electronic and Electrical Equipment (WEEE or e-waste) plays a critical role for different reasons. Firstly, WEEE is one of the fastest growing streams of waste in the world with the highest growth rate per year (3–5%). In fact, while in 2016 about 45 million tons of e-waste were generated globally (6.1 Kg per capita or Kgpc), it will achieve about 52.2 million tons in 2021 (6.8 Kgpc). The increase of e-waste is strongly fueled by the growing Electronic and Electrical Equipment (EEE) market demand which has in 2016 resulted in a 2.9% increase in the millions of tons of EEE put on the market.
Secondly, within e-wastes, there are different critical, valuable and hazardous substances which requires specific recycling processes and practices in order to avoid both environmental and health problems. Thirdly, the recycling and the recovery of the e-waste represents an opportunity to reduce greenhouse gas emissions and environmental impact. Furthermore, as a large quantity of precious and special metals are in WEEEs, their recovery represents a relevant economic opportunity as it allows for saving scarce and expensive resources necessary for the production of EEE itself and other devices. The economic convenience deriving from the reuse of materials embedded into e-wastes is indicated nowadays as one of the most important sustainability challenges able to ensure the development of the proper CE approaches.
Accordingly, the European Union (EU) have issued regulations, policies and actions to deal with the post-consumption phase of such EEEs. Despite the great efforts made, the collection of e-waste is very limited (after 10 years, the volume of WEEE collected in the period 2010–2016 is about 40% of EEE put on market) and is highly unbalanced across EU Member States (the WEEE collection rate per capita ranges from 1.6 kg in Romania to 16.5 kg in Sweden). Moreover, the more recent EU directives (2012/19/EU) has increased the collection target, thus enlarging the unbalances among different European countries. At the same time, the WEEEs that are treated coherently with the EU regulations (and then recycled and reused) are about the 90% of the WEEE collected. This data highlights that the collection system represents a critical element of the WEEE management system and in particular of the WEEE reverse logistic system. Moreover, the collection centers (CCs) represent a key element of the WEEE management system as a whole, being the initial point of the reverse logistic cycle. Its efficiency and effectiveness have a strong impact on all the performance of the WEEE management system and on the CE perspective that the EU intends to pursue. Moreover, WEEE collection plays an important role since it has a large effect on the actual recovery of critical raw materials (CRMs). For this reason, the European Commission individuates the increase of the collection centers as one out of four main activities to put in place the infrastructure needed and to improve the efficiency of the WEEE management system according to a CE perspective.
Therefore, within the context of the EU policy, the objective of this work is to evaluate the extent to which the Italian WEEE collection system is able to support the achievement of the collection targets with a specific focus on the provincial level. Since Italian reality is characterized by the existence of well-known strong territorial socio-economic differences, we expect that this could be reflected in the WEEE collection system organization. Such an analysis is necessary in order to grasp the specific strengthens and weaknesses at province-specific level in order to support the decision-makers about the more effective customized measures to be adopted (e.g., hard measures, such as investments in collection infrastructures soft measures such as initiatives having an impact on citizen behaviors) to improve the collection performance.
In this view, although there are some studies on the Italian WEEE collection system, they explore different aspects and adopt different levels of analysis. However, no research specifically evaluates the effectiveness of the current management of the WEEE collection process at the provincial level. In this view, the paper adopts the transition probability matrix method that is novel for e-waste analysis being adopted to analyze municipal separate wastes. The capillarity of a data analysis at the provincial level on the collection of the WEEE provides both a substantial internal homogeneity and the possibility of effectively interpreting the deviations and the heterogeneity of the data limiting the influences of the territorial and demographic dimension.
Based on these premises, a descriptive data
analysis, the paper aims to evaluate the extent to which the current
Italian organization of the WEEE collection system is able to support
the achievement of the targets defined for the e-waste collection by
European and Italian authorities with a specific focus on the role
played by the CCs. In particular, this study aims to verify the presence
of territorial divide about the WEEE collection performance, the
correlation between collection performance and the presence of CCs, and
the impact of external events on the trends of the collection results.
Our analysis focuses both on the amount of WEEE collected and on the
infrastructure represented by the distribution of CCs in the 110 Italian
provinces (NUTS-3). In this view, we use data provided by the national
clearing house "CdCRAEE" about e-waste collection and CC system for the
period 2008–2017.
The Theoretical Background
The circular economy concept is
increasingly gaining great relevance in academic research and on the
agenda of policy-makers. The main aim of the CE framework is to
create a regenerative system able to ensure optimal reuse, renovation,
remanufacturing and recycling of products, materials and waste by
handling them in closed loops. Such restorative economic system
should be intentionally developed and designed through the adoption
of strategies that close and narrow resource loops.
From the CE perspective, the reverse supply chain and the reverse logistic can be considered necessary approaches to "close the loops" of end-of-life (EOL) products. In literature, several definitions of Reverse Logistics exist (Table 1), which, although emphasizing different aspects, all highlight the importance of the recovery and reuse processes of EOL products and/or their disposal. In other words, the scholars aim to underline the environmental sustainability view of reverse logistics.
Table 1. Some principal definitions of Reverse Logistics present in literature.
Definition of Reverse Logistics |
---|
"… The term often used to refer the role of logistics in recycling, waste disposal and management of harzadous materials, a broader perspective includes all relating to logistics activities carried out in source reduction, recycling, substitution, reuse of materials and disposal". |
"…The process whereby companies can become environmentally efficient through recycling, reusing, and reducing the amount of materials used". |
"The process of planning, implementing, and controlling the efficient, cost-effective flow of raw materials, in-process inventory, finished goods, and related information from the point of consumption to the point of origin for the purpose of recapturing value or proper disposal". |
"… Reverse Logistics is the process of planning, implementing, and controlling the efficient, effective inbound flow and storage of secondary goods and related information opposite to the traditional supply chain direction for the purpose of recovering value or proper disposal". |
"The process of planning, implementing and controlling backward flow of row materials, in process inventory, packaging and finished goods, from a manufacturing, distribution or use point of proper disposal". |
"As an aspect of sustainable supply chain management (SSCM), Reverse Logistics can be regarded as a business strategy in which recovery activities are imposed for the purpose of increasing sustainability" |
The EU and National Law Basics of E-Waste Management
In the last few years, the European Union has issued various Directives aimed at supporting a correct and effective treatment and disposal of e-waste. The Directive 2002/96/EC aimed to promote the recycling, recovery and reuse of WEEE in order to reduce its disposal in landfills. To this aim, the main elements introduced by the 2002 Directive are: (a) the adoption of the Extended Producer Responsibility (EPR) principle, which is imposed on the producers to finance the WEEE collection activities; and (b) the adoption of the one-to-one principle which allows for consumers to deliver for free their WEEE if they buy an equivalent EEE. Moreover, such Directive defines a rate of WEEE collection equal to four Kgpc per year by 2008. In 2012, the EU Directive 2012/19/EU modified the collection thresholds and introduced a more severe target such that any State has to collect at least 45% (about 8 kgpc) of the total weight of the WEEE defined as a percentage of average weight of the EEE placed on the market (POM) from 2016, and 65% (about 12 kgpc) from 2019 (alternatively 85% of WEEE which has been generated). Finally, in 2017, the European Commission issued the Circular Economy Package, which gives further rise to the development of a reverse supply chain from consumers to producers where EPR plays a critical role in accomplishing the defined quantitative targets.
In Italy, the EU Directive 2002/96 was implemented through the Legislative Decree 2005/151 and the Ministerial Decree 185/2007 that regulated the management of WEEE by promoting the recycle, reuse and recovery and by restricting the use of certain hazardous substances contained in EEE.
Specifically, the first decree regulated the management of WEEE collection system and adopted the Clearing House Model as its own national system. The second decree focuses, instead, on the actors of the WEEE collection system. The Clearing House is a national system, whereas the end product manufacturers are grouped in organizations named Collective systems which are responsible for the entire WEEE management cycle. These actors cooperate with various operators (collectors, logistic operators, recyclers, waste management organizations, etc.) to provide WEEE management services. The government supervises the various Collective systems by means of the Coordination center (CdCRaee) which operates as a clearing house among the Collective systems to which they have to adhere. The Coordination center realizes the supervision of the entire system.
Finally, the legislative Decree 2005/151 has introduced efficiency awards for the Municipal CCs when they are able to reach a certain collection performance.
In 2014, Italy has issued the
Legislative Decree 2014/49, drawn on the Directive 2012/19/EU, that (a)
adopts the new and higher collection target since 2016; (b) establishes
appropriate measures and procedures to prevent the WEEE generation; and
(c) promotes reuse, recycling and other forms of WEEE recovery also by
introducing efficiency bonuses.
Methodological Approach
Following the Agovino et al. approach, we proceed in three steps. First, we analyze the transition probabilities of Italian provinces among three different e-waste collection states. Second, we investigate the state of the e-waste collection points in the Italian provinces. To perform these analyses, the probability transition matrix methodology was adopted. Thirdly, we have analysed the presence of a correlation between the WEEE collection rate and the dynamics of the CCs in different areas and provinces in order to better comprehend the role that can play both the investments in the CC system and other soft measures (e.g.,: communication, information and education campaigns)in achieving the WEEE collection targets.
In particular, the transition matrix is a methodology to
measure the probability of moving an element from initial state (at time
t) to a new state at time . Ina transition probability matrix (P),
the generic term
is defined as
, where as
represents the number of elements moving from an initial i state to a
final j state (Table 2). The rows and columns of the matrix describe,
respectively, the initial state and the final state, while the terms on
the main diagonal represent the steady state, namely the probability of
an element to remain in the same condition during a given unit of time.
Furthermore, it is possible to recognize an "absorbing state" which
occurs, in general, whenever the probability that an element exits at
from that state is zero. In other words, when one of the diagonal
transition probabilities of the matrix is unity.
Table 2. Transition probability matrix.
... | ... | total (a) |
||||
---|---|---|---|---|---|---|
... | ... | |||||
... | ... | ... | ... | ... | ... | ... |
... | ... | |||||
... | ... | ... | ... | ... | ... | ... |
... | ... | |||||
total (b) |
... | ... |
The elements of matrix
The data about WEEE's management system have been extracted from the records of the Italian Coordination center which is the institution set by WEEE Legislative Decree 151/05 (art. 13) for "the optimization of the activities of competence of the collective systems, to guarantee common, homogeneous and uniform operating conditions".
In particular,
we used data about kg of WEEE collected and about the CCs related to
the 110 Italian provinces, corresponding to the European level NUTS-3
over the period 2009–2017. Level NUTS-3 represents a more widespread
WEEE collection data, having substantial internal homogeneity and the
possibility to interpret the data deviations caused by limiting the
influences of the territorial and demographic dimension.
The Field Analysis: WEEE Collection Targets
WEEE Collection State
The WEEE collected in Europe is measured through the kg of WEEE pro capite (kgpc) which is the index adopted by the European Union as the target of the collection efficacy. The target was 4 kgpc per year until 2015 (Directive 2002/96/EU, art. 5). Then, the new Directive (2012/19/EU) has introduced stricter measures that correspond to a target of about8kgpcfrom 2016 and about 10 kgpc from 2019.
As described in Section 4, to set up the probability transition matrix, we have defined three states based on the value of the kgpc of WEEE collected: low (LWC), medium (MWC) and high (HWC). Considered that the HWC state contains several provinces, it was further divided into three secondary states: H1WC, H2WC and H3WC (Table 3).
Table 3. Summary of state about WEEE performance.
State. | Range of WEEE kgpc |
---|---|
LWC | 0–2 |
MWC | 2–4 |
HWC | >4 |
-H1WC | 4–6 |
-H2WC | 6–8 |
-H3WC | ≥8 |
Descriptive Results
Table 4 reports the percentage of Italian provinces according to the first three defined states (LWC, MWC, HWC) of WEEE collection statistics by macro areas for the period 2008–2017. The HWC state (mainly related to Northern provinces) ranges from 5.1 to 12.1 kg.
Table 4. WEEE statistics by collection states, macro-area and years (2008–2017): % of provinces.
Years | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | ||
LWC (%) | North | 28.2 | 1.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Central | 20.0 | 5.5 | 2.7 | 2.7 | 2.7 | 0.9 | 1.8 | 1.8 | 0.9 | 0.9 | |
South | 31.8 | 23.6 | 13.6 | 10.9 | 9.1 | 13.6 | 16.4 | 14.5 | 10.0 | 11.8 | |
Italy | 80.0 | 30.9 | 16.4 | 13.6 | 11.8 | 14.5 | 18.2 | 16.4 | 10.9 | 12.7 | |
MWC (%) | North | 13.6 | 13.6 | 1.8 | 0 | 5.5 | 8.2 | 4.5 | 1.8 | 1.8 | 0.9 |
Central | 3.6 | 10.9 | 10.0 | 7.3 | 5.5 | 10.0 | 10.9 | 7.3 | 5.5 | 5.5 | |
South | 1.8 | 6.4 | 12.7 | 13.6 | 17.3 | 13.6 | 10.9 | 10.9 | 12.7 | 10.9 | |
Italy | 19.1 | 30.9 | 24.5 | 20.9 | 28.2 | 31.8 | 26.4 | 20.0 | 20.0 | 17.3 | |
HWC (%) | North | 0.9 | 27.3 | 40.9 | 42.7 | 37.3 | 34.5 | 38.2 | 40.9 | 40.9 | 41.8 |
Central | 0 | 7.3 | 10.9 | 13.6 | 15.5 | 12.7 | 10.9 | 14.5 | 17.3 | 17.3 | |
South | 0 | 3.6 | 7.3 | 9.1 | 7.3 | 6.4 | 6.4 | 8.2 | 10.9 | 10.9 | |
Italy | 0.9 | 38.2 | 59.1 | 65.5 | 60.0 | 53.6 | 55.5 | 63.6 | 69.1 | 70.0 |
Transition Probability Matrix Results
2008–2017 | LWC | MWC | H1WC | H2WC | H3WC | Total (a) |
---|---|---|---|---|---|---|
LWC | 14 (15.9%) * | 18 (20.5%) | 35 (39.8%) | 15 (17%) | 6 (6.8%) | 88 (80%) |
MWC | 1 (4.8%) ** | 1 (4.8%) | 9 (42.9%) | 8 (38.1%) | 2 (9.5%) | 21 (19.1%) |
HWC | 0 (0%) | 0 (0%) | 0 (0%) | 1 (100%) | 0 (0%) | 1 (0.9%) |
Total (b) | 15 (13.6%) | 19 (17.3%) | 44 (40%) | 24 (21.8%) | 8 (7.3%) | 110 (100%) |
Group | Description | States Gained | Province (Area) | |
---|---|---|---|---|
Best performing provinces (6) | Collection rate > 8 kgpc and moved from LWC state to H3WC state. (More than virtuous provinces) | 5 | Aosta (N) | Gorizia (N) |
Bologna (N) | Isernia (S) | |||
Como (N) | Nuoro (S) | |||
Second-best performing provinces (2) | Collection rate > 8 kgpc and moved from MWC state to H3WC state. (Virtuous provinces) | 4 | Sassari (S) | Trento (N) |
Worst performing provinces * (11) | After ten years remained or recedes in LWC (Structurally blocked provinces) | 0 | Agrigento (S) Barletta-A-T (S) Caltanissetta (S) Cosenza (S) Crotone (S) Enna (S) |
Foggia (S) Pescara (C) Siracusa (S) Taranto (S) Vibo V. (S) |
The second-best performing provinces group is that of the second highest provinces, which improved their state of 4 positions conditional on being in the MWC state. This group includes only two provinces, Sassari (Southern Italy) and Trento (Northern Italy), which in 2017 collected more than 8 kgpc. They are indicated as "virtuous".
LWC | MWC | H1WC | H2WC | H3WC | Total (a) | ||
---|---|---|---|---|---|---|---|
2008–2012 | LWC | 13 (14.8%) | 30 (34.1%) | 35 (39.8%) | 9 (10.2%) | 1 (1.1%) | 88 (80%) |
MWC | 0 (0%) | 1 (4.8%) | 13 (61.9%) | 6 (28.6%) | 1 (4.8%) | 21 (19.1%) | |
H1WC | 0 (0%) | 0 (0%) | 1 (100%) | 0 (0%) | 0 (0%) | 1 (0.9%) | |
Total (b) | 13 (11.8%) | 31 (28.2%) | 49 (44.5%) | 15 (13.6%) | 2 (1.8%) | 110 (100%) | |
2013–2017 | LWC | 10 (62.5%) | 5 (31.3%) | 0 (0%) | 0 (0%) | 1 (6.3%) | 16 (14.5%) |
MWC | 3 (8.6%) | 14 (40%) | 17 (48.6%) | 1 (2.9%) | 0 (0%) | 35 (31.8%) | |
H1WC | 1 (2.1%) | 0 (0%) | 27 (57.4%) | 16 (34%) | 3 (6.4%) | 47 (42.7%) | |
H2WC | 0 (0%) | 0 (0%) | 0 (0%) | 7 (77.8%) | 2 (22.2%) | 9 (8.2%) | |
H3WC | 1 (33.3%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (66.7%) | 3 (2.7%) | |
Total (b) | 14 (12.7%) | 19 (17.3%) | 44 (40%) | 20 (18.2%) | 13 (11.8%) | 110 |
The Field Analysis: WEEE Collection Center Analysis
Definition of the Collection Centers
The collection centers are key components of WEEE's management system. The effectiveness of the CC system (the amount of WEEE collected per inhabitant per CC) depends on several factors, among which we can mainly indicate the distribution of the CCs in an area, the relative number of inhabitants and the management of the single CC. In this section, we adopt the transaction matrix methodology to analyze the dynamics of the contribution of the CCs of the Italian provinces at the WEEE collection process. In particular, we consider the number of CCs per 50,000 inhabitants for the Italian provinces in order to take into account the dimension of the province's population. In this way, we intend to analyze the role played by the appropriateness of the structure (in terms of number of CCs per province and their distribution) of the collecting system.
The model of Transaction matrix studied adopts three states: Low (LPC), Medium (MPC) and High (LPC) Presence of CCs. These three states correspond at three different levels of CCs per 50,000 inhabitants that have been calculated by means the standard deviation (2.7) of the distribution of the CCs across the Italian provinces. Table 8 summarizes the three states used in the analysis.
Table 8. Number of collection centers per 50,000 inhabitants.
State | WEEE CC |
---|---|
LPC | 0–2.7 |
MPC | 2.7–5.4 |
HPC | ≥5.4 |
Descriptive Results
![](https://learn.saylor.org/pluginfile.php/5619133/mod_book/chapter/49998/sustainability-11-02430-g001-550.jpeg)
Transition Probability Matrix Results
2009–2017 | LWC | MWC | HWC | Total (a) |
---|---|---|---|---|
LWC | 46 (69.7%) ** | 17 (25.8%) | 3 (4.5%) | 66 (60%) |
MWC | 2 (7.7%) * | 14 (53.8%) | 10 (38.5%) | 26 (23.6%) |
HWC | 1 (5.6%) * | 2 (11.1%) | 15 (83.3%) | 18 (16.4%) |
Total (b) | 49 (44.5%) | 33 (30%) | 28 (25.5%) | 110 (100%) |
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 |
- 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.
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 (-) |
Discussion of the Results
This study is focused on the collection activity of the reverse supply chain of the WEEE since it represents a critical phase impacting the overall performance.
(1) A first result of this research has underlined that a territorial divide exists in Italy about the e-waste collection rate. In fact, even though the 70% of provinces have moved in the HWC, almost ¾ of these provinces are localized in Northern Italy. Furthermore, even though the provinces in the LWC and MWC states have decreased over ten years, those that still remain in these states are mainly Southern and Central of Italy provinces. This divide is also confirmed by the fact that almost all the provinces of the worst ("structurally blocked") group are located in Southern Italy. It is interesting to underline that such territorial divide could depend on the existence of a socio-economic gap among Southern and Northern provinces. This finding confirms the results of other authors about the Italian territorial divide even though this study shows more detailed evidences at provincial level. On the contrary, the results about the best performers are not confirmed since only one province of this study is consistent with the best performing regions.
Similarly, the divide is confirmed with regard to the Collection Centers. After 10 years, in a context of a general shift towards medium and high states, Southern provinces are more than half of those in the LPC state, although they remarkably decrease in MPC. Even though some positive elements emerge (more southern presence in higher state), the territorial distribution is still uneven. This result is coherent with other research that aims to optimize the distribution of the CCs in the territorial areas. Moreover, the results concerning the presence of a territorial divide in the Italian WEEE collection are also consistent with those of other contributions, even if for a different stream of waste. In fact, Agovino M. et al. highlights that the legislative measures on a separate municipal waste collection in Italy in the period 1999–2011, while increasing collection rates at the national level, do not reduce the gap among provinces of different areas.
Moreover, the transition probability matrix analysis provides an explanation of this divide since it shows the presence of a remarkable steady state condition which mirrors the difficulty to carry out structural investments aimed to increase the CCs. It is possible to claim that this is particularly true for those provinces that, at the beginning of the introduction of a new EU WEEE management system, halfway through the 2000–2010 decade, did not already have a WEEE collection infrastructure.
(2) As for the impact of external
legislative events on the WEEE collection performance, a second result
emerged from the transition probability matrix analysis. In fact, the
matrix shows that the increase of provinces in the HWC state happened in
two different periods due to two diverse external events caused by
legislative changes (the digital switchover and the entry into force of
LD 49/2014). These events shocked the system provoking a rapid positive
reaction.
Moreover, we have verified that the first event caused a higher mobility probability toward the best states than the second one. The sole absorbing state for the entire period 2008–2017 was related to the HWC state and worked mainly during the first sub-period under consideration.
(3) A third result highlights the presence of a correlation between the WEEE collection and the CC distribution among provinces. This finding appears to be in line with several research works despite being in different territorial contexts and with diverse methodologies. The correlation value is high for the entire period for all the Italian provinces. Deepening the analysis by groups of provinces, it emerges that Southern provinces have the higher share among those that have invested in the CCs. By exploiting this positive correlation, these virtuous provinces have also raised the collection results. Specifically, according to the empirical results, Isernia, Nuoro and Sassari could represent an important model of the WEEE collection system to imitate. Indeed, in a few years, these Italian provinces of Southern of Italy were able to outperform the WEEE collection target defined by EU. By focusing on each province, we can try to understand the factors that strongly affect their WEEE collection performance in order to consider them in the definition of future measures and initiatives for different Italian provinces. Specifically, it is possible to underline that the main factors are: (i) a wider diffusion of CCs at the provincial level; and (ii) the organization of several events to sensitize citizens to adopt virtuous behaviours.
In
fact, Isernia, Nuoro and Sassari in the last several years have
strongly improved the network of CCs in the Southern area; specifically,
in 2017, they have respectively 17, 21 and 12 CCs for 100,000
inhabitants. This data is very important if we consider that, on
average, there are 5 CCs for 100,000 habitants in the Southern of Italy.
Additionally, in the considered provinces, several events, initiatives
and information campaign were organized in the last few years.
Conclusions
This paper has shed light on the extent to which the current Italian e-waste collection system has been able to achieve the targets defined by European and Italian authorities. The research, which has a basic descriptive nature, has focused on the role played by the collection centers by analyzing both the performance of the provincial collection system and the infrastructure represented by the provincial distribution of CCs in Italy in the period 2008–2017. In particular, the work has aimed to verify the presence of territorial divide about the WEEE collection at the provincial level and the presence of the correlation between collection performance and distribution of CCs. Three main results emerged from the adoption of the transition probability matrix methodology, which is novel for the WEEE waste stream.
The first result is that a territorial divide exists in Italy
between different areas of the country about the e-waste collection
rate and the e-waste Collection Centers infrastructure. Nevertheless,
some Southern provinces show positive WEEE collection performance so
that it is possible to claim that viable solutions can be found by
adopting appropriate local policy measures (actions, investments,
initiatives, etc.) which could be replicated by other lagging provinces.
A second result is the existence of a positive relationship between the
WEEE collection and the distribution of CCs among Italian provinces
which, in turn, contributes to feeding the territorial divide. Moreover,
we have found that external legislative events have had impact in
different periods of time on the Collection results of the WEEE
management system.
The transition probability matrix has resulted in
being an effective methodology able to recognize specific province
behaviors for both the WEEE collection performance and the CC
distribution. Additionally, more absorbing states of different intensity
emerged in the analysis. These absorbing states are more intense for
the CC distribution than for the WEEE collection coherently with the
higher constraints characterizing the investments in CC infrastructure. A
summary of main findings is shown in Table 12.
Table 12. Summary of findings.
Variable | Results |
---|---|
WEEE collected rate | - A territorial divide exists among different geographical area in Italy - Generally, provinces of Northern of Italy perform better than ones of Southern and Central Italy - The worst performing provinces are all in Southern Italy - In the period 2008–2012, there is a higher mobility probability than in the period 2013–2017 (probably caused by external legislative events) - In Southern Italy, there are two virtuous provinces, namely Isernia and Nuoro |
WEEE Collection Centers | - High probability of permanence in a same state in the entire period. - The higher steady state condition is related to HWC (83.3%), followed by LWC (70%) - Difficulty to make infrastructural investments (e.g., opening of new CCs) - Higher number of CCs in Northern Italy |
Correlation analysis between WEEE collected rate and WEEE collection Centers | - A correlation between WEEE collection and distribution of CCs exists - High number of CCs could explain different WEEE collection performance - Although the role of the CCs is becoming less crucial, it still remains a critical aspect of the Italian WEEE management system - Three different groups of provinces have been identified on the basis of the two considered dimensions (WEEE collection rate and CCs) |