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Proceedings SUM2016, Third Symposium on Urban Mining, 23 - 25 May 2016 Old Monastery of St. Augustine, Bergamo, Italy 2016 by CISA Publisher, Italy

COLLECTION PERFORMANCE OF E-WASTE IN ITALY. WHICH ARE THE FACTORS THAT CAN EXPLAIN IT?

Marinella Favota and Luca Grassettia

a Department of Economics and Statistics, University of Udine, Italy

ABSTRACT: This research aims at studying the collection performance of the Italian systems for

household electric and electronic waste (WEEE) in order to find out which are the factors that can explain it. WEEE is an important stream of waste for its growing quantity as well as the content of precious and hazardous substance. In December 2015 the European Union issued a circular economy package that aims at closing the loop of material. In this paradigm the Extended Producer Responsibility (EPR) principle that regulates the WEEE management, plays an important role. The European collection target of the first WEEE Directive (which is one of several instruments of EPR) was set to 4 kg per inhabitant per year. The collection phase can be a bottleneck or a factor of success in the EPR system and therefore in circular economy paradigm. In this paper we analysed the results of the 20 Italian regions from 2008 to 2015. We considered socio-demographic variables that could influence these results as well as the variable directly connected with the phenomenon: the collection presence variable expressed as number of collection point per 100.000 inhabitants. The dependent variables considered are: population density, territorial classification as north, centre and south, pro-capita GDP and presence of metropolis in the region. The results show that the kg collected and the collection point presence are highly correlated while GDP presents multicollinearity issues and the metropole presence factor is not significant. Moreover, there is a negative correlation with the population density and the year factor shows that in the first year (2008) the system was underperforming compared with the other years that do not present discrepancy. The macro region conditions show high discrepancy between the south regions, that are underperforming, and the centre and north regions. The Stochastic Frontier Model is also estimated. This allows the identification of the optimal production function among the twenty Italian regions. The best performing region is Tuscany. A in depth study of the WEEE system structure of this region would help understanding the factors affecting the efficiency of the system in order to replicate them and improve the collection performance which is one of the factors that help closing the loop of circular economy.

Keywords: e-waste, circular economy, waste collection, convenience

SUM2016 Third Symposium on Urban Mining

1. INTRODUCTION

Waste from electrical and electronic equipment (WEEE) is one of the fastest growing stream of waste in the world and about 42 million metric tons of e-waste was generated globally in 2014 (Baldé et al., 2015). The reduction of useful life span of existing devices and the transformation of several everyday products into Electric Electronic Equipment (EEE) increase the quantity and volume of sold products that will become WEEE at the end of their life. The reduction of the life span of products can be explained by the even faster release of new products with new features (Saphores et al., 2012). The transformation of things or objects in to EEE is due to the rapid growing of the so called “Internet of Things” (IoT). The “Internet of Things” firstly defined by Kevin Aston in 1999 (Ashton, 2009) has taken different routes and definitions. In this paradigm, more and more objects around us will be able to interact with each other and cooperate with their neighbors to reach common goals thanks to sensors, Radio-Frequency Identification (RFID) tags, actuators, mobile phones and so on (Atzori et al., 2010 and Gubbi et al., 2013). In 2013 there were 9 billion interconnected devices and they are expected to reach 24 billion by 2020 (Gubbi et al., 2013). Atzori and colleagues (2010) report that the US National Intelligence Council foresees that “by 2025 Internet nodes may reside in everyday things – food packages, furniture, paper documents, and more”. The National Strategy for Electronics Stewardship, issued by the United States Interagency Task Force on Electronics Stewardship, warns that these new technologies increase the challenge of protecting human health and the environment from the harmful effects of unsafe handling and disposal (US ITF on Electronics Stewardship, 2011). Worldwide the governments issued regulations to deal with e-waste. For example the United States Congress addressed the issue of toxic e-waste in 2013 with the Responsible Electronics Recycling Act (1) while the European Union issued the first Directive on WEEE in 2003 (2002/96/EC) (2). The objective was to improve the environmental management of WEEE including the collection, treatment, recovery and recycling of WEEE. The Directive includes the Extended Producer Responsibility principle that is defined by OECD (2001) as an environmental policy approach in which a producer’s responsibility for a product is extended to the post-consumer stage of a product’s life cycle. The 2003 WEEE Directive set a rate of separate collection of e-waste from private households of at least four kilograms per inhabitant per year. This flat rate should have been achieved by 31 December 2006 at the latest, by each Member State. This collection rate target is one of the several policy instruments that can be set within the Extended Producer Responsibility (EPR) policy. According to Potočnik (European Commission, 2012) this 4 kg target, represents about 2 million tons per year out of around 10 million tons of WEEE generated in EU. This binding collection target proved to be too low for some European States and too demanding for other States. It was therefore reviewed in 2012 by the European Parliament with the recast of the WEEE Directive (3) which set percentage targets: 45% of e-waste collected based on three years of EEE Placed on the Market (POM) from 2016 and 65% from 2019 (alternatively 85% of WEEE generated). In December 2015 the European Commission adopted an ambitious Circular Economy Package, where the goal of “closing the loop” of product lifecycles includes greater recycling and re-use (5). In this proposal the EPR plays an important role in attain at least the quantity targets as defined in the WEEE Directive. The collection phase is an important phase in the circular economy approach. It can be a bottleneck in EPR schemes, or a benefit if effectively managed because it could improve the quantity as well as the quality of waste collected. Therefore, in this research we focus on the collection results of the Italian systems of household WEEE and we analyse the variable that influence such outcomes. Additionally, we study the efficiency of the collection of the twenty Italian regions by adopting the stochastic frontier model theory in order to identify the optimal


production function. In Italy the producers created 17 consortia to comply with the law on WEEE and they are coordinated by the national clearinghouse. This autonomous body provides annual reports that include data and statistics. We also accessed data from the National Institute of Statistics (ISTAT) in order to study the collection results linked to other socio-economic variables. The paper is structured as follow: after the introduction we provide a theoretical background, followed by the material and methods section, results and discussion section and conclusions.

2. THEORETICAL BACKGROUND

In May 2014 the European Commission reviewed the list of the critical raw material for EU to include 20 materials (4). A study commissioned by ENEP to Öko Institute (2009) on critical metals reports that “a very relevant secondary material in a global scale is WEEE”. According to a study by the Copenhagen Resource Institute (2014), the current recycling of critical metals in Europe is low and can be improved by several initiatives: better collection, better pre-processing and end-processing, limiting the export of WEEE outside the EU as well as better design of EEE. The recast WEEE Directive, as did the first directive, reports that “Separate collection is a precondition for ensuring specific treatment and recycling of WEEE and is necessary to achieve the chosen level of protection of human health and the environment in the Union”. In literature it is recognized that collection is one of the bottlenecks in most EPR schemes in terms of effectiveness and cost (Rotter, 2011). In the aim at closing the loop of materials, in a circular economy framework, the role of waste collection is very important since, as reported by Gallardo and colleague (2015), an effective collection method can improve the quality and quantity of recovered materials.

Therefore, as reported by Bouvier and Wagner (2011) it is important to focus on those attributes that can be manipulated by policy makers in an effort to increase voluntary participation in household e-waste collection. In general terms, there are three main collection systems available, other than pay-as-you-throw system: curbside collection, neighborhood collection and clean point (González-Torre and Adenso-Diaz, 2005). Hornik and colleagues (1995) defined external and internal factors as determinants of recycling behaviour. Defection from recycling is linked to high external barriers by several researches. Such external barriers are time, money and effort needed to prepare, store and transport recyclables (Hornik et al., 1995). Regarding researches focused on e-waste, Saphores and colleagues (2006) studied the willingness to recycle e-waste with the case study of California. Omran and Shiopu (2015) in their research found out that statistical analysis shows that the negative perception of the distance to the recycling points and the lack of recycling in their neighbourhood refrain from recycling.

In term of convenience, two authors provided important contributions. In 2006 Saphores and colleagues report that convenience may impact recycling behaviour and this includes the proximity of recycling containers, the provision of curbside collection, available storage space and difficulty of recycling some materials. Wagner (2013) examines the concept of “convenient collection” for Extended Producer Responsibility programs (EPR) and Product Stewardship programs (PS). He considers the convenience of collection system a key factor to maximize consumer participation to increase the quantity of waste collected. Five elements of convenience are analysed: knowledge requirements, proximity to the collection site, opportunity to drop-off materials, draw of collection site and ease of the process. Proximity is measured by distance and time ‘expenditure.

In 2015 Ylä-Mella and colleagues studied the consumers’ perceptions towards recycling and re-use of mobile phones. For this specific type of e-waste, the study conducted in Finland shows that even if the permanent collection points are regarded simple to use, and consumers are


aware of the existence of a WEEE recovery system, the current system is not adequate to motivate the return of old mobile phone that are stored at home for potential future use that might not even occur. Therefore they suggest improving the information and publicity of the recovery options available as well as introducing a monetary deposit system.

In the dataset provided by the national clearing house, the proximity of collection point is not provided but there are full data available on the collection points per region. Therefore, we use the collection point rate (expressed as the number of collection point per 100.00 inhabitants) as a proxy of the proximity factor. This study aims at analyzing the collection performance of the Italian system created starting from 2007 and based on more than four thousand collection points, distributed in the twenty regions.

3. MATERIAL AND METHODS

We analyze the Italian collection system for household WEEE. The data is provided by the national clearinghouse “CdC RAEE”, while the socio-economic data source is the National Institute of Statistics (ISTAT) (6). We want to focus on the collection performance of the system measured as the kilograms collected per inhabitant, taking into consideration the variables as described in section 3.1.

The e-waste collected and treated before and after the introduction of the WEEE Directive shows an important increase starting from the 2007 when (on the 1st of September) producers became responsible for their e-waste. Between 2001 and 2006 the quantity of WEEE collected from households in Italy ranged between 1.2 and 1.8 per kg per inhabitant (ISPRA, waste report, 2006). Then the following years the collection rate was 2 kg per inhabitant in 2007, 2.6 kg in 2008 (Eurostat, 2013), 3.21 kg in 2009, and 4.06 Kg in 2010 when Italy reached the goal of collection set by the WEEE Directive. The target was missed again in 2013 and 2012. The data provided by the national clearinghouse allows an analysis based on the twenty Italian regional administrations. We report the data in table 1.

Year Collection points (n.)*

Population served (%)*

Tot WEEE collected (kg)*

Collection rate (kg/inhabitant)

2008 2,785 75.8% 65,713,414 2.60** 2009 3,044 86.3% 193,042,777 3.21 2010 3,564 89.6% 245,350,782 4.06 2011 3,511 - 260,090,413 4.29 2012 3,767 - 237,965,563 4.00 2013 3,900 - 225,931,218 3.80 2014 4,038 - 231,717,031 3.81 2015 4,260 - 249,253,917 4.09

Table 1: Collection outlook in Italy years 2008-2015 Data source: * Italian national clearinghouse (CdCRAEE) **Eurostat data Population data source: National Institute of Statistics (ISTAT)

The collection rate measured as the quantity of kilograms collected per inhabitant per year


(per-capital collection rate), can be subject to different variables. As reported by Bach and colleagues (2004), the collected waste, in general terms, are subjects to two determinants: demographics (that determines the waste potential) and logistic parameters (that influence the convenience of collection). Moreover, waste potential is considered the frame for decision makers as it can be hardly influenced, while convenience can be influenced by factors such as average distance to the next collection center or collection frequency (Bach, 2004). In our study we aim at presenting a model that takes the collection rate as the independent variables and assess the other variables as parameters which can influence it.

The data available spans from 2008 to 2015 and this allows a pooled analysis (without considering the time dimension) as well as a panel data specific analysis. Additionally, the annual reports by the clearinghouse picture a situation differentiated not only by regional administrations but also by macro regional areas namely north, centre and south macro regions. We mainly use the subdivision provided by the National Institute of Statistics (ISTAT). We consider Sardinia in the centre macro regions as its performances are aligned to the other central regions in all sampled years. Therefore the central macro area includes: Tuscany, Marche, Umbria, Lazio and Sardinia. The North macro area include: Friuli Venezia Giulia, Veneto, Trentino Alto Adige, Lombardia, Piemonte, Valle d’Aosta, Liguria and Emilia Romagna. The South macro area includes: Abruzzo, Basilicata, Molise, Calabria, Campania, Puglia and Sicily. The other socio-economic variables considered are: GDP per capita, population density, and the presence of metropolis in the regions. This data is provided by ISTAT and we considered the definition of metropolis as included in the Italian Constitutional Law.

3.1 Preliminary data analysis and data treatment

A preliminary data analysis is conducted in order to describe the variables involved in the study, to identify possible issues (as for instance outliers’ presence). The dataset presents a panel structure considering the twenty regions of Italy observed over an eight year time span (from 2008 to 2015). The phenomena are observed at the regional level. In particular, we focus on: • Some socio-demographic variables (that we will use as control variables in the following analyses): – The population (Pop), – The surface (Surf), – The factor that identifies the territorial classification (north, centre and south), – The number of metropolis, – The pro-capita GDP (procapGDP). • The WEEE variables (directly connected with the interest phenomenon): – The total collected WEEEs in kilograms (CollKg), – The number of collection points (CollPoints). The analyses of correlation between collected variables show that the quantitative phenomena are all positively related and that, given the peculiar kind of phenomenon we are observing, a first data transformation is surely needed. In particular, in order to study the productive capability of the waste collection service in Italy the available measures are transformed in order to define:

The collection rate expressed as the quantity of kilograms collected per inhabitant. The per-capita collection points defined as the number of collection points per 100000

inhabitants.


The population density given by the population and surface ratio.

After the data treatment the analysis can focus on the behavior of collection rates. The descriptive statistics (mean, standard deviation, minimum and maximum) are reported in Table 3 conditionally to: year, macro region subdivision and presence of metropolis in the considered region. An ulterior analysis on the correlation structure in our dataset can be based on the correlation matrix which can be used to analyse the possible sources of collinearity issues. The presence of correlation between the so called covariates can in fact course the estimation process. The results reported in the following table show that the correlations between the explicative variables are not so high to suggest the presence of multicollinearity.

Descriptive conditional statistics for Collection Rates

mean sd min max

General 3.77 1.94 0.13 8.29

2008 1.18 0.85 0.13 2.9

2009 3.33 1.75 0.82 6.43

2010 4.16 1.83 1.63 7.16

2011 4.43 1.88 1.9 7.41

2012 4.32 1.74 2.22 8.29

2013 4.18 1.7 1.74 8.20

2014 4.11 1.73 1.72 7.80

2015 4.43 1.68 2.06 8.24

Central Regions 3.99 1.54 0.56 7.16

North Regions 5.03 1.71 0.57 8.29

South Regions 2.40 1.34 0.13 5.83

NoMetropolis 4.18 2.17 0.36 8.29

Metropolis 3.49 1.72 0.13 6.43

Correlations between quantitative variables

CollRate procapGDP procapCP PopDens

CollRate 1.0000 0.5492 0.7124 ‐0.1718

procapGDP 0.5492 1.0000 0.5622 0.1275

procapCP 0.7124 0.5622 1.0000 ‐0.3515

PopDens ‐0.1718 0.1275 ‐0.3515 1.0000Table 3: Conditional descriptive statistics for the collection rates and correlations between

quantitative variables The results of an in-depth analysis of the correlations between the collection rates and the per-

capita collection points, the per-capita GDP and the population density are summarized in the Table 2. The intensity, the direction and the significance of the correlations between the response variable and the quantitative explicative variables are provided. The global correlation index is considered along with the year specific results in order to study the possible changes in the correlation structure. The correlations for the first two variables are all significant and positive while the population density is negatively correlated with the collection rate and the observed relationship is significant only for analysis on the pooled data. CPpc GDPpc PopDens


Correlation p‐value Correlation p‐value Correlation p‐value

General 0.71 0.00 0.55 0.00 ‐0.17 0.03

2015 0.78 0.00 0.00 0.00 ‐0.28 0.23

2014 0.78 0.00 0.78 0.00 ‐0.23 0.32

2013 0.82 0.00 0.74 0.00 ‐0.31 0.18

2012 0.75 0.00 0.75 0.00 ‐0.23 0.32

2011 0.72 0.00 0.73 0.00 ‐0.13 0.59

2010 0.71 0.00 0.61 0.00 ‐0.12 0.62

2009 0.75 0.00 0.58 0.01 ‐0.18 0.45

2008 0.83 0.00 0.60 0.01 ‐0.20 0.39Table 2: Correlations Index and p-values of correlation tests (conditional to the year of observation) The most important relationship in our analysis is the one between collection rates and per-capita collection points. In Figure 1 this relationship is studied conditionally to the year of observation. The relationship between the two variables is quite similar for all the observed years but the year 2008 observations. In fact, while the effect of a new collection point is almost equal in all the sub-samples, the absolute level of collection changed significantly after the first year when the WEEE system was operative. We will use this evidence to simplify the model specification analysed in the next section.

Figure 1: The relationship between collection rates and per-capita collection points conditional


to the year of observation

A similar study has been conducted with the three sub-samples obtained considering the macro-region definition. The results reported in Figure 2 show that while between the north and south regions (dotted and dashed lines respectively) it exists a significant difference in terms of absolute WEEE collection level. The behavior of the central region is different. The plot suggests that the benefit of additional collection points is higher for these regions (the estimated linear relationship present a higher slope).

Figure 2: The relationship between collection rates and per-capita collection points conditional

to the macro-region classification

4. RESULTS AND DISCUSSION

Using the descriptive analysis results we want now to focus on the regression analysis of the productivity of the collection points. We first analyse the productive framework with a multivariate regression. All the following results are obtained in terms of logarithms. This is done for two different reasons: _ to reduce the issues connected with the heteroscedasticity in the observed dataset, _ to be able to use the classical approach to productivity analysis based on the Cobb-Douglas Productive function.


4.1 The multiple linear regression on panel data A first step in the linear model estimation regards the evaluation of multicollinearity issue in model

specification. Looking at the VIF (Variance Inflation Factor) results it is possible to identify the presence of multicollinearity issue in the model specification as the per-capita GDP presents a value equal to 5.89. Therefore the “procapGDP” causes the problem (which is probably related with territorial classification and presence of metropolis). Consequently we simplify the model specification as:

Log (CollRate) = β0 + β1 log (CollPoints) + β2 log (PopDens) + β3Ds + β4DN + β5Dmetr + β6D2008 + εit

where DS represents the dummy for the south macro area, DN the north macro area, Dmetr the regions with the presence of metropolis, D2008 represents the dummy for year 2008 and is the idiosyncratic error term.

The choice of using a single time dummy for year 2008 is due to the evidences given in Figure 1 and to the results of a preliminary regression analysis considering the time factor with 8 levels. The estimation results show that the coefficients of the time dummies are similar. They are all significant because the level of collection is different between the first year (the benchmark one) and the following ones. These results are omitted because they are redundant. This is the reason why, we substitute the time factor with a single dummy variable for the first year of WEEE collection, namely year 2008.

We first estimated this model with the ordinary least square method (Pooled linear model). In order to consider the specific panel structure of the observed dataset it is necessary to adopt the specific model estimation process (see Baltagi, 2008 for a full review on panel data models). The panel data regression model can be specified considering the same variables as the linear model. The panel data model specification can be considered in order to take into account of the correlation existing between replicated observations of the same individuals and/or to consider the time dimension introducing the correlation between observations which refer to the same year. Some different model specifications and some different estimation strategies are available but after a preliminary analysis we decided to adopt the random effects paradigm and to estimate a model introducing the time random effect only. The model we finally estimated is as specified in liner model reported before.

In Table 4 the results of linear model with time dummy and panel data specific models (with and without variable selection) are summarized. All reported model estimates present similar coefficients. The goodness of fit indexes is higher than 0.8 and the model diagnosis (specifically developed on the pooled linear model) show that the model estimates can be considered reliable (see Figure 3). As shown in the figure, there are no relevant outliers and the error distribution is very close to normality.


Figure 3: Linear model diagnostic plots

Pooled Linear Model

Panel data LM

Selected var. PDLM

Estimate p-value Estimate p-value Estimate p-value Intercept 1.033 0.000 1.105 0.000 0.919 0.000 per-capita CP 0.349 0.000 0.328 0.000 0.346 0.000 Dummy N -0.010 0.872 0.004 0.951 -0.011 0.844 Dummy S -0.634 0.000 -0.644 0.000 -0.635 0.000 Pop Dens -0.026 0.557 -0.033 0.457 Metr 0.021 0.716 0.013 0.821 Year 2008 -1.300 0.000 -1.310 0.000 -1.301 0.000 Variance terms Idiosyncratic 0.257 0.250 0.248 Time effect 0.078 0.066 Goodness of fit R^2/pseudoR^2 0.881 0.838 0.844

Table 4: Linear model estimation results As one can note, the estimation results in panel data context are very close to the simple linear


regression ones. The estimated coefficients show that, even considering all the available control variables, a positive and significant relationship between the collection rate and the per-capita collection point is observed. Given the peculiar linear model we decided to specify, the estimated coefficient corresponds to the elasticity of collection rate to input changes. In particular, we are interested in the estimated value of the coefficient for the per-capita collection points. The estimated values (0.349 for the pooled linear model) are the elasticities of production to specific input changes. In other words the effect of additional collection points on collection rates is positive. A negative and significant effect is observed for the south macro-region identifying the lower productivity of the WEEE collection system in these regions if compared with the central Italy ones. The dummy identifying the north macro-region presents a small negative coefficient. This means that the northern regions are not statistically different from the central ones in terms of average collection rates. Both the population density and the presence of metropolis correspond to irrelevant effects. Finally, the average collection rate is lower in year 2008.

Good evidence in favour of results reliability can be obtained considering the estimation of the model excluding the non-significant variables. After a selection process based on the classical information criteria (Akaike Information Criterion - AIC and Bayesian Information Criterion - BIC) we finally estimate the reduced model and the estimation results (reported in the last two columns) are coherent with the full model ones.

4.1 Panel data Stochastic Frontier Model

In order to study the efficiency of the WEEE collection process one can adopt the stochastic

frontier model theory (see Kumbhakar and Lovell, 2003). Instead of computing the average production function, the SF model specification aims at identifying the optimal production function. This kind of model specification allows estimating the technical efficiencies of the considered statistical units. The Stochastic Frontier Model specification can be obtained considering the model specification given in the previous section adding an efficiency related effect connected with a positive error term. The model is as

Log (CollRate) = β0 + β1 log (CollPoints) + β2 log (PopDens) + β3Ds + β4DN + β5Dmetr + β6D2008 – ui + εit

where ui is the one sided error component (we will consider a Truncated Normal distribution for this term).

In order to study the real efficiency of WEEE collection processes one must consider only the input of the collection process in the model specification. For this reason we excluded the macro-regional factor from the model specification. The full model is also estimated in order to compare the model estimates and the efficiencies scores.

The results of the model estimation are reported in table 5. Estimated Stochastic Frontier Production Functions

Without Territorial

Dummies With Territorial

Dummies Estimate p-value Estimate p-value Intercept 0.371 0.601 0.677 0.194 per-capita CP 0.644 0.000 0.520 0.000 Dummy N - - -0.104 0.313 Dummy S - - -0.514 0.000


Pop Dens 0.014 0.924 0.050 0.600 Metr 0.177 0.139 0.024 0.832 Year 2008 -1.158 0.000 -1.217 0.000 sigmaSq 0.197 0.178 0.069 0.000 gamma 0.784 0.000 0.383 0.008 mu 0.183 0.683 0.325 0.040

Table 5: Estimated Stochastic Frontier Production Functions The elasticity of collection point presence per inhabitant is higher than the “simple” linear

model previously estimated. All the other effects are substantially similar. The two model estimates are quite similar but some relevant differences can be observed

between the efficiency measures obtained considering the two model specifications.

Efficiencies

Region Without With Macro Regions

Abruzzo 0.913 0.950 S Basilicata 0.402 0.561 S Calabria 0.516 0.712 S Campania 0.532 0.713 S Emilia Romagna 0.706 0.723 N Friuli Venezia Giulia 0.710 0.682 N Lazio 0.729 0.594 C Liguria 0.883 0.811 N Lombardia 0.660 0.668 N Marche 0.844 0.670 C Molise 0.391 0.548 S Piemonte 0.763 0.756 N Puglia 0.458 0.627 S Sardegna 0.829 0.775 C Sicilia 0.716 0.854 S Toscana 0.952 0.858 C Trentino 0.673 0.688 N Umbria 0.930 0.771 C Valle d'Aosta 0.777 0.762 N Veneto 0.634 0.661 N

Table 7: Efficiency measures without and with the macro-regional factor (MR factor) in the

model specification. Looking at the second column in Table 7 the best productive condition is the one observed for

region Tuscany. The macro regions have the same influence (north is slightly underperforming compared to the center but the difference is no significant while the south is significantly underperforming);


population density and presence of metropolis are not significant as in the linear model. Therefore, all south regions are performing better with the territorial dummies; the north regions are almost unchanged while all the central regions are underperforming. The best performing region is Tuscany which has a high efficiency rate with and without the macro-region dummies and can be considered the benchmark. Also Umbria and Abruzzo show good performances. The efficiency is not a direct result of the collection point presence.

5. CONCLUSIONS

Cahill and co-authors (2011) pointed out that national EPR systems vary considerably in design, methods of achieving producer compliance, fee structures, influence of pre-existing policy and systems, waste stream prioritization and local authority involvement. Savage (2006) enlarges the spectrum of variables that influence the operation and performance of a scheme. He recognises that a range of “environmental” factors such as population size and density, labour cost, legal requirements and so on, together with the national recycling culture and the public wiliness to engage, have a direct impact on the scheme operation and performance. Therefore, it is clear that there is no single best solution or legislation for WEEE suitable for all: even if the e-waste problems are similar, there are several ways to tackle them in order to reach the same final objective of efficient and effective management of EoL products (Khetriwal, 2011). This problem has to be tackled by the national system working not only with PROs but with all the stakeholders involved in order to reach the new target goals. The recast Directive sets higher level of collection from 2016 (45% of the average weight placed on the market –POM- in the preceding 3 years) and 2019 (65% of the average weight POM in the preceding 3 years or 85% of WEEE generated). These goals are ambitious. However, these goals are national-specific and the recast directive provides some changes that can improve the collection rate. The first one is that waste flows include not only waste from households (that can be referred as Business to Consumers - B2C) but also from Business sources (Business to Business - B2B). The policy implication of this study is that a fix collection target can be a good starting point in the first stage of the implementation the EPR system. However, after the first stage this collection target has to be modified in order to force compliance systems to reach higher level of WEEE collection and recycling. This action has already been done by the European Commission with the WEEE Recast. However, it is important to address several other aspects of each national system such as the collection system, the roles and duties of stakeholders, the importance of the public awareness of the importance of collecting WEEE and so on.

In this article we studied the collection results of the Italian system for household WEEE in order to understand the factors that can improve them. Collection goals are considered a bottleneck in the EPR system and therefore in the more comprehensive circular economy paradigm that aims at closing the loop of product lifecycles. The Italian system managed to reach the European target of 4 kg per inhabitant per year set by the first WEEE directive. The research aims at studying the performance of the household WEEE system, expressed by the kilograms collected per inhabitant per year. The dependent variables taken into considerations are socio-demographic variables (population density, territorial classification as north, centre and south, pro-capita GDP, presence of metropolis in the region) and a variable directly connected with the interested phenomenon namely the number of collection points per 100.000 inhabitants.

In the first part of the paper we used a descriptive analysis to study the collection rate behavior. The analysis is carried out conditionally to the year, the three macro regions and the presence of metropolis in the region. The results are coherent from 2009 to 2015, while 2008 had a very low performance that can be explained by the fact that this was the first full operating year


and the structure was still not completely organized. Having said that, the following years do not present discrepancy meaning that the improvement has been constant throughout the years. The macro region condition showed a remarkable discrepancy between the center and the south regions, while the north regions are performing similarly to the center ones. This implies that an additional effort has to be considered for this region from a national level perspective. The presence of the metropolis in the region is not significant; therefore no specific attention is addressed to this variable.

The analyses of the correlations show that the depended variable (kg collected per inhabitant) are highly correlated with collection point presence (n. of collection points per 100.000 inhabitants) and with the per-capita GDP, while there is a negative correlation with the population density. The multiple regression with time effect shows that the per-capita GDP presents multicollinearity issue. It is therefore excluded from the model. This can be explained by the fact that the effect of this variable is already included in the other variables. The Stochastic Frontier Model is also estimated in order to identify the optimal production function (in the twenty regional administrations) with and without the macro-regions factor. The results show that the best performing region is Tuscany. A in depth study of the WEEE system structure of this region and the other good performing regions, would help understanding the factors affecting the efficiency of the system.

ACKNOWLEDGEMENTS

Raphael Veit at Sagis Ltd, Bangkok, Thailand for sponsoring the post-doc and providing useful advices.

Prof. Antonio Massarutto, University of Udine, Italy for supervising the full project.

REFERENCES

(1)

https://www.congress.gov/bill/113th-congress/house-bill/2791 Responsible Electronics Recycling Act (United States Congress)

(2) http://eur-lex.europa.eu/resource.html?uri=cellar:ac89e64f-a4a5-4c13-8d96-1fd1d6bcaa49.0004.02/DOC_1&format=PDF WEEE Directive (European Parliament and the Council)

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