Comparing environmental product footprint for electronic and electric equipment: a multi-criteria approach

Abstract

Electronic and electric devices are now applied in most human activities: their diffusion is increasing worldwide; furthermore, most of them are characterized by a high replacement rate due to technological obsolescence. Consequently, environmental problems due to their diffusion are increasing; several aspects are involved from the energy consumption derived from their manufacturing processes and their use phases to their end-of-life (EOL) management. Such legislative (e.g. the European Energy Efficiency directive for household appliances) or voluntary interventions (e.g. based on the ISO standards) have been introduced for such devices: the aim is to incorporate environmental considerations in product design and manufacturing in order to benefit the environment. Some attempts are focusing on defining standardized models for the overall lifecycle including waste management. The aim of this paper is to introduce a reference model for comparing environmental product footprint of electrical and electronic equipment (EEE). All life cycles of EEE will be evaluated: a specific focus is on the EOL management process as their waste management represents a complex problem for developed and developing countries. A multi-criteria decision-making model will be developed based on the well-known analytical hierarchy process (AHP) method: differently from traditional AHP applications, an absolute model has been proposed in order to compare EEE effectively from an environmental point of view. A case study validation regarding large household appliances is proposed.

Keywords::

• environmental product footprint
• electronic and electric products
• analytical hierarchy process
• absolute model

1. Introduction

The rapid and increasing diffusion of consumer electronics in our society is now forcing emerging environmental problems in developed as well as developing countries. Even if the large diffusion of information and communication technologies and the Internet could contribute to positive environmental impacts (mainly due to a dematerialization of processes), several negative impacts have to be analysed (Berkhout and Hertin 2004; Falcone et al. 2005; Gnoni and Elia 2013). One main contribution is due to the end-of-life (EOL) phase which mainly involves an integrated and effective waste management system (Babu, Anand, and Basha 2007; Jin and Zhang 2007; Abu Bakar and Rahimifard 2008; Katsamaki and Bilalis 2012; Kuo 2013; Song, Wang, and Li 2013; Song, Wang, Li, and Wenyi 2013). Quantities of electrical and electronic equipment waste (WEEE) are now quickly increasing all over the world (Bhat, Rao, and Patil 2012). Several reasons are contributing to this trend: from the quick diffusion of electronic devices in new economies to the rapid obsolescence of technological products in developed countries, as people are upgrading their electronic devices – especially mobile phones, computers and televisions – more frequently than ever before (Kiatkittipong et al. 2008; UNEP 2009).

The increasing quantity of WEEE is now determining high environmental impacts mainly due to the presence of several hazardous substances (Leung, Cai, and Wong 2006): thus, the awareness of producers, as well as legislators and customers, is quickly growing. Pioneer examples are the European directives on restriction of the use of certain hazardous substances (ROHS) in electrical and electronic equipment (EEE; EC 2002a) and the WEEE directive (EC 2002b). The ROHS directive aims to reduce the presence of hazardous substances (e.g. heavy metals and flame retardants) by assessing safer alternatives. Recent studies have outlined the importance at national level to support manufacturing lead-free electronics (Zhou et al. 2011; Fuse and Tsunemi 2012); furthermore, Tang and Bhamra (2012) outlined how an effective involvement of consumers in the design phase of an electronic product could effectively support an increase in product environmental sustainability. Furthermore, The WEEE directive points out technical rules, procedures and environmental targets for managing EOL of WEEE. A recast activity has recently been developed for the ROHS (EC 2011) as well as the WEEE directives (EC 2012a). It should be noted that, although the EOL phase is in absolute an extremely critical phase for electronic devices, the use phase could be the most important one in the direct chain of these products (Rünger et al. 2011; Evrard, Brissaud, and Mathieux 2013; Wu et al. 2013; De Felice and Petrillo 2012).

Several approaches can be outlined to measure environmental sustainability of EEE during the whole life cycle. The two main types are (Vinodh et al. 2013) the following: the first deals with identifying the sustainability status at the process level; the second focuses on assessing and communicating environmental sustainability of a product by analysing its overall environmental impacts (e.g. due to energy absorption and waste generated).

The first category of approaches is based on quantitative estimation of environmental performance characterizing processes involved in all product life cycle stages: the two main examples are life cycle analysis (LCA) – based on a multi-criteria approach – and the carbon footprint assessment, which is a single-criterion model. As they are based on quantitative data source, the computational effort required is usually high.

One main example of the latter category is environmental declaration and labelling: the ISO 14020 series defines the general requirements. Three main types are outlined by the ISO standard in detail:

• Type I – Environmental labelling: it is a multi-criteria label developed by a third party: it is based on the fulfillment of a set of criteria defined by a third party.

• Type II – Self-declared environmental claims: it is a single-criteria label developed by the producer; thus, manufacturers simply declare a set of information about the environmental attributes of their products.

• Type III – Environmental declarations: it outlines a formalized set of environmental data describing the environmental aspects of a product. It mainly provides a product profile rather than a verifiable claim or an assurance that set criteria have been met. It is multi-criteria (as in Type I labelling), but there is no minimum threshold to pass (as in Type II declaration).

The last one is the most complete as it involves information about the overall product life cycle; on the other hand, its one main pitfall is a lack of a global harmonization among different products: thus, an alignment activity is still essential in order to effectively compare different products (Ingwersen and Stevenson 2012; Subramanian et al. 2012). One contribution could derive from a recent draft publication of the European Commission which published a draft of methodology for the calculation of the environmental footprint of products defined as product environmental footprint (EC 2012b).

A few examples of environmental declarations could be outlined for electronic products. One example is the environmental product declarations (EPDs) system defined by the Swedish Environmental Management Council. The EPD system provides quantifiable environmental data to compare products that fulfil the same function (Steen et al. 2008; Magerholm, Christofer, and Ottar 2009; Ingwersen and Stevenson 2012): thus, aiming to create comparable EPD, they must follow the same rules and guidelines defined in the so-called Product Category Rules (PCR) documents. Currently, only one PCR – i.e. defined for laser printers used with data processing machines – has been published as guideline for environmental performance of electronic products. Furthermore, a more specific standard focused on electronic products is the EPEAT (electronic product environmental assessment tool): EPEAT was developed using a grant by the US EPA (Environmental Protection Agency) and is managed by the Green Electronics Council, a non-profit organization. EPEAT is a self-declaration system aiming to identify greener electronic products. It is a multi-criteria standard involving design, production, energy use and recycling characterizing different electronic devices (i.e. PCs, monitors, imaging equipment and televisions). All of the criteria used in EPEAT are based on ANSI-approved public standards. In brief, the EPEAT standard ranks products based on several environmental performance criteria – a total number of 58 criteria (some obligatory and some optional) addressing the main impacts of the products' lifecycle, including toxics reduction, recycled content and recyclability, product longevity, EOL management, energy efficiency, packaging and corporate responsibility. Products are rated according to the number of optional criteria they meet above the baseline of the required criteria. EPEAT also defines a verification system to ensure the accuracy of the product declarations and ratings.

By analysing research studies, several examples have recently been proposed in scientific literature to measure environmental impacts of EEE. Duan et al. (2009) applied LCA for assessing overall environmental impacts regarding a PC desktop assembled in China. Furthermore, two main issues have also been analysed: the design and the EOL phases. By focusing on the design phase, Muñoz et al. (2009) proposed an LCA model to support effectively eco-design measures in electronic toys. Yung et al. (2009, 2012) applied LCA to support eco-design activities of electronic products in order to fulfil the European directive on energy-using products. A large number of papers focus on EOL management: Scharnhorst, Hilty, and Jolliet (2006) proposed an LCA model for evaluating environmental impacts due to two different mobile phone network technologies (i.e. GSM (Global system for mobile communication) and UMTS(Universal mobile telecommunications system)) by focusing on the EOL phase. Ahluwalia and Nema (2007) proposed an LCA study for evaluating most environmental effective scenarios in managing electronic wastes. Niu et al. (2012) applied LCA to evaluate alternative treatment technologies of cathode ray tube displays. Song, Wang, Li, and Wenyi (2013) proposed an LCA on desktop PCs assuming that the use and the EOL phases are carried out in a specific territorial area (i.e. Macau). However, even if LCA has been applied widely in this context, it should be noted that the obtained results from each study cannot easily be generalized, as model consistency has to be verified. A recent study (Andrae and Andersen 2010) has outlined several pitfalls in LCA studies regarding consumer electronics: these results could be due to subjective choices, different system boundaries and lifetime, rather than lack of standardization.

Some attempts have also been developed in order to overcome the limits of the two methodologies. Sandholzer and Narodoslawsky (2007) proposed a software tool based on the sustainable process index (SPI) method which allows to assess the ecological footprint of a process and the SPI of a product or service through the input that characterizes the process given by an eco-inventory model.

Song, Wang, and Li (2013) integrated LCA with an ‘energy analysis’ for assessing the effectiveness of an e-waste treatment trial project.

The aim of this study is to develop a tool for quantitatively outlining environmental performance of electronic products. The main innovative features of the proposed model are the following:

• A common basic reference model – based on a multi-criteria approach – was defined for each EEE; a feature typical of a specific product category was also added. This allows to compare environmental performance of electronic products belonging to different as well as the same categories.

• Environmental performance will be assessed over the whole life cycle characterizing electric products as a cradle-to-cradle approach has been applied. Thus, the model integrates information derived from the design, the use and the EOL phases aiming to contribute in reducing the overall environmental impact of goods and services taking into account supply chain activities (from extraction of raw materials, through production and use, to final waste management).

The proposed model applied the well-known multi-criteria technique analytical hierarchy process (AHP) to assess critical environmental impacts characterizing a consumer electronic product: the AHP was applied in an absolute way allowing to support an effective comparison between electronic products from an environmental point of view. Particular attention was given to the EOL management process, as it represents a critical phase for these products. An application for large household appliances was also proposed in order to validate the approach. The work is organized as follows: the rationale of the model is discussed in Section 2, the AHP model and its validation are fully described in Section 3. Lastly, the conclusions are presented.

2. The rationale

The aim of the proposed methodology is to define an effective approach to compare environmental performance of electronic products based on the whole life cycle from the design to the EOL phase. As reported in the previous section, quantitative methods (e.g. LCA) have outlined that all life cycle phases affecting electronic products require great attention as they could affect the overall environmental performance of the product. As the quantity of WEEE is quickly increasing all over the world, the proposed approach aims to incline decision-makers (such as producers, designers, but also waste managers and consumers) towards more environmental options in the field of consumer electronics. The model logic is depicted in Figure 1. Each model phase is discussed in brief as follows.

Figure 1 The proposed methodological approach.

2.1 Phase 1 – the proposal: a multi-criteria model to assess the degree of EEE environmental performance

As reported in the introduction section, measuring and comparing environmental performance of EEE is usually carried out by complex quantitative methods, such as LCA approaches. On the contrary, multi-criteria decision-making (MCDM) techniques could be effectively applied as they supply quantitative results with reduced computational efforts (compared with LCA techniques). Thus, a multi-criteria model based on the well-known AHP technique was developed in order to supply an effective evaluation of electronic products able to increase the overall sustainability of specific products. The AHP is a structured technique for supporting complex decisions in an effective way. Based on the integration of mathematics and psychology theories, it was developed by Thomas L. Saaty in the 1970s and it has been extensively studied nowadays (Saaty 1978, 1980, 1996). Applying AHP allows to quantify the interdependence among different types of criteria by a structured approach (Gómez-Navarro et al. 2009). This technique has been applied in several research papers focusing on evaluating environmental performance involving several phases of EEE life cycle. Several research studies have recently applied AHP for outlining the most effective alternatives by focusing on the EOL phase of EEE. Queiruga et al. (2008) and Achillas et al. (2010) focused on determining ‘optimal’ locations for EOL treatment plants. Rousis et al. (2008) developed a multi-criteria model for defining the most effective EOL management scenario based on social, as well as economic and technical remarks. Tsai and Hung (2009) proposed a multi-criteria decision tool in order to optimize treatment and recycling phases of electronic appliances; environmental constraints were also added for assessing profit maximization. A more strategic application was proposed by Lin, Wen, and Tsai (2010): the AHP was applied to define the most critical waste stream originating from EEE at a national level. Very few papers are focusing on applying the AHP technique for measuring environmental performance of EEE. A recent study (Chiang et al. 2011) has been proposed focusing on a specific topic: the AHP was applied for outlining the most critical factors affecting environmental performance after the adoption of lead-free manufacturing by the electronics industry.

2.2 Phase 2 – the conceptual development based on an AHP absolute model

The AHP is a technique which aims to break down a complex decision-making problem into several levels: the quantitative model is based on a hierarchy of criteria with unidirectional relationships among levels. The top level of the hierarchy represents the goal of the decision problem; lower levels are tangible and/or intangible criteria and sub-criteria that contribute to the goal. In traditional AHP models, the bottom level is formed by the alternatives (which are potential solutions of the decision problem); the latter are quantitatively evaluated in terms of the previous criteria. This procedure is usually defined as relative AHP model.

Different from the traditional applications reported previously, the proposed approach aims to apply the AHP as an absolute model of comparison: paired comparisons will be carried out among elements of a predefined set with respect to a common attribute. The absolute method is usually applied when one (or more decision alternatives) must be selected from several decision alternatives on the basis of multiple decision criteria of a competing or conflicting nature; other applications are when a large number of alternatives need to be analysed (Kinoshita and Nakanishi 1999; McCarthy 2000; Ohnishi et al. 2011). The main difference between absolute and relative models is that the latter allow to compare elements towards a predefined standard value: thus, this approach could be more suitable for scoring items with a defined target level, which can be defined as ‘the best in class’. Furthermore, the absolute model allows to rank independent alternatives one at a time in terms of rating intensities for each criteria.

The procedure for developing the absolute model is the same as the relative model: defining the hierarchical structure, assessing weights and validating the model. In brief the main steps are as follows:

• Step 1: Identify the criteria, sub-criteria and alternative (i.e. EEE to be compared) for evaluation; thus, the AHP based on previous information. The hierarchy development is similar to relative AHP models.

• Step 2: Calculate the weights of the decision criteria by the relative measurement of AHP, i.e. by constructing the pairwise comparison matrix for all the criteria and computing the normalized principal right eigenvector of the matrix. This vector gives the weights of the criteria. The following action is to divide the criteria into sub-criteria and to calculate the weights of these sub-criteria in the same manner. Next, these weights have to be multiplied by the weights of the parent criteria.

• Step 3: Divide each sub-criterion into several intensities or grades. An intensity is a range of variations defined for a criterion: it defines how each alternative differs from the other according to each criterion. An intensity may be expressed as a numerical range of values if the criterion is measurable or in qualitative terms (Saaty, Peniwati, and Shang 2007; Rafikul and Mohd Rasad 2005). The absolute AHP model requires a pairwise comparison procedure between indicator categories (for each lowest level criterion) to establish the relative weights for these categories using the eigenvector approach (Park and Lim 1999). Properties of an element are compared or ‘rated’ against a standard in the absolute measurement (Leskinen 2000): thus, an element is compared against an ideal property; i.e. a ‘memory’ of that property (Saaty, Vargas, and Dellmann 2003). The next action is to set priorities on the intensities by comparing them pairwise under each sub-criterion; then, these priorities have to be multiplied by the priority of the parent sub-criterion.From a quantitative point of view, the global weight r _kg of the kth intensity with respect to the jth sub-criterion of the ith criterion (where 1 = 1,2, …, m and j = 1, 2, …, n) is defined in Equation (1):

  (1) $r_{\text{kg}}=p_i\times q_{ij}\times r_k,$(1)
  where r _k is the local weight of the kth intensity, p _i is the weight of the ith main criterion and q _i is the weight characterizing the jth sub-criterion defined for the ith criterion. In brief, a global weight is the weight of a node in respect of the goal: the weight of the goal is 1. Thus, local weights r _k characterizing each sub-criteria at the main level (e.g. the first level in a hierarchy) must be added directly one to one; on the other hand, the global weights r _kg of each sub-criterion must be added to specific global weights of their respective parent criterion. Finally, the global weight of a node in a hierarchy can be calculated by multiplying its local weight by the global weight of its parent node.

• Step 4: The environmental footprint of each alternative (i.e. an electronic or electric product) is measured first by its performance intensity under each sub-criterion; next, the global priorities of the intensities will be added for each alternative. Alternatives are usually measured absolutely: this will allow to define an effective rating of EEE based on its own environmental footprint.

2.3 Phase 3 – designing the reference model based on a hierarchy structure

One of the crucial elements in measuring the effectiveness of sustainability is to obtain consistent results according to common standards. Thus, the proposed hierarchical structure has been derived by introducing criteria proposed by the EPEAT, a US non-profit organization for certifying sustainability of electronic products which has defined a comprehensive environmental rating, helping to identify greener computers and other electronic equipment. The EPEAT standard is applied for certifying specific groups of electronics products worldwide (Omelchuck et al. 2006). The main limit of this standard is its field of application: currently, it can be applied only to a set of electronic products (e.g. mainly PCs). In detail, the EPEAT standard ranks a specific electronic group of products in relation to a total number of 51 sustainability criteria: these criteria have also been derived from other international standards, such as IEEE 1680 standard (IEEE 2006), which defines standards for environmental assessment of electronic products. Thus, in order to expand its applicability to a major group of electronics and electric equipment, the proposed multi-criteria model has been modified by adding new criteria. The set of criteria has been integrated according to the well-known waste hierarchy applied by the European Legislation for waste management and, specifically, for WEEE management. The updated proposed set of criteria is given in Table 1; a brief description is reported as follows:

• Criteria regarding eco-design procedures: three groups of first level criteria have been introduced at the first level of the proposed hierarchy. The first (defined as C1) refers to reducing environmental impacts of electronic products by reducing (or eliminating) the presence of environmentally sensitive materials (e.g. mercury). The second (defined as C2) refers to more greener approaches in material selection: for instance, the use of recycled materials. The third (defined as C3) refers specifically to EOL processes.

  Table 1 The proposed set of criteria.

  Criterion	Source	Description
  C.1	EPEAT	Reduction/elimination of environmentally sensitive materials
  C1.1	EPEAT	Compliance with provisions of European ROHS directive upon its effective date
  C1.2	EPEAT	Reporting on amount of mercury used in light sources (mg)
  C1.3	EPEAT	Elimination of intentionally added short chain chlorinated paraffins flame retardants and plasticizers in certain applications
  C1.4	NEW	Reporting on amount of Chlorofluorocarbon
  C.2	EPEAT	Materials selection
  C2.1	EPEAT	Declaration of postconsumer recycled plastic content (%)
  C2.2	EPEAT	Declaration of renewable/bio-based plastic materials content (%)
  C2.3	EPEAT	Declaration of product weight (lbs)
  C.3	EPEAT	Design for EOL
  C3.1	EPEAT	Identification of materials with special handling needs
  C3.2	EPEAT	Elimination of paints or coatings that are not compatible with recycling or reuse
  C3.3	EPEAT	Easy disassembly of external enclosure
  C3.4	EPEAT	Marking of plastic components
  C3.5	EPEAT	Identification and removal of components containing hazardous materials
  C3.6	EPEAT	Minimum target of 65% of reusable/recyclable
  C.4	EPEAT	Product longevity/life cycle extension
  C4.1	EPEAT	Availability of additional three-year warranty or service agreement
  C4.2	EPEAT	Easiness in upgrading (e.g. by common tools)
  C.5	EPEAT	Energy conservation
  C5.1	EPEAT	ENERGY STAR®
  C5.2	NEW	Energy label for appliances
  C5.3	NEW	Ecolabel
  C5.4	NEW	Energy consumption for the production of raw materials (MJ/ton)
  C.6	EPEAT	EOL management
  C6.1	EPEAT	Provision of product take-back service
  C6.2	EPEAT	Provision of rechargeable battery take-back service
  C6.3	NEW	Provision of recoverable components
  C.7	EPEAT	Corporate performance
  C7.1	EPEAT	Demonstration of corporate environmental policy consistent with ISO 14001
  C7.2	EPEAT	Self-certified environmental management system for design and manufacturing organizations
  C7.3	EPEAT	Corporate report consistent with performance track or Global Reporting Initiative
  C.8	EPEAT	Packaging
  C8.1	EPEAT	Reduction/elimination of intentionally added toxics in packaging
  C8.2	EPEAT	Separable packing materials
  C8.3	EPEAT	Declaration of recycled content in packaging

• Criteria regarding the production phase: this group (defined as C8) affects the packaging process which is a critical part of the overall manufacturing process.

• Criteria regarding the use phase: two groups of first level criteria have been introduced: the first (defined as C4) will affect procedures to increase product longevity, such as the easiness in upgrading product functionalities as no dedicated tools are required. The latter (defined as C5) will affect the overall energy consumption required by the product. At the second level, three new criteria (defined as C5.2, C5.3 and C5.4) have been introduced in order to outline the actual resource consumption derived also by the overall life cycle of the product.

• Criteria regarding the post-user phase: this group (defined as C6) will affect the EOL process starting after the use phase. One main criterion (defined as C6.3) has been introduced at the second level of the hierarchy: it outlines the potential level of materials which could be recycled after corrective treatments.

• Criteria regarding the company performance: this group (defined as C7) regards the overall organization of the company such as a corporate environmental policy or the application of a standardized environmental management system.

According to these criteria groups, the proposed hierarchical structure is depicted in Figure 2; it is composed of:

1. A total number of 8 first-level criteria: derived directly by the EPEAT standard.

   Figure 2 The proposed AHP model and development of the alternatives.

   

2. A total number of 28 second-level criteria: five new criteria have been introduced aiming to expand the validity of the EPEAT for all EEE.

The proposed hierarchical structure will commonly measure environment footprint of all different categories of EEE, thus allowing to compare, with the same scoring model, different types of electronic products.

3. The model validation: a case study regarding large household appliances

A case study analysis was carried out aiming to test and validate the proposed approach. At first, a characterization analysis regarding the WEEE stream in analysis was carried out. Table 2 represents a collection of WEEE, by country, year and EEE-category, in tones updated in February 2013: data were reported by information supplied by EUROSTAT on its web site for the European area. Observed data show that waste produced from used ‘white goods’ (e.g. washing machines, dishwashers and so on.) is very high: thus, the environmental impact due to these waste flows is potentially high.

Table 2 Statistical data about quantity of waste collected for large household appliances in EU 27.

	Quantitity per year (ton)
Area	2005	2006	2007	2008	2009	2010
Austria	10,969.3	32,896.6	31,102.1	34,926	34,559.1	32,225.3
Belgium	29,373	31,383.4	32,856.1	39,171.6	45,701.8	47,440.6
Bulgaria	–	–	16,499.5	31,589.5	24,010.4	32,832.6
Cyprus	3020	3450	2260.7	1956.1	–	–
Czech Republic	–	–	16,723.3	21,643.5	28,190.1	22,725.5
Germany	–	462,066	232,277	260,269	287,140.8	249,149.3
Denmark	–	31,462	51,933	35,579	37,157	35,404
Estonia	–	3445.9	3382.3	2950.8	1869.7	2380.3
Greece	132	8711	24,939.3	34,456.9	48,737.5	29,101.6
Spain	–	–	238,440	251,403	86,448.8	92,626.3
Finland	9352	21,548	24,580	26,643	25,805.1	27,698.3
France	NA	3897	103,865	173,570	228,469	241,137
Hungary	10,867.6	13,573.4	23,113.8	28,321.9	28,592.5	21,337.1
Ireland	–	–	25,293	25,440	26,290.3	25,263
Italy	54,463	57,553	112,689	106,386	130,554	144,554
Liechtenstein	–	–	–	–	–	–
Lithuania	552	4925	6038	6187	3722.5	5054.9
Luxembourg	–	1898.6	1741.3	1704.7	2064.3	2051.8
Latvia	–	–	3083.4	2989.9	2096.9	2024.8
Netherlands	42,332	40,078	38,540	38,946	47,811	63,951
Norway	43,645	43,649	46,613	46,933	45,347	44,031
Poland	NA	2746	7535.2	17,682.3	51,509.9	54,227.9
Portugal	23.8	2339.8	19,675.5	21,736.6	22,125	28,772
Romania	–	517.7	1625.5	8923.7	21,385.6	14,119.9
Sweden	22,308	59,488	76,723	70,229.4	69,240	75,341
Slovenia	–	–	3251	3660	4220	3894.9
Slovakia	1863	4880.7	9213.4	12,457.8	14,573.2	12,325.4
United Kingdom	–	–	117,387.2	492,748	253,904.7	244,645.8

Source: Eurostat.

Next, the sample in analysis was outlined: the top 10 washing machines on the Italian market in 2011. The most relevant features for the model application, characterizing the 10 models in analysis, are synthesized in Table 3.

Table 3 Main features characterizing the case study sample.

Model	Energy label class	Dual input	Declared electricity consumption (Kwh/kg)	Declared water consumption (l/kg)
A1	A+++ AB	NO	0.13	7.40
A2	A+++ AA	NO	0.13	7.40
A3	A++ AA	NO	0.13	7.56
A4	A++ AA	NO	0.15	4.73
A5	A+AA	NO	0.15	6.56
A6	A++ AA	NO	0.15	7.56
A7	A++ AB	NO	0.15	7.00
A8	A++ AA	NO	0.15	7.00
A9	A+AB	NO	0.15	9.44
A10	A+AB	YES	0.17	7.67

According to the data for the ‘Energy Label Class’ (EC 2005; see the second column in Table 3), the first letter in the notation outlines the efficiency level in washing activities; the second letter refers to the spin-drying efficiency class (expressed in terms of the remaining moisture content, i.e. the mass of water divided by the dry mass of cotton fabrics); the third letter refers to the drying efficiency functionality. Products characterized by the notation ‘++’after the first letter (e.g. A++) are characterized by savings higher than the target limit defined for the ‘simple’ class A. Each ‘+’ symbol certifies a saving of 10%.

Then, a comparison analysis was carried out (see previously defined Step 3). First, the expert team to carry out the quantitative comparison for the AHP model development was defined. The expert team was composed of a production manager, a waste treatment manager, a marketing manager and an expert on environmental issues. The expert team developed the pairwise comparison matrices to determine the criteria and sub-criteria weights. The determination of relative weights in the absolute AHP model is based on the pairwise comparison conducted with respect to their relative importance towards their control criterion. A 9-point scale was applied for the comparison: scale values are namely unimportant (1), somewhat important (3), important (5), very important (7) and extremely important (9). This is an absolute scale; thus, priorities derived from it are normalized or idealized to obtain an absolute scale. Thus, pairwise comparisons will be carried out based on this evaluation scale: a comparison matrix can be defined: each matrix element defined as dominance coefficient (a _ij ) represents the relative importance of the ith (i.e. the row index in matrix A) component over the jth (the column index in matrix A) component. Each matrix element derives from a set of numerical weights (w ₁ , w ₂, …, w _m ) which reflects the recorded judgments: a _ij is defined as w _i /w _j :

$Aequals;\begin{array}{c}\\ A_1\\ A_2\\ \cdots \\ A_m\end{array}|\begin{array}{cccc}{w}_1/w_1& w_1/w_2& & w_1/w_m\\ w_2/w_1& w_2/w_2& & w_2/w_m\\ & & & \\ \cdots & \cdots & \cdots & \cdots \\ w_m/w_1& w_m/w_2& \cdots & w_m/w_m\end{array}|.$

If the decision-maker quantifies that the criterion i is equally important to another criterion j, the comparison matrix will contain the value of a _ij  = 1 = a _ji ; on the other hand, the ith criterion is absolutely more important than a jth criterion (a _ij 9; a _ji  = 1/9). The comparison matrix obtained for the first level criteria is given in Table 4: results show that the most important criterion is C1, that is the reduction/elimination of environmentally sensitive materials.

Table 4 Estimated values for first level criteria pairwise comparison.

	C1	C2	C3	C4	C5	C6	C7	C8	Weight
C1	1	2	3	4	3	3	5	5	0.289
C2		1	2	2	3	2	4	4	0.190
C3			1	4	3	2	4	4	0.170
C4				1	3	1/2	3	3	0.071
C5					1	1/2	2	2	0.086
C6						1	3	3	0.111
C7							1	3	0.036
C8								1	0.043
Consistency: 0.050

The quantitative pairwise comparisons for all sub-criteria are given in Table 5.

Table 5 The quantitative pairwise comparison for all sub-criteria in the proposed AHP model.

One main problem of MCDM is that judgments are potentially inconsistent, a consistency analysis was carried out. Saaty (1990) proposed to apply the consistency index (CI) calculation aiming to verify the consistency of the comparison matrix. The CI of the derived weights could then be calculated by Equation (2):

(2) $\text{CI}=\frac{{\lambda }_{\text{max}}-n}{n-1},$(2)

where λ_max is the maximum eigenvalue of the comparison matrix, and n is the rank of the matrix. If CI is less than 0.10, satisfaction of judgments may be assumed.

Data on consistency analysis are reported for each level in Table 5: all judgments were verified as consistent.

Next, all estimated data about each level and each criterion are reported in Figure 3: criterion C1 – reduction/elimination of environmentally sensitive materials – is preferred over the others with a weight of 0.289. However, this result could not be generalized as the aim of the case study application is to validate the proposed model.

Figure 3 Results obtained for all levels in the proposed AHP model.

Finally, after developing criteria comparison analysis, alternatives are rated one at a time using intensities (see Step 4 previously defined). The expert team quantified priorities on the intensities by comparing them pairwise under each sub-criterion. The qualitative scale used for intensity assessment is the following: excellent (E), good (G), average (A), satisfactory (S) and poor (P). The obtained pairwise comparison matrix for the intensities is given in Table 6. The global weights of the intensities (see values in the last column of Table 6) have been estimated according to Equation (1) previously defined.

Table 6 The pairwise comparison matrix for the intensities.

	E	G	A	S	P	Weights
E	1	3	5	6	8	0.507
G	–	1	3	5	6	0.268
A	–	–	1	3	5	0.118
S	–	–	–	1	3	0.068
P	–	–	–	–	1	0.036

Based on this scale, criteria comparison analysis was developed: qualitative results obtained from expert team judgments are reported in Table 7; overall weights and scoring are also reported in Table 8. In detail, quantitative data given in Table 8 show the numerical weights of the alternative performances on individual criteria; the second last column of the table provides the overall weight of the alternatives. Ranking of analysed alternatives was obtained from these values in the proposed absolute AHP model. One important feedback was defined from the expert team: they pointed out that adopting this technique supplied them a more reliable set of information for assessing environmental footprint of each product in analysis.

Table 7 Final performance rating estimated for each electronic product in the analysed sample.

	C1		C2			C3						C4		C5		C6		C7			C8
#	C1.1	C1.4	C2.1	C2.2	C2.3	C3.1	C3.2	C3.3	C3.4	C3.5	C3.6	C4.1	C4.2	C5.2	C5.4	C6.1	C6.3	C7.1	C7.2	C7.3	C8.1	C8.2	C8.3
A1	E	P	G	G	G	E	G	A	A	G	E	A	E	P	P	A	A	P	E	A	E	P	A
A2	G	G	A	E	A	G	S	E	A	G	A	E	A	G	G	S	G	A	G	G	G	G	A
A3	G	G	A	A	A	A	P	G	A	G	E	G	G	G	G	A	A	S	P	A	A	A	A
A4	A	G	P	A	S	E	E	A	G	G	G	E	P	E	P	G	P	P	G	G	S	E	A
A5	A	A	S	S	P	G	G	E	G	G	A	G	P	P	A	S	G	A	E	A	P	P	A
A6	S	S	S	E	A	A	A	S	E	G	S	P	A	P	E	P	P	A	E	A	E	P	A
A7	E	G	G	S	G	E	E	E	G	G	A	P	E	A	P	S	G	A	P	G	A	G	A
A8	G	S	G	S	S	A	A	G	G	G	S	G	P	A	A	S	G	G	E	G	S	A	A
A9	P	P	A	G	E	E	E	G	E	G	S	A	A	A	G	S	G	S	E	G	S	A	A
A10	E	A	S	P	E	A	G	G	A	A	G	A	E	A	G	G	G	P	A	S	E	A	G

Table 8 Overall weights and ranking estimated for the analysed electronic products in the case study.

			Weights
			0.057	0.231	0.100	0.026	0.063	0.016	0.011	0.060	0.010	0.041	0.030	0.017
	Total	Priorities	C1.1	C1.4	C2.1	C2.2	C2.3	C3.1	C3.2	C3.3	C3.4	C3.5	C3.6	C4.1
A1	0.427	0.127	0.072	1.000	0.134	0.134	0.134	0.072	0.134	0.266	0.266	0.134	0.072	0.266
A2	0.186	0.055	0.134	0.134	0.266	0.072	0.266	0.134	0.522	0.072	0.266	0.134	0.266	0.072
A3	0.207	0.061	0.134	0.134	0.266	0.266	0.266	0.266	1.000	0.134	0.266	0.134	0.072	0.134
A4	0.421	0.125	0.266	0.134	1.000	0.266	0.522	0.072	0.072	0.266	0.134	0.134	0.134	0.0723
A5	0.383	0.114	0.266	0.266	0.522	0.522	1.000	0.134	0.134	0.072	0.134	0.134	0.266	0.134
A6	0.477	0.142	0.522	0.522	0.522	0.072	0.266	0.266	0.266	0.522	0.072	0.134	0.522	1.000
A7	0.206	0.061	0.072	0.134	0.134	0.522	0.134	0.072	0.072	0.072	0.134	0.134	0.266	1.000
A8	0.352	0.105	0.134	0.522	0.134	0.522	0.522	0.266	0.266	0.134	0.134	0.134	0.522	0.134
A9	0.445	0.133	1.000	1.000	0.266	0.134	0.072	0.072	0.072	0.134	0.072	0.134	0.522	0.266
A10	0.237	0.070	0.072	0.266	0.522	1.000	0.072	0.266	0.134	0.134	0.266	0.266	0.134	0.266
			Weights
			0.0535	0.014	0.071	0.037	0.074	0.021	0.005	0.009	0.006	0.0248	0.012
	Total	Priorities	C4.2	C5.2	C5.4	C6.1	C6.3	C7.1	C7.2	C7.3	C8.1	C8.2	C8.3
A1	0.427	0.127	0.072	0.522	0.524	0.266	0.266	1.000	0.072	0.266	0.072	1.000	0.266
A2	0.186	0.055	0.266	0.134	0.144	0.522	0.134	0.266	0.134	0.134	0.134	0.134	0.266
A3	0.207	0.061	0.134	0.134	0.144	0.266	0.266	0.522	1.000	0.266	0.266	0.266	0.266
A4	0.421	0.125	1.000	0.072	0.524	0.134	1.000	1.000	0.134	0.134	0.522	0.072	0.266
A5	0.383	0.1143	1.000	0.522	0.242	0.522	0.134	0.266	0.072	0.266	1.000	1.000	0.266
A6	0.477	0.142	0.266	0.522	0.0729	1.000	1.000	0.266	0.072	0.266	0.072	1.000	0.266
A7	0.206	0.061	0.072	0.266	0.524	0.522	0.134	0.266	1.000	0.134	0.266	0.134	0.266
A8	0.352	0.105	1.000	0.266	0.242	0.522	0.134	0.134	0.072	0.134	0.522	0.266	0.266
A9	0.445	0.133	0.266	0.266	0.144	0.522	0.134	0.522	0.072	0.134	0.522	0.266	0.266
A10	0.237	0.070	0.072	0.266	0.144	0.134	0.134	0.134	0.266	0.522	0.072	0.266	0.134

Final scores about environmental footprint characterizing each alternative in the sample (i.e. the 10 washing machines) are reported in Table 9: product A6 is characterized by the best scoring, i.e. the product with the highest environmental performance. In detail, product A6 presents a high value for ‘C1.1 Compliance with provisions of European ROHS directive upon its effective date’ with a score of 0.522 and for C3.1 ‘Identification of materials with special handling needs’ with a score of 0.266. Thus, parameters C1.1 and C3.1 were considered very important in assessing weights of each criteria and sub-criteria (as defined in Step 2); they represent the ‘best’ features to guarantee its performance according to an environmental point of view. This result could be modified if different data or conditions vary: for example, if the quantity of recycled materials used for new products and/or the overall efficiency of recycling processes increases, comparison matrixes as well as weights have to be computed again. Thus, the proposed model could easily be adaptable if new information is available.

Table 9 Final ranking estimated by the AHP model.

Alternative	Estimated value	Ranking (°)
A1	0.127800	3
A2	0.055674	10
A3	0.061913	8
A4	0.125968	4
A5	0.114653	5
A6	0.142650	1
A7	0.061748	9
A8	0.105344	6
A9	0.133271	2
A10	0.070979	7

In conclusion, the case study showed the effectiveness of the proposed multi-criteria approach for comparing environmental footprint of an electronic or electric product. The model provided a reliable rating.

4. Conclusions

This paper proposes a multi-criteria reference approach to compare environmental performance of electronic products: a unified and common hierarchical model was proposed to point out environmental pressures derived from all life cycle phases characterizing these types of products. The multi-criteria model was developed based on the well-known AHP technique; furthermore, criteria were also deducted by the EPEAT standard, which is a voluntary standard defined for PC and printers by a US non-profit organization. The proposed AHP application is based on an absolute multi-criteria model: traditional AHP applications are based on relative models, which do not fit well in the analysed research problems as the number of alternatives (i.e. the number of electronic goods) is potentially high. This method also allows the aggregation of the experts' judgments, thus avoiding complex compensation operations, e.g. based on weighted sums. Furthermore, the use of AHP – unlike other multi-criteria techniques – could help to effectively outline relationships among dependent criteria: this is an important feature for scoring environment product footprint for electronic equipments.

The model was validated by a case study: an expert team was formed to carry out judgments required for assessing quantitative values in the hierarchical proposed structure. The results obtained showed the potentiality of the proposed approach in supporting electronic industries as well as consumers in outlining critical phases in the overall life cycle of an electronic product. Furthermore, the model can supply effective information to customers allowing them to purchase more greener products. Further developments will be oriented to apply the proposed approach to a wider group of electronic devices (e.g. mobile phones) aiming to test one model against different types of products with an absolute score.