Forecasting methods of generation of waste electrical and electronic equipment: a systematic review

Universidade Federal do Rio Grande do Sul (UFRGS) – Mestre e Doutora em Engenharia de Produção, Graduada em Estatística Professora do Departamento de Estatística – Porto Alegre/RS – Brasil. werner.liane@gmail.com Abstract In recent decades there have been a significant increase in the use of electronic equipment in homes, offices and industries. When these equipment are discarded after have been used they become Waste of Electric and Electronic Equipment (WEEE). To know how the forecasting of WEEE ́s generation were made, it was necessary to carry out a systematic review. The search was done in 5 databases by using the key words "electronic waste" or "WEEE" or "e-waste" and "forecasting" and it was found 854 articles. After applying the inclusion and exclusion criteria 28 articles were reached. As a result, it is noted that the selected articles are concentrated in the USA and China and in a few newspapers. The most independent variable used was the dada about the commercialization of the equipment. The majority of the articles have as variable response the unit and weight. There was also strong use of statistical tools and forecasting methods, especially the regression and the MFA (Material Flow Analysis).


Introduction
The equipment that rely on electric current or electromagnetic fields, which does not exceed the rated voltage of 1,000 V for alternating current and 1,500 V for direct current, is called  (Osibanjo, Nnorom, & Ogbonna, 2008;Franco, 2008).
The United Nations Environment Program (UNEP, 2015) estimates that 90% of WEEE is traded or discarded in landfills illegally. Although the countries that most manage with this type of waste are concentrated in Europe and North America, the most frequent destination for disposal of them are the poorer or less developed countries in Africa and Asia, such as Ghana, Nigeria and Pakistan. Ghana, according to Hoeltl, Brandtweinder and Müller (2017), imported a considerable EEE volume from Europe, Asia and the USA in 2009 and 15% of these equipments could not be reused. The underdeveloped countries suffer with illegal recycling deposits and unhealthy and poorly paid work.
In China, Habuer and Moriguchi (2014) have got as result of their research that between 4.8 and 5.1 billion units of household´s appliances (mainly computers and air conditioners) will be discarded in the next 20 year. The amount expected of the Latin America's WEEE is near to 4800 kilotonnes in 2018 (Magalini, Kuehr, & Baldé, 2015). In Brazil, Araújo, Magrini, Mahler and Bilitewski (2012)  However, there is paucity of data on lifespan and disposal of electrical and electronic equipment in Brazil, essentials informations to make estimates of WEEE, according to Polák and Drápalová (2012). Some papers, such as the one by Franco and Lange (2011) (Oliveira et al., 2012). It is necessary to change the materials and the methods of EEE´s production to meet the requirements of the RoHS directive, creating new forms of production that reduce the energy consumed during the process, minimizing packaging, maximizing the lifespan of products, and using materials that are renewable, recyclable and less harmful to the environment. For this purpose, Wang and Gupta (2001) (Brasil, 2010). Reverse logistics is a system (actions, procedures and means) that aims at facilitating the return of goods or their constituent materials to the production cycle for reuse or proper disposal (Brasil, 2010;Leite, 2009 In order to answer this question, keywords related to "forecasting" and "waste electrical and electronic equipment" as well as its synonyms were chosen. The following logic was used to   The following details were collected from the 28 articles selected for further analysis: information about the author, country where the research was conducted, journal in which the article was published, methods, tools used, variables considered in the research, and other data which were considered relevant. Tables and graphs were prepared with this information in order to enable the analysis.

Results
The articles that were selected after the application of this systematic review method are show in the Figure 3. Forecasting quantities of disused household CRT appliances -A regional case study approach and its application to Baden-Württemberg   The journal in which nine of the 28 selected articles were found was Waste Management. Table 2 shows the concentration of selected articles in a few journals, with 57% of the selected papers published in three journals. The waste generated by computers that is theme in most of the papers assessed, according to this EEE was detected in residences, academic environment and companies. Television sets were also object of more than half of the articles evaluated, and some articles focused on this device due to the change from analogy to digital transmission systems that happened in some countries. Table 3 shows the number of articles for each type of equipment to which some kind of forecasting of WEEE generation was performed.
Note that some articles performed forecast in relation to more than one device.   Table   4 details these information. Weight and its variances are important data to size these EEE´s disassembly, storage and transportation to a reverse logistics system. Table   5 presents information on the response variables grouped according to similarity. Note that some articles presented results with more than one response variable and the "Time" refers to how much time the device takes to become obsolete or be disposed. The relationship between the independent and response variables used to forecasting the WEEE generation is shown in Table 6. It can be noted that half of the articles analysed (14 of 28) used equipment commercialization data as independent variables and the unit as variable response. Several articles used more than one type of independent variable to perform the forecasting, as well as many articles presented the results using more than one response variable. Its justifies the sum of some rows and columns of table 6 being greater than the total number of articles analyzed in this work.  Brunner and Rechberger (2005), was used in six articles. These two methods were used in 14 articles, representing 50% of the selected articles.
The simulation was used in six articles as a primary or secondary method. One article (Linton, Yeomans, & Yoogalingam, 2004) (2012).
Sensitivity analysis was performed in six articles. Table 7 shows the number of articles by methods or tools used to forecast of WEEE generation. The total of articles are more than 28 because many articles use two or more methods or tools. Another interesting point is to analyse how companies will comply with the new National Policy on Solid Waste of Brazil, which was approved in 2010 and requires reverse logistics of electrical and electronic equipment. The sectorial agreement is in standby but it is expected to be finalized in next years (SINIR, 2017). To implement reverse logistics is important to forecast the amount of equipment to be discarded and when and where it will be done, so that it is possible to determine the necessary infrastructure for this system, as highlighted by Kang and Schoenung (2006).