Consumer E-Waste Recycling in South Africa

Critical Infrastructures: Reliability, Resilience and Wastage

“…These are common traits within other articles in different domains. For example, Barreiro et al [8] discuss climate action within the urban resilience domain, Deng et al [66] within the water domain and Ichikoitz et al [54] within the waste and recycling domain (specifically highlighting the growing volume of e-waste in South Africa). Furthermore, Ichikoitz et al [54] also discuss climate-related needs.…”

“…For example, Barreiro et al [8] discuss climate action within the urban resilience domain, Deng et al [66] within the water domain and Ichikoitz et al [54] within the waste and recycling domain (specifically highlighting the growing volume of e-waste in South Africa). Furthermore, Ichikoitz et al [54] also discuss climate-related needs. Other notable points include protection of infrastructures from weather in [58], consumer participation in food waste management in [68] and the need for greater education programs for supporting customers with purchases to reduce waste and on waste-sorting programs to reduce the strain on landfill or collection networks [33,48,49].…”

“…Regarding Ecology and Health, COVID-19 was discussed in [74,77,79], with other topics such as the pervasiveness of takeaway culture in [33] and other people-driven behaviours relating to the proper sorting and separation of waste [72] causing strain on landfill and collection networks. Uncertainty over weather patterns [56] and climate change [44,54] were also listed as barriers. The full list of strains identified in the 51 articles is presented in Table 9 below.…”

“…In total, 10 articles discuss carbon emissions, with a further 10 discussing landfill/trash and the environment in general. For example, Ichikowitz et al [54] discuss the strain caused by e-waste, and [64][65][66] are examples of works discussing the straining impact on the environment. Six articles discuss energy burdens, for instance, Khahro et al [81] discuss the benefits of Building Information Models (BIM) in this domain, and Xu et al [47] discuss waste heat recovery in power plants.…”

“…As with the needs, barriers and strains, duplicates are removed, resulting in a final identification of 60 approaches, as listed in Table 10. Many could be categorised under statistical analysis (for example, ANOVA [82], Chi-square [68], Pearson correlation [54], t-test [82], Wilcoxon-Mann-Whitney rank-sum test [37], and Welch [50], etc.). Other categories could include machine learning (k-means [65], and Logistic regression [72], etc.…”