Understanding the individuals' motivators and barriers of e-waste recycling: A mixed-method approach

Influence of Canadian provincial stewardship model attributes on the cost effectiveness of e-waste management

This paper presents a comprehensive analysis of e-waste collection and management trends across six Canadian provinces, focusing on e-waste collection rates, provincial stewardship model attributes, program strategies and budget allocations from 2013 to 2020. Temporal and regression analyses were conducted using data from Electronic Product Recycling Association reports. A group characterization based on geographical proximity is proposed, aiming to explore the potential outcomes of fostering collaboration among neighboring provinces. The analysis emphasizes the significant impact of stewardship model attributes on e-waste collection rates, with Quebec emerging as a standout case, showcasing a remarkable 61.5% surge in collection rates. Findings from group analysis reveal a positive correlation between per capita e-waste collection rate and the growth of businesses and collection sites in Western Canada (Group A - British Columbia, Saskatchewan, and Manitoba). This highlights the potential benefits of a coordinated waste management approach, emphasizing the importance of shared resources and collaborative policies. Saskatchewan and Manitoba allocated only 6.6% and 7.0% of their respective budgets to e-waste transfer and storage. British Columbia's observed steady decrease of e-waste collection rate. In Group A, stewards handled 2.18-13.95 tonnes of e-waste during the study period. The cost per tonne of e-waste tended to be lower when more e-waste is managed per steward, suggesting the potential benefits of an integrated e-waste collection and management system.

Tackling climate change at the city level: insights from Lighthouse Cities' climate mitigation efforts

Introduction

The link between lifestyles and Greenhouse Gas (GHG) emissions has prioritized climate mitigation strategies of cities worldwide. As cities have increasingly generated GHG emissions by their industrial and transportation activities, their role in climate mitigation has gained prominence. Cities' climate mitigation policies to reduce the GHG intensity of their residents' daily lives are one of their significant efforts to tackle climate change. Lighthouse Cities (LCs), in particular, have emerged as remarkable actors in promoting lifestyle changes for their residents.

Methods

This study examines climate mitigation strategies of LCs of Climate CAMPAIGNers project, including Baku, Vilnius, Lahti, Izmir, Trujillo, Athens, Linz, Milan, Cape Town, Dublin, and Skopelos, addressing lifestyle changes by conducting an expert survey in 11 LCs involving 89 respondents. The findings of the expert survey are comparatively analyzed across 11 LCs.

Results

The results show that experts form Lighthouse Cities identify increasing awareness and information provision as a significant component of climate mitigation policies. Concerning lifestyle changes, strategies toward energy efficiency and sustainable mobility are highlighted as the primary areas to be prioritized.

Discussion

This study enhances the understanding of cities' capacity to reduce their residents' GHG emissions. The findings can be utilized to identify and tailor policies for supporting the Lighthouse Cities in their climate change mitigation efforts and provide pointers for selecting the lifestyle changes that can be promoted and prioritized in Lighthouse Cities.

A computer vision-based system for real-time component identification from waste printed circuit boards

With an exponential increase in consumers' need for electronic products, the world is facing an ever-increasing economic and environmental threat of electronic waste (e-waste). To minimize their adverse effects, e-waste recycling is one of the pivotal factors that can help in minimizing the environmental pollution andto increase recovery of valuable materials. For instance, Printed Circuit Boards (PCBs), while they have several valuable elements, they are hazardous too; and therefore, they form a large chunk of e-waste being generated today. Thus, in recycling PCBs, Electronic Components (ECs) are segregated at first, and separately processed for recovering key elements that could be re-used. However, in the current recycling process, especially in developing nations, humans manually screen ECs, which goes on to affect their health. It also causes losses of valuable materials. Therefore, automated solutions need to be adopted for both to classify and to segregate ECs from waste PCBs. The study proposes a robust EC identification system based on computer vision and deep learning algorithms (YOLOv3) to automate sorting process which would help in further processing. The study uses a publicly available dataset, and a PCB dataset which reflect challenging recycling environments like lighting conditions, cast shadows, orientations, viewpoints, and different cameras/resolutions. The outcome of YOLOv3 detection model based on training of both datasets presents satisfactory classification accuracy and capability of real-time competent identification, which in turn, could help in automatically segregating ECs, while leading towards effective e-waste recycling.