| Title | Promoting digital transformation in waste collection service and waste recycling in Moscow (Russia): Applying a circular economy paradigm to mitigate climate change impacts on the environment |
|---|---|
| ID_Doc | 23095 |
| Authors | Maiurova, A; Kurniawan, TA; Kustikova, M; Bykovskaia, E; Othman, MHD; Singh, D; Goh, HH |
| Title | Promoting digital transformation in waste collection service and waste recycling in Moscow (Russia): Applying a circular economy paradigm to mitigate climate change impacts on the environment |
| Year | 2022 |
| Published | |
| Abstract | Due to industrialization, recently Moscow (Russia) has been inundated with municipal solid waste (MSW), while the capital does not have an organized waste collection and waste recycling system. Digitalization enables smart cities such as Moscow to do more work with less resources. This article identifies and analyzes the existing waste management facilities in Moscow with respect to drawbacks and the ways forward to mitigate the bottlenecks. To improve its waste management, lessons drawn from Berlin's experiences in waste management are discussed to inspire a transformation in the city's waste sector in the framewok of resource recovery. In line with the 2030 UN Agenda, this work proposes a digitalization to accelerate a societal transition through waste recycling industry. Its global relevance is elaborated by presenting a perspective of digitalization in waste management practices. In this work, case-study was selected as the research method to provide a means to investigate a complex waste problem in Moscow and Berlin (Germany). It was evident from a cleaner production paradigm that digital technology can minimize the amount of unrecycled MSW, while conserving raw materials and reducing operational cost and GHG emissions. Digitalization builds cities' resilience by strengthening local waste management practices to respond to the Covid-19 global pandemic. In Moscow, the transition towards the digitalization of waste recycling through informal waste sectors has created 5,000 new jobs that reduces unemployment rate. This maximizes pick up time and enhances efficiency with a lower cost of operating trucks up to 75%. A convolutional neural network-based identification system that classifies identified materials yields almost perfect accuracy. A single robot arm can handle four varying fractions of construction and demolition waste with 99% of purity. Robotic deployment could reduce the volume of unrecycled waste by 20%. This could be replicated worldwide to resist the pressure of resource consumption and deliver socio-economic and environmental benefits. |
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