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Unraveling the challenges of waste-to-energy transition in emerging economies: Implications for sustainability

The recent geopolitical events, such as the conflict between Russia and Ukraine, have strained the available resources worldwide. In emerging economies like Bangladesh, which is heavily reliant on imported gas, oil, and coal, this has created a severe energy crisis. In response to the energy crisis and to support eco-friendly waste management, converting waste into energy is being recognized as a promising solution. However, introducing waste-to-energy systems in developing economies faces many intricate challenges that require careful examination. This study, therefore, aims to explore and evaluate the challenges associated with adopting a waste to energy (WtE) conversion system in emerging economies like Bangladesh. The research methodology involves identifying challenges from an extensive review of existing literature and expert feedback and then combining Bayesian theory with Best Worst Method (BWM) to evaluate the challenges. Among the 21 challenges analyzed, the ‘need for well-developed planning and incentivized policymaking’, ‘ineffectiveness in waste segregation at the source’, and ‘high cost for installation, maintenance, and infrastructure development’ appear to be the most significant challenges with weight values 0.071, 0.067, and 0.066, respectively. The study can enhance managers' understanding of the challenges faced by this sector and thus facilitate informed decision-making. The outcomes of this study are expected to enrich the existing body of knowledge, promote the diffusion of WtE technology in emerging economies, reduce dependency on the international energy market, and achieve global sustainable development goals (SDGs) such as affordable and clean energy (SDG 7), sustainable cities and communities (SDG 11), and climate action (SDG 13).
Renewable energy, Technology, Waste-to-energy, T, Environmental sciences, Emerging economy, Sustainable development, GE1-350, Bayesian best worst method
Renewable energy, Technology, Waste-to-energy, T, Environmental sciences, Emerging economy, Sustainable development, GE1-350, Bayesian best worst method
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).55 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 1%
