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The relationship between municipal solid waste and greenhouse gas emissions: Evidence from Switzerland

Municipal solid waste generation is becoming a prominent issue in the environmental arena. The aim of this paper is to investigate the relationship among municipal waste generation, greenhouse gas emissions, and GDP in Switzerland over the period 1990-2017. We apply both time series procedures (stationarity and causality tests) and a Machine Learning approach. Empirical findings underline a bidirectional causal relationship between municipal solid waste generation and GDP, indicating that the Environmental Kuznets Curve hypothesis holds for Switzerland. Moreover, we found that waste recovery (recycling and composting) is a key driver in mitigating greenhouse gas emissions. In fact, in the Tree Model, the probability that a change in the waste recovery variable could lead to a reduction in the greenhouse gas emissions registered a value of 87%.
- University of Teramo Italy
- University of Teramo Italy
- Sorbonne Paris Cité France
- Roma Tre University Italy
- Pantheon-Sorbonne University France
Greenhouse Effect, Waste sector, Municipal solid waste, Solid Waste, Greenhouse gas, Refuse Disposal, Machine Learning, Municipal solid waste; Greenhouse gas; Waste sector; Recycling; Environmental sustainability; Machine Learning, Greenhouse Gases, Recycling, Environmental sustainability, Switzerland
Greenhouse Effect, Waste sector, Municipal solid waste, Solid Waste, Greenhouse gas, Refuse Disposal, Machine Learning, Municipal solid waste; Greenhouse gas; Waste sector; Recycling; Environmental sustainability; Machine Learning, Greenhouse Gases, Recycling, Environmental sustainability, Switzerland
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).145 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.Top 1% 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 0.1% visibility views 7 - 7views
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