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Can biomass energy curtail environmental pollution? A quantum model approach to Germany

This paper aims to investigate the causal relationship among renewable energy technologies, biomass energy consumption, per capita GDP, and CO2 emissions for Germany. We constructed an innovative algorithm, the Quantum model, and applied it with Machine Learning experiments - through a software capable of emulating a quantum system - to data over the period of 1990-2018. This process is possible after eliminating the "irreversibility" of classical computations (unitary transformations) by making the process "reversible". The empirical findings support the powerful role of biomass energy in reducing carbon dioxide emissions, although the effect of renewable energy technology displays a much stronger magnitude. Moreover, income remains an important determinant of environmental pollution in Germany.
- Beijing Institute of Technology China (People's Republic of)
- Roma Tre University Italy
- Sorbonne Paris Cité France
- Pantheon-Sorbonne University France
- Roma Tre University Italy
Biomass energy, Biomass energy; Carbon emissions; Environmental pollution; Machine learning; Germany, Carbon Dioxide, Environmental pollution, Germany, Machine learning, Biomass, Economic Development, Renewable Energy, Environmental Pollution, Carbon emissions
Biomass energy, Biomass energy; Carbon emissions; Environmental pollution; Machine learning; Germany, Carbon Dioxide, Environmental pollution, Germany, Machine learning, Biomass, Economic Development, Renewable Energy, Environmental Pollution, Carbon emissions
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).59 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 1% visibility views 3 - 3views
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