Literature DB >> 33714048

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

Cosimo Magazzino1, Marco Mele2, Nicolas Schneider3, Muhammad Shahbaz4.   

Abstract

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.
Copyright © 2021 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Biomass energy; Carbon emissions; Environmental pollution; Germany; Machine learning

Mesh:

Substances:

Year:  2021        PMID: 33714048     DOI: 10.1016/j.jenvman.2021.112293

Source DB:  PubMed          Journal:  J Environ Manage        ISSN: 0301-4797            Impact factor:   6.789


  2 in total

1.  Assessing a fossil fuels externality with a new neural networks and image optimisation algorithm: the case of atmospheric pollutants as confounders to COVID-19 lethality.

Authors:  Cosimo Magazzino; Marco Mele; Nicolas Schneider
Journal:  Epidemiol Infect       Date:  2021-11-16       Impact factor: 2.451

2.  Convergence behaviours of energy series and GDP nexus hypothesis: A non-parametric Bayesian application.

Authors:  Mihaela Simionescu; Wadim Strielkowski; Nicolas Schneider; Luboš Smutka
Journal:  PLoS One       Date:  2022-08-04       Impact factor: 3.752

  2 in total

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