| Literature DB >> 33714048 |
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.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