Can biomass energy curtail environmental pollution? A quantum model approach to Germany. (1st June 2021)

• Record Type: Journal Article
• Authors: Magazzino, Cosimo; Mele, Marco; Schneider, Nicolas; Shahbaz, Muhammad
• Is Part Of: Journal of environmental management. Volume 287(2021)
• Publication Date: 2021-06-01
• Subjects: Biomass energy -- Carbon emissions -- Environmental pollution -- Machine learning -- Germany; O13 -- Q5; Environmental policy -- Periodicals; Environmental management -- Periodicals; Environment -- Periodicals; Ecology -- Periodicals; 363.705
• DOI: 10.1016/j.jenvman.2021.112293
• Languages: English
• ISSNs: 0301-4797

Abstract

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. Highlights: An innovative Machine Learning algorithm (the Quantum model) has been constructed and applied. The empirical findings support the powerful role of biomass energy in reducing CO2 emissions. GDP represents an important determinant of environmental pollution in Germany.