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Authors
Advisor(s)
Abstract(s)
Sustainability assessments are crucial for more balanced economic
growth, and in these contexts, the new technologies, namely those associated with
the digital transition, play a fundamental role. Indeed, the technologies related to
the Era 4.0 are a hope for governments and international organisations, for example,
to deal with the need of promoting economic growth and, at the same time, reduce
the carbon footprint. Nonetheless, these digital approaches require expertise and,
sometimes, the stakeholders are not prepared to use these new methodologies, or the
infrastructures available are not adjusted to the new requirements. In this framework,
this study proposes to analyse the contributions of machine and deep learning for
economic growth and sustainability assessments. For that, bibliometric analysis and
systematic review were carried out, considering documents related to topics associated with the issues here addressed. The insights obtained highlight the potentialities
of the machine and deep learning methodologies for sustainability assessments and
economic growth.
Description
Keywords
Agriculture 4.0 Life cycle cost analysis Bibliometric analysis Literature insights
Pedagogical Context
Citation
Publisher
Springer