Exploring research on ecotechnology through artificial intelligence and bibliometric maps

Abstract

Ecotechnology, quintessential for crafting sustainable socio-environmental strategies, remains tantalizingly uncharted. Our analysis, steered by the nuances of machine learning and augmented by bibliometric insights, delineates the expansive terrain of this domain, elucidates pivotal research themes and conundrums, and discerns the vanguard nations in this field. Furthermore, we deftly connect our discoveries to the United Nations' 2030 Sustainable Development Goals, thereby accentuating the profound societal ramifications of ecotechnology.

Graphical abstract

Highlights

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  Research on ECT has a sustained growing during the last 30 years.

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  ECT scientific production has had an increasing visibility.

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  Machine learning was useful to multidimensionally compare countries ECT profiles.

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  ECT research is highly related to the goals of the UN 2030 Agenda.

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  Developing countries with higher levels of renewable energy consumption are more specialized in ECT compared to developed countries.

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  Prominent research fronts are related to wastewater treatment and the preservation of ecosystems.