The role of natural resources in the management of environmental sustainability: Machine learning approach

Amar Rao, Amogh Talan, Shujaat Abbas, Dhairya Dev and Farhad Taghizadeh-Hesary

Resources Policy, 2023, vol. 82, issue C

Abstract: This study examines the ability of Asia's natural resources to manage environmental sustainability through digitalization. We analyse 19 Asian nations from 1990 to 2020. In addition to natural resource rents and digitalization, we consider human capital, economic complexity, trade, urbanisation, energy use, renewable energy consumption, academic freedom, and corruption as variables affecting environmental sustainability. To achieve our goal, we use adaptable machine learning algorithms. The sparse algorithm of ANN suggests that when the range is 0.1–9.87, CO2 is decreasing, but when the range is 0–0.1 and above 9.87, the impact on CO2 emission is positive. When ICT is between 0.18 and .32, its impact on CO2 is negative, but above.32, it's positive. The interaction between natural resources and ICT has a positive impact on CO2 in the.05-0.25,0.85-1.45, and 14.7–17.45 ranges, but a negative impact in the.35-0.65 and 5.65–8.45 ranges. Urbanization, energy use, and economic complexity play the biggest roles in Asian CO2 predictions. The findings suggest that governments in Asian countries should prioritize sustainable management of natural resources through the utilization of information and communication technology (ICT) resources. Additionally, the article recommends increasing trade integration and cooperation with other Asian countries in the ICT industry and investing in smart urbanization to improve environmental quality. To address energy sustainability, the research suggests prioritizing energy efficiency and promoting renewable energy sources to achieve the goals of SDG7 and SDG13.

Keywords: Asia; Digitalization; Environmental sustainability; Machine learning; Natural resource (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (20)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jrpoli:v:82:y:2023:i:c:s0301420723002593

DOI: 10.1016/j.resourpol.2023.103548

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