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How do natural resources and economic growth impact load capacity factor in selected Next-11 countries? Assessing the role of digitalization and government stability

pmid: 37392299
With growing environmental concerns, everyone's attention has shifted to how we use our limited materials supplies. Rapid economic expansion is dependent on heavy resource use, decreasing biodiversity and raising the ecological footprints (EF), resulting in a reduction in the load capacity factor (LCF). Because of this, scholars and policymakers are actively looking for approaches to improve the LCF without hindering economic growth (GDP). For similar reasons, this research aims at how the selected next eleven economies improved their LCF from 1990 to 2018 by analyzing the effect of digitalization (DIG), natural resources (NAT), GDP, globalization, and governance. To account for dependence across sections and slope variation, the cross-sectional augmented ARDL model is used in this research. The long-term findings indicate that LCF was diminished by dependence on NAT, globalization, and economic growth and was bolstered by DIG and sound governance. The work recommends that financial and policy support is needed for initiatives such as zero-emission vehicle production and energy-efficient building construction. By offering a line of credit at low interest rates, renewable energy projects can attract domestic and private investors.
- University of the Punjab Pakistan
- University of Management and Technology Pakistan
- University of Management and Technology Pakistan
- Gyeongsang National University Korea (Republic of)
- Nanjing University China (People's Republic of)
Internationality, Carbon Dioxide, Cross-Sectional Studies, Natural Resources, Government, Economic Development, Renewable Energy
Internationality, Carbon Dioxide, Cross-Sectional Studies, Natural Resources, Government, Economic Development, Renewable Energy
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).33 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 1%
