The Load Capacity Curve Hypothesis: Natural Resource Rent, Financial Inclusion, and the Pathways to Global Environmental Sustainability

The load capacity factor (LCF) hypothesis extends the environmental Kuznets curve (EKC) framework by incorporating both the demand and supply sides of natural resources. This study investigates the LCF hypothesis and the environmental impacts of the natural resources, financial inclusion, population density, and energy consumption on LCF globally and across income groups. The Driscoll-Kraay standard error approach is employed on the panel data from 1995 to 2021. For robustness analysis, the system GMM estimation approach is also utilized. The results reveal the validity of the LCF hypothesis. Natural resource rent negatively affects LCF across all panels except the low-income group, where an insignificant relationship is observed. Financial inclusion reveals mixed findings; it improves LCF in high-income and high-middle-income economies whereas reducing LCF in low-middle-income economies. Comparatively, financial inclusion measures reflect mixed effects. An increase in domestic credit and bank branches leads to environmental degradation by reducing LCF across all panels. The proliferation of ATMs tends to improve LCF in global, high-income, and upper-middle-income economies, whereas diminishing it in low-middle-income and low-income economies. This research suggests the importance of developing an inclusive financial system, along with adopting cleaner energy technologies to improve the load capacity of the planet.

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Journal
Pakistan journal of commerce and social sciences
Published
2025-12-31
DOI
https://doi.org/10.64534/commer.2025.665
Primary Topic
Energy, Environment, Economic Growth
Type
article
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article

The Load Capacity Curve Hypothesis: Natural Resource Rent, Financial Inclusion, and the Pathways to Global Environmental Sustainability

Sajid Ali, Maryum Bashir, Ayesha Rehman
Pakistan journal of commerce and social sciences
Energy, Environment, Economic Growth
article

The Load Capacity Curve Hypothesis: Natural Resource Rent, Financial Inclusion, and the Pathways to Global Environmental Sustainability

Sajid Ali, Maryum Bashir, Ayesha Rehman
article en

Abstract

The load capacity factor (LCF) hypothesis extends the environmental Kuznets curve (EKC) framework by incorporating both the demand and supply sides of natural resources. This study investigates the LCF hypothesis and the environmental impacts of the natural resources, financial inclusion, population density, and energy consumption on LCF globally and across income groups. The Driscoll-Kraay standard error approach is employed on the panel data from 1995 to 2021. For robustness analysis, the system GMM estimation approach is also utilized. The results reveal the validity of the LCF hypothesis. Natural resource rent negatively affects LCF across all panels except the low-income group, where an insignificant relationship is observed. Financial inclusion reveals mixed findings; it improves LCF in high-income and high-middle-income economies whereas reducing LCF in low-middle-income economies. Comparatively, financial inclusion measures reflect mixed effects. An increase in domestic credit and bank branches leads to environmental degradation by reducing LCF across all panels. The proliferation of ATMs tends to improve LCF in global, high-income, and upper-middle-income economies, whereas diminishing it in low-middle-income and low-income economies. This research suggests the importance of developing an inclusive financial system, along with adopting cleaner energy technologies to improve the load capacity of the planet.

Pakistan journal of commerce and social sciencesVol. 19(4)
Quaid-i-Azam University (PK)
Reduced inequalities
Openalex Percentile: Top 16%
Energy, Environment, Economic Growth
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The Load Capacity Curve Hypothesis: Natural Resource Rent, Financial Inclusion, and the Pathways to Global Environmental Sustainability — Sajid Ali, Maryum Bashir, et al. · Pakistan journal of commerce and social sciences (2025) | TGRS Research Map | TGRS