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ABSTRACT
The concern that over-depleting natural resources (NAT) may somehow be costly for environmental quality is a recurrent topic in empirical analysis. However, the literature has predominantly focused on studying the effects of increases in NAT use on the environment, ignoring potential non-linear interactions. The current study seeks to expand the previous literature by examining the non-linear environmental ramifications of NAT in Russia, home to one of the world’s richest natural resource reserves. In this regard, adopting the load capacity factor (LCF) as an environmental indicator, the study examines how the positive and negative changes in NAT affect the country’s environmental well-being within the framework of the N-ARDL model for 1992–2021. This study uses artificial intelligence unit root and asymmetric Fourier causality approach for robustness analysis. Empirical evidence suggests that positive shocks to NAT have a deleterious influence on the LCF, while negative changes in NAT contribute to environmental well-being. Nonetheless, the long-term influence of a unit positive shock in NAT decreases the LCF by a greater amount than a unit negative shock increases it. The results also demonstrate a ‘U-shaped’ connection between income growth and ecological well-being, confirming the validity of the LCC hypothesis. Furthermore, the outcomes reveal that renewable energy consumption and human capital upgrade environmental quality. Therefore, Russian policymakers should focus on the SDG 7, 12, and 13 targets, which refer to substantially increasing the quality of primary and secondary education, the share of green energy in the total energy mix, and effective resource management and utilization.
Highlights
Applied SDG policies focused on environmental quality are presented.
Renewable energy consumption helps to increase environmental quality.
For the first time, natural resources are considered asymmetrically in Russia.
Natural resource extraction worsens environmental quality.
Nomenclature
| ARDL | = | Autoregressive Distributed Lag |
| BIC | = | Biocapacity |
| BRICS | = | Brazil, Russia, India, China, South Korea, |
| BRICS-T | = | Brazil, Russia, India, China, South Korea, Turkey |
| BP-LM | = | Breusch-Pagan Lagrange Multiplier |
| BP | = | British Petroleum |
| CAT | = | Climate Action Tracker |
| CE | = | Carbon Emissions |
| CS-ARDL | = | Cross Sectional ARDL |
| DOLS | = | Dynamic Ordinary Least Squares |
| EC | = | Energy Consumption |
| EF | = | Ecological Footprint |
| EKC | = | Environmental Kuznets Curve |
| EP | = | Environmental Pollution |
| EQ | = | Environmental Quality |
| FMOLS | = | Fully Modified Ordinary Least Squares |
| GFN | = | Global Footprint Network |
| GDP | = | Gross Domestic Product |
| GDPSQ | = | Square of Gross Domestic Product |
| GHG | = | Greenhouse Gases |
| G7 | = | The Group of Seven |
| HC | = | Human Capital |
| IPCC | = | Intergovernmental Panel on Climate Change |
| LCC | = | Load Capacity Curve |
| LCF | = | Load Capacity Factor |
| NARDL | = | Non-linear Autoregressive Distributed Lag |
| NAT | = | Natural Resources Rent |
| REC | = | Renewable Energy Consumption |
| SDG | = | Sustainable Development Goal |
| UNFCCC | = | United Nations Framework Convention on Climate Change |
Disclosure statement
The authors declare that they have no known competing personal or financial interests that could have appeared to influence the work reported in this paper.
Data availability statement
Data is available at request from the corresponding author.