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Drivers of green growth in the Kingdom of Saudi Arabia: can financial development promote environmentally sustainable economic growth?

Asif Ali AbroNaushad AlamMuntasir MurshedHaider MahmoodMohammed MusahA K M Atiqur Rahman
Published in: Environmental science and pollution research international (2022)
The Kingdom of Saudi Arabia has recently declared its vision of turning carbon neutral by 2060. This declaration has motivated policymakers in this Arab nation to design policies that can green economic activities in Saudi Arabia so that environmentally sustainable growth can be ensured. Against this backdrop, this study models the independent and joint effects of financial development, globalization, and energy efficiency rates on green growth of the Saudi Arabian economy. In this regard, green growth in the Kingdom of Saudi Arabia is proxied by the difference between the nation's annual per capita growth rates of gross domestic product and carbon dioxide emission. Utilizing data from 1972 to 2018 and controlling for structural break-induced problems found in the data, the findings from the regression and causality analyses confirm the green growth-inhibiting impacts of financial development and trade globalization. In contrast, greater financial globalization is evidenced to drive green growth in the Kingdom of Saudi Arabia. Furthermore, more efficient uses of energy resources are found to not only directly boost green growth but also partially neutralize the long-run green growth-dampening impacts associated with the development of the financial sector. In addition, financial development and trade globalization are observed to jointly inhibit green growth attainment both in the short and long run. In line with these important findings, it is recommended that the government of Saudi Arabia conceptualizes new green growth policies so that the nation's annual per capita economic growth rate outpaces its annual per capita growth rate of carbon dioxide emissions.
Keyphrases
  • saudi arabia
  • carbon dioxide
  • emergency department
  • public health
  • machine learning
  • oxidative stress
  • artificial intelligence
  • deep learning
  • data analysis
  • life cycle