Technological progress in achieving environmental sustainability in Central and Southern Africa: The role of fossil fuels and institutional quality

ABSTRACT

In the face of growing efforts to achieve sustainable development Goal 7, 9, 13, and 16 in Africa, this study examines the environmental impacts of technological progress, fossil fuels, and institutional quality in achieving environmental sustainability in Central and Southern countries. The empirical analysis draws from a panel dataset spanning from 1996 to 2021, and utilizes the innovative method of Moments quantile regression technique, adjusted with the bootstrap variance algorithm and Half-Panel Jackknife (HPJ) Wald-type test with a bias-corrected pooled estimator. The findings reveal that fossil fuel consumption has a significant negative impact on sustainability. Surprisingly, institutional quality does not affect environmental outcomes. Central Africa faces challenges in adopting a sustainable technological path, while South African nations show a U-shaped relationship between technological progress and CO2 emissions. In both Central and South Africa, high GDP per capita and fossil fuel consumption have a detrimental effect on environmental sustainability. In South Africa, there is bidirectional causality between CO2 emissions and GDPsq, FOSSIL, while a unidirectional causality exists from fossil fuels to TECH. Overall, the study suggests a promising link between technological advancements and environmental sustainability in the South African region.

KEYWORDS:

• Africa
• environmental sustainability
• fossil fuels
• institutional quality
• Technological

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary Material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/15567249.2024.2361746.

Notes

¹ https://images.transparencycdn.org/images/Report_CPI2022_English.pdf

² A detailed explanation of the variables mentioned in Note 1 and Appendix D relate.

³ Details explanation of the variables in highlighted in the Appendix D and Note 2 respectively.

⁴ The regression out is generated using eviews 12.

⁵ See Figure 2