This study investigates the nexus between environmental degradation, energy use, and globalization using Markov-switching (MS) models which previous studies on Ghana have not considered. We utilize this method because of its ability to detect possible non-linear relationships. The Neural Network Autoregression (NNAR [p, k]) model is also employed to predict carbon-dioxide (CO2) emissions for the country over the next decade. In doing so, secondary time-series data on CO2 releases, per capita gross domestic product (GDP), energy use, and KOF (Konjunkturforschungsstelle) globalization indexes spanning the period 1971–2016 are employed. The results from all three MS estimations show no support for the existence of the Environmental Kuznets Curve (EKC) in Ghana. The results further demonstrate that energy use and an overall globalization index result in more CO2 emissions causing deterioration of the environment. Economic globalization is also revealed to harm the environment whereas social and political globalization have different effects in different regimes. The forecast results from the NNAR (14, 8) estimation also indicate that Ghana will have an upward trajectory of CO2 discharge for the next decade. The implication of the findings is that there is an urgent need for strengthening and/or revising environmental policies in the country with greater focus on mitigation strategies in line with the Paris Agreement and Kyoto Protocol. These measures are likely to curb CO2 emissions as the economy expands. Recommendations and areas for further research to improve the environmental quality in Ghana are also provided for policy consideration.

Environmental degradation, energy use, and globalization in Ghana: New empirical evidence from regime switching and neural network autoregression models / B. Tetteh, S.T. Baidoo. - In: SUSTAINABILITY: SCIENCE, PRACTICE, & POLICY. - ISSN 1548-7733. - 18:1(2022), pp. 679-695. [10.1080/15487733.2022.2110680]

Environmental degradation, energy use, and globalization in Ghana: New empirical evidence from regime switching and neural network autoregression models

B. Tetteh
Primo
;
2022

Abstract

This study investigates the nexus between environmental degradation, energy use, and globalization using Markov-switching (MS) models which previous studies on Ghana have not considered. We utilize this method because of its ability to detect possible non-linear relationships. The Neural Network Autoregression (NNAR [p, k]) model is also employed to predict carbon-dioxide (CO2) emissions for the country over the next decade. In doing so, secondary time-series data on CO2 releases, per capita gross domestic product (GDP), energy use, and KOF (Konjunkturforschungsstelle) globalization indexes spanning the period 1971–2016 are employed. The results from all three MS estimations show no support for the existence of the Environmental Kuznets Curve (EKC) in Ghana. The results further demonstrate that energy use and an overall globalization index result in more CO2 emissions causing deterioration of the environment. Economic globalization is also revealed to harm the environment whereas social and political globalization have different effects in different regimes. The forecast results from the NNAR (14, 8) estimation also indicate that Ghana will have an upward trajectory of CO2 discharge for the next decade. The implication of the findings is that there is an urgent need for strengthening and/or revising environmental policies in the country with greater focus on mitigation strategies in line with the Paris Agreement and Kyoto Protocol. These measures are likely to curb CO2 emissions as the economy expands. Recommendations and areas for further research to improve the environmental quality in Ghana are also provided for policy consideration.
carbon-dioxide emissions; Environmental Kuznets Curve; globalization; Markov-switching; neural network autoregression; Sustainable Development Goals
Settore SECS-P/06 - Economia Applicata
2022
Article (author)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/1031537
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