{"title":"加纳的环境退化、能源使用和全球化:来自制度转换和神经网络自回归模型的新经验证据","authors":"Bright Tetteh, Samuel Tawiah Baidoo","doi":"10.1080/15487733.2022.2110680","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":35192,"journal":{"name":"Sustainability: Science, Practice, and Policy","volume":"111 1","pages":"679 - 695"},"PeriodicalIF":3.6000,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Environmental degradation, energy use, and globalization in Ghana: New empirical evidence from regime switching and neural network autoregression models\",\"authors\":\"Bright Tetteh, Samuel Tawiah Baidoo\",\"doi\":\"10.1080/15487733.2022.2110680\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":35192,\"journal\":{\"name\":\"Sustainability: Science, Practice, and Policy\",\"volume\":\"111 1\",\"pages\":\"679 - 695\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2022-09-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sustainability: Science, Practice, and Policy\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/15487733.2022.2110680\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENVIRONMENTAL STUDIES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sustainability: Science, Practice, and Policy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/15487733.2022.2110680","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL STUDIES","Score":null,"Total":0}
Environmental degradation, energy use, and globalization in Ghana: New empirical evidence from regime switching and neural network autoregression models
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.
期刊介绍:
Sustainability: Science, Practice and Policy is a refereed, open-access journal which recognizes that climate change and other socio-environmental challenges require significant transformation of existing systems of consumption and production. Complex and diverse arrays of societal factors and institutions will in coming decades need to reconfigure agro-food systems, implement renewable energy sources, and reinvent housing, modes of mobility, and lifestyles for the current century and beyond. These innovations will need to be formulated in ways that enhance global equity, reduce unequal access to resources, and enable all people on the planet to lead flourishing lives within biophysical constraints. The journal seeks to advance scientific and political perspectives and to cultivate transdisciplinary discussions involving researchers, policy makers, civic entrepreneurs, and others. The ultimate objective is to encourage the design and deployment of both local experiments and system innovations that contribute to a more sustainable future by empowering individuals and organizations and facilitating processes of social learning.