The effect of changes in industrial structure on carbon dioxide emissions in Iranian provinces: A spatial econometric approach
Introduction: Considering the services that the environment provides for human society, environmental pollution related issues have great importance. Air pollution is one of the environmental pollutions had more attention in recent decades. According to the economic literature, one of the importan...
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Shahid Beheshti University
2022-03-01
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| author | soraya khodadadi mosayeb pahlavani Ramezan Hossein Zadeh |
| author_facet | soraya khodadadi mosayeb pahlavani Ramezan Hossein Zadeh |
| author_sort | soraya khodadadi |
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| description | Introduction: Considering the services that the environment provides for human society, environmental pollution related issues have great importance. Air pollution is one of the environmental pollutions had more attention in recent decades. According to the economic literature, one of the important factors affected the emission of air pollutants is the industrialization of economic activities and the structure of production in the industry sector. Accordingly, the current study investigated the direct and indirect effects (spillovers) of structural changes in the industrial sector on carbon dioxide emissions from energy consumption. Material and methods: This research was carried out using the spatial econometric model for thirty provinces of Iran during 2006-2015. To calculate the index of structural change in production in this study, the index of structural change index presented by Lillian (2012) was used. Before estimating the model, , to check the stationarity of the variables, the Levin, Lin, Chou (LLC) unit root test was estimated, and finally, after performing the Hausman and multiple Wald test, , the spatial Durbin model was estimated with spatial random effects.Results and discussion: Based on the results of the Levin, Lin, Chou (LLC) unit root test, all variables of the model were I (0). Hausmann test also suggested the estimation by random effect method (RF). Based on the results of estimating the model, the effect of GDP per capita on carbon dioxide emissions was positive and significant; If GDP per capita increases by 1%, carbon dioxide emissions will increase by 0.22 %. The effect of energy intensity on carbon dioxide emissions was positive and significant; If the energy intensity increases by 1%, carbon dioxide emissions will increase by 0.06 %. The effect of population on carbon dioxide emissions was positive and significant; If the population increases by 1%; emissions of carbon dioxide will increase by 0.95%. The effect of changes in industrial structure on carbon dioxide emissions has been negative. This means that the industrial structure in different provinces has changed in order to reduce carbon dioxide emissions, although the contribution of these changes in reducing this pollutant was small. On the other hand, the spatial impact of industrial structure changes on carbon dioxide emissions was negative and significant. The coefficient related to the effects of industrial overflow showed that if the index of changes in the industrial structure increases by 1%, carbon dioxide emissions in adjacent areas will decrease by 0.03 %. Conclusion: Model estimation has shown that the restructuring of industry has a direct and negative effect on carbon emissions in the provinces. This indicates that the structure of the industry in different provinces has changed to industries with lower carbon dioxide emissions. One of the reasons for this could be the increase in the share of sub-sectors of the industry that have low energy consumption. Another reason for this could be the increase in the share of sectors that use fossil fuels with less pollution. Other results of the model have shown that the change in the structure of production in the industrial sector of the provinces has had indirect effects (spillovers) on the production of the provinces. Based on the results of the model, it is suggested that policies be adopted to transfer factors and resources from low-efficiency sectors to high-efficiency sectors. Also, energy efficiency policies in high-energy intensive industrial sectors and sub-sectors that use high-emission fossil fuels such as diesel and fuel oil should be considered. |
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| spelling | doaj-art-824791a93db44f54baad6f6bb636fe8d2025-08-20T02:18:32ZfasShahid Beheshti Universityعلوم محیطی1735-13242588-61772022-03-0120122123610.52547/envs.2021.36979101694The effect of changes in industrial structure on carbon dioxide emissions in Iranian provinces: A spatial econometric approachsoraya khodadadi0mosayeb pahlavani1Ramezan Hossein Zadeh2Department of Economics, Faculty of Economics and Management, Sistan and Baluchestan University, Zahedan, IranDepartment of Economics, Faculty of Economics and Management, Sistan and Baluchestan University, Zahedan, IranDepartment of Economics, Faculty of Economics and Management, Sistan and Baluchestan University, Zahedan, IranIntroduction: Considering the services that the environment provides for human society, environmental pollution related issues have great importance. Air pollution is one of the environmental pollutions had more attention in recent decades. According to the economic literature, one of the important factors affected the emission of air pollutants is the industrialization of economic activities and the structure of production in the industry sector. Accordingly, the current study investigated the direct and indirect effects (spillovers) of structural changes in the industrial sector on carbon dioxide emissions from energy consumption. Material and methods: This research was carried out using the spatial econometric model for thirty provinces of Iran during 2006-2015. To calculate the index of structural change in production in this study, the index of structural change index presented by Lillian (2012) was used. Before estimating the model, , to check the stationarity of the variables, the Levin, Lin, Chou (LLC) unit root test was estimated, and finally, after performing the Hausman and multiple Wald test, , the spatial Durbin model was estimated with spatial random effects.Results and discussion: Based on the results of the Levin, Lin, Chou (LLC) unit root test, all variables of the model were I (0). Hausmann test also suggested the estimation by random effect method (RF). Based on the results of estimating the model, the effect of GDP per capita on carbon dioxide emissions was positive and significant; If GDP per capita increases by 1%, carbon dioxide emissions will increase by 0.22 %. The effect of energy intensity on carbon dioxide emissions was positive and significant; If the energy intensity increases by 1%, carbon dioxide emissions will increase by 0.06 %. The effect of population on carbon dioxide emissions was positive and significant; If the population increases by 1%; emissions of carbon dioxide will increase by 0.95%. The effect of changes in industrial structure on carbon dioxide emissions has been negative. This means that the industrial structure in different provinces has changed in order to reduce carbon dioxide emissions, although the contribution of these changes in reducing this pollutant was small. On the other hand, the spatial impact of industrial structure changes on carbon dioxide emissions was negative and significant. The coefficient related to the effects of industrial overflow showed that if the index of changes in the industrial structure increases by 1%, carbon dioxide emissions in adjacent areas will decrease by 0.03 %. Conclusion: Model estimation has shown that the restructuring of industry has a direct and negative effect on carbon emissions in the provinces. This indicates that the structure of the industry in different provinces has changed to industries with lower carbon dioxide emissions. One of the reasons for this could be the increase in the share of sub-sectors of the industry that have low energy consumption. Another reason for this could be the increase in the share of sectors that use fossil fuels with less pollution. Other results of the model have shown that the change in the structure of production in the industrial sector of the provinces has had indirect effects (spillovers) on the production of the provinces. Based on the results of the model, it is suggested that policies be adopted to transfer factors and resources from low-efficiency sectors to high-efficiency sectors. Also, energy efficiency policies in high-energy intensive industrial sectors and sub-sectors that use high-emission fossil fuels such as diesel and fuel oil should be considered.https://envs.sbu.ac.ir/article_101694_8d264fef25c28ec66a43670f96be38ea.pdf1carbon dioxide emissions2- industrialization3- industrial structure4- pollution5- spatial econometrics |
| spellingShingle | soraya khodadadi mosayeb pahlavani Ramezan Hossein Zadeh The effect of changes in industrial structure on carbon dioxide emissions in Iranian provinces: A spatial econometric approach علوم محیطی 1carbon dioxide emissions 2- industrialization 3- industrial structure 4- pollution 5- spatial econometrics |
| title | The effect of changes in industrial structure on carbon dioxide emissions in Iranian provinces: A spatial econometric approach |
| title_full | The effect of changes in industrial structure on carbon dioxide emissions in Iranian provinces: A spatial econometric approach |
| title_fullStr | The effect of changes in industrial structure on carbon dioxide emissions in Iranian provinces: A spatial econometric approach |
| title_full_unstemmed | The effect of changes in industrial structure on carbon dioxide emissions in Iranian provinces: A spatial econometric approach |
| title_short | The effect of changes in industrial structure on carbon dioxide emissions in Iranian provinces: A spatial econometric approach |
| title_sort | effect of changes in industrial structure on carbon dioxide emissions in iranian provinces a spatial econometric approach |
| topic | 1carbon dioxide emissions 2- industrialization 3- industrial structure 4- pollution 5- spatial econometrics |
| url | https://envs.sbu.ac.ir/article_101694_8d264fef25c28ec66a43670f96be38ea.pdf |
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