Forecasting Carbon Emissions with Dynamic Model Averaging Approach: Time-Varying Evidence from China
Although energy-related factors, such as energy intensity and energy consumption, are well recognized as major drivers of carbon dioxide emission in China, little is known about the time-varying impacts of other macrolevel nonenergy factors on carbon emission, especially those from macroeconomic, fi...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2020/8827440 |
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| author | Siqi Xu Yifeng Zhang Xiaodan Chen |
| author_facet | Siqi Xu Yifeng Zhang Xiaodan Chen |
| author_sort | Siqi Xu |
| collection | DOAJ |
| description | Although energy-related factors, such as energy intensity and energy consumption, are well recognized as major drivers of carbon dioxide emission in China, little is known about the time-varying impacts of other macrolevel nonenergy factors on carbon emission, especially those from macroeconomic, financial, household, and technology progress indicators in China. This paper contributes to the literature by investigating the time-varying predictive ability of 15 macrolevel indicators for China’s carbon dioxide emission from 1982 to 2017 with a dynamic model averaging (DMA) method. The empirical results show that, firstly, the explanatory power of each nonenergy predictor changes significantly with time and no predictor has a stable positive/negative impact on China’s carbon emissions throughout the whole sample period. Secondly, all these predictors present a distinct predictive ability for carbon emission in China. The proportion of industry production in GDP (IP) shows the greatest predictive power, while the proportion of FDI in GDP has the smallest forecasting ability. Interestingly, those Chinese household features, such as Engel’s coefficient and household savings rate, play very important roles in the prediction of China’s carbon emission. In addition, we find that IP are losing its predictive power in recent years, while the proportion of value-added of the service sector in GDP presents not only a leading forecasting weight, but a continuous increasing prediction power in recent years. Finally, the dynamic model averaging (DMA) method can produce the most accurate forecasts of carbon emission in China compared to other commonly used forecasting methods. |
| format | Article |
| id | doaj-art-91ed9b7f16f24787be58027851372900 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-91ed9b7f16f24787be580278513729002025-02-03T01:28:02ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2020-01-01202010.1155/2020/88274408827440Forecasting Carbon Emissions with Dynamic Model Averaging Approach: Time-Varying Evidence from ChinaSiqi Xu0Yifeng Zhang1Xiaodan Chen2School of Social Development, Xihua University, Chengdu, ChinaSchool of Finance, Yunnan University of Finance and Economics, Kunming, ChinaSchool of Finance, Yunnan University of Finance and Economics, Kunming, ChinaAlthough energy-related factors, such as energy intensity and energy consumption, are well recognized as major drivers of carbon dioxide emission in China, little is known about the time-varying impacts of other macrolevel nonenergy factors on carbon emission, especially those from macroeconomic, financial, household, and technology progress indicators in China. This paper contributes to the literature by investigating the time-varying predictive ability of 15 macrolevel indicators for China’s carbon dioxide emission from 1982 to 2017 with a dynamic model averaging (DMA) method. The empirical results show that, firstly, the explanatory power of each nonenergy predictor changes significantly with time and no predictor has a stable positive/negative impact on China’s carbon emissions throughout the whole sample period. Secondly, all these predictors present a distinct predictive ability for carbon emission in China. The proportion of industry production in GDP (IP) shows the greatest predictive power, while the proportion of FDI in GDP has the smallest forecasting ability. Interestingly, those Chinese household features, such as Engel’s coefficient and household savings rate, play very important roles in the prediction of China’s carbon emission. In addition, we find that IP are losing its predictive power in recent years, while the proportion of value-added of the service sector in GDP presents not only a leading forecasting weight, but a continuous increasing prediction power in recent years. Finally, the dynamic model averaging (DMA) method can produce the most accurate forecasts of carbon emission in China compared to other commonly used forecasting methods.http://dx.doi.org/10.1155/2020/8827440 |
| spellingShingle | Siqi Xu Yifeng Zhang Xiaodan Chen Forecasting Carbon Emissions with Dynamic Model Averaging Approach: Time-Varying Evidence from China Discrete Dynamics in Nature and Society |
| title | Forecasting Carbon Emissions with Dynamic Model Averaging Approach: Time-Varying Evidence from China |
| title_full | Forecasting Carbon Emissions with Dynamic Model Averaging Approach: Time-Varying Evidence from China |
| title_fullStr | Forecasting Carbon Emissions with Dynamic Model Averaging Approach: Time-Varying Evidence from China |
| title_full_unstemmed | Forecasting Carbon Emissions with Dynamic Model Averaging Approach: Time-Varying Evidence from China |
| title_short | Forecasting Carbon Emissions with Dynamic Model Averaging Approach: Time-Varying Evidence from China |
| title_sort | forecasting carbon emissions with dynamic model averaging approach time varying evidence from china |
| url | http://dx.doi.org/10.1155/2020/8827440 |
| work_keys_str_mv | AT siqixu forecastingcarbonemissionswithdynamicmodelaveragingapproachtimevaryingevidencefromchina AT yifengzhang forecastingcarbonemissionswithdynamicmodelaveragingapproachtimevaryingevidencefromchina AT xiaodanchen forecastingcarbonemissionswithdynamicmodelaveragingapproachtimevaryingevidencefromchina |