Forecasting Carbon Emissions with Dynamic Model Averaging Approach: Time-Varying Evidence from China

Joint Authors

Xu, Siqi
Zhang, Yifeng
Chen, Xiaodan

Source

Discrete Dynamics in Nature and Society

Issue

Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-14, 14 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2020-10-28

Country of Publication

Egypt

No. of Pages

14

Main Subjects

Mathematics

Abstract EN

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.

American Psychological Association (APA)

Xu, Siqi& Zhang, Yifeng& Chen, Xiaodan. 2020. Forecasting Carbon Emissions with Dynamic Model Averaging Approach: Time-Varying Evidence from China. Discrete Dynamics in Nature and Society،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1153542

Modern Language Association (MLA)

Xu, Siqi…[et al.]. Forecasting Carbon Emissions with Dynamic Model Averaging Approach: Time-Varying Evidence from China. Discrete Dynamics in Nature and Society No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1153542

American Medical Association (AMA)

Xu, Siqi& Zhang, Yifeng& Chen, Xiaodan. Forecasting Carbon Emissions with Dynamic Model Averaging Approach: Time-Varying Evidence from China. Discrete Dynamics in Nature and Society. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1153542

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1153542