Forecast of China’s Carbon Emissions Based on ARIMA Method
Global warming caused by carbon emissions has become increasingly prominent. As the world’s second-largest economy, China is under enormous pressure to cut down its carbon dioxide emissions. It is urgent to seek effective methods to forecast carbon emissions and put forward the pointed and effective...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2021/1441942 |
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| author | Longqi Ning Lijun Pei Feng Li |
| author_facet | Longqi Ning Lijun Pei Feng Li |
| author_sort | Longqi Ning |
| collection | DOAJ |
| description | Global warming caused by carbon emissions has become increasingly prominent. As the world’s second-largest economy, China is under enormous pressure to cut down its carbon dioxide emissions. It is urgent to seek effective methods to forecast carbon emissions and put forward the pointed and effective measures to reduce emissions. In this paper, we first use the software Eviews to make an analysis of randomness on data of carbon emissions in the four representative provinces and city, Beijing, Henan, Guangdong, and Zhejiang, in terms of their carbon emissions data from 1997 to 2017. Then, according to their distinct characteristics, the methods of stationary processing of the difference, moving average, and substituting strong impact points, respectively, are adopted to perform the data preprocessing. Then, model identification, parameter estimation, and model test are carried out to establish the model of ARIMA(p, d, q) for the prediction of the carbon emissions of the four regions, respectively. Finally, the model is used to forecast the data and analyze their tendency for their carbon emissions in the next three years. The results can provide guidance for decision-makers to set reasonable carbon emission reduction targets and take appropriate energy conservation and emission reduction measures. |
| format | Article |
| id | doaj-art-c8e98d4df9d146f58694ce4ea2222d27 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-c8e98d4df9d146f58694ce4ea2222d272025-02-03T05:45:11ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2021-01-01202110.1155/2021/14419421441942Forecast of China’s Carbon Emissions Based on ARIMA MethodLongqi Ning0Lijun Pei1Feng Li2School of Mathematics and Statistics, Zhengzhou University, Zhengzhou, Henan 450001, ChinaSchool of Mathematics and Statistics, Zhengzhou University, Zhengzhou, Henan 450001, ChinaSchool of Mathematics and Statistics, Zhengzhou University, Zhengzhou, Henan 450001, ChinaGlobal warming caused by carbon emissions has become increasingly prominent. As the world’s second-largest economy, China is under enormous pressure to cut down its carbon dioxide emissions. It is urgent to seek effective methods to forecast carbon emissions and put forward the pointed and effective measures to reduce emissions. In this paper, we first use the software Eviews to make an analysis of randomness on data of carbon emissions in the four representative provinces and city, Beijing, Henan, Guangdong, and Zhejiang, in terms of their carbon emissions data from 1997 to 2017. Then, according to their distinct characteristics, the methods of stationary processing of the difference, moving average, and substituting strong impact points, respectively, are adopted to perform the data preprocessing. Then, model identification, parameter estimation, and model test are carried out to establish the model of ARIMA(p, d, q) for the prediction of the carbon emissions of the four regions, respectively. Finally, the model is used to forecast the data and analyze their tendency for their carbon emissions in the next three years. The results can provide guidance for decision-makers to set reasonable carbon emission reduction targets and take appropriate energy conservation and emission reduction measures.http://dx.doi.org/10.1155/2021/1441942 |
| spellingShingle | Longqi Ning Lijun Pei Feng Li Forecast of China’s Carbon Emissions Based on ARIMA Method Discrete Dynamics in Nature and Society |
| title | Forecast of China’s Carbon Emissions Based on ARIMA Method |
| title_full | Forecast of China’s Carbon Emissions Based on ARIMA Method |
| title_fullStr | Forecast of China’s Carbon Emissions Based on ARIMA Method |
| title_full_unstemmed | Forecast of China’s Carbon Emissions Based on ARIMA Method |
| title_short | Forecast of China’s Carbon Emissions Based on ARIMA Method |
| title_sort | forecast of china s carbon emissions based on arima method |
| url | http://dx.doi.org/10.1155/2021/1441942 |
| work_keys_str_mv | AT longqining forecastofchinascarbonemissionsbasedonarimamethod AT lijunpei forecastofchinascarbonemissionsbasedonarimamethod AT fengli forecastofchinascarbonemissionsbasedonarimamethod |