Abstract
With the fast growth of Chinese economy and the society's progress, the environment issues in China begin to attract people's attention more and more. After United Nations Climate Change Conference in Copenhagen, Chinese begin to concern about the "carbon emissions" and the low carbon lifestyle. According to the expert's forecast, China's automobile industry's carbon emissions possibly occupy about 5% of total emissions. And the automobile industry of our country is developing quickly, its carbon emissions rate in total emissions will be more and more. In this paper, it's demonstrated that Rough Set Theory can find some potential knowledge by data analysis. Based on Rough Set Theory and the demand of prediction of car sales volume, a procedure of car sales forecast is proposed. Then automobile carbon emissions can be forecasted with obtained rules set and calculation of carbon emissions. The prediction is expected to have some effect on the establishment of Chinese twelfth five-year plan, especially the policy about the carbon emissions.
| Original language | English |
|---|---|
| Title of host publication | Emerging Research in Artificial Intelligence and Computational Intelligence - International Conference, AICI 2011, Proceedings |
| Pages | 582-589 |
| Number of pages | 8 |
| DOIs | |
| State | Published - 2011 |
| Event | 2011 International Conference on Artificial Intelligence and Computational Intelligence, AICI 2011 - Taiyuan, China Duration: 23 Sep 2011 → 25 Sep 2011 |
Publication series
| Name | Communications in Computer and Information Science |
|---|---|
| Volume | 237 CCIS |
| ISSN (Print) | 1865-0929 |
Conference
| Conference | 2011 International Conference on Artificial Intelligence and Computational Intelligence, AICI 2011 |
|---|---|
| Country/Territory | China |
| City | Taiyuan |
| Period | 23/09/11 → 25/09/11 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 13 Climate Action
Keywords
- Rough Set Theory
- car sales volume
- carbon emissions
- forecast
- low carbon
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