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Forecast of Energy Consumption and Carbon Emissions in China's Building Sector to 2060
| Content Provider | MDPI |
|---|---|
| Author | Pu, Xingfan Yao, Jian Zheng, Rongyue |
| Copyright Year | 2022 |
| Description | The goal of reaching the peak of carbon in the construction industry is urgent. However, the research on the feasibility of realizing this goal and the implementation of relevant policies in China is relatively superficial. In view of the historical data of energy consumption and building $CO_{2}$ emission from 1995 to 2019, this paper establishes a BP neural network model for predicting building $CO_{2}$ emissions. Moreover, the influencing factors, such as population, GDP, and total construction output, are introduced as the parameters in the model. Through the scenario analysis method explores the practical path to accomplish the peak of building $CO_{2}$ emissions. When using traditional prediction methods to predict building carbon emissions, the long prediction cycle will increase the possibility of significant errors. Therefore, this paper constructs the calculation model of building carbon emission and forecasts the future carbon emission value through the BP neural network to avoid the error caused by the nonlinear relationship between influencing factors and predicted value. It will effectively predict the feasibility of the carbon peak and the carbon-neutral target set by government, and provide a useful predictive tool for adjusting the new energy structure and formulating related emission reduction policies. |
| Starting Page | 4950 |
| e-ISSN | 19961073 |
| DOI | 10.3390/en15144950 |
| Journal | Energies |
| Issue Number | 14 |
| Volume Number | 15 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-07-06 |
| Access Restriction | Open |
| Subject Keyword | Energies Environmental Engineering Industrial Engineering Carbon Emissions Peak Carbon Emissions Carbon Neutral Energy Consumption Scenario Analysis Bp Neural Network Model |
| Content Type | Text |
| Resource Type | Article |
