Forecast of Energy Consumption and Carbon Emissions in China’s Building Sector to 2060
Xingfan Pu,
Jian Yao and
Rongyue Zheng
Additional contact information
Xingfan Pu: Department of Civil Engineering, Ningbo University, Ningbo 315211, China
Jian Yao: Department of Architecture, Ningbo University, Ningbo 315211, China
Rongyue Zheng: Department of Civil Engineering, Ningbo University, Ningbo 315211, China
Energies, 2022, vol. 15, issue 14, 1-20
Abstract:
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.
Keywords: carbon emissions; peak carbon emissions; carbon neutral; energy consumption; scenario analysis; BP neural network model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)
Downloads: (external link)
https://www.mdpi.com/1996-1073/15/14/4950/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/14/4950/ (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:14:p:4950-:d:857093
Access Statistics for this article
Energies is currently edited by Ms. Agatha Cao
More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().