Scenarios of energy efficiency and CO2 emissions reduction potential in the buildings sector in China to year 2050
Abstract
As China’s rapid urbanization continues and urban dwellers become more affluent, energy use in buildings is expected to grow. To understand how this growth can be slowed, here we explore four scenarios for Chinese buildings, ranging from a high-energy-demand scenario with no new energy policies to lowest energy demand under a techno-economic-potential scenario that assumes full deployment of cost-effective efficient and renewable technologies by 2050. We show that, in the high energy demand scenario, building energy demand has an average annual growth rate of about 2.8%, with slower growth rates in the other three scenarios. In all scenarios, CO2 emissions grow slower than energy, with building CO2 peaking around 2045 in the high energy demand scenario, and as early as 2030 in the techno-economic-potential scenario. We show that although various technological solutions, systems and practices can be very effective in minimizing building energy use, rigorous policies are needed to overcome multiple implementation barriers.
- Authors:
-
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Energy Technologies Area, Energy Analysis and Environmental Impacts Division, China Energy Group
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1650036
- Grant/Contract Number:
- AC02-05CH11231
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Nature Energy
- Additional Journal Information:
- Journal Volume: 3; Journal Issue: 11; Journal ID: ISSN 2058-7546
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 29 ENERGY PLANNING, POLICY, AND ECONOMY; energy efficiency; energy policy
Citation Formats
Zhou, Nan, Khanna, Nina, Feng, Wei, Ke, Jing, and Levine, Mark. Scenarios of energy efficiency and CO2 emissions reduction potential in the buildings sector in China to year 2050. United States: N. p., 2018.
Web. doi:10.1038/s41560-018-0253-6.
Zhou, Nan, Khanna, Nina, Feng, Wei, Ke, Jing, & Levine, Mark. Scenarios of energy efficiency and CO2 emissions reduction potential in the buildings sector in China to year 2050. United States. https://doi.org/10.1038/s41560-018-0253-6
Zhou, Nan, Khanna, Nina, Feng, Wei, Ke, Jing, and Levine, Mark. Mon .
"Scenarios of energy efficiency and CO2 emissions reduction potential in the buildings sector in China to year 2050". United States. https://doi.org/10.1038/s41560-018-0253-6. https://www.osti.gov/servlets/purl/1650036.
@article{osti_1650036,
title = {Scenarios of energy efficiency and CO2 emissions reduction potential in the buildings sector in China to year 2050},
author = {Zhou, Nan and Khanna, Nina and Feng, Wei and Ke, Jing and Levine, Mark},
abstractNote = {As China’s rapid urbanization continues and urban dwellers become more affluent, energy use in buildings is expected to grow. To understand how this growth can be slowed, here we explore four scenarios for Chinese buildings, ranging from a high-energy-demand scenario with no new energy policies to lowest energy demand under a techno-economic-potential scenario that assumes full deployment of cost-effective efficient and renewable technologies by 2050. We show that, in the high energy demand scenario, building energy demand has an average annual growth rate of about 2.8%, with slower growth rates in the other three scenarios. In all scenarios, CO2 emissions grow slower than energy, with building CO2 peaking around 2045 in the high energy demand scenario, and as early as 2030 in the techno-economic-potential scenario. We show that although various technological solutions, systems and practices can be very effective in minimizing building energy use, rigorous policies are needed to overcome multiple implementation barriers.},
doi = {10.1038/s41560-018-0253-6},
journal = {Nature Energy},
number = 11,
volume = 3,
place = {United States},
year = {Mon Oct 08 00:00:00 EDT 2018},
month = {Mon Oct 08 00:00:00 EDT 2018}
}
Web of Science
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