Near and long-term perspectives on strategies to decarbonize China's heavy-duty trucks through 2050
Abstract
China needs to drastically reduce carbon dioxide (CO2) emissions from heavy-duty trucks (HDTs), a key emitter in the growing transport sector, in order to address energy security concerns and meet its climate targets. We address existing research gaps by modeling feasibility, applicability, and energy and emissions impacts of multiple decarbonization strategies at different points in time. China still relies heavily on coal power, so impacts of new HDT technologies depend on the timing of their introduction relative to progress toward non-fossil power. We use a bottom-up model to simulate HDT energy consumption and CO2 emissions through 2050. Results show that beginning to deploy battery electric and fuel-cell HDTs as early as 2020 and 2035, respectively, could achieve significant and the largest CO2 emissions reduction by 2050 with a decarbonized power sector. However, viable near-term strategies-improving efficiency and logistics, switching to liquefied natural gas-could halve HDTs' current diesel consumption and CO2 emissions by 2050. Our results underscore the need for a mix of near- and long-term policy and technology options to decarbonize China's HDTs.
- Authors:
-
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1834588
- Grant/Contract Number:
- AC02-05CH11231
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Scientific Reports
- Additional Journal Information:
- Journal Volume: 11; Journal Issue: 1; Journal ID: ISSN 2045-2322
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION
Citation Formats
Khanna, Nina, Lu, Hongyou, Fridley, David, and Zhou, Nan. Near and long-term perspectives on strategies to decarbonize China's heavy-duty trucks through 2050. United States: N. p., 2021.
Web. doi:10.1038/s41598-021-99715-w.
Khanna, Nina, Lu, Hongyou, Fridley, David, & Zhou, Nan. Near and long-term perspectives on strategies to decarbonize China's heavy-duty trucks through 2050. United States. https://doi.org/10.1038/s41598-021-99715-w
Khanna, Nina, Lu, Hongyou, Fridley, David, and Zhou, Nan. Thu .
"Near and long-term perspectives on strategies to decarbonize China's heavy-duty trucks through 2050". United States. https://doi.org/10.1038/s41598-021-99715-w. https://www.osti.gov/servlets/purl/1834588.
@article{osti_1834588,
title = {Near and long-term perspectives on strategies to decarbonize China's heavy-duty trucks through 2050},
author = {Khanna, Nina and Lu, Hongyou and Fridley, David and Zhou, Nan},
abstractNote = {China needs to drastically reduce carbon dioxide (CO2) emissions from heavy-duty trucks (HDTs), a key emitter in the growing transport sector, in order to address energy security concerns and meet its climate targets. We address existing research gaps by modeling feasibility, applicability, and energy and emissions impacts of multiple decarbonization strategies at different points in time. China still relies heavily on coal power, so impacts of new HDT technologies depend on the timing of their introduction relative to progress toward non-fossil power. We use a bottom-up model to simulate HDT energy consumption and CO2 emissions through 2050. Results show that beginning to deploy battery electric and fuel-cell HDTs as early as 2020 and 2035, respectively, could achieve significant and the largest CO2 emissions reduction by 2050 with a decarbonized power sector. However, viable near-term strategies-improving efficiency and logistics, switching to liquefied natural gas-could halve HDTs' current diesel consumption and CO2 emissions by 2050. Our results underscore the need for a mix of near- and long-term policy and technology options to decarbonize China's HDTs.},
doi = {10.1038/s41598-021-99715-w},
journal = {Scientific Reports},
number = 1,
volume = 11,
place = {United States},
year = {Thu Oct 14 00:00:00 EDT 2021},
month = {Thu Oct 14 00:00:00 EDT 2021}
}
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