Spatial-temporal differentiation and influencing factors of carbon emission trajectory in Chinese cities - A case study of 247 prefecture-level cities
(2024) In Science of the Total Environment 928.- Abstract
Cities, where human energy activities and greenhouse gas emissions are concentrated, contribute significantly to alleviating the impacts of global climate change. Utilizing the China Carbon Emissions Accounting Database (CEADs) to provide carbon dioxide emission inventories for urban areas in China at the prefecture level, this study closely examines the historical evolution trajectories of carbon emissions across 247 urban units from 2005 to 2019. The logarithmic cubic function model was employed to simulate these trajectories, evaluating urban emission peaks and classifying the different carbon emission trajectories. Further, the Geographical and Temporal Weighted Regression model was employed to explore spatiotemporal traits and... (More)
Cities, where human energy activities and greenhouse gas emissions are concentrated, contribute significantly to alleviating the impacts of global climate change. Utilizing the China Carbon Emissions Accounting Database (CEADs) to provide carbon dioxide emission inventories for urban areas in China at the prefecture level, this study closely examines the historical evolution trajectories of carbon emissions across 247 urban units from 2005 to 2019. The logarithmic cubic function model was employed to simulate these trajectories, evaluating urban emission peaks and classifying the different carbon emission trajectories. Further, the Geographical and Temporal Weighted Regression model was employed to explore spatiotemporal traits and essential variables that impact the variations in carbon emissions among four identified trajectory types. Our results showed that Chinese urban carbon emission trajectories can be classified into four categories: a) peaking emissions, b) fluctuating growth, c) continuous growth, and d) passive decline. Specifically, 43 cities, primarily in North China, proactively attained their emission peak post-2010, driven by the reduction in secondary industry and energy intensity. 90 cities, largely industrial hubs in the southeast coast and inland, reached an emission plateau around 2015, exhibiting fluctuating growth due to dependencies on secondary industries. 101 cities, predominantly located in western and central regions, demonstrated a clear upward trend in carbon emissions, propelled by rapid urbanization and heavy industry-oriented economic development. Lastly, 13 cities, typically in the northeastern and southwestern regions, experienced a passive decline in carbon emissions, attributable to resource depletion or economic downturns. It is evident that China's city-level carbon peaking has demonstrated some effectiveness, yet considerable progress is still required.
(Less)
- author
- Yang, Xinlian ; Jin, Ke ; Duan, Zheng LU ; Gao, Yuhe ; Sun, Yanwei and Gao, Chao
- organization
- publishing date
- 2024-06
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Carbon emission trajectory, Influencing factors, Spatial-temporal differentiation, Urbanization
- in
- Science of the Total Environment
- volume
- 928
- article number
- 172325
- publisher
- Elsevier
- external identifiers
-
- pmid:38604371
- scopus:85190579544
- ISSN
- 0048-9697
- DOI
- 10.1016/j.scitotenv.2024.172325
- language
- English
- LU publication?
- yes
- id
- 063300a9-7f0b-438b-a017-fa9f08aa6de4
- date added to LUP
- 2024-04-29 14:34:29
- date last changed
- 2024-05-13 15:52:56
@article{063300a9-7f0b-438b-a017-fa9f08aa6de4,
abstract = {{<p>Cities, where human energy activities and greenhouse gas emissions are concentrated, contribute significantly to alleviating the impacts of global climate change. Utilizing the China Carbon Emissions Accounting Database (CEADs) to provide carbon dioxide emission inventories for urban areas in China at the prefecture level, this study closely examines the historical evolution trajectories of carbon emissions across 247 urban units from 2005 to 2019. The logarithmic cubic function model was employed to simulate these trajectories, evaluating urban emission peaks and classifying the different carbon emission trajectories. Further, the Geographical and Temporal Weighted Regression model was employed to explore spatiotemporal traits and essential variables that impact the variations in carbon emissions among four identified trajectory types. Our results showed that Chinese urban carbon emission trajectories can be classified into four categories: a) peaking emissions, b) fluctuating growth, c) continuous growth, and d) passive decline. Specifically, 43 cities, primarily in North China, proactively attained their emission peak post-2010, driven by the reduction in secondary industry and energy intensity. 90 cities, largely industrial hubs in the southeast coast and inland, reached an emission plateau around 2015, exhibiting fluctuating growth due to dependencies on secondary industries. 101 cities, predominantly located in western and central regions, demonstrated a clear upward trend in carbon emissions, propelled by rapid urbanization and heavy industry-oriented economic development. Lastly, 13 cities, typically in the northeastern and southwestern regions, experienced a passive decline in carbon emissions, attributable to resource depletion or economic downturns. It is evident that China's city-level carbon peaking has demonstrated some effectiveness, yet considerable progress is still required.</p>}},
author = {{Yang, Xinlian and Jin, Ke and Duan, Zheng and Gao, Yuhe and Sun, Yanwei and Gao, Chao}},
issn = {{0048-9697}},
keywords = {{Carbon emission trajectory; Influencing factors; Spatial-temporal differentiation; Urbanization}},
language = {{eng}},
publisher = {{Elsevier}},
series = {{Science of the Total Environment}},
title = {{Spatial-temporal differentiation and influencing factors of carbon emission trajectory in Chinese cities - A case study of 247 prefecture-level cities}},
url = {{http://dx.doi.org/10.1016/j.scitotenv.2024.172325}},
doi = {{10.1016/j.scitotenv.2024.172325}},
volume = {{928}},
year = {{2024}},
}