High-resolution spatiotemporal dynamic simulation and driving force analysis of carbon emissions in coastal cities : The case of Ningbo City
(2026) In Sustainable Cities and Society 138.- Abstract
Coastal port cities are key regions for carbon emissions (CEs) research in China, due to their industrial density, frequent transportation, and diverse human activities. Taking Ningbo City as a case study, this study employed a top-down accounting method and integrated multiple sources of geospatial data to reveal the spatiotemporal distribution characteristics of CEs from four sectors, including industrial, residential, logistical, and commercial, from 2013 to 2022. Monte Carlo simulation was further applied to assess uncertainty. The driving factors were analyzed using the Logarithmic Mean Divisia Index (LMDI) decomposition model. The findings show that the total CEs in Ningbo City increased steadily, with the spatial pattern evolving... (More)
Coastal port cities are key regions for carbon emissions (CEs) research in China, due to their industrial density, frequent transportation, and diverse human activities. Taking Ningbo City as a case study, this study employed a top-down accounting method and integrated multiple sources of geospatial data to reveal the spatiotemporal distribution characteristics of CEs from four sectors, including industrial, residential, logistical, and commercial, from 2013 to 2022. Monte Carlo simulation was further applied to assess uncertainty. The driving factors were analyzed using the Logarithmic Mean Divisia Index (LMDI) decomposition model. The findings show that the total CEs in Ningbo City increased steadily, with the spatial pattern evolving from early single-core aggregation to coastal multi-center expansion. The industrial sector is the primary source, with emissions concentrated in ports and coastal industrial zones. Residential and commercial emissions exhibit a multi-point distribution and low-value expansion pattern, while logistical emissions have remained relatively stable and are primarily concentrated in the urban area. Economic development is the core driver of CEs growth, while energy intensity and carbon emission factors have had an inhibitory effect. This study provides a theoretical basis and practical support for high-resolution urban CEs monitoring and low-carbon policy formulation in coastal cities.
(Less)
- author
- Cui, Guangxin ; Cheng, Xusheng ; Sun, Yanwei ; Gao, Chao ; Duan, Zheng LU and Ruan, Tian
- organization
- publishing date
- 2026-03
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Carbon emissions accounting, Coastal port city, Downscaling, LMDI model, POI data, Spatiotemporal distribution
- in
- Sustainable Cities and Society
- volume
- 138
- article number
- 107195
- publisher
- Elsevier
- external identifiers
-
- scopus:105029227668
- ISSN
- 2210-6707
- DOI
- 10.1016/j.scs.2026.107195
- language
- English
- LU publication?
- yes
- id
- d725f28e-d751-4f32-947d-6d53ad029dc9
- date added to LUP
- 2026-02-18 11:59:28
- date last changed
- 2026-03-18 18:52:50
@article{d725f28e-d751-4f32-947d-6d53ad029dc9,
abstract = {{<p>Coastal port cities are key regions for carbon emissions (CEs) research in China, due to their industrial density, frequent transportation, and diverse human activities. Taking Ningbo City as a case study, this study employed a top-down accounting method and integrated multiple sources of geospatial data to reveal the spatiotemporal distribution characteristics of CEs from four sectors, including industrial, residential, logistical, and commercial, from 2013 to 2022. Monte Carlo simulation was further applied to assess uncertainty. The driving factors were analyzed using the Logarithmic Mean Divisia Index (LMDI) decomposition model. The findings show that the total CEs in Ningbo City increased steadily, with the spatial pattern evolving from early single-core aggregation to coastal multi-center expansion. The industrial sector is the primary source, with emissions concentrated in ports and coastal industrial zones. Residential and commercial emissions exhibit a multi-point distribution and low-value expansion pattern, while logistical emissions have remained relatively stable and are primarily concentrated in the urban area. Economic development is the core driver of CEs growth, while energy intensity and carbon emission factors have had an inhibitory effect. This study provides a theoretical basis and practical support for high-resolution urban CEs monitoring and low-carbon policy formulation in coastal cities.</p>}},
author = {{Cui, Guangxin and Cheng, Xusheng and Sun, Yanwei and Gao, Chao and Duan, Zheng and Ruan, Tian}},
issn = {{2210-6707}},
keywords = {{Carbon emissions accounting; Coastal port city; Downscaling; LMDI model; POI data; Spatiotemporal distribution}},
language = {{eng}},
publisher = {{Elsevier}},
series = {{Sustainable Cities and Society}},
title = {{High-resolution spatiotemporal dynamic simulation and driving force analysis of carbon emissions in coastal cities : The case of Ningbo City}},
url = {{http://dx.doi.org/10.1016/j.scs.2026.107195}},
doi = {{10.1016/j.scs.2026.107195}},
volume = {{138}},
year = {{2026}},
}