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Dear Colleagues,

Global warming has become a severe threat to the environment and human health, and has received extensive attention from the international community and academia. The major contributor to global warming was widely reported to be the growth of carbon dioxide (CO₂) emissions from human activities, and thereinto, urban carbon emission is an important part of global greenhouse gas emissions. With social development and population increase, the energy consumption and CO₂ emissions of cities will continue to increase, supporting the requirements of economic growth and human living. Therefore, the assessment of urban carbon emissions using multi-source data (e.g., remote sensing) is a vital research topic. For this Special Issue, we would like to invite you to submit original research that assesses urban carbon emissions using multi-source data, working to develop effective ways to reduce urban carbon emissions and realize carbon neutrality.

Potential topics include, but are not limited to:

Urban carbon emissions monitoring and calculation;
Spatio-temporal distributions of urban carbon emissions;
Simulation and analysis of urban carbon dynamics;
Carbon sources and carbon sinks in urban and rural areas;
Urban transportation and energy consumption;
Emission reduction technology towards carbon neutrality;
Policies and optimization paths of carbon neutrality;
Green building and low-carbon city;
Land use/cover changes and carbon emissions;
Urban form and carbon emissions;
Driving mechanism of urban carbon emissions;
Pattern and path of low-carbon construction.

Dr. Jinpei Ou
Dr. Guohua Hu
Dr. Jinyao Lin
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

urban carbon emissions
carbon neutrality
multi-source data
remote sensing
spatio-temporal distributions
energy consumption
green building
low-carbon city

Published Papers (1 paper)

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Research

17 pages, 7368 KiB

Open AccessArticle

Exploring the Spatiotemporal Dynamics of CO₂ Emissions through a Combination of Nighttime Light and MODIS NDVI Data

by Yongxing Li, Wei Guo, Peixian Li, Xuesheng Zhao and Jinke Liu

Sustainability 2023, 15(17), 13143; https://doi.org/10.3390/su151713143 - 31 Aug 2023

Viewed by 872

Abstract

Climate change caused by CO₂ emissions is posing a huge challenge to human survival, and it is crucial to precisely understand the spatial and temporal patterns and driving forces of CO₂ emissions in real time. However, the available CO₂ emission data are usually converted from fossil fuel combustion, which cannot capture spatial differences. Nighttime light (NTL) data can reveal human activities in detail and constitute the shortage of statistical data. Although NTL can be used as an indirect representation of CO₂ emissions, NTL data have limited utility. Therefore, it is necessary to develop a model that can capture spatiotemporal variations in CO₂ emissions at a fine scale. In this paper, we used the nighttime light and the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI), and proposed a normalized urban index based on combination variables (NUI-CV) to improve estimated CO₂ emissions. Based on this index, we used the Theil–Sen and Mann–Kendall trend analysis, standard deviational ellipse, and a spatial economics model to explore the spatial and temporal dynamics and influencing factors of CO₂ emissions over the period of 2000–2020. The experimental results indicate the following: (1) NUI-CV is more suitable than NTL for estimating the CO₂ emissions with a 6% increase in average

R^{2}

. (2) The center of China’s CO₂ emissions lies in the eastern regions and is gradually moving west. (3) Changes in industrial structure can strongly influence changes in CO₂ emissions, the tertiary sector playing an important role in carbon reduction. Full article

(This article belongs to the Special Issue Using Multi-Source Data to Assess Urban Carbon Emissions)

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