Carbon dioxide (CO2) emissions from the service industry, traffic, and secondary industry as revealed by the remotely sensed nighttime light data
Exploring carbon dioxide (CO2) emissions from human activities is essential for urban energy conservation and resource management. Remotely sensed nighttime lights from the Suomi NPP-VIIRS provide spatial consistency in and a low-cost way of revealing CO2 emissions. Although many researches have doc...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Taylor & Francis Group
2021-11-01
|
Series: | International Journal of Digital Earth |
Subjects: | |
Online Access: | http://dx.doi.org/10.1080/17538947.2021.1946605 |
_version_ | 1797678481169973248 |
---|---|
author | Kaifang Shi Jingwei Shen Yizhen Wu Shirao Liu Linyi Li |
author_facet | Kaifang Shi Jingwei Shen Yizhen Wu Shirao Liu Linyi Li |
author_sort | Kaifang Shi |
collection | DOAJ |
description | Exploring carbon dioxide (CO2) emissions from human activities is essential for urban energy conservation and resource management. Remotely sensed nighttime lights from the Suomi NPP-VIIRS provide spatial consistency in and a low-cost way of revealing CO2 emissions. Although many researches have documented the feasibility of the Suomi NPP-VIIRS data for assessing CO2 emissions, few have systematically revealed the ability of nighttime lights for evaluating CO2 emissions from different industries, such as service industry CO2 emissions (SC), traffic CO2 emissions (TC), and secondary industry CO2 emissions (IC). Here, China was selected as the experimental subject, and we comprehensively explored the relationships between the nighttime lights and SC, TC, and IC, and investigated the factors mediating these relationships. We found that without considering other factors, the nighttime lights only revealed up to 51.2% of TC, followed by 41.7% of IC and 22.7% of SC. When controlling for city characteristic variables, the models showed that there were positive correlations between the Suomi NPP-VIIRS data and SC, IC, and TC, and that nighttime lights have an Inverted-U relationship with SC. The Suomi NPP-VIIRS data are more suitable for revealing SC, TC, and IC in medium-sized and large-sized cities than in small-sized cities and megacities. |
first_indexed | 2024-03-11T23:00:23Z |
format | Article |
id | doaj.art-489c03a260f54612a1c144cea000a97b |
institution | Directory Open Access Journal |
issn | 1753-8947 1753-8955 |
language | English |
last_indexed | 2024-03-11T23:00:23Z |
publishDate | 2021-11-01 |
publisher | Taylor & Francis Group |
record_format | Article |
series | International Journal of Digital Earth |
spelling | doaj.art-489c03a260f54612a1c144cea000a97b2023-09-21T14:57:10ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552021-11-0114111514152710.1080/17538947.2021.19466051946605Carbon dioxide (CO2) emissions from the service industry, traffic, and secondary industry as revealed by the remotely sensed nighttime light dataKaifang Shi0Jingwei Shen1Yizhen Wu2Shirao Liu3Linyi Li4Southwest UniversitySouthwest UniversitySouthwest UniversitySouthwest UniversityWuhan UniversityExploring carbon dioxide (CO2) emissions from human activities is essential for urban energy conservation and resource management. Remotely sensed nighttime lights from the Suomi NPP-VIIRS provide spatial consistency in and a low-cost way of revealing CO2 emissions. Although many researches have documented the feasibility of the Suomi NPP-VIIRS data for assessing CO2 emissions, few have systematically revealed the ability of nighttime lights for evaluating CO2 emissions from different industries, such as service industry CO2 emissions (SC), traffic CO2 emissions (TC), and secondary industry CO2 emissions (IC). Here, China was selected as the experimental subject, and we comprehensively explored the relationships between the nighttime lights and SC, TC, and IC, and investigated the factors mediating these relationships. We found that without considering other factors, the nighttime lights only revealed up to 51.2% of TC, followed by 41.7% of IC and 22.7% of SC. When controlling for city characteristic variables, the models showed that there were positive correlations between the Suomi NPP-VIIRS data and SC, IC, and TC, and that nighttime lights have an Inverted-U relationship with SC. The Suomi NPP-VIIRS data are more suitable for revealing SC, TC, and IC in medium-sized and large-sized cities than in small-sized cities and megacities.http://dx.doi.org/10.1080/17538947.2021.1946605nighttime light datasuomi npp-viirsco2 emissionstransmission mechanismchina |
spellingShingle | Kaifang Shi Jingwei Shen Yizhen Wu Shirao Liu Linyi Li Carbon dioxide (CO2) emissions from the service industry, traffic, and secondary industry as revealed by the remotely sensed nighttime light data International Journal of Digital Earth nighttime light data suomi npp-viirs co2 emissions transmission mechanism china |
title | Carbon dioxide (CO2) emissions from the service industry, traffic, and secondary industry as revealed by the remotely sensed nighttime light data |
title_full | Carbon dioxide (CO2) emissions from the service industry, traffic, and secondary industry as revealed by the remotely sensed nighttime light data |
title_fullStr | Carbon dioxide (CO2) emissions from the service industry, traffic, and secondary industry as revealed by the remotely sensed nighttime light data |
title_full_unstemmed | Carbon dioxide (CO2) emissions from the service industry, traffic, and secondary industry as revealed by the remotely sensed nighttime light data |
title_short | Carbon dioxide (CO2) emissions from the service industry, traffic, and secondary industry as revealed by the remotely sensed nighttime light data |
title_sort | carbon dioxide co2 emissions from the service industry traffic and secondary industry as revealed by the remotely sensed nighttime light data |
topic | nighttime light data suomi npp-viirs co2 emissions transmission mechanism china |
url | http://dx.doi.org/10.1080/17538947.2021.1946605 |
work_keys_str_mv | AT kaifangshi carbondioxideco2emissionsfromtheserviceindustrytrafficandsecondaryindustryasrevealedbytheremotelysensednighttimelightdata AT jingweishen carbondioxideco2emissionsfromtheserviceindustrytrafficandsecondaryindustryasrevealedbytheremotelysensednighttimelightdata AT yizhenwu carbondioxideco2emissionsfromtheserviceindustrytrafficandsecondaryindustryasrevealedbytheremotelysensednighttimelightdata AT shiraoliu carbondioxideco2emissionsfromtheserviceindustrytrafficandsecondaryindustryasrevealedbytheremotelysensednighttimelightdata AT linyili carbondioxideco2emissionsfromtheserviceindustrytrafficandsecondaryindustryasrevealedbytheremotelysensednighttimelightdata |