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author:

Shi, Kaifang (Shi, Kaifang.) [1] | Chen, Zuoqi (Chen, Zuoqi.) [2] | Cui, Yuanzheng (Cui, Yuanzheng.) [3] | Wu, Jianping (Wu, Jianping.) [4] | Yu, Bailang (Yu, Bailang.) [5]

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EI

Abstract:

Remotely sensed nighttime light (NL) data collected by the Suomi National Polar-orbiting Partnership Satellite equipped with the Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) sensor have proven to be effective for evaluating fossil fuel combustion carbon emissions (CEs). However, few studies have analyzed the relationships between NL and CE originating from different sectors. The effects of impact factors on the NL-CE relationship have not been thoroughly examined and compared. Utilizing the corrected annual composite average of NPP-VIIRS data (NTL), this letter individually investigated the relationships between the NTL and CE from all types of fossil fuels total CE (TCE); CE from gasoline, diesel oil, natural gas, and cement urban carbon emission (UC); and CE from raw coal, cleaned coal, other washed coal, briquette, and coke industrial carbon emission (IC) in China at the provincial level. The impact factors governing the NTL-CE relationship were also examined. The results showed that total NLs (TNLs) may be a more effective means for estimating UC than other types of CE but may not be a good proxy for IC due to the mismatch between their amounts and brightness. The R2 values from TNL and TCE analyses were higher than those of TNL and IC within the eastern, central, and western regions. Meanwhile, we found that NTL could more accurately evaluate CE in urban areas with a large population size and a relatively developed social economy. Although the urbanization rate was the most important factor in the assessment of CE from NTL, China's urbanization rate presented an inverted U-shaped impact on the NTL-CE relationship in the long run. © 2004-2012 IEEE.

Keyword:

Carbon Coal Coal combustion Industrial emissions Integrated circuits Orbits Population statistics Radiometers Satellite imagery Waste incineration

Community:

  • [ 1 ] [Shi, Kaifang]School of Geographical Sciences, State Cultivation Base of Eco-Agriculture for Southwest Mountainous Land, Southwest University, Chongqing; 400715, China
  • [ 2 ] [Shi, Kaifang]Chongqing Jinfo Mountain Field Scientific Observation and Research Station for Kaster Ecosystem, Ministry of Education, Chongqing; 400715, China
  • [ 3 ] [Chen, Zuoqi]Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, National and Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Fuzhou University, Fuzhou; 35002, China
  • [ 4 ] [Chen, Zuoqi]The Academy of Digital China, Fuzhou University, Fuzhou; 350002, China
  • [ 5 ] [Cui, Yuanzheng]Institute of Land and Urban-Rural Development, Zhejiang University of Finance and Economics, Hangzhou; 310018, China
  • [ 6 ] [Wu, Jianping]Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai; 200241, China
  • [ 7 ] [Wu, Jianping]School of Geographic Sciences, East China Normal University, Shanghai; 200241, China
  • [ 8 ] [Yu, Bailang]Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai; 200241, China
  • [ 9 ] [Yu, Bailang]School of Geographic Sciences, East China Normal University, Shanghai; 200241, China

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Source :

IEEE Geoscience and Remote Sensing Letters

ISSN: 1545-598X

Year: 2021

Issue: 12

Volume: 18

Page: 2062-2066

5 . 3 4 3

JCR@2021

4 . 0 0 0

JCR@2023

ESI HC Threshold:77

JCR Journal Grade:1

CAS Journal Grade:2

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

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