Exploring the Spatiotemporal Dynamics of CO 2 Emissions through a Combination of Nighttime Light and MODIS NDVI Data

Yongxing Li, Wei Guo (), Peixian Li, Xuesheng Zhao and Jinke Liu
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Yongxing Li: College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, China
Wei Guo: College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, China
Peixian Li: College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, China
Xuesheng Zhao: College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, China
Jinke Liu: College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, China

Sustainability, 2023, vol. 15, issue 17, 1-17

Abstract: Climate change caused by CO 2 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 CO 2 emissions in real time. However, the available CO 2 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 CO 2 emissions, NTL data have limited utility. Therefore, it is necessary to develop a model that can capture spatiotemporal variations in CO 2 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 CO 2 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 CO 2 emissions over the period of 2000–2020. The experimental results indicate the following: (1) NUI-CV is more suitable than NTL for estimating the CO 2 emissions with a 6% increase in average R 2 . (2) The center of China’s CO 2 emissions lies in the eastern regions and is gradually moving west. (3) Changes in industrial structure can strongly influence changes in CO 2 emissions, the tertiary sector playing an important role in carbon reduction.

Keywords: CO 2 emissions; normalized urban index based on combination variables; standard deviational ellipse; Theil–Sen and Mann–Kendall trend analysis; nighttime light (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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