Differential Spatiotemporal Patterns of CO2 Emissions in Eastern China’s Urban Agglomerations from NPP/VIIRS Nighttime Light Data Based on a Neural Network Algorithm
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sources
2.3. Methods
2.3.1. Correction of NPP/VIIRS Nighttime Light Data
2.3.2. Estimating the Statistical Carbon Emissions Using the IPCC Method
2.3.3. Estimating CO2 Emissions by GABP Neural Networks and Nighttime Light Data
2.3.4. Spatiotemporal Dynamics of CO2 Emissions Based on GIS-Based Buffer Analysis, Kernel Density Estimation and Linear Regression Analysis
2.3.5. Statistical Analysis on the Influencing Factors of CO2 Emissions among Urban Agglomerations
3. Results
3.1. Spatiotemporal Variations of CO2 Emissions
3.2. Carbon Emissions within Cities
3.3. Influencing Factors of Spatial Pattern of CO2 Emissions
4. Discussion
4.1. NPP/VIIRS Nighttime Light Integrated Genetic Neural Network Showed a Good Performance in Estimating CO2 Emissions
4.2. Three Urban Agglomerations Exhibited Diverse Spatial Patterns of CO2 Emissions
4.3. Population Density Versus Terrain Slope Featured Opposite Effects on Spatial Pattern of CO2 Emissions
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Data Name | Data Description | Source |
---|---|---|
Nighttime light | NPP/VIIRS nighttime light at a spatial resolution of 500 m for 2014–2019 | Colorado School of Mines (https://payneinstitute.mines.edu/eog/, accessed on 11 October 2019) |
Fossil fuel combustion data | Annual total data of ten energy types, such as raw coal, coke, crude oil, gasoline, kerosene, diesel oil, fuel oil, natural gas, heat and electricity, during 2014–2019 | China Statistical Yearbook, China Regional Statistical Yearbook, China Energy Statistical Yearbook and statistical yearbooks of each city |
Socioeconomic data | Annual statistical data of six types, such as permanent population, GDP, per capita GDP, primary industry GDP, secondary industry GDP and tertiary industry GDP, during 2014–2019 | China Statistical Yearbook, China City Statistical Yearbook, China Regional Statistical Yearbook, and statistical yearbooks of each city |
Administrative boundaries | Vector files of provinces, prefectures in China | National Catalogue Service For Geographic Information |
Population density | Annual data with a spatial resolution of 30 arc-seconds (approximately 1km at the equator) | WorldPop (https://www.worldpop.org/, accessed on 13 August 2021) |
Terrain slope | Spatial resolution of 90 m | Geospatial Data Cloud (http://www.gscloud.cn/, accessed on 16 July 2021) |
Temperature | Annual mean temperature unit with a spatial resolution of 1 km | Resource and Environment Science and Data Center (https://www.resdc.cn/, accessed on 3 September 2021) |
Urban area | Redefined data in 2016 | Beijing City Lab Database [30] |
Factor | Urban Agglomeration | Urban | First-Level Urban Circle | Second-Level Urban Circle |
---|---|---|---|---|
Population density | Beijing-Tianjin-Hebei | 0.209 * | 0.184 * | 0.152 * |
Yangtze River Delta | 0.257 * | 0.087 * | 0.188 * | |
Pearl River Delta | 0.333 * | 0.172 * | 0.384 * | |
Annual mean temperature | Beijing-Tianjin-Hebei | −0.013 | −0.062 | −0.012 |
Yangtze River Delta | −0.020 * | −0.047 | −0.016 | |
Pearl River Delta | −0.283 * | −0.122 * | 0.025 | |
Terrain slope | Beijing-Tianjin-Hebei | −0.026 * | −0.070 * | −0.091 * |
Yangtze River Delta | −0.060 * | −0.101 * | −0.113 * | |
Pearl River Delta | −0.062 * | −0.178 * | −0.175 * |
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Zhou, L.; Song, J.; Chi, Y.; Yu, Q. Differential Spatiotemporal Patterns of CO2 Emissions in Eastern China’s Urban Agglomerations from NPP/VIIRS Nighttime Light Data Based on a Neural Network Algorithm. Remote Sens. 2023, 15, 404. https://doi.org/10.3390/rs15020404
Zhou L, Song J, Chi Y, Yu Q. Differential Spatiotemporal Patterns of CO2 Emissions in Eastern China’s Urban Agglomerations from NPP/VIIRS Nighttime Light Data Based on a Neural Network Algorithm. Remote Sensing. 2023; 15(2):404. https://doi.org/10.3390/rs15020404
Chicago/Turabian StyleZhou, Lei, Jun Song, Yonggang Chi, and Quanzhou Yu. 2023. "Differential Spatiotemporal Patterns of CO2 Emissions in Eastern China’s Urban Agglomerations from NPP/VIIRS Nighttime Light Data Based on a Neural Network Algorithm" Remote Sensing 15, no. 2: 404. https://doi.org/10.3390/rs15020404
APA StyleZhou, L., Song, J., Chi, Y., & Yu, Q. (2023). Differential Spatiotemporal Patterns of CO2 Emissions in Eastern China’s Urban Agglomerations from NPP/VIIRS Nighttime Light Data Based on a Neural Network Algorithm. Remote Sensing, 15(2), 404. https://doi.org/10.3390/rs15020404