Spatiotemporal dynamics evaluation of pixel-level gross domestic product, electric power consumption, and carbon emissions in countries along the belt and road

Zhong, Laifu; Liu, Xiaosheng; Ao, Jianfeng

doi:10.1016/j.energy.2021.121841

Cited by 11 publications

(3 citation statements)

References 40 publications

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“…The NTL data used in this study was published in our previous work [38], and was acquired from two satellite sensors, including the DMSP-OLS (satellite time interval from 1992 to 2013) and NPP-VIIRS (satellite time interval from 2012 to 2021). Most scholars only adopted one of these sensors to estimate carbon emissions, which is challenging when conducting a long-term comprehensive dynamic analysis of carbon emissions [24,25]. Taking the Sicily area of the F12 satellite in 1999 as the light-invariant area, the DMSP-OLS data of China from 1992 to 2013 was corrected based on the statistical relationship of the satellite data of other years through the quadratic polynomial function [39].…”

Section: Nighttime Lightmentioning

confidence: 99%

“…Shi et al [24] combined the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) data with carbon emissions data to simulate the spatiotemporal carbon emission in China. Similarly, Zhong et al [25] adopted a multi-period mask denoising method that combined the National Polar-Orbiting Partnership Satellite's Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) in "The Belt and Road" region with a spatial resolution of 0.5 km × 0.5 km to analyze the spatiotemporal characteristics of carbon emissions in this region. Lv et al [26] integrated DMSP-OLS and NPP-VIIRS data, and established a comprehensive dataset of nighttime lights from 1992 to 2016 to build a carbon emission estimation model that can analyze the spatiotemporal analysis of carbon emissions at multiple scales.…”

Section: Introductionmentioning

confidence: 99%

“…However, most of the studies in the second group [24][25][26] only considered the linear relationship between NTL data and carbon emissions statistics (CES) data. In fact, the relationship between NTL data and carbon emissions is not simply linear.…”

Section: Introductionmentioning

confidence: 99%

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Carbon Emissions Estimation and Spatiotemporal Analysis of China at City Level Based on Multi-Dimensional Data and Machine Learning

Lin

Chen

et al. 2022

Remote Sensing

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Carbon emissions caused by the massive consumption of energy have brought enormous pressure on the Chinese government. Accurately and rapidly characterizing the spatiotemporal characteristics of Chinese city-level carbon emissions is crucial for policy decision making. Based on multi-dimensional data, including nighttime light (NTL) data, land use (LU) data, land surface temperature (LST) data, and added-value secondary industry (AVSI) data, a deep neural network ensemble (DNNE) model was built to analyze the nonlinear relationship between multi-dimensional data and province-level carbon emission statistics (CES) data. The city-level carbon emissions data were estimated, and the spatiotemporal characteristics were analyzed. As compared to the energy statistics released by partial cities, the results showed that the DNNE model based on multi-dimensional data could well estimate city-level carbon emissions data. In addition, according to a linear trend analysis and standard deviational ellipse (SDE) analysis of China from 2001 to 2019, we concluded that the spatiotemporal changes in carbon emissions at the city level were in accordance with the development of China’s economy. Furthermore, the results can provide a useful reference for the scientific formulation, implementation, and evaluation of carbon emissions reduction policies.

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