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- Title
Research on Remote Sensing Estimation of Carbon Emissions in Anhui Province and Its Decoupling Relationship with Economic Growth.
- Authors
XU Jianhui; MA Wenhao; HU Feng; LI Yuanyuan; YANG Mengdie; ZHU Bingyi
- Abstract
Clarifying regional carbon emissions and their relationship with economic development is of great significance for regional green and low-carbon development. In this study, the night lighting dataset of Anhui Province from 2000 to 2020 was constructed by using multi-source remote sensing luminous images, simulating the carbon emissions of the Province from 2000 to 2020, investigating the change trend of carbon emissions in different time and space, and discussing the interaction mechanism between carbon emissions and economic development. The results showed that compared with the four prediction models, the accuracy verification showed that the CNN-BiLSTM deep learning estimation model had the best accuracy, with R² being 0.882 3, MAE being 23.006 7, MSPE being 16.39%, and RMSE being 33.616 1 under the significance level P<0.001. There were significant differences in the spatial distribution of regional carbon emissions in the province, with the highest annual average carbon emissions being 8.97 Mt/km², the number of carbon emission extreme hotspots decreased from 10 to 9, and the number of extreme cold spots increased from 0 to 3. From 2000 to 2020, carbon emissions showed an increasing trend, with the highest growth rate being 0.061 5 Mt/(km² • a), and carbon emissions and growth rates had high similarities in spatial distribution. The decoupling state between carbon emissions and economic development gradually improved, with an average decoupling coefficient of 0.481 4, and the decoupling state was mainly weak decoupling. Per capita GDP and population size are positive factors of carbon emissions, and energy structure and energy intensity are negative factors. The positive contribution gradually decreases, and the negative contribution gradually increases.
- Subjects
ANHUI Sheng (China); CARBON emissions; REMOTE sensing; ECONOMIC expansion; SUSTAINABLE development; DEEP learning
- Publication
Environmental Science & Technology (10036504), 2023, Vol 46, Issue 10, p198
- ISSN
1003-6504
- Publication type
Article
- DOI
10.19672/j.cnki.1003-6504.1174.23.338