Near Real-time CO$_2$ Emissions Based on Carbon Satellite And Artificial Intelligence

• URL: http://arxiv.org/abs/2210.09850v1
• Date: Tue, 11 Oct 2022 12:01:32 GMT
• Title: Near Real-time CO$_2$ Emissions Based on Carbon Satellite And Artificial Intelligence
• Authors: Zhengwen Zhang, Jingjin Gu, Junhua Zhao, Jianwei Huang, Haifeng Wu
• Abstract summary: We propose an integral AI based pipeline that contains both a data retrieval algorithm and a two-step data-driven solution. First, the data retrieval algorithm can generate effective datasets from multi-modal data including carbon satellite, the information of carbon sources, and several environmental factors. Second, the two-step data-driven solution that applies the powerful representation of deep learning techniques to learn to quantify anthropogenic CO$$ emissions.
• Score: 20.727982405167758
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To limit global warming to pre-industrial levels, global governments, industry and academia are taking aggressive efforts to reduce carbon emissions. The evaluation of anthropogenic carbon dioxide (CO$_2$) emissions, however, depends on the self-reporting information that is not always reliable. Society need to develop an objective, independent, and generalized system to meter CO$_2$ emissions. Satellite CO$_2$ observation from space that reports column-average regional CO$_2$ dry-air mole fractions has gradually indicated its potential to build such a system. Nevertheless, estimating anthropogenic CO$_2$ emissions from CO$_2$ observing satellite is bottlenecked by the influence of the highly complicated physical characteristics of atmospheric activities. Here we provide the first method that combines the advanced artificial intelligence (AI) techniques and the carbon satellite monitor to quantify anthropogenic CO$_2$ emissions. We propose an integral AI based pipeline that contains both a data retrieval algorithm and a two-step data-driven solution. First, the data retrieval algorithm can generate effective datasets from multi-modal data including carbon satellite, the information of carbon sources, and several environmental factors. Second, the two-step data-driven solution that applies the powerful representation of deep learning techniques to learn to quantify anthropogenic CO$_2$ emissions from satellite CO$_2$ observation with other factors. Our work unmasks the potential of quantifying CO$_2$ emissions based on the combination of deep learning algorithms and the carbon satellite monitor.

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