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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