Near-Real-Time Carbon Emission Accounting Technology Toward Carbon Neutrality

Climate change is the greatest environmental threat to humans and the planet in the 21st century.Global anthropogenic greenhouse gas emissions are one of the main causes of the increasing number of extreme climate events.Cumulative carbon dioxide(CO_(2))emissions showed a linear relationship with cumulative temperature rise since the pre-industrial stage,and this accounts for approximately 80%of the total anthropogenic greenhouse gases.Therefore,accurate and reliable carbon emission data are the foundation and scientific basis for most emission reduction policymaking and target setting.Currently,China has made clear the ambitious goal of achieving the peak of carbon emissions by 2030 and achieving carbon neutrality by 2060.The development of a finer-grained spatiotemporal carbon emission database is urgently needed to achieve more accurate carbon emission monitoring for continuous implementation and the iterative improvement of emission reduction policies.Near-real-time carbon emission monitoring is not only a major national demand but also a scientific question at the frontier of this discipline.This article reviews existing annual-based carbon accounting methods,with a focus on the newly developed real-time carbon emission technology and its current application trends.We also present a framework for the latest near-real-time carbon emission accounting technology that can be widely used.The development of relevant data and methods will provide strong database support to the policymaking for China’s“carbon neutrality”strategy.Finally,this article provides an outlook on the future of real-time carbon emission monitoring technology.

The status and development proposal of carbon sources and sinks monitoring satellite system

In order to mitigate global warming,the international communities actively explore low-carbon and green development methods.According to the Paris Agreement that came into effect in 2016,there will be a global stocktaking plan to carry out every 5 years from 2023 onwards.In September 2020,China proposed a"double carbon"target of carbon peaking before 2030 and carbon neutrality before 2060.Achieving carbon peaking and carbon neutrality goals requires accurate carbon emissions and carbon absorptions.China’s existing carbon monitoring methods have insufficient detection accuracy,low spatial resolution,and narrow swath,which are difficult to meet the monitoring requirement of carbon sources and sinks monitoring.In order to meet the needs of carbon stocktaking and support the monitoring and supervision of carbon sources and sinks,it is recommended to make full use of the foundation of the existing satellites,improve the detection technical specifications of carbon sources and sinks monitoring measures,and build a multi-means and comprehensive,LEO-GEO orbit carbon monitoring satellite system to achieve higher precision,higher resolution and multi-dimensional carbon monitoring.On this basis,it is recommended to strengthen international cooperation,improve data sharing policy,actively participate in the development of carbon retrieval algorithm and the setting of international carbon monitoring standards,establish an independent and controllable global carbon monitoring and evaluation system,and contribute China’s strength to the global realization of carbon peaking and carbon neutrality.

Gas leakage recognition for CO2 geological sequestration based on the time series neural network

The leakage of stored and transported CO2 is a risk for geological sequestration technology. One of the most challenging problems is to recognize and determine CO2 leakage signal in the complex atmosphere background. In this work, a time series model was proposed to forecast the atmospheric CO2 variation and the approximation error of the model was utilized to recognize the leakage. First, the fitting neural network trained with recently past CO2 data was applied to predict the daily atmospheric CO2. Further, the recurrent nonlinear autoregressive with exogenous input(NARX) model was adopted to get more accurate prediction. Compared with fitting neural network, the approximation errors of NARX have a clearer baseline, and the abnormal leakage signal can be seized more easily even in small release cases. Hence, the fitting approximation of time series prediction model is a potential excellent method to capture atmospheric abnormal signal for CO2 storage and transportation technologies.