Keeling Curve

Primary Project Category:

Project Summary / Description:

Consensually and scientifically, climate change relates to the globally uneven temperature rise caused by anthropogenic CO2 emissions since the pre-industrial level. One of the most humanity-focusing issues behind is whether such CO2 emissions can be near-real-time (NRT) monitored: specifically monitor the NRT changing dynamics by artificial intelligence and big data techniques with the continuous development of higher spatio-temporal resolution CO2 data. The derived most up-to-date information and revealed nature beyond from those data could serve as the robust scientific evidences for climate change research and policy-making. The extent to which those data contribute will be highly appreciated. In light of that, our project tracks the NRT carbon emissions from human activities for the first time. This dynamic dataset system is derived from various real-time activity data, including the national hourly electricity data, traffic data over 400 cities worldwide, etc. Currently, it is developed to be a collection of innovative and ground-breaking research based on this system to solve such problem for policy-makers and climate change experts, including more detailed estimates spatially at the regional level (the US, China, global cities), at the pixel level (the global first NRT carbon map) and temporally for fine-grained near-hourly data. The project was founded in 2019.

Regions of Operations:

Best Estimate of GHG Avoidance/Reduction of This Project (Tonnes CO2 Equivalent/Year):

Impact on Underrepresented Groups:

This solution is of global extent: We are continuously monitoring the global impacts of CO2 emissions near-real-time. In addition, this solution is a project, containing numerous works in various aspects based on the latest data sources and published methodologies. For instance, the global first high-resolution near-real-time carbon maps (see details in the official website) covers regions around the world, including the area for traditionally underrepresented populations to fully understand the status of the traditionally underrepresented, the normal and the most focused. Specifically, emissions dynamics for continents such as Africa, South America, are also explicitly shown in our emission datasets. In the past, Numerous middle and small -populated countries in those continents didn‚ t have vivid and clear impressions on how their anthropogenic emissions will affect their economic developments and whether those emissions dynamics can be reflected by some special events (religious rituals, ceremonies, etc.) in their countries, some of which may have significant commemorative meanings to the underrepresented groups. Our databases could help reveal such information to better unwind the information of those where underrepresented groups prevail and inhabit. This will be extended to more precise monitoring of those area in the future implementation to help underrepresented groups and the government better the trade-off between possible carbon neutrality goals and benefits of those groups, even leverage politics and sciences from this viewpoint.

Sub-Categories: