Page 277 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

2) Sparse and Inadequate Data

Monitoring networks in polar regions are sparse compared to those in more accessible areas.
This results in significant gaps in data coverage for both the Arctic and Antarctic. Change 4.2-Climate

Satellite data, while useful, often lacks the resolution or precision needed to monitor smaller-
scale ecological or climatic changes.

3) High Costs

Deploying and maintaining monitoring equipment in the polar regions is expensive due to the
logistical challenges and the need for specialized equipment designed to withstand extreme
conditions.

Research expeditions require significant financial and human resources, limiting the frequency
and geographic reach of such efforts.

4) Delayed Data Processing

Traditional data collection methods often rely on manual or semi-automated processing, which
can result in delays in interpreting and sharing critical findings.

This limits the ability to provide timely early warnings for events like ice sheet collapse,
permafrost thaw, or extreme weather.

5) Inadequate Early Warning Systems
Existing early warning systems may not be capable of forecasting extreme events in polar
regions (e.g., rapid ice shelf disintegration) with sufficient accuracy or lead time.

Systems often lack the ability to integrate diverse datasets from different sources (e.g., satellite
imagery, ocean sensors, and climate models).

PECIP integrates polar ecological monitoring, microalgae carbon capture, and ESG
(Environmental, Social, Governance) policy research to drive climate governance, carbon
neutrality, and cross-border collaboration through AI, advancing the synergistic achievement
of the UN SDGs. It uses real-time data and offline data. This hybrid approach ensures
comprehensive data coverage, combining the precision of historical data with the immediacy
of real-time inputs. It implements the following functions:

1) Real-time analysis of polar ecosystems via AI models (e.g., glacier melt prediction)
AI models enable real-time analysis of polar ecosystems, providing unprecedented
insights into glacier melt, sea ice dynamics, permafrost thaw, and marine ecosystems.
These technologies enhance predictive accuracy, detect subtle changes, and enable
early warnings for critical environmental events. By integrating AI into polar monitoring,
scientists and policymakers can better understand and respond to the rapid changes
occurring in these fragile ecosystems and their profound impacts on Earth's climate
system.
2) AI-driven microalgae cultivation increases carbon absorption by 30%
AI-driven microalgae cultivation represents a breakthrough in carbon sequestration,
increasing CO₂ absorption efficiency by 30% while addressing scalability, cost, and
resource challenges. By integrating advanced AI technologies, this approach offers a
sustainable and multi-functional solution to combat climate change. With further research
and investment, it holds great promise for reducing industrial emissions, generating

241