Earth Day 2020's call for climate action: Can AI Address the challenge?

By Jennifer Clemente Creative and Editorial Lead, Data and AI, IBM in AI, IBM Planning Analytics, data

With 2019 emerging as the warmest on record for the world’s oceans, the call to climate action continues as the theme for the 50-year anniversary of Earth Day 2020, described as the world’s largest environmental movement to drive transformative change for people and planet.

The eye on the prize

Alongside the pandemic, the climate crisis presents an opportunity to use data and AI in ways never before considered. IBM itself began focusing on environmental sustainability before the first Earth Day was ever celebrated — but its track record on greening its supply chain and driving innovative uses of tech has put it among the world’s top eco-friendly Fortune 500 companies.

Where IBM leads, customers reap benefits. Digital transformation efforts across industries has given the company a unique vantage point on critical challenges facing the world — putting AI to work on a number of different issues, from drastically reducing energy consumption to lowering C02 to optimizing large scale food production in the wake of climate chaos.

Learn how IBM has harnessed DataOps and Watson Studio combined with other services to build the world’s leading digital farming platform at Norway-based fertilizer company Yara.

Watch the video

Smaller, but formidable examples of AI enabling more responsible environmental stewardship are gaining steam — and hold the promise to drive impactful changes from individual behaviors and community practices to corporate policies — all the way up to the way energy is both created and consumed so it meets zero carbon aspirations.

Part I: Automated financial planning can help meet nations’ zero-carbon targets

In Greenland, the world's least-densely populated territory, energy provider Nukissiorfiit has tapped into data and AI capabilities to turn carbon zero aspirations into real outcomes.

Nukissiorfiit means "where energies are created” and today, 70 percent of Greenland’s electricity is produced by renewables. Tapping the country’s vast resources for hydro power is costly due to extreme Arctic weather and rugged terrain — but thanks to machine learning capabilities combined with IBM Planning Analytics, a labor intensive, six month financial planning cycle has been replaced by automated and continuous financial forecasting that a lean team of seven can easily tackle.

This means Greenland remains on track to go carbon zero by 2030 thanks to Nukissiorfiit’s ability to predict the future more precisely and invest hydroelectric, wind and solar projects more quickly.

Meanwhile, Finland is set to go carbon zero by 2035. Energy provider Vapo Oy has also announced its own target to half its CO2-emissions by 2025. To reach this goal Vapo is improving energy efficiency with the help of advanced IT in its own power and heating plants. IBM Planning Analytics helped Finland's Vapo Oy repurpose its stores of peat into an activated carbon portfolio — meeting an emerging market in Europe for water, air and gas purification with a low carbon footprint. The move means Vapo can continue to make investments that shift away from a declining demand for Peat — giving it a shot at making good on its promise to re-write its future.

Predictive maintenance capabilities can mean a brighter future for renewables

As Europe steps up its wind power capacity, suppliers face greater pressure to ensure reliability and consistency. The proper maintenance of windfarms is critical here. Not being able to detect and predict anomalies within a high voltage grid can shuttle operational costs and stymie competitive offerings in the booming renewables market.

Take UK engineer company James Fisher & Sons which maintains the subsea cables that transmit power from offshore wind farms to consumers on land. Subsea cables lie on the seabed and are extremely difficult to observe, which causes cable caretakers to lack insight into their condition or status.

The company now looks to data science and machine learning to detect and quickly alleviate issues with its windfarm customers’ subsea cables that transmit power from offshore wind farms to consumers on land. 

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