Mission-Driven AI: Addressing Climate Change


There is no question that Artificial Intelligence (AI) and Machine Learning (ML) are having a profound impact across every industry.  AI systems are now capable of problems previously considered untenable.  At Omega Venture Partners, we are seeing innovative companies focus on mission-driven use cases.  One such example has to do with the thorny problem of tackling climate change.

The effects of climate change are increasingly noticeable. In the past few decades, we have seen a significant increase in extreme weather events. Wildfire and drought seasons are longer, glaciers are melting at record rates, and sea levels are rising.  AI systems can predict and limit the impact of climate change in new ways.

Addressing climate change involves reducing emissions and taking actions to undo damage already caused. Governments, technology firms, and investors are interested in data-driven solutions and algorithms that can curate complex trends and patterns from massive datasets.  AI has the unique ability to translate large amounts of data into practical predictions, recommendations, and decisions. 

Several countries are already applying AI to combat climate change. For example, Japan uses AI software to predictively warn citizens of impending flood- and tsunami-risk. China is utilizing AI systems to design buildings that are optimized for eco-efficiency. And Brazil is using AI to guide its strategy for mitigating deforestation in the Amazon Rainforest.   

Make no mistake, climate change is a complex issue. However, AI and machine learning technologies are bringing new insights to the problem. Below are three examples of how AI is quickly becoming a key part of the solution to Climate Change:   


1. AI for Better Climate Predictions 

The application of Environmental Intelligence has gained increased importance in predicting extreme climate patterns and trends.  With Artificial Intelligence, we can extract new insights from the massive amounts of complex simulations generated by climate models. AI and ML can power dynamic models for scientists and researchers to use and make informed decisions.

More and more real-time geospatial data is becoming available from connected cameras, satellites, drones, and other terrestrial and aerial imagery. AI is key to analyzing these troves of data, both efficiently and effectively, to surface insights that are accurate as well as actionable.

For example, the US National Oceanic and Atmospheric Administration (NOAA) is using AI to build better predictions of extreme weather events such as hurricanes to unlock new insights from various data sources. Another example is Green Horizons, an IBM Research initiative, which is utilizing AI and IoT to predict and lower pollution levels. IBM has been successful in generating high-resolution pollution forecasts 72 hours in advance to warn citizens and give them time to prepare and plan for events such as hazardous air quality.   


2. AI to Manage Corporate Carbon Footprint 

Research from the Boston Consulting Group shows that companies can reliably use AI to monitor and predict their carbon emissions as well better adjust their practices to reduce these emissions.  AI’s greatest strength lies in its ability to learn from experience, to collect massive amounts of data from its environment, to infer connections, and to recommend appropriate actions. Companies wishing to reduce their carbon footprint may use AI to better monitor, predict, and reduce emissions.

Monitor Emissions

AI can collect and crunch data at the right velocity and volume to accurately calculate a company’s carbon footprint. The data collected would include all activities such as corporate travel, IT equipment, materials and suppliers, transportation, and even include data on how clients use the company’s products.  AI excels at filling gaps and approximating missing data to improve accuracy. 

Predict Emissions 

Predictive AI can utilize the company’s current carbon footprint data to calculate it’s future footprint in relation to current reduction efforts and future demand.  This information is detailed and specific to every part of the value chain. 

Reduce Emissions

Prescriptive AI can optimize and drive efficiencies in all departments including production and distribution. Not only can it detail reductions in emissions, but also costs. With AI, companies have access to deep insights into multiple aspects of their carbon footprint, allowing them to quickly cut costs. 


3. Advancing Renewable Energy through AI  

Renewable energy is collected from resources that never deplete and are sustainable such as, sunlight, wind, or tides. Several AI programs have been set up recently to optimize renewable energy.

An example is the US Department of Energy’s SunShot Initiative. This initiative aims to increase solar power to 15% of the United States’ electricity generation by 2030.  Researchers, in collaboration with industry participants, have developed a self-learning weather model and renewable forecasting technology known as SMT.  The SMT system blends AI and data from sensor networks, local weather stations, cloud motion information gathered from satellites, and multiple weather prediction models to analyze and improve the accuracy of solar forecasts.  


The bottom line is that AI is a powerful technology that has an important role to play in tackling one of the world’s most pressing problem—climate change. Addressing climate change is the type of mission-driven use case that we will see Artificial Intelligence utilized for on a more consistent basis going forward.