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Artificial Intelligence, News, Technology

The Four Steps to Combating Climate Change With AI (Part I)

Abhinav Raj

Abhinav Raj, Writer
@uxconnections

Artificial Intelligence (AI) has the potential to become one of our most powerful tools in enacting climate action measures. The Global Partnership on AI (GPAI) details four methods to accelerate climate action through AI.

Unprecedented heat waves, long droughts, intense floods, and biodiversity losses—the signs are all around us: humanity has locked itself in a long, drawn-out war against climate change

The vital signs of the planet are fluctuating, and only timely climate action involving governments, corporations and individuals can keep our ecosystems from incurring irreversible damage. 

Fortunately, the tools powered by artificial intelligence can play an instrumental role in preserving our planet and its biodiversity for posterity. 

AI Against Climate Change: Using Our Tools Right 

Artificial intelligence technology has been a subject of interest in the international discourse regarding sustainable development for years. 

The Global Partnership on Artificial Intelligence (GPAI) is a multi-stakeholder forum for world governments and experts leading the effort to devise strategies to bring AI solutions to aid climate action. 

In its latest report, the GPAI lays out four key methods to accelerate climate action through AI. 

Knowing What We Know 

(Image: United States Geological Survey on Unsplash)

The first is enabling action by leveraging AI to convert raw data into meaningful information. 

As of 2020, we generate more than 2.8 quintillion bytes of data every day, with every byte revealing a quantum of information about the world. In the context of climate action, space agencies and meteorology bureaus create enormous amounts of data that can be translated into actionable information.  

But the question arises: what are we doing with this information? 

It is futile to expend human effort to analyze and interpret this data—so we must employ machine learning in data analytics. Modern machine learning algorithms can analyze large amounts of raw data and convert it into useful knowledge for experts and governments to act upon. 

For example, an adequately trained ML algorithm can analyze satellite imagery released by meteorological departments to monitor deforestation, identify sites vulnerable to inundation in real time, or chart long-term changes to glaciers and ice covers.

Predicting What Will Come 

(Image: William Bossen on Unsplash)

By knowing what we know, we can know what’s about to come. 

The next key contributor to the battle against climate change is predictive analytics. 

The Big Tech are no strangers to harnessing ML’s predictive capabilities (enabled by pattern recognition) to forecast events before they’ve occurred. Google’s flood forecasting initiative originating in the Indian subcontinent is an exemplary undertaking that evinces the power of predictive analytics backed by machine learning. 

Proactive ML models can provide experts with real-time analytics on estimates of greenhouse gas emissions, forecasts of agricultural yields for a year, and even predict the rise in sea levels for pre-emptive action.

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