Accelerating Climate Action with AI

James Zhang

10 years: CEO and co-founder

Climate-related data is booming. More organisations are publishing data related to their net zero targets and sustainability commitments. But how can we track it all? Join James Zhang as he explains how artificial intelligence is solving climate data challenges.

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Accelerating Climate Action with AI

11 mins 14 secs

Key learning objectives:

Understand how AI is addressing data challenges in climate change
Describe the types of net zero-related queries AI can now answer
Identify the techniques machine learning uses to provide reliable and accurate climate data

Overview:

As more organisations, governments and regulatory bodies issue climate reports and sustainability-focused documents, the human power needed to process this data is becoming increasingly challenging to manage. With the advances in artificial intelligence, it is now possible to leverage technology to analyse, understand and verify net zero pledges and other sustainability commitments in a more efficient way.

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Summary

Why is artificial intelligence (AI) necessary in the journey towards net zero?

As the world races to meet its goal of becoming net zero by 2050, the world’s largest polluters are falling under a greater spotlight and being held accountable for their sustainability commitments. The public needs credible information to understand who is taking meaningful action and committing fully to their net zero pledges.

There remains a significant gap between pledges and actual actions, and transparency requires rapid processing of massive amounts of net zero data. This is where artificial intelligence comes into play and helps in the journey towards net zero.

AI and machine learning can complement human expertise and help process climate data to decipher who is doing what and doing it in a robust way. This technological advancement will accelerate climate action and the world aims to reach net zero.

Why are climate researchers and scientists hesitant to use artificial intelligence?

Climate scientists and researchers are hesitant to use artificial intelligence because of two major concerns: sourcing and hallucinations.

The answers AI (specifically chatbots) produce can be vague and the sourcing can be unclear. This makes it hard for scientists to rely on AI for accurate and dependable information.

Hallucinations also make using AI unpredictable. This is when the content produced appears sound and fluent, but it is factually wrong.

For these reasons, climate researchers and scientists hesitate to incorporate AI into their workflows.

What techniques underpin the AI chatbot ChatNetZero?

There are four main techniques that underpin ChatNetZero. The first is an anti-hallucination algorithm, the second is a fine-tuned process to ensure no relevant information is overlooked, the third is data-processing steps to help the chatbot answer analytical questions about net zero targets, and finally, the user experience design.

These techniques ensure a trusted, source-based information technology that helps demystify the net zero concept.

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James Zhang

James Zhang, co-founder and CEO of Arboretica, is a Dutch startup that uses data science to accelerate environmental analysis. They provide the Net Zero Tracker data smarts alongside Data Driven EnviroLab. He is a data scientist specialising in Natural Language Processing, a technique that has gained popularity due to AI advancements.

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