How is data crucial in reacting to the climate emergency?

Elisabeth Goos, IBM Consulting Sustainability Services Leader for EMEA, shares insight on data and AI as sustainability solutions for the climate emergency

Elisabeth Goos has led Sustainability Services across the EMEA market for IBM Consulting since March 2023, before which she led the Strategy and Sustainability Services for the DACH market. 

As a sustainability strategist, she helps clients across all sectors activate their ESG and sustainability strategies, and brings research, consulting, technology and IBM’s vast ecosystem together to accelerate towards the company’s targets. Prior to joining IBM Europe in 2021, she headed Innovation and Digital Strategy Consulting for Capgemini Invent in Asia-Pacific and worked for the Strategy consultancy Roland Berger. She holds a sustainability strategy certification from Cambridge University and a Master's Degree in Political Science from the Universities Osnabrueck (Germany) and Harvard (USA).

How is data crucial in reacting to the climate emergency?

One of the key components in the fight against climate change is data, but effectively managing and interrogating it is becoming increasingly difficult due to the growth in its volume and complexity. As a result, potentially important insights may get overlooked, leaving significant solutions with real-world applications hidden in isolated data silos.  

Fortunately, advances in generative artificial intelligence (AI) have the potential to make a difference. Its ability to employ machine learning (ML) algorithms and its capacity to automate data processing – at speed and at scale – offers the prospect of humanity getting better insights into the climate challenge, helping ensure we make better informed decisions.  

How can AI be used to boost sustainability?

One of the great advantages of generative AI is its capacity to ingest large volumes of pre-existing data while simultaneously learning from it. It does this through the use of foundation models, which are pre-trained on vast amounts of data which is less expensive to prepare and can be adapted to a wide range of tasks and operations. Once trained, these models can easily be adapted to different use cases.  

This ability is already being harnessed by companies around the world to create automated, scalable and, most importantly, trustworthy reporting mechanisms. It’s good that organisations are making self-assured environmental, social and corporate governance commitments, but if you can’t prove you’ve met your targets there can be trouble ahead. As a result, companies are increasingly exploring how the successes of generative AI and large language models can be leveraged to streamline reporting, increase accuracy and enhance their position as a sustainable organisation for a green future.  

The scale of the benefits this provides was recently revealed in a survey from the IBM Institute of Business Value, which asked 2,500 senior executives about their sustainability goals. The response was unequivocal; 95% of executives stated that their company had a clear value proposition around their environmental and social targets. However, despite these objectives, only 10% claimed to have made significant headway.

What are the challenges in data and AI sustainability solutions?

The single biggest brake on progress, is a lack of data insights – an issue where generative AI is starting to gain traction. A recent survey from the IBM Institute of Business Value showed that 77% of CEOs pursuing transformational sustainability expect their organisation’s digitised workflows to leverage AI automation by 2025, with 46% viewing AI as important for advancing their companies’ sustainability efforts.  

The technology also has applications in developing remedial technologies, such as carbon capture and sequestration (CCS), which requires an extensive understanding of the Earth's subsurface and geological processes. Fortunately, decades of oil and gas exploration have created a vast knowledge bank that contains a massive, but unindexed, repository of potentially valuable documents. 

Traditionally, this store of information was inaccessible to advanced analytics. However, a collaboration between Wintershall Dea, Germany's largest crude oil and natural gas producer, and IBM Consulting, created a generative AI-based knowledge extraction tool. This now enables the company to interrogate its siloed data to reveal previously inaccessible insights that are helping it to assess subsurface locations for suitability for CCS projects in terms of both profitability and safety.

Obstacles, such as unstructured or siloed data, complex regulations and stringent net zero obligations can all be addressed with generative AI. Fundamentally, it enables enterprises to streamline measurement and reporting procedures, while also driving quantifiable progress towards ESG commitments. 

What is the future of AI in sustainability?

Generative AI is a hugely transformative technology that has the potential to raise humanity’s game against climate change, while at the same time enhancing productivity, sustainably and securely. The insights it’s already beginning to deliver are helping shape environmental policies, supported by robust data analytics. Organisations are also starting to reap the benefits and better understand their operations, identify decarbonisation opportunities and in the process create a robust roadmap to net zero.  

However, in order to ensure the creation of robust and trustworthy AI tools it is essential for businesses, governments and regulators to work with technology partners with a proven track record of developing and implementing scalable generative AI systems that perform from the beginning. It’s also important to ensure that the chosen company has the capacity to adapt its offer to meet changing environmental requirements. With so much at stake, only the best tools should be applied.  


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