Andrew McHardy is a partner and national leader of the decarbonization hub and Ally Karmali is a partner and national leader of ESG data and technology at KPMG in Canada.
On the heels of the worst forest fire season in Canadian history, the need to get a handle on climate change has never felt more real for Canadian businesses. The data around the true costs of the fires and recent extreme weather events are still rolling in, but we know these disruptions had a significant impact on lives, livelihoods and our economy.
Devastating climate disasters are increasingly becoming the rule rather than the exception, and Canada’s CEOs agree. KPMG International’s 2023 CEO Outlook found that three quarters of Canadian business leaders believe natural disasters and extreme weather events will hinder their organization’s prosperity over the next three years.
The uncertainty that comes with a changing climate poses several complex challenges to Canadian businesses, including the need for climate adaptation and resilience plans. To minimize further changes to our climate, governments and other stakeholders are mandating that companies take actions to reduce their emissions, and harnessing the power of data and digital technology will help companies find the most efficient ways to decarbonize.
That’s where artificial intelligence comes in.
While still in the earlier stages of deployment, AI’s ability to digest massive amounts of complex, unstructured data and deliver interpretations and predictions makes it an important tool in determining how to reduce our carbon footprint. It’s already helping companies make more informed decisions in the transition to lower carbon operations, including moving the dial on measuring, tracking and forecasting greenhouse gas (GHG) emissions reductions.
Identifying, tracking and predicting emissions
Building an effective decarbonization strategy requires accurate and real-time information about where your emissions come from, and their intensity.
That means looking at the emissions a company directly produces (Scope 1) and the energy purchased to power its operations, such as electricity and heat (Scope 2). Companies are also increasingly expected to tackle upstream and downstream emissions that stem from other parts of their business, including their investments, suppliers and even what happens to those products when disposed of at the end of their use (Scope 3).
It’s a lot to capture and gaps in emissions data are a common challenge that AI is well positioned to solve.
It’s rapid ability to collect and consolidate quality data from diverse sources – think Internet of Things devices, energy meters, transportation records and even historical weather data and satellite imagery – provides organizations the ability to understand their emissions footprint in a way that has been impossible previously.
With a more accurate and integrated data map of GHG emissions, AI’s algorithms do the heavy lifting to analyze these emissions across a company’s value chain. AI can then predict and model multiple variables to determine what emissions outputs could be like in the future under a vast number of scenarios. That means rapid access to data-driven insights that better inform business decisions, laddering up to your decarbonization goals and ultimately better outcomes.
To maximize AI’s potential, it’s important to stay focused on two key elements: keeping your data in good shape and always improving your AI models to optimize future predictions.
Once you have a more complete picture of where your emissions come from, it’s easier to track emissions in real-time to predict future trends, and most importantly, find efficiencies that prioritize decarbonization.
Organizations increasingly see the power of AI through:
- Energy optimization: To manage energy outputs at the asset level, companies are already leveraging AI in building systems to track climate control and lighting usage patterns or enable automated climate zones to minimize energy consumption.
- Electricity grid modernization: Using AI to forecast when renewable sources (such as solar or wind power) are most available, manage microgrids to store unused or excess energy and track electricity consumption habits for efficiency purposes can reduce emissions along your operations on a much larger scale. A power and utility company, for example, could harness AI to track consumer energy usage habits and identify changing peak consumption times much faster than what’s traditionally been possible, shortening the feedback loop. Access to real-time information on demand translates into smarter energy supply processes to keep emissions at a minimum.
- Transportation optimization: Using AI-powered route optimization algorithms, companies can reduce fuel consumption from their fleet based on analyzed traffic and weather patterns and fleet loads or find the most efficient and environmentally friendly route using different available modes of transportation, which could be by ship, air, freight train or truck.
- Supply chain optimization: Scope 3 emissions along your supply chain are some of the most challenging to track, and for many industries, most of their emissions fall under this category. That makes choosing suppliers aligned to your decarbonization goals very important. The process of seeking sustainable sources gets a boost from AI’s ability to identify a supplier’s sustainability approach, from their environmental policies all the way down to their manufacturing processes, including whether circular economy practices like minimizing emissions and waste are prioritized.
As part of AI’s evolution, generative AI will also be a key enabler for investigating decarbonization options, including facilitation of knowledge sharing and analysis of available energy alternatives.
There is no one-size-fits all technology that can solve every decarbonization challenge, and it’s important that companies seeking an AI aid in their decarbonization strategy also make sure to choose responsible AI and factor in AI’s own carbon contributions. However, one of the world’s most disruptive technologies has an important role to play in mitigating climate change, and time is of the essence. Companies need to take advantage of AI’s powerful capabilities to accelerate the transition to a lower carbon future.
This column is part of Globe Careers’ Leadership Lab series, where executives and experts share their views and advice about the world of work. Find all Leadership Lab stories at tgam.ca/leadershiplab and guidelines for how to contribute to the column here.