Nature and Biodiversity

Data to decisions: How technology can solve a $1.2 trillion climate change problem

Sheep graze during the annual shepherd festival on the Gemmi pass: Climate data can help decision making for businesses in the short and long term.

Climate data can help decision making for businesses in the short and long term. Image: REUTERS/Pascal Lauener

Himanshu Gupta
Chief Executive Officer, ClimateAi
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Climate and Nature

  • Climate risks can lead to business losses but organizations can mitigate such problems with better data and data-driven decision-making.
  • Emerging climate technologies can help gain better data such as remote sensing technologies, AI-driven models and Internet of Things devices.
  • Climate-related data can be integrated into decision-making strategies alongside industry-specific data to inform short or long-term actions.

After a summer with the hottest stretch in 120,000 years, climate change alarm bells are ringing.

But companies haven’t yet fully accounted for the profound risks of a warming planet – from shifting weather patterns to rising sea levels to environmental impacts that threaten natural capital, infrastructure, operations and supply chains. In fact, climate risks threaten to cause $1.26 trillion in revenue losses for suppliers within the next five years, a CDP Global Supply Chain Report found.

These factors, plus geopolitics, war and inflation, significantly impact corporate top and bottom lines. But when peace comes and inflation cools, climate change will continue, prompting the urgent need to mitigate risk and build resilience within supply chains.

Corporate leaders want to mitigate climate risk – but it’s complicated. When it comes to climate change, companies have two major pain points: a data problem and, subsequently, a data-driven decision-making problem.

Forward-thinking business leaders are proactively taking steps to tackle some of these issues, from using emerging technologies to forging new collaborative efforts.

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Climate data and decision-making problems

Companies’ decision-makers need accessible, reliable and contextual climate information to understand their risks and increase resilience. But climate data can be hard to find, understand and incorporate into decision-making.

Too often, companies rely upon anecdotal and historical data to inform their operational and strategic decision-making. But climate change is here and creating profound new volatility. What’s more, the past is no longer a reliable predictor of the future. That means businesses have considerable knowledge gaps as they set and operationalize their strategies. Industry leaders must tackle this complex challenge as climate change accelerates and brings even more extremes.

In general, there is a lack of data about climate change-caused extreme weather events that pose some of the greatest risks to companies and their supply chains. These events are, by nature, rare. Historical observational data doesn’t go back very far, as reliable satellite technology is only about 50 years old. That means such recorded instances seem infrequent but have become more common in the past 20 years as climate change has worsened.

Improving climate data for business

To access more data, companies can leverage emerging climate technologies, which have advanced significantly in recent years, for more accurate and comprehensive measurements. These include remote sensing technologies, which involve using satellites, aircraft or drones equipped with sensors and cameras to monitor and capture data about the Earth’s surface.

They can also involve Internet of Things (IoT) devices, such as weather stations and environmental sensors, which are increasingly used to collect real-time climate data at a localized level. Plus, advanced climate models that leverage machine learning and artificial intelligence (AI) algorithms can process vast amounts of climate data, identifying patterns, trends and correlations that might not be evident through conventional analysis. These technologies can enhance climate data processing and support predictive modelling efforts in an accessible way.

Similar to buzzy new-age generative AI models such as ChatGPT, AI-based weather and climate forecasting models train on simulated climate data (without historical data) and transfer these learnings onto observational data. They simulate centuries of extreme weather events to get more accurate data to learn about these types of events. They then use these learnings for forward-looking forecasts. For example, advanced forecasting tools can reveal the shifting patterns of the hurricane season in the United States, giving key insights for businesses about the long-term risk to their operating sites, suppliers and key ports.

By leveraging actionable climate data, companies are more prepared for the major shifts in global temperatures, precipitation and other patterns that climate change brings.

Himanshu Gupta, Chief Executive Officer, ClimateAi

Integrating climate data into decision-making

Once companies have improved climate data, they must integrate it into decision-making strategies. Advanced climate tools can translate this data into impacts that matter most to businesses by factoring in industry-specific data.

For example, when applied to agriculture, AI that models not just weather and climate conditions but also crop phenology and location-specific characteristics can help agribusinesses make climate-smart decisions. That can mean taking short-term actions, including moving a planting window to avoid rain, or long-term endeavours, such as developing certain seed traits to deal with future droughts.

By leveraging actionable climate data, companies are more prepared for the major shifts in global temperatures, precipitation and other patterns that climate change brings. Understanding business-specific climate change impacts is key for enabling better operational decisions, including sourcing, contracting, demand planning and logistics, as well as decisions on long-term investments and strategy.

Climate data and decision-making in action

Climate data and technology can also build business continuity and capture new opportunities. For example, businesses turn to AI-based climate forecasting technologies during hurricane season to analyze the impact on sales or sourcing regions, identifying which commodities have high risk and what actions companies can take to ensure their supply chains are safe.

In one case in 2022, a building materials company used these tools to assess the impacts of the upcoming hurricane season on the demand for building materials to inform better supply planning. The company received a forecast that showed a substantially elevated risk of hurricane impacts in Florida in September and the potential of damage and subsequent demand for roofing products. The company was able to adjust its tactical decisions and get ready for the increased chances of a landfalling hurricane in or around Florida.

It gathered resources at its nearby facilities to ramp up the production of its Florida-specific roofing shingles once the hurricane was en route. Once Hurricane Ian started tracking towards Florida, they could take swift action to meet the increased demand while many other companies were left flat-footed. With meaningful climate insights for the company, it captured a large market share and helped the affected communities recover more quickly.

Technologies can be a major tool for businesses in extreme weather situations. Without them, companies will face unprecedented new environmental and business landscapes.

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