After severe flooding disrupted production at Audi’s Neckarsulm plant in 2016, engineers and scientists from Zurich Resilience Solutions were brought in to find a solution. After analysing the site and surrounding region, they helped Audi erect water-filled barriers around the facility that have prevented surface flooding ever since.
As the weather-related risk becomes more volatile, resilience will move further up board-room agendas. A study soon to be released by the World Economic Council of Sustainable Development predicts businesses – particularly those in the food and beverages industry – could see their net profits wiped out entirely by the impact of extreme weather events within the next five-to-ten years.[1]
“Shareholders should understand that these are now threats to the value and profitability of companies,” Johan Rockström, director of the Potsdam Institute for Climate Impact Research told journalists in Davos. “Businesses have a great opportunity but also a high degree of responsibility to map risks of extreme events across their entire value chain.”
Yet as businesses in California can attest, this is not as simple as it may seem. Start with physical risk. “Climate risks accumulate through a combination of interacting physical processes,” explains Professor Emily Shuckburgh, academic director of the Institute of Computing for Climate Science at Cambridge University and director of Cambridge Zero, the university’s climate change initiative. The businesses in California, for example, which are recovering from some of the deadliest wildfires in modern history, must now prepare for the increased risk of flooding as rainwater struggles to permeate the scorched earth; floods, in turn, can promote vegetation growth and perpetuate the risk of fires.
Next, consider the challenges of collecting accurate data across value chains. When analysts at the Bank of Japan assessed their global exposure to environmental disruption, they found that economic data provided in regions such as Columbia was provided as a national average because the government didn’t have the precise location of assets of Colombian banks. Other problems, such as the limited availability of data on subsidiaries or raw materials can also hamper analysis.