Under the high-impact climate change scenario in 2050, 66% of the largest international corporations have at least one asset that is subject to physical climate risk. Among these risks, the major threats come from climatic stresses (i.e., heat and water stress) and natural hazards converted into disasters due to a lack of informed planning and decision-making at the early stages of risk management.
Though massive amounts of climate data are produced globally, it has become a challenge to transform this data into information and knowledge that can provide useful insights to support decision-making and planning for a sustainable future.
It is noted that at least 2.5 quintillion bytes of data are produced each day globally (that’s 2.5 followed by a staggering 18 zeros!). While this situation is being observed across different fields, the physical climate research field is no exception.
For instance, the Google Earth Engine, now hosts 40+ petabytes of planetary-scale geospatial data from several individual platforms (NASA, NOAA, ESA, USGS, etc), which are readily available to perform countless analyses under different scenarios (e.g., climate change scenarios and stress testing). This geospatial data can be processed at various scales ranging from global to granular levels to provide location-specific intelligence, which can help us prepare more effectively for the future—especially under climate change and associated uncertainties.
The problem is, such a massive amount of data (aka Big Data) creates further challenges, like Big Data paralysis. We as humans are not equipped to effectively and efficiently handle, process, and transform such big data into useful insights for decisions.
Luckily, AI can potentially resolve a variety of complications, including this too-much-data conundrum through proper processing and reasonable understanding of data as well as AI-integrated modelling. This integrated modelling coupled with advanced analytics and user-friendly dashboards can progressively help manage challenging issues associated with climate change in the context of enhanced adaptation and resilience of organisations, businesses, and large corporations.
Through its sophisticated AI-assisted climate modelling approaches, Intensel helps companies to identify the exposure of different facilities owned by the company including capital assets, as well as supply chains, from climate-related physical risks, such as long- short-term sea-level rise, inland and coastal flooding, extreme heat/cold waves, droughts, and hurricanes/typhoons.
Our machine learning and geo-information modelling-based capabilities deliver insights regarding low, medium, and high-impact climate change scenarios across different time intervals, covering long-term forward-looking situations and short-term challenges, to help corporations, businesses institutes, and financial organizations to manage vulnerabilities and inform adaptation strategies at early stages of risk planning.
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