Beehive’s Financial Impact Calculator Documentation
This document describes Beehive’s Financial Impact Calculator product. We explain the equations used to calculate dollar value of risk, our reasoning for selecting those equations, and known limitations and ideas to improve the results.
Table of Contents:
Methodology & Phislophy
Quantifying climate-related financial risk is hard—but critically important.
Executives and decision-makers need to understand: Is this a material risk to our business?
That’s why Beehive’s Financial Impact Calculator exists: to translate complex, uncertain climate risks into dollar estimates that drive clarity, urgency, and action.
We believe that putting a dollar value on climate disruption helps:
Communicate risk in a language executives understand
Prioritize the most urgent vulnerabilities
Organize messy or incomplete data into actionable insights
But it’s also important to say: this will never be exact.
Every company is different.
Every insurance policy is different.
Every employee works differently.
And every disaster affects companies in different ways.
For example, a book store might lose millions in revenue from hurricane closures, but a home improvement store might gain millions in revenue as demand spikes.
We can’t model every nuance. What we can do is give you:
Simple, transparent formulas you can understand
Editable assumptions so you can fine-tune the math
Ballpark estimates that get you into the right order of magnitude
If we say the risk is $1M, maybe it’s actually $800K or $1.2M. But it’s probably not $5K, and it’s definitely not $100 million.
This tool is designed to get you started:
If a number feels material, dig deeper.
If you have better inputs, use them.
If you need a refined analysis, hire help—or tell us what to build next.
Our commitment is to be transparent, helpful, and grounded in reality, so you can turn climate risk from a vague concern into a strategic conversation.
Employees
Equation:
Lost Workdays per Year × Cost per Lost Workday
Why we use this equation:
This approach assumes that climate-related events (e.g., heatwaves, hurricanes, wildfires) can prevent employees from working, either due to safety concerns, caregiving responsibilities, or utility/infrastructure failures. Lost workdays translate directly into productivity loss or deferred output.
Limitations:
Does not account for remote work flexibility, asynchronous productivity, or cross-team backups.
Assumes each lost day has the same cost impact, which may not reflect real operational dynamics. (For example, the lost workday of an intern and an executive have different costs to the business.)
How to get a more accurate estimate:
Adjust “Cost per Lost Workday” to reflect a more accurate estimate for your company
Create different People asset types to differentiate between Executives, Employees, and Contractors
Offices
Equation:
Closure Days per Year × Cost per Closure Day × (1 + Headcount / 1000) × Ownership Type Multiplier
Why we use this equation:
Office closures due to floods, wildfires, or other climate risks can incur direct and indirect costs. We scale cost based on headcount (assuming more people = more disruption) and apply multipliers for ownership type, since owned buildings tend to have higher financial exposure (e.g., asset damage, insurance gaps).
Ownership Type
Own
Lease
Co-working
Multiplier
1.5x
1.25x
1x
Limitations:
Does not capture capital asset damage, insurance coverage, or localized backup operations.
Ownership multipliers are generalized and may not reflect real lease terms or property value.
How to get a more accurate estimate:
Use true occupancy numbers and cost breakdowns (rent, security, IT, utilities).
Replace default multipliers with internal real estate valuations or insurance models.
Cloud Data Centers
Equation:
Hours of Downtime × Cost per Hour of Downtime
Why we use this equation:
Even though cloud providers have strong redundancies, extreme climate events can lead to latency, degraded performance, or outages. For many businesses, even brief interruptions in core services can have measurable revenue or SLA impacts.
Limitations:
Doesn’t differentiate between critical vs. non-critical systems.
Downtime likelihood is based on regional exposure, not provider-specific resilience.
Some costs may be recovered later (e.g., deferred transactions).
How to get a more accurate estimate:
Refine downtime costs by workload tier (customer-facing vs. internal).
Map cloud dependencies by region and vendor architecture.
Consider SLAs or third-party uptime monitoring data
Retail
Equation:
Daily Revenue per Location × Closure Days per Year
Why we use this equation:
If a store can’t open due to an extreme heat event, flooding, or power outage, it likely misses a day of sales. This simple formula estimates the revenue not captured during those closures.
Limitations:
Doesn’t factor in customer rebound (e.g., make-up purchases the next day).
Assumes revenue loss is 100% rather than partially mitigated through e-commerce or other stores.
How to get a more accurate estimate:
Segment by store type or importance (e.g., flagship vs. low-traffic).
Adjust for seasonal fluctuations or high-revenue days.
Include mitigation strategies like online sales, nearby store substitution, or delivery options.
Legal, Privacy, and Disclaimers
The climate risk assessments and projections provided by Beehive are based on complex models and data analysis techniques that attempt to predict future climate-related events and risks. These projections are inherently uncertain and subject to numerous variables beyond our control.
Beehive does not guarantee the accuracy of any climate risk projections or assessments. The risk categorizations provided are best estimates based on available data and modeling techniques at the time of the assessment.