Introducing Environmental ROI (EROI): A Dynamic Metric for Valuing Climate Action
Billy Richards • July 22, 2025
It was in a room that huffed and hummed, where the air was sterile and gloves stuck like glue to my hands, that I learned the art of modeling dynamic systems. Under the guidance of the late Professor Béla Novák—one of the most inspiring and brilliant mathematicians, chemists, and biophysicists in the world—I discovered how complex cellular systems with millions of variables could be distilled into elegant equations that, from first principles, could accurately predict the future.
Today, I believe we need a metric that moves beyond basic cost-benefit logic and captures the true return of climate investments—one that satisfies commercial requirements while simultaneously aligning with sustainable objectives.
The Objective
My goal is to develop a non-static metric for valuing climate action. Enter Environmental Return on Investment (EROI): a dynamic ratio that links economic gains from CO₂ mitigation to the actual costs of achieving them. I'm working on two approaches, with the intention of comparing them against real-world and historical data to develop their accuracy and utility.
Approach 1: The Core Equation
The fundamental EROI equation is:
EROI = (Annual Economic Benefit from Mitigation) / (Annualized Cost of Mitigation)
Or in expanded form:
Where:
- M_rate = Annual CO₂ mitigated (tCO₂/year)
- V = Economic value per tonne CO₂ ($/tCO₂)
- CRF = Capital Recovery Factor (based on discount rate r and project lifetime L)
- CAPEX = Initial investment
- OPEX = Annual operating cost
Worked Example: Carbon Capture Project
Let's apply this to a real scenario:
Given:
- CAPEX = $100M
- OPEX = $10M/year
- Biochar mitigates 787,500 tCO₂/year
- V = $60/tCO₂ (based on $150/t biochar with 2.5 tCO₂ offset per tonne)
- CRF (8%, 25 years) ≈ 0.0937
Calculations:
- Annualized CAPEX = 0.0937 × $100M ≈ $9.37M
- Annual Benefit = 787,500 × $60 = $47.25M/year
- Total Annual Cost = $9.37M + $10M = $19.37M/year
Result: EROI ≈ 2.44 – A solid return indicating the project generates $2.44 in value for every $1 spent.
Full derivation can be found here
Why Static Models Fall Short
Traditional models assume linear relationships, but environmental economics is inherently nonlinear—characterized by feedbacks, thresholds, delays, and path dependence. The costs of technologies like Direct Air Capture (DAC) or Carbon Capture and Storage (CCS) fluctuate wildly with scale, policy, and carbon market volatility.
Consider the range:
- CCS: $40–120/tCO₂
- Early DAC: Over $1,000/tCO₂
- EU ETS carbon prices: €70–100
Yet many technologies need $150–200+ per tonne to be economically viable. We need a model that adapts to these realities.
Modeling Inspired by Life Itself
Drawing from Professor Novák's cell cycle models—which use feedback loops and thresholds to show how cells make irreversible decisions—I've developed a systems dynamics approach using nonlinear differential equations that model changes in capacity, investment, and value over time.
Just as cells decide whether to divide based on complex signals, the global economy can be modeled as shifting between low and high mitigation states based on internal system dynamics and external signals like carbon pricing. This approach brings economics far closer to reality than traditional models, making externalities implicitly important.
Dynamic Elements to Map
- Investment thresholds: Economic signals must cross a "switch" point before investment accelerates
- Feedback loops: Learning-by-doing reduces CAPEX/OPEX over time → higher EROI → more investment
- Hysteresis: Once investment is triggered and infrastructure is in place, the system resists reverting—even if prices drop
- Oscillations: Overshooting (e.g., too much supply of credits) can crash prices, reduce EROI, and cycle investment behavior
Key Variables in the Dynamic Model
- M (Mitigation): Total CO₂ removed
- K (Capacity): Infrastructure scale
- I (Investment): Cumulative capital input
- V (Value): Market signal, often carbon price adjusted for policy risk
- Pc (Carbon price): Either external input or dynamic output
As costs decrease with increasing K (learning effect), EROI improves, making investment more attractive. However, if too much mitigation floods the market, V drops—creating a negative feedback. The model captures this intricate dance.
Worked Example: Rainforest Conservation Project
Let's examine a forestry project focused on rainforest conservation:
Inputs:
- Annual CO₂ Mitigated (M_rate): 100,000 tCO₂
- Price per Credit (V): $2.50
- CAPEX: $450,000
- OPEX: $40,000/year
- Discount Rate (r): 8%
- Lifetime (L): 25 years
Step 1: Calculate Capital Recovery Factor (CRF)
Step 2: Annualized CAPEX Annualized CAPEX = 0.0937 × $450,000 = $42,165
Step 3: Annual Economic Benefit Benefit = 100,000 × $2.50 = $250,000
Step 4: Total Annualized Cost Total Cost = $42,165 + $40,000 = $82,165
Step 5: Calculate EROI EROI = $250,000 / $82,165 ≈ 3.04
Result: EROI ≈ 3.04 – The project returns $3.04 in value for every $1 spent.
The Real Power of EROI
EROI is not just a ratio—it's a lever. A dynamic signal that helps investors and policymakers see when, where, and how climate investments actually pay off. It captures the economic pulse of environmental action in real time, across different technologies, scales, and policies.
Think of EROI as:
- An alternative to static "cost of avoidance" metrics
- A forward-looking, risk-adjusted investment signal
- A framework to model tipping points, not just trends
Final Takeaway
The path to a low-carbon economy won't be linear. We need to understand its feedbacks, thresholds, and momentum. By merging systems biology logic with environmental economics, we not only get a clearer picture of ROI but also understand how to design policy that nudges the system across critical tipping points.
EROI isn't just a metric. It's a tool for navigating complexity.
The Novák Framework Summary
The dynamic EROI model incorporates:
- Nonlinear differential equations for system evolution
- Feedback mechanisms between investment, capacity, and returns
- Threshold behaviors mimicking biological decision points
- Hysteresis effects preventing system reversal
- Oscillatory dynamics capturing market cycles
Coming Soon
- Release of comprehensive research and worked example sets
- Case studies including historical analysis
- Bridge analysis connecting biochemical and biological processes to economic systems
- Open-sourcing of the basic EROI calculator
- And much more...
For more insights on environmental markets and dynamic systems modeling, follow my work and upcoming releases.