Do the Math: Why Financial Modeling Is Essential for Sustainability

Todd Cort, MS, PE, PhD, is a Senior Lecturer at Yale School of Management and Yale School of the Environment and serves as Faculty Co-director of both the Yale Center for Business and the Environment and the Yale Initiative on Sustainable Finance. In our July blog, he sheds light on the fundamental importance of financial modeling for sustainability to be a core part of business strategy.

Do the Math: Why Financial Modeling Is Essential for Sustainability

As global markets begin to internalize the financial impacts of climate change and other environmental and social risks, I’ve seen expectations rise sharply for companies to provide financially robust disclosures. Standards and regulations are evolving, and the International Sustainability Standards Board (ISSB) has made it clear that sustainability disclosures must be useful to investors by linking environmental and social risks to enterprise value over the short, medium, and long term. Similarly, the EU’s Corporate Sustainability Due Diligence Directive (CSDDD) requires us to identify and mitigate adverse environmental and human rights impacts across our company’s value chain—including integrating these risks into our corporate strategy and financial planning. These frameworks don’t just ask us to be aware; they demand that we develop a quantitative understanding of how environmental and social risks affect our financial outlook. Simply put: we can no longer talk about sustainability in broad, qualitative terms. We have to do the math.

And yet, despite these evolving expectations, I see little movement in risk disclosures and still see almost all in the corporate world treating sustainability as a reporting task, not a financial modeling challenge. The one possible exception being the calculation of appropriate shadow prices for carbon emissions for oil and gas asset planning by companies like Occidental Petroleum and ConocoPhillips. Although even these can be difficult to reconcile with global energy scenarios. The ISSB and CSDDD reference enterprise value, financial planning, and risk mitigation, but I notice that many corporate responses stop at narrative statements—talking about reputational risk, regulatory uncertainty, or stakeholder pressure. While those qualitative insights provide context, they fall short of supporting sound financial decision-making. As a board member, CFO, or investor, I must move beyond vague statements like "climate change may impact our operations" and instead ask: by how much, under what assumptions, and with what financial consequences? Without this level of rigor, we can’t prioritize investments, adjust capital allocation, or weigh transition risks against emerging opportunities.

The data challenge

I understand the lack of financial modeling and the prevalence of qualitative risk assessment and mitigation narratives issued by corporations today. The data that underlies and explains environmental and social risks feels like it is not up to the challenge of quantitative financial modeling and making statements of financial risk based on shaky data is a good recipe for inviting litigation. Even for the most well documented risks such as climate adaptation, the data can leave enormous gaps in our ability to forecast financial impact. How frequent and of what duration are the expected climate events? Where in my supply chain am I likely to see the greatest disruption? What components or operations will prove to be most vulnerable and most critical in the face of disruption? How will critical stakeholders such as regulators react and respond in the face of severe events? How strained will our backstops such as insurance coverage become in the face of widespread events? These and other questions are important to calculating severity and likelihood of financial risks, but the available data may leave us with enormous sensitivities and error bars in our analysis. However, I have found in practice that the data challenge is frequently not as daunting as it appears. Many variables turn out to be less important to the model, thereby making the data challenge less relevant. In other cases, we are able to find new data sets that provide meaningful insights to critical variables. Even in those cases where the data are lacking and the question is critical, I find that knowing the range and likelihood of outcomes is more useful than an unsubstantiated narrative.

Net present value

Looking forward, companies must integrate sustainability risk and opportunity into financial modeling tools typically used in capital budgeting and investment analysis to make better strategic decisions. That means projecting the net present value (NPV) of sustainability-related projects, whether it's decarbonizing operations or installing renewable energy systems. NPV is a fundamental tool for companies to assess whether these projects will create or erode value over time, especially when compared to the cost of inaction—such as paying for carbon emissions or recovering from extreme weather damage. A key part of this is choosing the right discount rate—one that reflects our risk-adjusted cost of capital and the long-term calculations of climate investments. If I choose a rate that’s too high, I risk undervaluing the future benefits of resilience; too low, and I might overstate the returns.

Embedding sustainability into financial models

Practitioners must also recognize that environmental and social risks directly influence key financial metrics like free cash flow, leverage ratios, and cost of capital. For instance, rising water stress and deforestation policies can drive up input costs and squeeze margins in some circumstances and for some companies. Exposure to carbon pricing can increase earnings volatility, which affects beta and ultimately raises the cost of equity. Lenders and insurers are beginning to price environmental risk into debt and premiums, which means corporate cost of capital is increasingly tied to how well companies manage sustainability. If we want to integrate sustainability into our enterprise valuation and ensure that our initiatives are financially sound—not just aspirational—we have to model these dynamics accurately. Equally importantly, we must be cognizant of which financial metrics are most critical to financial health and whether these are the most sensitive factors to sustainability risks. For example, earlier ventures typically live and die by free cash flow whereas larger companies may be much more sensitive to leveraged ratios. Matching the sustainability risk and opportunity to the appropriate line item can be the difference between critical and meaningless insights.

At the end of the day, I see financial modeling as the essential bridge connecting sustainability goals, enterprise valuation, and fiduciary duties. By quantifying the financial implications of our net-zero targets, carbon transition risks, nature-positive investments, labor disruptions, and resource constraints, we can move beyond abstract narratives and deliver forecasts that truly guide action. This shift allows wise capital allocation, sets credible decarbonization paths, and communicates sustainability risks and opportunities in ways that matter to investors. For sustainability to be a core part of business strategy—not just a footnote in a report—we must embed it in our financial models. In today’s world of tightening regulation and growing risk, doing the math isn’t optional. It’s essential.

About the Author:

Todd Cort, MS, PE, PhD
Senior Lecturer, Yale School of Management and Yale School of the Environment
Faculty Co-director, Yale Center for Business and the Environment

Faculty Co-director, Yale Initiative on Sustainable Finance

PHOTO: George Dagerotip | Wind Turbines on Jeju Island, South Korea, 2024 | Unsplash

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Can sustainability be saved by tackling loneliness, not just CO₂ emissions?

By Raz Godelnik, Associate Professor • November 20, 2025

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By Nicole Cacal, MSc, • October 30, 2025

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But to do that, we need to move beyond binary thinking. Right-Sizing AI: Why Bigger Isn't Always Better One of the most overlooked levers we have for sustainable AI is also one of the simplest: choosing the right model for the job. The AI industry has been caught in a "bigger is better" arms race for years now. Every new model release touts more parameters, more capabilities, more everything. And sure, these massive general-purpose models are impressive. But they've created a dangerous assumption: that every task requires maximum firepower. This is where my strategic design training from Parsons kicks in. Good design isn't about having the biggest toolkit. It's about matching the tool to the task. It's about elegance through constraint. The same principle applies to AI deployment. The emerging concept of "Small is Sufficient " is gaining traction for good reason. 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Measuring What Matters: Beyond Energy to Net Benefit But here's the challenge: if we only measure AI's direct energy consumption, we miss the full picture. We need frameworks that capture both the operational cost and the systemic benefit. This is where life cycle assessment combined with comparative modeling becomes essential. We need to ask: compared to what? And over what timeframe? The sectoral success stories are compelling when you run the numbers: Building automation systems powered by AI are consistently achieving energy savings in the range of 20-30% across diverse building types. One documented case study of a commercial office building in the United States showed a 32% reduction in overall energy consumption with a 2.4-year return on investment (a $2.1 million system investment generating $875,000 in annual savings). In Stockholm, the SISAB school building portfolio achieved similar results with a two-year payback period. 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It requires companies to demand more of their technology providers and deploy their systems sustainably when greener options become available. But (and this is crucial) these benefits only materialize when we pair the right AI with the right infrastructure and the right deployment strategy. Which brings us to governance. The Path Forward: Governance, Transparency, and Adaptive Thinking The sustainability community, including organizations like ISSP, is actively developing shared frameworks for assessing AI's net impact. These emerging approaches include system-level energy auditing, selective task deployment protocols, and strategies for minimizing "dark data" (the vast amounts of stored data that's never used but still requires energy to maintain). Multi-stakeholder governance initiatives are bringing together technologists, policymakers, environmental scientists, and business leaders to create adaptive standards. 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