Beyond the Narrative: Modeling Sustainable Risk

This semester, my practicum work has changed my understanding of sustainable investment. In class, it is easy for us to discuss “impact” and “risk” as two separate topics. In practice, however, they are closely connected through cash flow, corporate governance, and data. Our team is building a project financing model for a sustainable timber-to-housing value chain in northern Brazil. What surprises me most is that this work is not so much about writing a narrative of sustainable development as it is about building a rigorous hypothesis. People often say “put theory into practice”; that’s exactly what we did: we turned grand goals into a model that can withstand scrutiny.

In order to avoid falling into a single “big box,” we built the model in stages. We divided the value chain into several modules: primary sawn processing, panels, secondary engineered products, commercialization, and construction. This phased structure helps me clearly see the creation point of value and where cash flow may have problems. This also reinforces a principle that we have repeatedly emphasized in our meetings: do not overmodel. We narrowed down the geographical scope, used the best available data, and made all the assumptions public, because complex models may not be reliable.

After carefully studying the operational hypothesis, the financial considerations are more specific and real for me. The inventory turnover days of logs, wet wood, dry wood, and finished engineering materials are by no means just input data in an electronic form; every additional day inventory is delayed means that more cash will be tied up for a long time before generating any revenue. The “Days Payable Outstanding” (DPO) presents another side of the story: if we can delay payment to suppliers, liquidity pressure will be reduced; on the contrary, if we have to settle payments quickly, the project needs to invest more cash in the early stage. In our value chain model, it is these details involving time nodes that may cause a funding gap, requiring the project to seek the support of bridge financing before it enters the steady-state operation stage. This finding also coincides with recent academic research. Research by Huynh and Le (2025) shows that when supply chain risks rise, enterprises tend to increase their inventory holdings and adjust working capital to act as a buffer. Our model reveals the same operating mechanism: even if the project is reasonable in terms of long-term economic benefits, a longer inventory turnover cycle and shorter payment period may still put heavy pressure on the liquidity of the enterprise.

Another constraint in our model comes from the income side. For subsidized housing projects, pricing is not a variable that we can optimize at will. We anchor the income of housing units on the “affordability framework” formulated by the federal government, which sets a price ceiling at the municipal level, thus forming a hard ceiling on the cash inflow generated by each housing set. This constraint forces us to maintain a high degree of rigor and self-discipline in the project feasibility assessment. It is at this point that the research of Krueger, Sautner, and Starks (2020) has given me particularly important inspiration. Their research shows that although institutional investors regard climate risks as factors with substantial financial implications, they also emphasize the inherent uncertainty of such risks and the challenges faced in quantitative measurement. In our model construction work, I also observed the same mechanism. The real difficulty is not just listing all kinds of risks, but how to turn this uncertainty into a premise that can be accepted by lenders and investors, and then verifying whether the investment plan is still valid through sensitivity analysis. In other words, the “price ceiling” on the income side makes originally obscure uncertainties more explicit, and the model forces us to face and identify which key assumptions really constitute the “load-bearing pillars” that support the bankability of the project.

Looking forward, I would like to know how these lessons can be popularized and applied in different contexts. As a student from China who focuses on energy transformation and infrastructure financing, I find that there are similar contradictions in many emerging markets. Policy goals, people’s livelihoods, and capital market needs are not always automatically coordinated. Building a phased model made me understand that we should start from a clear scope, face uncertainty frankly, and let the analytical results reveal where preferential or catalytic capital is really needed. I hope to apply this pragmatic attitude to future work in China and other countries because our goal is not only to finance projects but also to design financing structures that can withstand practical tests.

References

[1] Huynh, N., & Le, Q. N. (2025). From chain to capital: Supply chain risks and working capital management. Economics Letters, 247, 112100.

[2] Krueger, P., Sautner, Z., & Starks, L. T. (2020). The importance of climate risks for institutional investors. The Review of financial studies, 33(3), 1067-1111.