It’s great to see an economic perspective brought into the circular bioeconomy discussion. That said, I do wonder how practical the social cost-benefit framework you propose really is when it comes to valuing non-market outcomes like biodiversity or long-term soil regeneration. These are notoriously hard to quantify, especially across different geographies and farming systems. How do you see this framework holding up in regions where data is sparse or uncertainty is high?
Also, while you touch on equity and propose compensation mechanisms for those who might be disadvantaged by the transition, it feels like these concerns come in a bit late in the process. Shouldn’t equity be more central to the policy design itself rather than something we patch up afterward?
And finally, the framework seems to assume that producers and consumers will respond to incentives in predictable, rational ways, but given the behavioral barriers and institutional lock-ins you describe earlier (like risk aversion, entrenched habits, or policy inertia), can we really expect economic tools alone to drive this transition? I’m curious how your model might evolve if it took more cues from behavioral economics or complex systems thinking.
To address the thoughtful points raised by the previous commenter, I would like to offer a few reflections that may help clarify the authors’ intent and scope:
1. The commenter rightly points out the profound challenge of quantifying values for biodiversity and soil health, especially in data-sparse regions. It’s important to view the proposed framework not as a prescriptive calculator demanding perfect data, but as a conceptual guide for prioritization and policy design. Even with high uncertainty, the framework forces a structured, transparent conversation about trade-offs. It argues that we must attempt to assign values, using best-available methods like benefit transfer, deliberative valuation, or avoided cost estimates, because ignoring these costs entirely (effectively valuing them at zero) leads to the sub-optimal linear outcomes we have today. The framework’s greatest strength may be in highlighting the direction of change needed, even if the precise monetary value is debated.
2. This is a critical point; While the paper addresses equity explicitly in the “Pathways” section, I read the framework itself as being fundamentally about maximizing social welfare, which is inherently distributional. The social marginal benefit (SB) curve can and should be constructed to reflect societal preferences for equity. The authors’ call for “interdisciplinary collaborations” is key here; integrating methods from political economy and ethics (e.g., weighting benefits to vulnerable groups) into the valuation process would make equity a first-order input into determining the “socially optimal” point (C*), rather than merely an afterthought. This is a vital area for the authors and others to elaborate on in future work.
3.The commenter’s point about behavioral economics is well-taken. The authors expertly catalogue these barriers (risk aversion, lock-ins, etc.) in the section “Why do linear systems persist?”. The economic framework presented in Figure 2 is a normative model, it describes an optimal outcome if incentives are aligned. The authors acknowledge that achieving this requires more than just price signals; it necessitates the suite of “carrots and sticks” they outline, including institutional change, education, and verification systems. These are the mechanisms designed to overcome the very behavioral and institutional hurdles described. Incorporating insights from behavioral economics, such as how to design default options, leverage social norms, or structure choice architecture within the proposed policies, would be a powerful enhancement to this already robust framework.