| 03 Feb 2026
Schumpeterian disaggregation and integrated assessment: An endogenous, stock-flow consistent economy in disequilibrium for FRIDA v2.1
Abstract. Integrated assessments of climate change require models capable of capturing the coupled dynamics of natural and socioeconomic systems. This paper presents the economy module of FRIDA v2.1, a Schumpeterian, disequilibrium framework of endogenous growth designed to address several limitations of contemporary integrated assessment models (IAMs). The module incorporates monetary and financial dynamics, innovation-driven productivity, and endogenous business cycles, allowing explicit representation of how climate impacts propagate through various institutional sectors and economic processes. Its process-based structure replaces aggregated damage functions with disaggregated, empirically grounded mechanisms, improving the traceability of assumptions and enabling the study of climate-finance interactions—including risks of disorderly transitions—absent from mainstream IAMs. Calibration against historical data demonstrates the model’s ability to reproduce key macroeconomic developments. A 100,000-member ensemble simulation communicates the uncertainty in projections through 2150 while revealing endogenous constraints on economic activity. We show that without further action to combat climate change, expected climate impacts not only affect economic production, primarily through reduced investment growth and financial fragility, but also government budgets which come under stress owing to the increasing burdens of unemployment and demographic change. By providing a transparent, modifiable platform for simulating monetary, financial, and innovation dynamics under climate constraints, FRIDA v2.1 expands the analytical scope of IAMs and supports richer exploration of transition pathways.
Referee Report: Schumpeterian disaggregation and integrated assessment: An endogenous, stock–flow consistent economy in disequilibrium for FRIDA v2.1
Summary
The manuscript presents an ambitious attempt to embed a Schumpeterian, stock–flow consistent, disequilibrium macro‑economy within an integrated assessment modeling (IAM) framework. The conceptual motivation is strong: IAMs typically lack explicit financial dynamics, innovation processes, and macro‑economic instability. The authors aim to fill this gap by developing a macro module capable of generating endogenous cycles, downturns, and financial stress.
However, the manuscript’s central empirical claim, that the model reproduces major historical downturns and macroeconomic cycles, is not supported by the evidence presented. Figures 5-7, which are intended to demonstrate the model’s empirical validity, instead reveal substantial mismatches with observed data. These discrepancies raise fundamental questions about the model’s suitability for long‑run IAM applications, especially given the extensive literature showing the inherent difficulty of predicting business cycles even a few years ahead.
1. Scientific Significance
The conceptual ambition is high, and the integration of Schumpeterian innovation and stock-flow consistent (SFC) accounting into an IAM is potentially valuable. However, the claimed contribution (capturing macroeconomic cycles and major downturns) is not demonstrated.
Given these issues, the manuscript does not yet demonstrate a substantial advance in modeling science for IAMs. Many existing IAMs can reproduce long‑run trends; the novelty claimed here is not supported by the results.
2. Scientific Quality
The modeling framework is internally coherent, but the empirical validation is insufficient and, in some cases, contradictory to the claims.
2.1. Mismatch between claims and results
The manuscript repeatedly asserts that the model “reproduces major downturns” and “captures macroeconomic cycles.” However:
2.2. Lack of engagement with DSGE and macro‑forecasting literature
There is a large body of work such as DSGE, VAR, and macro‑forecasting studies, demonstrating that:
The manuscript needs to acknowledge these fundamental limitations, and justify why this model should be able to do what DSGE models cannot.
2.3. Implications for IAMs
For IAMs, the question is whether this macro block provides reliable, policy‑relevant dynamics. Given the weak empirical performance, it is unclear whether the model adds value beyond simpler trend‑based representations.
3. Presentation Quality
The manuscript is generally well written, but the presentation of results is not balanced. A more transparent discussion of model limitations would strengthen the manuscript.
4. Improvements
To move toward publication, the authors would need to: