EGUsphere - Schumpeterian disaggregation and integrated assessment: An endogenous, stock-flow consistent economy in disequilibri

EGUsphere - Schumpeterian disaggregation and integrated assessment: An endogenous, stock-flow consistent economy in disequilibrium for FRIDA v2.1
Preprints
Abstract
Discussion
Metrics
Preprints
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Preprints
Abstract
Discussion
Metrics
03 Feb 2026
03 Feb 2026
Schumpeterian disaggregation and integrated assessment: An endogenous, stock-flow consistent economy in disequilibrium for FRIDA v2.1
Martin Breda Grimeland
Benjamin Blanz
William Schoenberg
and
Beniamino Callegari
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.
How to cite.
Grimeland, M. B., Blanz, B., Schoenberg, W., and Callegari, B.: Schumpeterian disaggregation and integrated assessment: An endogenous, stock-flow consistent economy in disequilibrium for FRIDA v2.1, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-6342, 2026.
Received: 18 Dec 2025
Discussion started: 03 Feb 2026
Publisher's note
: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
Download & links
Preprint (PDF, 1983 KB)
Download & links
Preprint
(1983 KB)
Metadata XML
BibTeX
EndNote
Share
Martin Breda Grimeland
Benjamin Blanz
William Schoenberg
and
Beniamino Callegari
Status
: final response (author comments only)
Comment types
AC
– author |
RC
– referee |
CC
– community |
EC
– editor |
CEC
– chief editor

: Report abuse
Login for authors/topic editors
Subscribe to comment alert
RC1
'Comment on egusphere-2025-6342'
, Anonymous Referee #1, 18 Mar 2026
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.
Figure 5 shows that the model fails to reproduce the 2009 and 2015 downturns, despite these being among the most significant macroeconomic shocks of the past decades.
The unemployment simulation moves in the opposite direction of observed unemployment in several periods.
The simulated labor share lies outside the historical range for 1980-2000.
Figure 6 shows no meaningful cyclical behavior, only trend‑like projections with wide confidence intervals.
Figure 7 shows cycles only in the sense that the confidence interval is extremely wide; the median itself exhibits a downward drift.
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:
The model does not reproduce the 2009 or 2015 downturns.
The unemployment dynamics are counterfactual.
The labor share simulation is systematically misaligned with historical data.
No cycle‑focused diagnostics (e.g., year‑on‑year changes, HP‑filtered cycles, turning‑point analysis) are provided.
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:
Business cycles are extremely difficult to predict, even 1-3 years ahead.
Models that match long‑run trends often fail to match short‑run dynamics.
Predicting cycles over 50-100 years is not considered feasible in the macroeconomics literature.
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:
Reassess the claim of capturing cycles, given the DSGE literature and the model’s performance.
Provide rigorous cycle‑focused validation (year‑on‑year changes, filtered cycles, turning‑point analysis).
Discuss why the model misses major downturns and what this implies for long‑run projections.
Clarify the intended use case: is the model meant to capture cycles, or long‑run structural trends?
Consider reframing the contribution as a conceptual integration rather than an empirically validated cycle‑generating macro module.
Citation
: https://doi.org/
10.5194/egusphere-2025-6342-RC1
RC2
'Reply on RC1'
, Anonymous Referee #2, 19 Mar 2026
Publisher’s note: the content of this comment was removed on 4 April 2026 since the comment was posted by mistake.
Citation
: https://doi.org/
10.5194/egusphere-2025-6342-RC2
RC3
'Comment on egusphere-2025-6342'
, Anonymous Referee #3, 21 Apr 2026
This is an impressive SD work worth celebrating.  A strong incrementation for similar integrated models. My main comments are about the presentation and positioning of the model so it can be of use for policymakers, industry and academia.
1. Please elaborate in the Introduction or appendix clearly contrasting this economy module with previous versions and other climate–finance/ SFC macro‑climate models. Ideally, a small table is desired that lists key dimensions (finance, business cycles, innovation treatment, damage representation, uncertainty) and shows what is new or improved in FRIDA v2.1 relative to those models
2. Section 2 mixes motivations, theory, and trade‑offs  - I would focus a section per each.
3. In my opinion, the model is hard to make sense for a policy maker as it is now. It scares with complexity, and does not involve the user into the experiments. Also model description is quite static (looks like a validation - that you did the job good), at the same time misses holding the hand of the reader - so you can navigable the readership towards detailed reading, not skimming.
4. You can possibly add a more detailed “model roadmap” at the start of Sec. 3: a one‑paragraph overview of each submodule (Circular Flow, Finance, Innovation, Government, Employment, GDP/Inflation) and a one‑sentence statement of its main role in the overall feedback structure
5. Please consider explicitly reference the main equations/variables from simplified stock‑and‑flow diagrams (Figures 2–5) in the text (e.g. “as shown in Fig. 3, the risk premium depends on X)
6. Elaboration of results is the weakest point of the paper. Given the complexity of the model, please consider adding the key schematic that highlights just the key feedback loops (e.g., climate → productivity → employment → transfers → debt → risk premium) - that would greatly help readers who would like to use findings from the study.
7. Please be clear on the shortcomings and elaborate limitations with more detail. Its currently unclear for me which aspects of the historical series are deliberately not reproduced apart from mentioning 2009, 2020 pandemic. Missing what range of crises this is acceptable for the model’s intended use as an exploratory tool? Say we have pandemic every 5 years from now  - what time range of the model would still be feasible for use?Moreover, I would be surprised if model covered all business cycles related to innovation the model reproduces. Say the AI that is embedded at the large scale in the society? scarcity of rare earth?  Potential wars and conflicts?
8. The current contribution is vague for a policy maker to take an action. Suggest adding one illustrative figure showing the distribution (histogram) of a key outcome (e.g. real GDP in 2100) to make the probabilistic reading more concrete. Make sure the that is improved so readers have an interactive tool to play with.
9. I would argue there is a need to explain the story by binding variables to macro outcomes and the need for interventions, e.g.: higher STA → higher failure rates → investment slowdown → higher unemployment → more transfers → higher debt → higher risk premia → further slowdown.
10. Consider adding a short comparison of results using the best‑fit parameter set vs the median of the ensemble, to illustrate the role of uncertainty and what can be done to improve the outcome
11.Ensure terms such as “safe loans”, “exploratory loans”, “performing/nonperforming loans”, and “risk premium” are defined once and used consistently.  Same with other terms that have synonyms in the paper.
12. For Figures 6–8 please consider explicitly stating in each caption what the lines and shaded areas represent (median, 67%, 95%),
Good luck with the revision, I hope my comments will make the paper stronger
Citation
: https://doi.org/
10.5194/egusphere-2025-6342-RC3
Share
Martin Breda Grimeland
Benjamin Blanz
William Schoenberg
and
Beniamino Callegari
Martin Breda Grimeland
Benjamin Blanz
William Schoenberg
and
Beniamino Callegari
Viewed
Total article views: 318
(including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
184
106
28
318
16
35
HTML: 184
PDF: 106
XML: 28
Total: 318
BibTeX: 16
EndNote: 35
Views and downloads
(calculated since 03 Feb 2026)
Cumulative views and downloads
(calculated since 03 Feb 2026)
Viewed (geographical distribution)
Total article views: 313
(including HTML, PDF, and XML)
Thereof 313 with geography defined
and 0 with unknown origin.
Country
Views
Total:
HTML:
PDF:
XML:
Latest update: 23 Apr 2026
Martin Breda Grimeland
CORRESPONDING AUTHOR
martin.grimeland@kristiania.no
School of Economics, Innovation, and Technology, Kristiania University of Applied Sciences, Oslo, 0107, Norway
Benjamin Blanz
Research Unit Sustainability and Climate Risk, University of Hamburg, Hamburg, 20144, Germany
William Schoenberg
System Dynamics Group, University of Bergen, P.O. Box 7802, 5020, Bergen, 5020, Norway
isee systems inc., 24 Hanover St. Suite 8A, Lebanon, New Hampshire, 03766, USA
Beniamino Callegari
School of Economics, Innovation, and Technology, Kristiania University of Applied Sciences, Oslo, 0107, Norway
Preprint
(1983 KB)
Metadata XML
BibTeX
EndNote
Short summary
This study develops a novel global economic model to better capture how climate change interacts with finance, innovation, employment, and public budgets. Instead of treating climate damage as a simple output loss, the model traces how rising temperatures affect investment risk, productivity, unemployment, and government spending. Large simulation ensembles show that without stronger climate action, growth slows, financial fragility rises, and welfare and debt pressures increase.
This study develops a novel global economic model to better capture how climate change interacts...
Share