This paper presents a system-dynamics model that couples the climate system with the drivers of anthropogenic emissions such as demographics, affluence, and energy-demand. This is an important modeling advance in my view, in that it adds structural diversity to the suite of models typically used to project emissions pathways (i.e. detailed process-based energy-system models with exogenously-prescribed trajectories of demographics, economic growth and, sometimes, technological change).

Key distinctions in this model that add value in my opinion are:

1. Endogenizing many processes that are either excluded or specified exogenously in most models used to asses future climate pathways. This includes both drivers of energy demand and emissions, as well as feedbacks from the impacts of climate change on those processes (e.g. impacts on heating and cooling demand, mortality, consumption growth).
2. A focus on quantifying parametric uncertainties. Because of the computational complexity of many detailed process-based integrated assessment models, results are rarely presented with comprehensive assessments of parametric uncertainties. The very different structure of this model allows a more comprehensive assessment of parametric uncertainties, which is key to placing model projections in an appropriate context.

While adding significant value, it is important to note that these capabilities come with trade-offs. The manuscript notes the lack of regional differentiation which is an important limitation. I believe there are others though that should be more explicitly delt with in the paper, notably 1) the lack of any forward-looking belief formation or optimization, including for variables with a significant forward-looking component such as investments or fertility choices; and 2) the ability, at least in current form, to only model baseline behavior – there is no endogenous representation of collective action or policy formation and no clear means of representing policy decisions given the endogeneity of key variables. Given these limitations, particularly the latter, I would appreciate if the manuscript could speak more clearly to intended either policy or research use-cases of the model.

There are a number of additional comments I have on the current manuscript:

• Firstly, I note that evaluating the model as presented is challenging because only high-level information on model components is presented. Details of functional forms, parameters, and calibration for individual model components are referenced to other papers that are, in most case, still unpublished. Given these components may well change as part of that publication process, and because, until publication, these important details are unavailable for review, it seems it might be appropriate to wait on publication of the full model until those processes are finalized. I believe this is an editorial decision.

• The current manuscript provides a high-level overview of model structure and the results of a full sensitivity analysis to parametric uncertainty. I believe the reader would benefit from additional details to better understand model behavior and dynamics and the calibration process. For instance, some elements that would be useful:
  • A supplementary table listing details on the 158-time series parameters used for calibration (e.g. reference, start and end date, variable, component)
  • A better sense of key pathways, feedbacks or parameters driving model results. I recognize there are a complex set of relationships in the model, but it does not seem unreasonable to highlight essential feedbacks for the reader operating either within or between model components.
  • Similarly, a better sense of model sensitivity to parameters. This seems relatively easy to do given existing results – what parameters (or interactions between parameters) are drive variance in key output variables?

• I find the interpretation of the set of 100,000 ensemble members quite confusing and believe this needs to be more clearly explicated. Page 25 implies that, given the lack of likelihood weighting in the sampling scheme, this set should not be interpreted probabilistically. But Figure 10 clearly suggests a probabilistic interpretation (i.e. describing 67% and 95% confidence bounds). If sampling is not likelihood weighted then can we really interpret these as confidence intervals?
  • One suggestion is that a comparison with probabilistic distributions of outcome variables (e.g. temperature, population, per-capita consumption) developed for Social Cost of Carbon purposes in Rennert et al. (2022) would be interesting. Unlike the SSPs, these are explicitly probabilistic ensembles, and so in that sense more comparable to the FRIDA output presented in Figure 10.

• I believe the abstract and introduction could do a better job of explicating the current typology of different models and distinguishing the contribution of the new FRIDA model. For instance:
  • The paper repeatedly references “IAMs” but that is a very broad class of models, and it is not always clear what types of IAMs (or whether its all of them) that are being referred to. I believe the distinction in Weyant (2017) between detailed-process IAMs (e.g. GCAM, IGSM) and cost-benefit IAMs (e.g. DICE, FUND, GIVE) is relevant here. My sense is that the paper is mostly referring to the former, in which case it is helpful to be specific
  • The paper and introduction draw a distinction between ESMs capturing the earth system and IAMs capturing human elements. But many IAMs include a representation of the climate system (e.g. IGSM uses the MIT Earth System Model, GCAM is often connected to HECTOR). Typically that representation is an intermediate complexity model similar to the FAIR model used in FRIDA. All cost-benefit IAMs include a model of the climate system in order to calculate the benefits of emissions reduction. I feel like this discussion needs more nuance and specificity.

Bibliography

Rennert, K., Errickson, F., Prest, B. C., Rennels, L., Newell, R. G., Pizer, W., Kingdon, C., Wingenroth, J., Cooke, R., Parthum, B., Smith, D., Cromar, K., Diaz, D., Moore, F. C., Müller, U. K., Plevin, R. J., Raftery, A. E., Ševčíková, H., Sheets, H., … Anthoff, D. (2022). Comprehensive evidence implies a higher social cost of CO2. Nature, 610(7933), 687–692. https://doi.org/10.1038/s41586-022-05224-9

Weyant, J. (2017). Some contributions of integrated assessment models of global climate change. Review of Environmental Economics and Policy, 11(1), 115–137. https://doi.org/10.1093/reep/rew018

General Comments:

The model presented in this paper is a feat of scope that demonstrates the feasibility of integrating endogenous feedback between climate and human processes. Comparing results of simulation ensembles to various SSPs highlights key differences and underscores the importance of including coupled climate-human feedback models in future IPCC assessments. 

The FRIDA model is described through an interconnected set of domain-specific modules that represent different aspects of the system: climate, demographics, economy, land use and agriculture, energy, resources, and behavioral change. These modules are not strictly necessary from a computational perspective, but they are helpful for communicating the scope and feedback complexity of the model.

Specific Comments:

In the introduction, lines 82-83, please elaborate on (define or explain) the named taxons.

In the last paragraph of the introduction, the emphasis on approach (lines 97 and 99) implies a methodological orientation, but it seems that the major contribution is the novelty of the integrative model structure. This emphasis on approach could be reframed or clarified (invoking “approach” in a broader sense, not specifically “methods”) to temper expectations about what follows (such as that section 2 will be a “methods” section).

My advice on reframing “approach” notwithstanding, I’d be interested in knowing more about the method by which the model structures were generated. For instance, in reference to line 115, it would be helpful to know more about how the “essential” loops were identified.

In section 2, the term “relative simplicity” (line 117 and caption for Figure 1) could be confusing after emphasizing the richness of FRIDA’s feedback complexity. It would be helpful to clarify what is meant here, perhaps by referring to the number of variables or using the phrase “relative computational simplicity.”

In section 3, before getting into each of the specific modules, it would be helpful to explain what modules are and why they were used. 

For the benefit of clarity, as the modules are presented, it would be helpful to be consistent about using the terms “module” to describe the modules at the highest level of aggregation and “sub-module” to describe the modules within the highest modules, even if they contain further sub-modules. Some of these instances are noted under Technical Corrections below.

In section 3.2.1, it would be worth pointing out in the context of Figure 4 that the Demographics module does not directly impact the Climate module, but rather that the feedback from population dynamics to the climate is mediated through other modules describing human processes.

In section 3.2.4, please clarify briefly what is meant by the “stepping on toes” effect (line 406).

Technical Corrections:

Line 41: Suggest “processes that include but extend beyond the economic” instead of “processes both economics and not”

Line 54: Suggest “represent known feedback” instead of “represent what is known to be the true feedback”

Line 58: Suggest “has been proposed” rather than “is proposed” to be clear that the sequential approach is not being proposed in this manuscript

Line 67: Suggest “has contributed to a division” rather than “has led to a division”

Line 72: Should be “WG II and WG III” not “WG II or WG III”

Line 105: Suggest “feedback loops” rather than “feedback processes” since “closing the loops” is more understandable than “closing the processes”

Line 119: Should be a semicolon after laptop, not a comma

In Figure 1, the legend for ESM and IAM colors should match the colors used in the figure.

In Figure 2, the arrow from the Demographics module to the Climate module appears to be in error. Such an arrow does not appear in the module-specific Figure 4.

Line 181: In the caption for Figure 3, the first instance of “modules” should probably be “sub-modules.”

Line 190: Both instances of “module” should be “sub-module”, as in “Emissions sub-module” and “Radiative Forcing sub-module”

Line 203: Suggest adding “chemical” before “species”, to read “eight chemical species”

Line 254: should be “impact” not “impacts”, as in “impact the climate system”

Line 258-9: the citation (Conveyor Computation, 2025) is missing from the reference list

Line 279: remove the extraneous word “becomes”

In Figure 5, the orange arrow from the Economy module to the Climate module should be positioned at the edge of the orange shaded box to imply it is coming from the overall module and not from a specific sub-module like GDP.

Line 309: capitalize “Economic” for consistency

Line 315-7: Suggest rephrasing of this sentence, mainly to avoid using the term “forthcoming” to refer to something elsewhere in the same paper: “While the Economy module produces little direct impact on climate, its indirect impacts through the modules described in the following sections that characterize other human processes necessary to represent the meeting of specific (emissions generating) human needs and desires do produce the very large majority of anthropogenic emissions that drive outcomes in the climate system.”

Line 322: Suggest specifying “Economy module” rather than “economy”

Line 325: Suggest adding the words “closes loops” after “system” to read: “economic system closes loops via the supply demand balance”

Line 341: suggest “sub-module” not “module” in the first instance, as in “Land Carbon sub-module”

Line 342: suggest “sub-module” not “module” in the second instance, as in “Food Demand sub-module”

Line 347: suggest “sub-module” not “module”, as in “Land Use sub-module”

Line 353: should be “parts are” not “part is”

Line 354: suggest “sub-module” not “module” in the first instance, as in “Land Carbon sub-module”

Line 356: suggest “sub-module” not “module”, as in “Land Carbon sub-module”

Line 363: suggest “sub-module” not “module” in the second instance, as in “Animal Products sub-module”

Line 367: suggest “sub-module” not “module”

Line 369: suggest “sub-module” not “module”

Line 374: suggest “sub-module” not “module”, as in “Land Carbon sub-module”

Line 416: add “by” before “changes” to read “as well as by changes”

Line 419: suggest adding “chemical” before “species”

Line 437: replace “Concrete module” with “Resources module” or “Resources (concrete) module”

Line 456: remove extraneous word “drive” to read “barriers to collective action”

Line 498: suggest removing “e.g.,” before “ordinary least squares”

Line 499-500: suggest adding “exists” after “solution” to read “an analytical solution exists” and removing it from the end of the sentence

Line 524: remove extraneous word “in” to read “across the global sensitivity analysis”

Line 567: suggest rephrasing with punctuation, to read “actions of government that change, such as energy taxes and subsidies, because”

Line 578: should be “were” not “was”

Line 579: should be “These data were” not “This data was”; and should be “are” not “is”, as in “are displayed”

Line 641: suggest removing the word “true” before “uncertainty”

This study presents a global coupled human and natural systems (CHANS) model built upon a system dynamics approach. In particular, the model incorporates various feedback that is not available in IAM models, and the results demonstrate the key role of this feedback. Also, I agree that SD-based models are important complementary tools to other models, including IAM and Earth system models. The manuscript could contribute to modeling CHANS dynamics and making future dynamic predictions.

My major comment on this work is that it did not track the recent progress on the CHANS theory and modeling efforts well. The review process needs improvement, as many important work is missing in the current literature and discussion. There are classic papers about CHANS (Boyd 2017; Alberti 2011; Liu 2007), and more recent papers highlighting the need and challenges of two-way coupling for modeling CHANS (Motesharrei 2017; Li 2023). In terms of models, there are similar global SD models, such as Felix 2.0 (Ye 2024), ANEMI3(Breach & Simonovic 2021),iSDG (Pedercini 2019), and country/regional models, T21-China (Qu 2020), ANEMI_Yangtze (Jiang 2022), and the Yellow River (Sang 2025). What are the key differences between this work and other global SD models? Although regional models are different from global models in scale, the modules share many similarities. For IAM models, E3SM-GCAM is the latest coupled IAM and ESM models (Di Vittorio 2025). These existing modeling efforts often share similar challenges and difficulties as summarized in (Li 2023).

The model emphasizes its ability to provide endogenous feedback. For a model, it is also critical to have the capability of designing policy influence and external intervention scenarios to answer the “what if” question. A full endogenous model implies that it is not easy to implement scenario design. The potential user case of the model should be introduced. 

The paper emphasizes the inclusion of two-way feedback compared to one-way feedback in IAM. What processes and results are influenced by the newly added feedback loop? Is there a way to quantify the effects? The authors also discussed different results with IAM, but the exact causes of the differences should be elaborated.

Specific comments

L67-68. Check Li 2023

L75-77 Check Motesharrei 2017

L93-100. There are many alternative models of IAM, including SD model and Agent-based models, ESM-IAM (Yang 2015). Each modeling method has its own pros and cons.

L115-116. The lack of regional breakdown also limits the model's applicability to support real-world policy making, which is important for CHANS models.

L385-386: How to determine the share of each energy type

What is the time step of the model?

References:

Alberti, M., Asbjornsen, H., Baker, L. A., Brozovic, N., Drinkwater, L. E., Drzyzga, S. A., et al. (2011). Research on Coupled Human and Natural Systems (CHANS): Approach, Challenges, and Strategies. Bulletin of the Ecological Society of America, 92(2), 218–228. https://doi.org/10.1890/0012-9623-92.2.218

Boyd, D., Hartter, J., Boag, A. E., Jain, M., Stevens, K., Nicholas, K. A., et al. (2017). Top 40 questions in coupled human and natural systems (CHANS) research. Ecology and Society, 22(2).

Breach, P. A., & Simonovic, S. P. (2021). ANEMI3: An updated tool for global change analysis. PLOS ONE, 16(5), e0251489. https://doi.org/10.1371/journal.pone.0251489

Di Vittorio, A. V., Sinha, E., Hao, D., Singh, B., Calvin, K. V., Shippert, T., et al. (2025). E3SM-GCAM: A Synchronously Coupled Human Component in the E3SM Earth System Model Enables Novel Human-Earth Feedback Research. Journal of Advances in Modeling Earth Systems, 17(6), e2024MS004806. https://doi.org/10.1029/2024MS004806

Jiang, H., Simonovic, S. P., & Yu, Z. (2022). ANEMI_Yangtze v1.0: a coupled human – natural systems model for the Yangtze Economic Belt – model description. Geoscientific Model Development, 15, 4503–4528.

Liu, J., Dietz, T., Carpenter, S. R., Alberti, M., Folke, C., Moran, E., et al. (2007). Complexity of Coupled Human and Natural Systems. Science, 317(September), 1513–1517.

Motesharrei, S., Rivas, J., Kalnay, E., Asrar, G. R., Busalacchi, A. J., Cahalan, R. F., et al. (2017). Modeling sustainability: Population, inequality, consumption, and bidirectional coupling of the Earth and human Systems. National Science Review, 3(4), 470–494. https://doi.org/10.1093/nsr/nww081

Sang, S., Li, Y., Zong, S., Yu, L., Wang, S., Liu, Y., et al. (2025). The modeling framework of the coupled human and natural systems in the Yellow River Basin. Geography and Sustainability, 100294. https://doi.org/10.1016/j.geosus.2025.100294

Qu, W., Shi, W., Zhang, J., & Liu, T. (2020). T21 China 2050: A Tool for National Sustainable Development Planning. Geography and Sustainability, 1(1), 33–46. https://doi.org/10.1016/j.geosus.2020.03.004

Li, Y., Sang, S., Mote, S., Rivas, J., & Kalnay, E. (2023). Challenges and opportunities for modeling coupled human and natural systems. National Science Review, 10(7), nwad054. https://doi.org/10.1093/nsr/nwad054

Pedercini, M., Arquitt, S., Collste, D., & Herren, H. (2019). Harvesting synergy from sustainable development goal interactions. Proceedings of the National Academy of Sciences, 116(46), 23021–23028. https://doi.org/10.1073/pnas.1817276116

Ye, Q., Liu, Q., Swamy, D., Gao, L., Moallemi, E. A., Rydzak, F., & Eker, S. (2024). FeliX 2.0: An integrated model of climate, economy, environment, and society interactions. Environmental Modelling & Software, 179, 106121. https://doi.org/10.1016/j.envsoft.2024.106121

Yang, S., Dong, W., Chou, J., Feng, J., Yan, X., Wei, Z., et al. (2015). A brief introduction to BNU-HESM1.0 and its earth surface temperature simulations. Advances in Atmospheric Sciences, 32(12), 1683–1688. https://doi.org/10.1007/s00376-015-5050-6