AD-MERGE 2.0: An Integrated Assessment of the Nexus Among Energy Transitions, Climate Impacts, and Adaptation Responses

Amirmoeini, Kamyar; Bahn, Olivier; de Bruin, Kelly; Everett, Kirsten; Kouchaki-Penchah, Hamed; Pineau, Pierre-Olivier

doi:10.5194/egusphere-2025-6408

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https://doi.org/10.5194/egusphere-2025-6408

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27 Jan 2026

| 27 Jan 2026

Status: this preprint is open for discussion and under review for Geoscientific Model Development (GMD).

AD-MERGE 2.0: An Integrated Assessment of the Nexus Among Energy Transitions, Climate Impacts, and Adaptation Responses

Kamyar Amirmoeini, Olivier Bahn, Kelly de Bruin, Kirsten Everett, Hamed Kouchaki-Penchah, and Pierre-Olivier Pineau

Abstract. This paper presents AD-MERGE 2.0, an enhanced integrated assessment model that evaluates reactive ('flow') and proactive ('stock') adaptation strategies along with climate mitigation policies. The updated model extends AD-MERGE 1.0 through seven enhancements: i) including a more recent base year, ii) increased regional details, iii) refined energy system modeling, iv) inclusion of variable renewable energy, v) direct air carbon capture and storage, vi) recalibrated damage and adaptation estimates, and vii) alignment with the latest Shared Socioeconomic Pathway (SSP2, version 3.0). Next, this study assesses five distinct scenarios using the enhanced AD-MERGE 2.0 framework: a Baseline (no mitigation or damage consideration) and two mitigation pathways, a Reference scenario (current policy-driven mitigation and climate damages), and an Announced Pledges scenario (emissions aligned with national commitments). Each of the mitigation scenarios is studied with and without adaptation. Collective advancements incorporated in the model refine analytical precision in scenario analysis, thus facilitating a more extensive examination of regional heterogeneity, energy system dynamics, technological innovation, and economic vulnerabilities associated with climate impacts. The results underscore critical trade-offs and synergies between adaptation and mitigation strategies, focusing on region-specific policy design and integration of clean energy technologies.

Received: 21 Dec 2025 – Discussion started: 27 Jan 2026

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Kamyar Amirmoeini, Olivier Bahn, Kelly de Bruin, Kirsten Everett, Hamed Kouchaki-Penchah, and Pierre-Olivier Pineau

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RC1:
'Comment on egusphere-2025-6408', Page Kyle, 20 Feb 2026 reply

This paper is well suited for this journal. It does an excellent job presenting a large number of model developments while still being readable, and not going too far into details. Below I'm listing some things I noticed in the order encountered in the document, using line numbers from the PDF.

243 - "PHS [pumped hydro storage] dominates global electricity storage when hydro reservoirs are excluded." I don't know what this means.

Figure 7 - please clarify that this refers to anthropogenic emissions only of methane and nitrous oxide.

399 - "In the Baseline scenario, primary energy supply reaches 1221 EJ and final energy use reaches 1050 EJ by 2100...By 2100, fossil fuels will account for 75% of the total primary energy supply, with coal leading at 28%, closely followed by oil at 27%"

Something seems wrong with the calculation of final or primary energy shown in Figure 8, which explains this very high ratio of final to primary energy in 2100. Globally, final energy is about 2/3 of primary energy, with most of the difference lost in electricity generation, with contributions from transmission and distribution of electricity and gas, and oil refinery energy use. This ratio can increase over time with renewable electricity deployment, but that isn't what is driving this ratio so high here. In the 2015 time period it looks like the final energy is equal to the primary energy, and natural gas final energy exceeds primary energy, which can't be the case, especially as this is a major fuel input to the power sector.

Figure 10 - The spread between the scenarios' estimates of climate damages in 2030 seems a bit wide. The baseline is 2% of GDP, and the lowest two are under 1%. This just doesn't seem like it's enough time for the adaptation scenarios to have built the infrastructures necessary to address climate impacts. Can the authors comment on what the timelines are here? Another consideration is that it is 2026 right now, so even though the model base year is 2015, it doesn't make sense to have that level of divergence taking place in years that in the real world are historical. I'm not suggesting re-running or revising the scenarios, but the appropriate caveat should be provided if the scenarios are going to have this level of divergence for a variable that in the real world would take decades of action to achieve. Or perhaps starting the chart in 2040 would address this.

The final question that I have pertains to the use of this model for charting out optimal policies that balance the costs of mitigation against the benefits of avoided damages, taking into account the cost of that adaptation. It seems like this model has all of the necessary pieces to do that sort of analysis, but I don't see any commentary about this, and the scenarios weren't constructed so as to estimate it.

Reply

Citation: https://doi.org/10.5194/egusphere-2025-6408-RC1
- AC1:
  'Reply on RC1', Kamyar Amirmoeini, 20 Mar 2026 reply
  We thank Reviewer #1 for the careful and constructive assessment of our manuscript, for the positive overall evaluation, and for providing comments and suggestions that will improve the manuscript.
  
  Clarification of the statement on pumped hydro storage (PHS).
  
  We agree that the sentence is unclear as currently written. We will revise it to explain more precisely what is meant by PHS dominance in the context of the storage categories represented in the model.
  
  Clarification of methane and nitrous oxide emissions in Figure 7.
  
  We thank the reviewer for noting this. We will clarify in the figure caption and related discussion that these refer to anthropogenic methane and nitrous oxide emissions.
  
  Interpretation of primary and final energy values in Figure 8 and related text.
  
  We appreciate this important comment. We will revisit both the calculation and presentation of primary and final energy, and clarify the underlying accounting conventions used in the model so that the reported values and trends are properly interpreted.
  
  Near-term divergence in climate damages across scenarios in Figure 10.
  
  We agree that this point deserves clearer discussion. We will revise the text to add an explicit caveat on the interpretation of near-term scenario differences in Figure 10, emphasizing that these reflect the model’s stylized treatment of adaptation timing and the role of NA and OA as bounding cases rather than a reconstruction of observed near-term outcomes. We will also review the figure presentation and accompanying discussion to reduce the risk of misinterpretation.
  
  Potential use of the model for jointly optimal mitigation–adaptation analysis.
  
  We thank the reviewer for raising this point. We agree that the model framework is well-suited for such analysis, and we will add a clearer discussion of this capability and its relevance, while also noting that such an application is beyond the scope of the present scenario design.
  
  We thank the reviewer again for these valuable comments. They will be carefully taken into account in the final response and revised manuscript.
  
  Reply
  
  Citation: https://doi.org/10.5194/egusphere-2025-6408-AC1
RC2:
'Comment on egusphere-2025-6408', Anonymous Referee #2, 07 Mar 2026 reply
Summary
This paper presents AD-MERGE 2.0, a substantially updated integrated assessment model extending the AD-MERGE 1.0 with seven enhancements: updated base year (2015), expanded regional disaggregation (9 to 15 regions), explicit VRE dynamics, hydrogen pathways, DACCS, recalibrated damage and adaptation functions, and alignment with SSP2 version 3.0. The model is applied across five scenarios ranging from a no-policy Baseline to an Announced Pledges scenario with optimal adaptation (APS-OA). The paper is well structured and represents a genuine technical contribution — the simultaneous treatment of adaptation and mitigation within a welfare-optimizing framework with an endogenous climate module is relatively rare in the IAM literature. I am not an energy system modeller and will not comment on the VRE, hydrogen, or DACCS components. My comments focus on the damage and adaptation framework, which the paper presents as a central contribution.
Major Comments
The adaptation and damage framework: a prior justification is required

Adaptation is presented as a key contribution of this paper, and the authors draw substantive policy conclusions from adaptation results, including claims about 'region-specific policy design' (abstract, l. 11) and 'optimal policy approaches tailored to the unique needs and capabilities of each region' (l. 566-567). However, the paper does not address a prior question: why does modelling adaptation as an aggregate welfare-optimizing choice variable over regions like 'Africa' or 'Other Asia' produce scientifically credible or policy-relevant outputs?
A central and well-established insight in adaptation science is that adaptation is inherently local, context-specific, and institutionally embedded (Smit & Wandel, 2006; Adger et al., 2007; IPCC AR6 WG2). There is no universal optimal adaptation level derivable from aggregate economic parameters. The UNEP Adaptation Gap Report (2025) documents a ‘widening gap’ between adaptation needs and realized action in many developing countries, which are precisely the regions where the model predicts the largest benefits (Africa, Other Asia). The calibration procedure (Appendix D) implicitly assumes that adaptation costs and effectiveness can be meaningfully expressed as a fraction of GDP at the regional scale, and that 'optimal adaptation' is achievable given sufficient investment. This follows the approach of earlier IAM-based adaptation models in the literature (de Bruin et al., 2009; Agrawala et al., 2011), and as such represents a reasonable starting point. However, to produce genuinely meaningful policy implications, the modeling community needs to start moving beyond this type of representation toward frameworks that are connected with empirical findings on how adaptation actually happens — taking into account characteristics such as adaptive capacity, institutional context, path dependency, and local governance conditions. I am aware this is a difficult challenge, but there are promising avenues in this direction, including adaptation pathways approaches new collection of papers coming in (https://www.nature.com/collections/gfebjgbjid) and efforts to couple IAMs with more bottom-up behaviourally rich simulation models (Filatova et al., 2025)
To strengthen the contribution of this paper, the authors should either: (a) demonstrate, with explicit reference to the empirical literature, that the structural assumptions are reasonable approximations and constrain policy conclusions accordingly; or (b) reframe contribution as modelling exercise looking at theoretical upper bounds under idealized conditions, not as achievable policy targets. The macro-level outputs the model can legitimately support — aggregate damage cost comparisons across scenarios and the identification of highly vulnerable regions — are genuinely valuable. But the claims to region-specific policy design cannot be sustained. This concern is actually acknowledged at l. 563-564 ('these results are obtained on the assumption of optimal adaptation, an idealized scenario in practice'), but a single end-of-paper caveat is insufficient given the policy framing used throughout Sections 4.3 and 5. This caveat needs to be made more explicit in the framing of the paper and its contribution.
A related issue: Figure D1b — the adaptation cost curve — is explicitly described in its own caption as 'conceptual' with 'shapes of the curves theoretical and not reflecting specific empirical estimates.' If the figure used to illustrate the adaptation calibration logic has no empirical grounding, the paper's claim to have empirically recalibrated adaptation is weakened. The paper needs to be transparent about what is empirically derived versus theoretically assumed throughout the adaptation framework. Where empirical adaptation investment data exist — for instance for European countries (Cortes-Arbuez et al., 2025) — these should be used to benchmark whether modeled adaptation expenditure levels are plausible.
Lack of model documentation

A broader concern across the paper is that key model components are insufficiently documented for a model description paper. The main text frequently defers to earlier publications (Bahn & Kypreos, 2003; Bahn et al., 2019) for core structural elements, and the appendices, while useful, do not fully compensate. The paper is clearly written with appropriate conciseness, and I am not asking for an exhaustive technical manual in the main text. However, at minimum, the appendices — or a linked online documentation resource — should provide enough information for a reader to understand the main modeling without having to track down earlier papers. The adaptation and damage framework is a case in point. Equation D2 governs the entire adaptation structure via parameters σ, β1,r, β2,r, and β3,r (l. 679–680), yet none of these values are reported anywhere. The 'maximum adaptation level' mentioned at l. 301 is stated but neither quantified nor explained. A reader cannot assess the plausibility of the adaptation results, connect them to current literature, or understand their sensitivity without access to these parameters. The same issue applies more broadly: the welfare function parameterization and the Negishi weights are not reported, and the climate module equations are not provided. The authors should either expand the appendices or provide a clearly signposted link to online technical documentation that fills these gaps. Minor Comments.
Table 1: The selection of IAMs for comparison is not justified. Why these specific models? Some (e.g. GCAM, REMIND) have been substantially updated and the version used for comparison is not stated. Please add a brief justification of the selection criteria.

Appendix D / Table D1: The paper estimates its own regional damage functions rather than using existing sector-specific, country-level approaches such as COACCH (Van Der Wijst et al,. 2023) . Given that Table D1 draws on many of the same underlying data sources that such frameworks use, why was an existing damage assessment not used or more systematically benchmarked against? This choice should be explicitly justified.

Scenarios / Figure 10: APS-NA does not appear in the energy figures (Figs. 7-9), which is justified at l. 367-369 because APS-NA and APS-OA share identical energy trajectories. However, showing APS-NA explicitly alongside APS-OA in Figure 10 and the adaptation decomposition figures (10b-10c) would allow clearer isolation of the marginal effect of optimal adaptation under the more ambitious mitigation pathway. It is not evident why this comparison is currently absent.

Section 3.1: The model is calibrated to 2015 and validated against observed 2015 and 2020 Baseline emissions (l. 326-330). It would strengthen the paper to also assess whether other outputs, such as GDP, align with historical data.

References Cited in This Review
Adger, W.N. et al. (2007). Assessment of adaptation practices, options, constraints and capacity. In: Climate Change 2007: Impacts, Adaptation and Vulnerability. IPCC AR4 WG2.
Cortés Arbués, Ignasi, et al. "Private investments in climate change adaptation are increasing in Europe, although sectoral differences remain." Communications Earth & Environment 6.1 (2025): 470. IPCC (2022). Climate Change 2022: Impacts, Adaptation and Vulnerability. WG2 Sixth Assessment Report. Cambridge University Press.
Patt, A.G. et al. (2010). Adaptation in integrated assessment modeling: where do we stand? Climatic Change, 99, 383-402.
Schipper, E.L.F. (2020). Maladaptation: when adaptation to climate change goes very wrong. One Earth, 3(4), 409-414.
Smit, B. & Wandel, J. (2006). Adaptation, adaptive capacity and vulnerability. Global Environmental Change, 16(3), 282-292.
Van Der Wijst, Kaj-Ivar, et al. "New damage curves and multimodel analysis suggest lower optimal temperature." Nature Climate Change 13.5 (2023): 434-441.
Filatova, T., Akkerman, J., Bosello, F., Chatzivasileiadis, T., Cortés Arbués, I., Ghorbani, A., ... & Wei, T. (2025). The power of bridging decision scales: Model coupling for advanced climate policy analysis. Proceedings of the National Academy of Sciences, 122(38), e2411592122.

Reply
Citation: https://doi.org/10.5194/egusphere-2025-6408-RC2
- AC2:
  'Reply on RC2', Kamyar Amirmoeini, 20 Mar 2026 reply
  We thank Reviewer #2 for the detailed, and constructive comments. The comments are very helpful and will improve both the framing and transparency of the paper.
  
  Major comments
  
  Framing and interpretation of the adaptation and damage framework.
  
  We agree that the manuscript should more clearly justify the use of aggregate adaptation in a macro-regional IAM framework and more carefully frame the interpretation of the results. In the revision, we will strengthen this discussion and make clearer throughout the paper that the adaptation module provides a macro-level, empirically informed but idealized representation that is useful for comparative analysis of aggregate damages, adaptation expenditures, residual damages, and mitigation–adaptation interactions. We will also revise the policy framing to avoid overstating the extent to which these results can be interpreted as direct region-specific policy prescriptions. We will also discuss more clearly how the modeled adaptation expenditure levels relate to the available empirical literature and how they should be interpreted within the scope and scale of the model. Furthermore, we will also clarify the role of Figure D1b in illustrating the calibration logic.
  
  Model documentation and transparency.
  
  We also agree that the documentation of key model components should be strengthened for a model description paper. In the revision, we will expand the appendices and/or supplementary material to provide clearer information on the main structure and parameterization of the model so that readers can better assess the assumptions and interpretation of the results without relying too heavily on earlier publications.
  
  Minor comments
  
  Table 1 and IAM comparison.
  
  We will clarify the criteria used to select the IAMs included in Table 1 and better specify the basis for comparison.
  
  Appendix D / Table D1 and the calibration approach.
  
  We will clarify why we adopted this calibration approach, how it relates to the underlying literature and data sources, and how it should be interpreted relative to other existing frameworks.
  
  Presentation of APS-NA in Figure 10 and related figures.
  
  We thank the reviewer for this comment. APS-NA and APS-OA are both included in Figure 10, but the decomposition panels are structured to show how gross damages are divided into residual damages, avoided damages, and adaptation expenditures rather than to present APS-NA and APS-OA as a standalone side-by-side comparison. As a result, APS-NA is represented implicitly through the decomposition, which may not be immediately apparent to the reader. We agree that this relationship should be clarified more explicitly. We will revise the figure caption and the related discussion to explain how APS-NA is inferred in the decomposition and to make the marginal effect of adaptation under the ambitious mitigation pathway more transparent.
  
  Historical validation beyond emissions.
  
  We will add further discussion on the historical consistency of other key outputs, in addition to the existing emissions validation.
  
  We will address all of these points in more detail in the final point-by-point response and the revised manuscript.
  
  Reply
  
  Citation: https://doi.org/10.5194/egusphere-2025-6408-AC2
RC3:
'Comment on egusphere-2025-6408', Anonymous Referee #3, 16 Mar 2026 reply

This manuscript presents AD-MERGE 2.0, an updated version of the AD-MERGE integrated assessment model designed to analyse the interaction between climate mitigation and adaptation. The model introduces several important improvements relative to AD-MERGE 1.0, including an updated base year, increased regional disaggregation, improved energy system representation, incorporation of variable renewable energy dynamics, inclusion of DACCS, recalibrated damage and adaptation functions, and alignment with SSP2 (version 3.0).
Overall, the paper makes a valuable contribution to the IAM literature. In particular, the explicit representation of both mitigation and adaptation—distinguishing between reactive (“flow”) and proactive (“stock”) adaptation—is an important advancement. The expanded regional resolution (15 regions) also enables a more detailed analysis of regional heterogeneity in climate risks and adaptive capacity.
The results demonstrate that while mitigation reduces global impacts, adaptation substantially lowers residual damages. The regional analysis is particularly valuable, showing significant benefits across both vulnerable regions (e.g., Africa, Other Asia, Central Latin America) and high-capacity regions (e.g., USA, China, Western Europe), while also revealing persistent damages in regions such as Africa and India. These findings underscore the importance of jointly considering mitigation and adaptation and highlight enduring regional inequalities in climate risk.
The manuscript is well structured and clearly written, and the modelling developments are relevant for both IAM research and climate policy analysis. I have one main question that would benefit from clarification.
Comment: Representation of Land Use and BECCS
The manuscript highlights the role of negative emission options such as Bioenergy with Carbon Capture and Storage (BECCS), particularly under mitigation scenarios where BECCS appears to play an important role. Given the strong link between BECCS deployment and land use dynamics, it would be helpful if the authors could clarify how land use change is represented in the AD-MERGE 2.0 framework.
It would be helpful to clarify whether the model represents land availability and competition (e.g., food, bioenergy, forests), how BECCS feedstock supply is determined, whether afforestation and bioenergy expansion share a land-use framework, and whether land constraints limit BECCS deployment. Clarification would help interpret the feasibility of the reported BECCS scenarios.

Reply

Citation: https://doi.org/10.5194/egusphere-2025-6408-RC3
- AC3: 'Reply on RC3', Kamyar Amirmoeini, 20 Mar 2026 reply
  
  We thank Reviewer #3 for the encouraging and constructive comments on our manuscript and appreciate the valuable comments and suggestions.
  
  Representation of land use and BECCS.
  
  We agree that the manuscript would benefit from a clearer explanation of how land use is treated in the current version of AD-MERGE 2.0 and how this affects the interpretation of BECCS deployment. In AD-MERGE 2.0, BECCS is represented within the enhanced energy system module as one of the carbon removal options, but the model does not include an explicit endogenous land-use allocation module. This means that competition among land uses, such as food production, bioenergy crops, and forests, is not represented explicitly, nor are afforestation and bioenergy expansion modeled within a shared land-use framework. Instead, BECCS deployment is based on broad regional techno-economic potentials and associated cost assumptions within the aggregated structure of the model.
  
  In the revised manuscript, we will clarify this limitation more explicitly and better frame the interpretation of BECCS feasibility in light of the model’s regional and structural scope.
  
  Reply
  
  Citation: https://doi.org/10.5194/egusphere-2025-6408-AC3

Kamyar Amirmoeini, Olivier Bahn, Kelly de Bruin, Kirsten Everett, Hamed Kouchaki-Penchah, and Pierre-Olivier Pineau

Data sets

AD-MERGE 2.0 Integrated Assessment Model (Data Sets) Kamyar Amirmoeini, Olivier Bahn, Kelly de Bruin, Kirsten Everett, Hamed Kouchaki-Penchah, and Pierre-Olivier Pineau https://doi.org/10.5281/zenodo.17989841

Model code and software

AD-MERGE 2.0 Integrated Assessment Model (Model Code) Kamyar Amirmoeini, Olivier Bahn, Kelly de Bruin, Kirsten Everett, Hamed Kouchaki-Penchah, and Pierre-Olivier Pineau https://doi.org/10.5281/zenodo.17989841

Kamyar Amirmoeini, Olivier Bahn, Kelly de Bruin, Kirsten Everett, Hamed Kouchaki-Penchah, and Pierre-Olivier Pineau

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Short summary

This paper presents AD-MERGE 2.0, an integrated assessment model designed to study emissions reductions and adaptation together, and to capture how decisions on one can change the benefits and costs of the other. Building on earlier versions, and by improving how the model represents both climate policy actions and climate damages, this paper provides new evidence on how regions can combine near-term protection from climate change impacts with long-term transitions toward cleaner energy systems.


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