the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 29 Oct 2024
SuCCESs – a global IAM for exploring the interactions between energy, materials, land-use and climate systems in long-term scenarios (model version 2024-10-23)
Abstract. SuCCESs is a bottom-up Integrated Assessment Model (IAM) that represents energy production and use, materials production, land-use and climate globally. The primary use-case for SuCCESs is to calculate long-term scenarios until 2100 that consider the interactions between these systems, for example the greenhouse gas emissions from energy, materials and land-use and their impact on climate change. The four systems are hard-linked in SuCCESs and scenarios are solved through intertemporal optimization by minimizing discounted system costs to satisfy projected demand and other constraints, e.g. climate targets. This yields a long-term equilibrium solution between the modelled systems. This article introduces the model logic and structure, describes the overall representation of each system, and provides an evaluation by comparing the scenarios produced by SuCCESs with different end-of-century radiative forcing targets to those from other IAMs. Towards this end, and to demonstrate the capability of SuCCESs for large-scale scenario exploration, we also conduct a sensitivity analysis employing Monte Carlo sampling with a 1000-member scenario ensemble for each radiative forcing target. Last, we discuss some practical aspects and different ways of using the model in long-term scenario analyses.
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RC1: 'Comment on gmd-2024-196', Anonymous Referee #1, 26 Nov 2024
The study introduces SuCCESs, a bottom-up Integrated Assessment Model (IAM) that integrates energy, materials, land-use, and climate systems globally to explore long-term scenarios through 2100. By hard-linking these systems, SuCCESs captures interactions such as greenhouse gas emissions and their climate impacts. Scenarios are solved using intertemporal optimization, minimizing system costs while meeting demand and climate constraints, yielding equilibrium solutions. The article details the model structure, evaluates its performance against other IAMs under varying radiative forcing targets, and demonstrates its capacity for large-scale scenario exploration through a Monte Carlo sensitivity analysis with a 1000-member ensemble. Below are specific comments and questions to enhance clarity and address potential uncertainties.
Below are specific comments and questions to enhance clarity and address potential uncertainties:
Major:
- The climate system in SuCCESs endogenously includes only three GHG emissions (CO2, CH4, and N2O). How do exogenous inputs for radiative forcing from other sources influence the outcomes of mitigation scenarios? Could the authors discuss the potential uncertainties introduced by these exogenous factors?
- Given the varying levels of detail in the representation of the energy, land, material, and climate systems, could the authors provide guidance on scenarios or cases where SuCCESs excels (e.g., scenarios benefiting from its lightweight design and detailed system representation)? In which scenarios or cases might the model produce higher uncertainties, and why? Such guidance would be valuable for potential users of the model.
- Lines 373–374: “Even small to moderate variations in input parameters (up to ±20%) can result in a diverse set of scenarios, spanning in some cases a broader range than those observed in the SSP scenario ensemble.” How does this model’s sensitivity to input parameters compare with other IAMs? Is this sensitivity within a reasonable range?
- The calibration, extensions, and exogenous inputs mentioned in the article are based on SSP2, e.g. in Lines 164, 233, 243, and 313. Additionally, the comparison with other IAMs and the sensitivity analysis are also based on SSP2. Does the model already include other SSPs (i.e., SSP1, SSP3, SSP4, and SSP5)? If so, and if it is not too much effort, how does the model perform under these SSPs compared to other IAMs? Providing this information would be helpful for potential users. However, I understand that comparing SuCCESs' performance under other SSPs with other IAMs may require significant effort. Even just clarifying whether the model includes other SSPs in the article would still be very useful.
Minor:
- Line 74, does the assumption imply that all demands are fixed? What are the potential impacts of this assumption on the model results, especially in scenarios where demand might vary dynamically?
- Line 74, how does the model account for supply elasticity? Please clarify how flexible responses in supply are modeled across different systems.
- Figure 2, why are wind and solar energy sources not included in the figure?
- Line 344: “SuCCESs does not include traditional bioenergy use.” How might the exclusion of traditional bioenergy affect future projections of energy use and related emissions? Please discuss the implications.
- Line 334, how is land use constrained in the model? Additional details on the mechanisms or assumptions for land-use constraints would be useful.
- Does the energy and material system provide all three major greenhouse gases (CO2, CH4, and N2O) to the climate system? If so, how are these emissions generated by the energy sector? Additional details on this linkage would be helpful.
- Figures 5, 6, and 7, the shaded areas representing the range of results from other IAMs in the SSP database are not easy to distinguish. Could the authors consider revising the visualization methods to make these areas clearer for readers?
Citation: https://doi.org/10.5194/gmd-2024-196-RC1 -
RC2: 'Comment on gmd-2024-196', Anonymous Referee #2, 02 Jan 2025
The study introduces SuCCESs, a lightweight global Integrated Assessment Model (IAM), providing documentation and test results. Overall, the paper is well-written and well-structured, offering comprehensive technical details of the modeling framework. While there are already several widely used IAMs, I believe there is value in developing new models. However, I encourage the authors to elaborate on the unique aspects of SuCCESs, add more comprehensive results, and discuss the limitations and future directions. Below are my detailed comments:
Motivation:
The introduction effectively summarizes the background of IAMs and categorizes SuCCESs as a bottom-up, process-based model relative to existing literature. While the information is useful, the research or modeling gap justifying the need for this new model remains unclear. Apart from its "lightweight" computational feature, which offers advantages in computational efficiency at the expense of resolution, the rationale for developing SuCCESs is insufficiently discussed. The inclusion of a comparison with SSP scenarios is appreciated; however, reproducing existing results is not necessarily a novel contribution. I recommend the authors emphasize the specific gaps SuCCESs addresses to ensure it makes a meaningful impact.
Furthermore, SSP scenarios are somewhat outdated. The authors should consider incorporating results from the IPCC AR6 scenario database or the recent SSP v3 database (IIASA), which includes updated GDP and population projections and is being evaluated under ScenarioMIP.
Model Features and Design:
SuCCESs is presented as a single-region model, but the associated CLASH model seems to offer more regional granularity.
Could the authors clarify how SuCCESs and CLASH are coupled, given their differing regional resolutions?
CLASH appears to have a detailed representation of agricultural and land-use sectors. Does it include price-responsive food demand and international trade?
While a single-region model offers computational simplicity, it assumes complete global market integration, ignoring regional market differences. Recent studies, such as Zhao et al. (2022; DOI: 10.1016/j.gloenvcha.2021.102413), underscore the importance of international trade in IAMs. Discussing the trade-offs involved in this design choice would be valuable.
Calibration and Baseline:
Around line 163, the paper mentions that future energy and material demands are calibrated to SSP2 until 2100. Does this imply that demand is fully exogenous? Similarly, the assumptions regarding supply and demand (e.g., whether they are exogenous or endogenous) in the context of partial economic equilibrium modeling should be clarified. For instance, around line 173, the description of resource extraction costs suggests these are used to construct supply curves. More clarity here would be beneficial.
The Baseline scenario shows emissions lower than those in the SSP database. Could the authors elaborate on whether this discrepancy is an artifact of model assumptions? If the definition of the baseline differs from SSP baselines, the comparison might not be consistent.
The constraint on land-use to LUH2 (Hurtt et al., 2020) is mentioned. What would the implications be if this constraint were not applied? Additionally, could the model incorporate land mitigation policies, such as differentiated carbon prices across scenarios?
Results:
Including a broader range of results, such as carbon prices, climate outcomes, and final energy and agricultural demands, would strengthen the paper.
Minor Comments:
Figure 6: Were the axes truncated inappropriately?
Line 370: Clarify the reference to "IPCC."
Model Uniqueness: Will the model solution always be unique?
Citation: https://doi.org/10.5194/gmd-2024-196-RC2 -
RC3: 'Comment on gmd-2024-196', Anonymous Referee #3, 05 Jan 2025
The paper introduces the SuCCESs model, a new integrated assessment model (IAM) representing global energy, materials, land-use and climate systems for long-term scenarios exploration up to 2100. The model is designed to be highly agregated in one region, as to lower computational requirements and to allow for Monte Carlo simulations. This provides an interesting model application, that is not yet commoly seen in the field of IAMs.
The paper is easy to read and provides a comprehensive overview of the model structure, and its potential for application. However, I believe it misses a few important references to existing literature, and could be improved in its structure to better highlight the model key features and relevance for IAMs-related research.
Below, there are specific comments and questions that can be relevant to enhance the clarity of the paper and the contribution that SuCCESs can bring to field of research.
Specific comments:
- Lines 37-41: it would be valuable to mention some specific examples of bottom-up process-based IAMs as described in this paragraph, with related references to the literature.
- Lines 46-48: I believe it would be worth mentioning here other IAMs similar to SuCCESs either in their systems representation and /or in their structural definition, and to clearly highlight how SuCCESs differ from them and what benefits it provides. Some examples: the GLUCOSE model, it provides a highly aggregated IAM developed using OSeMOSYS and representing energy, land and food, and materials systems (for reference, please see Beltramo et al, 2021 <https://doi.org/10.1016/j.envsoft.2021.105091>); the MESSAGEiX model, as you have briefly mention already it has recently started expanding its systems representation by adding a representation of materials flows and stocks (for reference, see Ünlü et al, 2024 <https://doi.org/10.5194/egusphere-2023-3035>).
- Line 85: could you please explain the reason behind the choice of a more detailed land-use representation as compared to the other systems in SuCCESs, and what are the benefits of this for the model application.
- Line 86-87: could you please explain the reasons behind the choice of adopting a ten-year time-step model resolution, and what could be the benefits and drawbacks of this choice particularly in consideration of the fast pace at which the technology transitions are expected to take place in order to mitigate climate change? In addition, could you please clarify if the ten-year time steps are solved consequentially (i.e. assuming myopia) or all at once (i.e. assuming perfect foresight)?
- Could you please clarify which parts of the SuCCESs model are individual, separate models that are hard linked to the SuCCESs model, and which parts instead are embedded elements of the SuCCESs model as it is? E.g. you mentioned in the text that the land-use system is represented using the dedicated CLASH model and the energy system is represented using the OSeMOSYS model: is there another dedicated model also used for the material sector?
- Lines 276-277: could you please provide a reference to the statement saying that the covered materials "were selected due to the high energy-use, emissions, and land-use impacts of their production"?
- Section 4: I believe this section is not fitting in its current position towards the end of the paper, as it does not add to the results and the scientific contribution that the SuCCESs model provides, nor discuss further some of the model results presented in section 3. I would recommend the authors to either integrate some of the information provided here in section 2, under the model structure, or to simply remove this section and use it for a separate model documentation in the supplementary material or on the dedicated GitHub repository.
- Line 461: I would recommend to move the reference to Keppo et al. (2021) if relevant to the earlier section of the paper (i.e. lines 86-87), also to better address comment 4 above.
- Line 465: here it is mentioned that in SuCCESs "a single scenario run takes roughly 10 seconds on a standard laptop using the CPLEX solver". Considering such a fast solving time, could you please expand earlier on in the paper on the reasons behind choosing a 10-year time steps for the inter-temporal optimisation of the SuCCESs model?
Technical corrections:
- Line 461: I would recommend the authors to check the wording of this sentence.
Citation: https://doi.org/10.5194/gmd-2024-196-RC3
Data sets
Results and plotting scripts for the manuscript Tommi Ekholm https://zenodo.org/records/13981206
Model code and software
SuCCESs Integrated Assessment Model Tommi Ekholm, Nadine-Cyra Freistetter, Tuukka Mattlar, Theresa Schaber, and Aapo Rautiainen https://zenodo.org/records/13981520
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