Assessing Effects of Climate and Technology Uncertainties in Large Natural Resource Allocation Problems
Abstract. The productivity of the world's natural resources is critically dependent on a variety of highly uncertain factors, which obscure individual investors and governments that seek to make long-term, sometimes irreversible investments in their exploration and utilization. These dynamic considerations are poorly represented in disaggregated resource models, as incorporating uncertainty into large-dimensional problems presents a challenging computational task. In this paper, we apply the SCEQ algorithm (Cai and Judd, 2021) to solve a large-scale dynamic stochastic global land resource use problem with stochastic crop yields due to adverse climate impacts and limits on further technological progress. For the same model parameters, the range of land conversion is considerably smaller for the dynamic stochastic model as compared to deterministic scenario analysis. The scenario analysis can thus significantly overstate the magnitude of expected land conversion under uncertain crop yields.
Jevgenijs Steinbuks et al.
Status: final response (author comments only)
- RC1: 'Comment on egusphere-2022-863', Anonymous Referee #1, 13 Mar 2023
- RC2: 'Comment on egusphere-2022-863', Xin Zhao, 23 May 2023
Jevgenijs Steinbuks et al.
Jevgenijs Steinbuks et al.
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The paper titled "Assessing Effects of Climate and Technology Uncertainties in Large Natural Resource Allocation Problems" addresses the important problem of how to deal with uncertainties in yields in simulations of future land use change. The approach of linking the SCEQ algorithm to the FABLE model appears promising and provides the necessary novelty to justify its publication. While the introduction is effective, the methods, discussions, and result sections might require some rework to enhance the overall approach's reproducibility and better justify the paper's main claims.
* Firstly, the SCEQ algorithm plays a crucial role in the paper, but it is only presented in the appendix and not in the methods section. The authors should introduce the algorithm in the methods section and, more importantly, explain how it has been integrated into the FABLE model.
* Similarly, the calibration of the yield development stages is explained only conceptually in the methods section and not specifically for this study. The appendix contains relevant details that should be part of the main text. Additionally, statements in the methods and appendix sections appear contradictory, and the authors should clarify the approach they used. While the methods section says that the authors "use the results of Rosenzweig et al. (2014)", the appendix says that the authors "follow the approach of Rosenzweig et al." to process the data, which sounds like slightly different data was used.
* The discussion section mostly analyzes model output and feels unrelated to the rest of the paper. The authors should either show the model results in the abstract and conclusions and place them in the context of other studies or put less emphasis on the model output discussion and focus on the paper's main findings. The authors should provide a more in-depth discussion and justification for the main claim that scenario analysis can overstate the magnitude of expected land conversion under uncertain crop yields. Specifically, the authors should explain what this overestimation implies and how to interpret it. Additionally, the authors should discuss the robustness of that finding. If I am not mistaken the reduced range should be a direct consequence of the used states in the markov chain and its implication that the stochastic model "knows" that being already in the best/worst stage the situation cannot get any better/worse in the future. It should be critically discussed whether this assumption about a bounded solution space is realistic or might actually lead to an underestimation of the range of potential land conversions under uncertainty.
* The code and data availability section does not provide sufficient information to make the analysis of the paper reproducible. To achieve reproducibility, the section should provide access to the model source code used for the analysis and the input data to the model. If access to the source code and/or data cannot be made available to the public, this should be explicitly stated and justified in the section. Code and data should be at least available to the reviewers.
p3.l83ff: The wording feels a bit harsh ("fail to account for") and it also creates in my perspective too high expectations for this paper. Like the other mentioned studies ignored uncertainties in yields this study is ignoring uncertainties the other paper considered. This study does not supersede previous studies but it instead expands the range of uncertainty studies.
p7.l184ff: The statement about incomplete coverage of GHG emissions might require some more context. I believe it might be put here to justify that a fixed RCP scenario can be used for the simulations without ensuring that total GHG emissions are in line with this scenario. Please explain in the text why this consideration is relevant. In addition, please also mention and justify in the methods and/or introduction section that RCP6 is used. Currently this is just mentioned in the appendix with no justification provided.
Besides RCP it is also unclear on what other scenario assumptions (e.g. SSP, mitigation policies) the simulations are based on, or to which scenario the simulations can be best compared to. Can the simulations be understood as a business as usual scenario with very limited to none mitigation efforts?
p7.l188ff: I was surprised to see the yield states having such a strongly negative bias with states of +15, +2, -15, -19, and -36 percent (are these percentage differences over the simulation period?). Given the details in the appendix this mainly seems to come from 1. having PEGASUS as a rather pessimistic model in the game and 2. considering also runs with deactivated CO2 fertilization effect. As these choices have a critical impact on the final outcomes of the model these choices require some justification in the text. It is also unclear why not a more recent analysis such as Jägermeyr 2021 ("Climate impacts on global agriculture emerge earlier in new generation of climate and crop models" Nature Food) was taken as point of reference.
p11. I am not sure if it helps to show the difference between deterministic and stochastic scenarios on the right hand side. Instead I could imagine that showing just the same plot as on the left but for the stochastic runs might ease the comparison between the two. To put the results into context it would be helpful to have the historic development of the variables shown in the plots as well. Otherwise it is difficult to evaluate the results.
p14.l338ff: Was this strong demand side elasticity to be expected? Is this in line with other studies?
p18.l387: I did not find a description in the text of how the high resolution outputs were plugged into the FABLE model. Is the FABLE model spatial explicit?