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Peer-Review Record

Exploring Low-Carbon Futures: A Web Service Approach to Linking Diverse Climate-Energy-Economy Models

Energies 2019, 12(15), 2880; https://doi.org/10.3390/en12152880
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Energies 2019, 12(15), 2880; https://doi.org/10.3390/en12152880
Received: 30 April 2019 / Revised: 18 July 2019 / Accepted: 25 July 2019 / Published: 26 July 2019
(This article belongs to the Special Issue Climate Changes and Energy Markets)

Round 1

Reviewer 1 Report

The paper systematically analyses model linking and presents a case study that demonstrates the advantages of the proposed linking strategy. The topic of the paper is highly relevant, although there already were a number of model linking attempts. The paper could be improved by including an extended overview of model linking exercises presented in the scientific literature.

 

The authors describe hard-linking as "creating automated data exchange between models". In praxis, most of the current linking attempts involve some kind of automatisation, especially in the case of iterative model runs. Considerations on soft- and hard-linking should also include the scope of linking (a few variables or many), and the level of convergence aimed. The last aspect could also be discussed in the result's section.

 

The paper might also be improved by highlighting the advantages of web service based approach in comparison to other approaches presented in the scientific literature (perhaps, access to different teams, etc.).

 

Finally, please carefully check the journal's instructions https://www.mdpi.com/journal/energies/instructions and use the correct template.


Author Response

Please see attachement

Author Response File: Author Response.docx

Reviewer 2 Report

The manuscript presents the outcome from a European project, COMPLEX, namely the integration of different numerical models to allow combining and linking different models at different scales and purposes. After introducing the general purpose, an example of linking three models is explained in detail including the development of wrappers to exchange data between models. The concept of integrating multiple models is indeed important and the approach proposed deserves some merit. Moreover, the paper is rather well written. Yet several things remain unclear, and the application and online reference are not fully self-explanatory, so I would suggest to invite the authors to provide a revised version of the paper to be considered for publication in “Energies”.

Major issues

·       In the example of three models there are many issues and concerns that are not addressed in the paper and should be at least discussed. Notably, the role of other variables, as all models have dozens to hundreds of input variables, so how can the system ensure consistency across all of them? Just as an example, baseline gdp drivers should also affect the ABM directly or must be used somehow. How is this data consistent with GCAM linked to EXIOMOD?) Or since GCAM produces an energy mix as well as the ABM in terms of renewables/fossil fuel mix. How are those two model outcomes made sure to be consistent with each other when linked through EXIOMOD?

·       It should be acknowledged that many models already in itself hard-link or even fully include other factors, see also many recent GCAM papers including energy deman responses, water, etc.

·       There is also a linked website with the DIMF at a (strange) URL: http://130.89.221.193:88/dmif, which is not fully clear in how far it has been already published somewhere else in the literature. If it is basically the framework here (Even though DIMF is not even in the title of the present paper) would be good to mention the working paper/article here.

 

Minor issues:

·       While in general the paper is well written, there are numerous instances of missing commata that make following the argument sometimes hard, please edit carefully. Similarly, in many instances an determinate/indeterminate article is missing for subjects of sentences (E.g., “a web based service, line 39; …)

·       There are many instances of weird jargon to be improved: “unclear metrics” (l51), “forcing functions (l53), scores of computer models (l58), “some sort of computer-aided design (l180)

·       The sentence l61-63 is incomplete.

·       L60: Connoly et al. could better be refereed to Weyant (1996) or (2017).

·       The introduction esp. page 3 are very lengthy and should be shortened.

·       In figure 2, What do “1 Dimensional” and “Static” refer to? Explain

·       L211: should read changes in electricity (with the “s”)

·       Table 1: GAMS instad of “GAMS programming”

·       L246: three models out of the three outlines above??

·       Page 13: the formula about the very simple share calculation and its description can be explained in one sentence without equation.

·       Figure 4 is important (maybe even show it earlier. But explain how the consistency between the energy variables form GCAM and the LCE/FF consumption and prices from BENCH are ensured!!

·       P16. The URL as an IP is strange. Explain how this paper links/contirbutes7derives from the “DMIF”.

·       Figures 8 and 9: The z-axis shows a wron unit “Energy consumption in 2007 Million Euros! Same for employment in Euros.

 

 

 

References

Weyant, John. “Some Contributions of Integrated Assessment Models of Global Climate Change.” Review of Environmental Economics and Policy 11, no. 1 (January 1, 2017): 115–37. https://doi.org/10.1093/reep/rew018.

Weyant, John, O Davidson, H Dowlabathi, J Edmonds, M Grubb, EA Parson, R Richels, et al. Integrated Assessment of Climate Change: An Overview and Comparison of Approaches and Results. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1996.

 


Author Response

Please see attached

Author Response File: Author Response.docx

Round 2

Reviewer 2 Report

Thank you for providing an updated version of the manuscript.

I think like this it is possible to publish it in "Energies".

While some doubt on the results due to the many variables and consistencies across models is still there in my opinion, the paper at least provides a first step.

Very minor issues to be addressed would be

(a) shorten the abstract!

(b) In figure 3, please move the variables without bulletpoints and put them in the white boxes to make it easier readable. 

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