An Approach to Identifying Factors Affecting Residential Energy Consumption at the Urban Block Scale: A Case Study of Gaziantep

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The paper addresses an important and timely topic: identifying urban-scale factors that influence residential energy consumption, with a focus on Gaziantep, Turkey. Using DesignBuilder simulations, the study highlights how layout angle, number of stories, inter-building distance, and flats per story affect heating and cooling loads. The research is valuable in connecting urban planning decisions with energy efficiency goals, aligning with global sustainability and climate objectives (SDG 11 and 13). While the paper presents a coherent methodology and relevant findings, several issues related to clarity, depth of analysis, methodological rigor, and presentation should be addressed before the article is suitable for publication.

• The title is clear and reflects the content. The abstract, however, is overly descriptive and lacks concise presentation of methodology, novelty, and quantitative findings. Revise the abstract to highlight (a) the research gap, (b) the methods used, (c) the key numerical results, and (d) the main contribution. Avoid repeating background information.

• The introduction provides extensive background, but it is too long, repetitive, and sometimes unfocused. The research gap and novelty are not clearly emphasized until later sections. Shorten the introduction. Clearly state the gap in previous research and how this study addresses it. Place emphasis on the unique contribution (integration of historical urban development with energy analysis at the block scale).

• The review covers many relevant studies, but some references are outdated or tangential. The novelty compared to prior works is not sufficiently highlighted. Add a comparative table summarizing previous works (parameters studied, tools used, scales) and how this study differs. Emphasize how your study extends the literature (e.g., historical zoning evolution + block-scale analysis).

• The five-stage methodology is systematic and well-structured. However, some important details are missing (simulation settings, climate data source, boundary conditions). The study relies only on four neighborhoods, which may limit representativeness.

  • Provide more details on simulation setup (time step, weather file, validation approach).

  • Justify the choice of the four neighborhoods more strongly.

  • Consider adding scenario analysis (e.g., systematically varying orientation, storeys, and spacing) to strengthen conclusions.

• The results are clearly presented in tables and figures, but they are mainly descriptive. The key finding (cooling energy is much higher than heating) is expected in hot-dry climates. The influence of parameters is observed but not quantified.

  • Use quantitative analysis (e.g., correlation or sensitivity analysis) to measure the impact of each parameter.

  • Improve figure clarity and resolution. Ensure all axes, scales, and units are clearly labeled.

  • Add percentage differences or comparative ratios to highlight significance.

• The discussion acknowledges limitations but does not fully interpret the broader implications. The interplay between parameters is mentioned but not deeply analyzed.

  • Expand discussion to explain why certain parameters (distance, orientation, storeys) behave as observed.

  • Strengthen policy implications (how zoning laws, urban planning, and building regulations could use these findings).

  • Compare with international studies more explicitly to show generalizability.

• Comment: The conclusion repeats results rather than synthesizing insights. It lacks strong, actionable recommendations.

  • Make the conclusion concise and focused.

  • Provide clear takeaways for (a) architects, (b) urban planners, and (c) policymakers.

  • Highlight the study’s unique contribution (framework + urban-scale focus).

  • Clearly outline directions for future research (e.g., scenario testing, inclusion of material variations, field validation).

• The manuscript contains grammatical errors, awkward phrasing, and formatting inconsistencies (e.g., “se lement” instead of “settlement”). Figures and tables sometimes lack clarity.

  • Undertake thorough English language editing.

  • Standardize terminology (e.g., “settlement” instead of “se lement”).

  • Improve figure resolution and ensure tables are self-explanatory.

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Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

The paper discusses an important topic backed by literature review. However, there are some issues that need to be addressed:

1. In the literature review, there are few instances where similar claims are repeated. In that scenario, please combine the literatures if multiple of literatures have similar claims
2. In the Introduction section in line 114-115, author made the statement "The study determined that the layout angle, number of floors, inter-building distance, and apartments per storey influence energy consumption." is it hypothesis or conclusion? Please do not make any claim in the introduction section only!
3.  In Table 1, any specific reason 'Floor Area Ratio' is not provided for Alleben and Akkent?
4. In Table 2, are this information actual envelope information or extracted based on the standards? All of them has same envelope components?
5. Please add reference in the statement in line no 360-361 "The values in Table 2 comply with the minimum insulation requirements specified in TS825."
6. Can author please clarify which variables are assumed so they can remain consistent across all the sites (eg., HVAC) and which ones are actual specifically? It will be helpful to have that clear
7. In Table 3, please check heating, cooling and total energy values! The values are not matching with the sum
8. How close these consumption values with actual buildings? Have author checked that? Although some factors are not designed as in the actual building, it is good to know how the consumption differs from the modeled values. This is to make sure the validity of the models
9. Add some information about the climate condition. What weather file is used for simulation
10. In line 377-379, it is mentioned that "The Alleben neighborhood had 30% lower cooling energy consumption than the other three regions (Table 3). The number of storeys, the distance between buildings, the apartments per storey, and the layout angle are key factors in this difference". However, the first 3 places have similar building characteristics. Please expand on what can be the reason behind such a big difference in cooling energy.
11. In line 380-382 it is mentioned "In Figure 8, "red" indicates a higher value, "blue" a lower value, and "white" denotes equality. For example, in the first row, “1 blue 2” means heating demand of “1<2”." Does equality mean exact same value? Or is there a range? If they are exactly same, how can 2 different buildings although with similar characteristics can have exactly same heating or cooling load?
12. What is the significance of Figure 8 and what more information does it carry over Table 3 as it is only showing which one is higher or lower and nothing more. The plot also does not show the level of differences
13. How can author specifically correlate building angle with the heating, cooling load as there is only a single building of that type.
14. There are different claims made in the manuscript, but not enough results to back them. Please add more results to support the claims

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The study has been found to be publishable in its current form due to the authors addressing the suggested corrections. This manuscript is suitable for publication. 

Reviewer 2 Report

Comments and Suggestions for Authors

Thanks for addressing all the comments and updating the manuscript