Research on the Driving Factors and Prediction Model of Urban Underground Space Demand in China

Round 1

Reviewer 1 Report

By collecting comprehensive data on developed UUS demand and potential driving factors in different cities, this research employs descriptive statistics, correlation analysis, and path analysis to identify the inherent driving factors of UUS development demand. Subsequently, a series of regression models are proposed and analyzed, with primary driving factors as independent variables and UUS demand as the dependent variable, including univariate optimal models, multiple linear regression models, and LASSO regression models. Additionally, the correlation between UUS demand and primary driving factors in each city is investigated and discussed separately. The paper is well organized. It can be considered to be published in Sustainability only if the following suggestions would be addressed appropriately.

(1) In line 119, what is the specific relationship between temperature-related indicators and sustainable development ?

(2) In table 4, the CV value of F7, F8 and F9 is small, the fluctuation of independent variables is relatively small, Is the influence on the dependent variables not obvious enough ?

(3) In the literature review, it is emphasized that the factors and methods related to energy conservation or sustainable development are difficult to quantify and predict. The following research has also been carried out, but the corresponding conclusions have not been obtained.

(4) In line 335, it is mentioned that this model needs further verification. Since this article contains data from 16 cities, why are all used as data analysis generation models, and why can 't some data be retained for verification ?

Author Response

Please see the attachment. Thanks!

Author Response File: Author Response.pdf

Reviewer 2 Report

1.       The introduction uses the acronym "UUS" without defining it, which may confuse readers unfamiliar with the term. It's crucial to define all acronyms and jargon upon their first use to ensure accessibility for all readers.

2.       The introduction includes detailed statistical data and outcomes early on, which might be more appropriate in the body of the paper where detailed analysis is discussed. Introductions should broadly set up the problem and state the objectives of the research without delving into deep specifics or results.

3.       The introduction does not clearly state the main thesis or research question. A concise statement of the primary research question or objective would help focus the reader on what the study aims to achieve.

4.       The literature review section sometimes uses technical terms and acronyms without clear explanations, which could confuse readers not familiar with the subject. It is essential to define all terms and acronyms clearly when they are first introduced.

5.       The review appears somewhat fragmented and jumps from one topic to another without clear transitions. A more structured approach that groups similar studies or approaches together would help the reader better understand the progression of research in this field.

6.       The section is titled "Methodology," but it includes elements typically found in a results section, such as detailed descriptions of data acquisition and analysis methods. This can confuse readers expecting to find information solely about how the research was conducted. It would be clearer if the section were divided appropriately into methodology and results sections or accurately labeled to reflect its contents.

7.       The section might be overloading the reader with detailed descriptions of data sources and specific cities included in the study. While thoroughness is valuable, excessive detail can detract from the main points and could be condensed or presented in an appendix or supplementary material.

8.       The equations and models are presented in a way that might be confusing due to formatting issues. Clearer presentation and perhaps a brief explanation of each variable within the text could enhance understanding. It's essential to ensure that formulas are legible and correctly formatted in academic documents.

9.       In the results section .The results are presented with a heavy emphasis on numerical data and statistical details, such as coefficients of variation, correlation coefficients, and variance inflation factors. However, there is a lack of context or interpretation to help the reader understand what these figures mean in practical terms. This could be improved by providing more detailed explanations of how these statistics relate to the study's hypotheses or objectives.

10.   The section appears to conflate different types of statistical analyses (descriptive statistics, correlation analysis, path analysis) without clear transitions or subsections. This can make it difficult for readers to follow the progression of the analysis and understand how each part contributes to the overall findings of the study.

11.   The factors (e.g., F1, F2, F3, etc.) are listed with their descriptive statistics but without adequate explanations of what these factors represent. This lack of clarity could confuse readers who are not familiar with the specific jargon or abbreviations used in the study.

12.   The conclusion mostly reiterates findings without synthesizing them into a strong, overarching statement about the implications or significance of the study. A powerful concluding remark could better encapsulate the contributions and significance of the research.

13.   The conclusion enumerates findings without integrating them into a coherent narrative. This list-like format can conclude feel somewhat disjointed. Instead, it would be more impactful to weave these findings into a narrative that discusses the broader implications or potential applications of the research.

Not required

Author Response

Please see the attachment. Thanks!

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

The manuscript can be accepted as is

Proofreading is not required