Assessment of Urban Local High-Temperature Disaster Risk and the Spatially Heterogeneous Impacts of Blue-Green Space

Round 1

Reviewer 1 Report

The article is devoted to the study of the risk of high temperatures in large cities. The study was carried out for the territory of Harbin. The research is relevant and interesting. The strength of the work is that it does not simply focus on describing the urban heat island, but pays attention to assessing risks and studying the factors influencing them.

The methodology is described in detail and qualitatively. The results are well described and well illustrated. The conclusions are beyond doubt. The most interesting aspect of the work is the use of spectral indices, landscape metrics and the method of geographically weighted regression. The combination of remote sensing, GIS and spatial statistics is an effective methodology for studying urban heat islands and assessing thermal risks.

I believe that the manuscript can be accepted for publication in the form presented.

Reviewer 2 Report

This article developed an assessment process for mitigating the risk of urban high-temperature disasters in the main urban area of Harbin, China, using the layout of blue-green spaces (UBGS). They used Geographic Information Systems (GIS) and remote sensing imagery to create local climate zone (LCZ) maps for the study area. The assessment process calculates the total high-temperature risk (T) based on disaster-causing danger (D), disaster-environment sensitivity (S), and disaster-bearing vulnerability (V). They identified UBGS factors that influence T and assessed their impact using geographical weighted regression. The findings reveal a low overall risk level with high-risk areas concentrated within the second ring road. The study also explores the influence of building categories on T and identifies specific factors (AREA_MN, SHAPE_MN, PD, and NP) that significantly affect T. The research provides insights and a scientific basis for urban high-temperature disaster risk mitigation planning using UBGS and LCZ classification. I think there are still some places in this manuscript that need further revision to warrant publication as a high-quality research article. Detailed comments follow.

Abstract: The abstract might exceed the length limits. Please streamline according to the journal policy.

Introduction: place this study in the context of broader literature (e.g., blue-green space and urban extreme heat). Consider citing the following work: (1) The changing dynamics of population exposure to extreme heat in the contiguous United States from 2001 to 2020, (2) Built environment influences on urban climate resilience: evidence from extreme heat events in Macau.

Discussion: More in-depth discussions are needed. The authors should compare this study with the prior literature in this field and highlight the contribution of this research. How does this research advance the existing literature?

Last but not least, it is recommended that the authors perform a thorough edit to ensure proper grammar and accurate citation. I would encourage the authors to invite a native speaker to proofread this manuscript.

In conclusion, I think there are some interesting points in this paper, but it could benefit from some changes as suggested. 

It is recommended that the authors perform a thorough edit to ensure proper grammar and accurate citation. I would encourage the authors to invite a native speaker to proofread this manuscript.

Reviewer 3 Report

This study constructed a set of assessment processes for mitigating urban high-temperature disasters risk using UBGS structure layout specifically for the main urban area of Harbin, China. In general, this study has clear ideas, reasonable logic, and appropriate methods, and has certain guiding significance for mitigating urban high-temperature disasters risk. Some suggestions:

1.     Introduction part: This part is too complicated, it is suggested to elaborate from the spatial and temporal distribution characteristics and influencing factors of high-temperature disasters risk respectively.

2.     Why choose a multi-scale geographical weighted regression model? The multi-scale characteristics are not reflected in the paper.

3.     Method section: each method does not need to be exhaustive, it is recommended to list only the important methods, the rest can be placed in the supplementary documentation.

4.     Figure 5: It is recommended that all landscape boundaries be displayed.

Minor editing of English language required