Review Reports - Study on Spatiotemporal Pattern Evolution and Regional Heterogeneity of Carbon Emissions at the County Scale of Major Cities, Inner Mongolia Autonomous Region

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This study investigates the spatiotemporal evolution and regional heterogeneity of carbon emissions at the county scale in the Hohhot-Baotou-Ordos (HBO) urban agglomeration in Inner Mongolia, China. The research combines remote sensing data, spatial metrics, and a Geographically and Temporally Weighted Regression (GTWR) model to analyze the impact of urban spatial form on carbon emissions from 2013 to 2021. The topic is highly relevant to regional low-carbon development and spatial planning, especially under China’s “dual-carbon” goals. The manuscript is well-structured, the methodology is robust, and the findings provide valuable insights into the spatial heterogeneity of carbon emissions. However, several issues need to be addressed before publication.

The study uses data from only five years (2013, 2015, 2017, 2019, 2021). While this may be due to data availability, a more continuous annual series would strengthen the temporal analysis. The authors should justify this sampling strategy or discuss its potential limitations.
The bidirectional effects of certain indicators (e.g., LPI and PD) are discussed, but the underlying mechanisms could be explained more deeply. For instance, how does monocentric vs. polycentric structure specifically affect energy use in different sectors?
The model only includes urban form indicators. Incorporating socioeconomic factors (e.g., GDP per capita, industrial structure, energy efficiency) could help isolate the pure effect of spatial form and improve model robustness.
While Global and Local Moran’s I are reported, the discussion of spatial dependence is somewhat superficial. A deeper interpretation of the shifting H-H and L-L clusters over time would enhance the spatial analysis.
The abstract should briefly mention the data sources (e.g., NPP-VIIRS, CLCD) and the time range of the study.
Please ensure all figures are properly numbered and referenced.
The conclusion section could be more concise and focused on key findings and policy recommendations.
Some acronyms are used without full names (e.g., JJI in the abstract should be written as Interspersion and Juxtaposition Index at first use).

Author Response

Dear reviewer,

Thank you for offering us an opportunity to improve the quality of our submitted manuscript (article number: 3881890). We appreciated very much the reviewers’ constructive and insightful comments. In this revision, we have addressed all of these comments. We hope the revised manuscript has now met the publication standard of your journal.

We highlighted all the revisions in red colour.

Our point-to-point response to the questions raised by the reviewers is as follows：

Comment 1: The study uses data from only five years (2013, 2015, 2017, 2019, 2021). While this may be due to data availability, a more continuous annual series would strengthen the temporal analysis. The authors should justify this sampling strategy or discuss its potential limitations.

Response: Thank you for pointing out this issue. This study constructed a complete time series based on continuous remote sensing and statistical data from 2013 to 2021. To avoid redundancy and present the trends more clearly, five representative time points (2013, 2015, 2017, 2019, and 2021) were selected. These points are evenly distributed across the study period and sufficiently capture the overall temporal trends of carbon emissions, urban spatial morphology, and their interrelationship.

Comment 2: The bidirectional effects of certain indicators (e.g., LPI and PD) are discussed, but the underlying mechanisms could be explained more deeply. For instance, how does monocentric vs. polycentric structure specifically affect energy use in different sectors?

Response: Thanks for your suggestion. We have thoroughly discussed the bidirectional effects of LPI and PD as well as their underlying driving factors.

In Baotou’s central urban area, the concentration of industrial facilities combined with high centrality (LPI) intensifies carbon emissions due to the aggregation of energy-intensive industries. By contrast, Hohhot is dominated by services and administrative functions, with a relatively low share of energy-intensive industries and a higher degree of low-carbon development in the urban core. In this case, higher LPI implies dense population distribution with mixed land-use functions, which reduces commuting distances and energy consumption.
Similarly, in Baotou and Ordos, where industrial sectors dominate the urban core, an increase in patch density (PD) reflects the subdivision of industrial, residential, or commercial parcels into smaller and more dispersed units, which reduces energy intensity within single areas. In contrast, Hohhot’s core is primarily administrative, commercial, educational, and residential. Here, higher PD often corresponds to inefficient land use and fragmented development, which undermines the efficiency of infrastructure and transportation systems.

The revised sections can be found in the last paragraph on page 23 and the second paragraph on page 24 of the revised manuscript, with the changes highlighted in red.

Comment 3: The model only includes urban form indicators. Incorporating socioeconomic factors (e.g., GDP per capita, industrial structure, energy efficiency) could help isolate the pure effect of spatial form and improve model robustness.

Response: We sincerely appreciate the reviewer’s insightful comment. We fully acknowledge the importance of incorporating socioeconomic factors (e.g., GDP per capita, industrial structure, and energy efficiency) into the analysis, as this would provide a more comprehensive understanding of the drivers of carbon emissions. However, the main purpose of this study is to isolate and quantify the independent effect of urban spatial form on carbon emissions. For this reason, socioeconomic variables were intentionally excluded to avoid confounding effects and to provide clearer evidence on the role of spatial morphology itself, which is particularly relevant for urban planning and spatial governance.

Moreover, the influence of socioeconomic factors on carbon emissions has been extensively examined in the existing literature, while studies focusing on the role of urban form remain relatively scarce, especially at the county level. We believe our contribution lies in filling this gap by providing new evidence on the spatiotemporal heterogeneity of the form–emission relationship. Nevertheless, we fully agree that future research should integrate both spatial morphology and socioeconomic dimensions to build a more comprehensive explanatory framework. Therefore, we have decided not to add economic indicators. Thank you again for your suggestion.

Comment 4: While Global and Local Moran’s I are reported, the discussion of spatial dependence is somewhat superficial. A deeper interpretation of the shifting H-H and L-L clusters over time would enhance the spatial analysis.

Response: Thanks for your advice. We agree that in the original manuscript the explanation of the global and local Moran’s I indices was indeed somewhat limited. Following your advice, we have provided a more detailed discussion of the temporal evolution of H–H and L–L clusters in the revised version. Specifically, we not only report the spatial distribution of the clusters but also analyze their evolution between 2013 and 2021 and the underlying driving factors. For example, we find that H–H clusters are mainly distributed in the central urban districts of Baotou, including industrial areas, and in the energy exploitation and processing zones of Ordos, reflecting the persistent influence of industrial and energy structures. In contrast, L–L clusters are mainly located in the non-core areas of Hohhot and the northern areas of Baotou, and we observe that their extent gradually increases. This suggests that some previously less prominent low-emission areas are undergoing industrial and energy restructuring, showing signs of emission reduction, which deserves further attention.

The revised sections can also be found in Section 4.3.1 Spatial Statistical Analysis on page 14, paragraphs 2 and 3, with the changes highlighted in red.

Comment 5: The abstract should briefly mention the data sources (e.g., NPP-VIIRS, CLCD) and the time range of the study.

Response: We have made this correction in the abstract as your suggestion. The revised sentence is as follows: This study focuses on the three major cities of Hohhot, Baotou, and Ordos in Inner Mongolia. By integrating NPP-VIIRS nighttime light data, the CLCD(China Land Cover Dataset) dataset, and statistical yearbooks, it quantifies county-level carbon emissions and establishes a spatiotemporal analysis framework of urban morphology–carbon emissions from 2013 to 2021.

The above passage has been added to lines 4–8 of the abstract on the first page of the revised manuscript.

Comment 6: Please ensure all figures are properly numbered and referenced.

Response: We have carefully reviewed the manuscript to ensure that all figures and tables are properly cited, and all corresponding citations have been highlighted in red in the revised version.

Comment 7: The conclusion section could be more concise and focused on key findings and policy recommendations.

Response: Thanks for your suggestion. We found that there were indeed some issues in the conclusion section of the original manuscript. Based on your suggestions, we have made the following revisions:

We first removed the redundant content in the conclusion, which makes the section more concise.
After removing the repetitive parts, we summarized and refined the original conclusions. For example, the opening paragraph of the conclusion now highlights the research process, innovations, and significance. In subsection (1), we summarize and analyze the changes in carbon emissions and their driving factors. In subsection (2), we incorporate a comparison with studies reviewed earlier in the paper to emphasize the distinctiveness of our research.
We further analyzed the variation patterns of each indicator and their underlying causes, and provided targeted policy recommendations accordingly. For instance, subsection (3) offers policy suggestions regarding city size (CA) and compactness（COHESION）; subsection (4) addresses Urban Complexity (LSI) and Land-Use Adjacency (IJI); and subsection (5) discusses recommendations for Centrality (LPI) and Fragmentation (PD).

We have made revisions in Section 6, Conclusions and Suggestions, on pages 24–25 of the revised manuscript, and the changes have been highlighted in red.

Comment 8: Some acronyms are used without full names (e.g., JJI in the abstract should be written as Interspersion and Juxtaposition Index at first use).

Response: Thank you for pointing out this issue. We carefully checked all abbreviations throughout the manuscript and corrected any improper usage. For example, we revised the use of Interspersion Juxtaposition Index (IJI).

The revision can be found on line 10 of the abstract on the first page of the revised manuscript.

We sincerely appreciate the reviewer’s constructive comments and suggestions. We have carefully revised the manuscript accordingly, and we believe these revisions have significantly improved the quality of our paper.

Yours Sincerely,

Yun Xue

School of Architecture and Art Design, Inner Mongolia University of Science and Technology, Baotou 014010, China

Email: xueyun_cn@126.com

Reviewer 2 Report

Comments and Suggestions for Authors

What is the main question addressed by the research?

The paper is centered around the key research question of how urban spatial form shapes the spatiotemporal heterogeneity of carbon emissions in the Hohhot–Baotou–Ordos urban agglomeration. This question is clearly defined and thoroughly explored through long-term monitoring and the application of spatial morphology indicators.

Do you consider the topic original or relevant to the field? Does it address a specific gap in the field? Please also explain why this is/is not the case.

The topic is both original and highly relevant. Most previous studies have focused on national or provincial scales, while county-level emissions—where spatial differences are most visible—have been largely overlooked. This research fills an important gap, particularly in the context of Inner Mongolia, a region of both strategic energy importance and ecological sensitivity.

What does it add to the subject area compared with other published material?

The contribution lies in linking spatial indicators with emission dynamics and demonstrating their varying effects across different stages of urban development. This provides a new and more detailed perspective compared with earlier studies, enabling a deeper understanding of urbanization processes and their influence on carbon emissions.

What specific improvements should the authors consider regarding the methodology? What further controls should be considered?

The methodological approach—combining spatial analysis with the GTWR model—is applied appropriately and successfully captures spatiotemporal variation. The selection of morphological factors and their interpretation in relation to carbon emissions provides a strong analytical framework that fully supports the study’s findings. No additional improvements or controls are required, as the methodology is rigorous and well-suited to the research aim.

Are the conclusions consistent with the evidence and arguments presented and do they address the main question posed? Please also explain why this is/is not the case.

The conclusions are entirely consistent with the evidence and arguments presented, directly addressing the core research question. They highlight spatiotemporal heterogeneity and clearly identify which spatial characteristics are most influential in shaping carbon emissions. In doing so, the paper not only makes a strong scientific contribution but also provides a solid theoretical basis for practical sustainable development policies.

Final evaluation: Given the clear research focus, originality, and relevance of the topic, along with a solid methodology and well-substantiated conclusions, the paper fully meets academic standards and should be accepted without reservations revisions.

Author Response

Dear Reviewer，

We are delighted to learn that our manuscript entitled "Study on Spatiotemporal Pattern Evolution and Regional Heterogeneity of Carbon Emissions at the County Scale of Major Cities, Inner Mongolia Autonomous Region" (Manuscript ID:article number: 3881890) has completed the review process.

We wish to express our sincere gratitude to you and the reviewers for the time and effort dedicated to evaluating our work. We are truly honored and pleased that the reviewers found our manuscript acceptable without requiring further revisions. This positive outcome is a tremendous encouragement to our team.

We believe this paper will contribute valuable insights to the field of yours, and we are very pleased and excited that it will be published in your esteemed journal.

Thank you once again for your efficient handling of our manuscript.

Yours Sincerely,

Yun Xue

School of Architecture and Art Design, Inner Mongolia University of Science and Technology, Baotou 014010, China

Email: xueyun_cn@126.com

Reviewer 3 Report

Comments and Suggestions for Authors

The relevance of this study stems from the urgent need to implement China's "double carbon neutrality" strategy at the subregional level, which requires a detailed understanding of spatiotemporal emission patterns. This study fills a critical gap by conducting the first comprehensive county-level carbon emissions analysis for the key energy and environmentally vulnerable Hohhot-Baotou-Ordos metropolitan area, using an advanced GTWR model and nighttime light data to identify the heterogeneous impacts of urban planning factors. This provides a scientific basis for developing targeted and adaptive low-carbon planning strategies in the specific conditions of resource-dependent regions.

How can the results of the spatiotemporal analysis of county-level carbon emissions be used to develop targeted and differentiated decarbonization strategies for different districts in the Hohhot-Baotou-Ordos (HBO) metropolitan area?
What specific urban planning measures (e.g. zoning policy, polycentric development, infrastructure projects) can be proposed to regulate the key spatial form factors (CA, LSI, LPI) to reduce the carbon footprint in the HBO agglomeration?
In the context of industrial zone redevelopment (using the Baogang Group site transformation as an example) and greenfield development, how can optimizing the land use contiguity index (IJI) and fragmentation index (PD) contribute to the creation of low-carbon neighborhoods?
Considering the bidirectional influence of some factors (LPI, PD) on emissions, how can adaptive thresholds for spatial development indicators be developed that would ensure low-carbon growth at different stages of urbanization of individual counties?

Author Response

Dear reviewer,

We highlighted all the revisions in red colour.

Our point-to-point response to the questions raised by the reviewers is as follows：

Comment 1: How can the results of the spatiotemporal analysis of county-level carbon emissions be used to develop targeted and differentiated decarbonization strategies for different districts in the Hohhot-Baotou-Ordos (HBO) metropolitan area?

Response: We sincerely thank the reviewer for raising this important question. We fully understand your concern regarding how the spatiotemporal analysis of county-level carbon emissions can be translated into differentiated decarbonization strategies for different counties within the Hohhot–Baotou–Ordos metropolitan area. To address this point, we have substantially revised and enriched the conclusion section of the manuscript.

Specifically:

In addition to summarizing the spatiotemporal emission patterns and the heterogeneous effects of urban spatial form indicators, we further proposed targeted policy recommendations for different counties.

In the revised conclusion, we presented five main findings and policy suggestions in a numbered format. For example:

For areas experiencing disorderly urban sprawl, we recommend controlling land expansion and promoting polycentric and mixed-use development.
For highly compact areas, we suggest adopting polycentric layouts to mitigate traffic and energy demand pressures.
For areas with higher urban complexity and land-use mixing, we emphasized their positive effect on emission reduction and proposed strengthening ecological mosaics and “15-minute living circles.”
For bidirectional indicators such as centrality (LPI) and fragmentation (PD), we recommend setting appropriate thresholds according to local conditions. For instance, in industrial districts such as Baotou’s Kundulun District, reducing centrality and relocating energy-intensive industries are advised, while in other cities, maintaining moderate centrality can enhance efficiency.

These discussions not only reinforce the academic significance of the spatiotemporal analysis but also provide clear policy relevance and practical guidance, offering scientific support for differentiated decarbonization pathways in the HBO metropolitan region.

The revised conclusions and recommendations are presented in Section 6 Conclusions and Suggestions on pages 24–25 of the revised manuscript, with the changes highlighted in red. We believe these improvements directly address the reviewer’s concern by showing how the research findings can be translated into actionable policy strategies.

Comment 2: What specific urban planning measures (e.g. zoning policy, polycentric development, infrastructure projects) can be proposed to regulate the key spatial form factors (CA, LSI, LPI) to reduce the carbon footprint in the HBO agglomeration?

Response: We sincerely thank you for your question and fully agree with your viewpoint. In response, we have made the following additions:

First, for the 27 counties, the policy aims to reduce disorderly urban sprawl, since urban expansion increases land consumption and consequently raises carbon emissions.

Second, the regression coefficients of urban complexity (LSI) show a significant negative correlation with carbon emissions, indicating that more irregular urban forms can help suppress emissions. Our proposed strategy is to strengthen the urban ecological mosaic and reasonably enhance spatial complexity. Specifically, this can be achieved by preserving and restoring water bodies, wetlands, and green spaces, and by constructing urban ecological zones to increase edge irregularity, expand carbon sink areas, alleviate the urban heat island effect, and reduce cooling-related energy consumption.

Finally, regarding urban centrality (LPI), the bidirectional effects of this indicator determine that policies must be context-specific. For industrial areas, we recommend relocating energy-intensive industries to reduce emissions, while for general urban areas we suggest maintaining a certain degree of centrality to improve urban functional efficiency and thereby reduce carbon emissions.

The revised sections can be found on page 25 of the revised manuscript, lines 4–8 of part (4) and lines 2–7 of part (5), with the changes highlighted in red.

Comment 3: In the context of industrial zone redevelopment (using the Baogang Group site transformation as an example) and greenfield development, how can optimizing the land use contiguity index (IJI) and fragmentation index (PD) contribute to the creation of low-carbon neighborhoods?

Response: We sincerely thank you for your question and fully agree with your viewpoint. In response, we have made the following additions:

The regression coefficients of land-use adjacency (IJI) show a significant negative correlation with carbon emissions, indicating that higher land-use mixing contributes to emission reduction. How to optimize IJI is therefore crucial for building low-carbon communities. We propose the following policy recommendations: land-use interspersion and mixing should be enhanced to optimize urban adjacency patterns. Mixed residential–employment and multifunctional layouts should be encouraged, while “15-minute living circles” should be developed at the community scale to shorten commuting distances, promote walking and public transport, and thereby reduce transport-related emissions. At the same time, distributed energy systems and public services should be prioritized in mixed-use areas to improve infrastructure efficiency.

Regarding urban fragmentation (PD), a certain degree of fragmentation is necessary within cities to maintain ecological patches and corridors as natural carbon sinks, while ensuring efficient infrastructure layouts. In industrial areas such as Hondlon District of Baotou and Dongsheng District of Ordos, fragmented spaces can be used to introduce innovative industries, service facilities, or distributed energy systems, thereby improving land-use efficiency without compromising carbon reduction. In the urban core of Hohhot, inefficient and underutilized land can be consolidated through urban renewal to create larger functional zones, optimize transportation routes, and improve infrastructure layouts. Overall, maintaining moderate levels of centrality and fragmentation is essential to balance spatial efficiency with the provision of green and commercial spaces.

The revised sections can be found on page 25 of the revised manuscript, lines 8–15 of part (4) and lines 9–15 of part (5), with the changes highlighted in red.

Comment 4: Considering the bidirectional influence of some factors (LPI, PD) on emissions, how can adaptive thresholds for spatial development indicators be developed that would ensure low-carbon growth at different stages of urbanization of individual counties?

Response: Thank you for your suggestion. We fully agree with your viewpoint and have made the following additions:

Our analysis shows that industrial urban areas and general urban areas should be treated differently. For industrial urban areas, it is necessary to reduce centrality (LPI) to lower carbon emissions by relocating energy-intensive industries to suburban areas and promoting green industrial upgrading. For general urban areas, maintaining a certain level of centrality helps improve urban functional efficiency, effectively shorten commuting distances, and thereby reduce carbon emissions. Similarly, within cities, an appropriate degree of fragmentation(PD) should be maintained. While ensuring efficient infrastructure layouts, ecological patches and corridors should be preserved to sustain natural carbon sinks.

The revised content of part (5) on page 25 has also been highlighted in red.

Yours Sincerely,

Yun Xue

School of Architecture and Art Design, Inner Mongolia University of Science and Technology, Baotou 014010, China

Email: xueyun_cn@126.com

Reviewer 4 Report

Comments and Suggestions for Authors The current manuscript examines the temporal and spatial characteristics of urban carbon emission at a rather detailed scale in the region of Inner Mongolia. The study focuses on the three major cities—Hohhot, Baotou, and Ordos—and quantifies the distribution of carbon emission, constructing a long-term analysis framework (from 2013 to 2021). Six morphological factors—urban scale (CA), urban complexity (LSI), centrality (LPI), cohesion (COHESION), patch density (PD), and interspersion and juxtaposition index (IJI)—are analyzed. The study is certainly important and it does deliver some interesting results. However, prior to publication, certain important revisions are absolutely necessary, both regarding the presentation aspects as well as from the viewpoint of the data analysis; please address the points below. The introduction and literature review should be more structured. Not only the results and findings are to be listed here somewhat assortedely, but also some comparative analysis should be presented. Some strange URL web links should be removed from the main text and put into the bibliography instead, with the full citations of the respective literature source. The axis captions of the plots should not be too small. The font size for the axis captions should be the same as---or even larger than---the font size of the standard text in the finally formatted paper. The explicit math definitions of all quantities presented, and in particular of the Global Moran's I, should be given in the revised text. The same for the math details of the models and definitions of the six main parameters listed above used to rationalize the observed heterogeneities. Currently, literally zero details is presented about them in the main text. Moreover, their actual meaning is described first on page 24 out of 29, in the discussion section. It will make much more sense to shift these descriptions to the start of the modelling section, to give the reader a clear view regarding the meaning of these key parameters. The conclusions should be presented much more clearly. Also, some repetitions from the section with the results should at best be removed. One can easily deliver the same information in only half of the space, if one would want to and read the text critically and them optimize the presentation. Additionally, in the conclusions the readers often prefer VERY CLEAR---may be even numbered---4-5 statements regarding the novelty and the importance of the obtained results. After this, the authors are STRONGLY encouraged to critically compare and contrast their main results to the finding of the most related studies available in the literature (and described in the introduction, i presume/hope). Finally, the authors consider the spatial correlations, but what about the temporal ones? For instance, from the data of Fig. 3 it is easy to compute the so-called temporal averages and from them to forecast the properties of further growth of the CO2 level. The prediction of this growth is evidently of paramount importance, even more so than the temporal pattern of growth, possibly. The authors can therefore consult Ref. [https://doi.org/10.1088/1367-2630/aa7199] for the math details of the methods of such an analysis (the so-called time-averaged mean-squared displacement, TAMSD) for other temporal data. The authors are encouraged to compute such forecasts for their data too. A serious and deep discussion of the analysis of temporal growth of CO2 emission should certainly be presented in the revised version, with the detailed description of the relevant math methods mentioned above.

Author Response

Dear reviewer,

We highlighted all the revisions in red colour.

Our point-to-point response to the questions raised by the reviewers is as follows：

Comment 1: The introduction and literature review should be more structured. Not only the results and findings are to be listed here somewhat assortedely, but also some comparative analysis should be presented.

Response: Thanks for your suggestion. We fully agree that in the original version the introduction and literature review were somewhat fragmented, with findings listed in a rather descriptive way. Following your advice, we have thoroughly revised and restructured this section as follows:

Structured Organization: The literature is now reviewed across three dimensions—research scale, research focus, and research methodology. Studies are systematically summarized at national, provincial, city, and county levels, and categorized into socioeconomic drivers, spatial morphology drivers, and methodological advances.
Comparative Analysis: Instead of simple listing, we now highlight differences and limitations across studies. For example, we contrast macro-scale versus county-scale studies in terms of data accessibility and their ability to capture spatial heterogeneity.
Research Gaps and Value: We emphasize that while most existing studies on Inner Mongolia focus on socioeconomic drivers, relatively little attention has been given to urban spatial morphology. This underscores the necessity and novelty of our research.

The revised literature review section has been incorporated into the revised manuscript on pages 2–4, Section 2 Literature Review, with the corresponding changes highlighted in red. We believe that the improved structure and comparative analysis strengthen the coherence of the introduction and literature review and directly address the reviewer’s concern.

Comment 2: Some strange URL web links should be removed from the main text and put into the bibliography instead, with the full citations of the respective literature source.

Response: Thank you for pointing out this issue. We have moved all web links for the data sources to the reference section to keep the manuscript neat and consistent.
The revised content has been incorporated into the revised manuscript on page 6, Section 3.2 Data, with the changes highlighted in red.

Comment 3: The axis captions of the plots should not be too small. The font size for the axis captions should be the same as---or even larger than---the font size of the standard text in the finally formatted paper.

Response: Thank you for raising this issue. We have revised the coordinates in Figure 2. The updated figure can be found on page 7of the revised manuscript.

Comment 4: The explicit math definitions of all quantities presented, and in particular of the Global Moran's I, should be given in the revised text.

Response: Thanks for your advice. We have carefully checked all mathematical definitions in the manuscript and added a supplementary explanation for the definition of Moran’s I as follows:

“Spatial autocorrelation analysis is applied to reveal the spatial association and heterogeneity of regional carbon emissions. This study employs both global and local spatial autocorrelation methods to examine the county-level carbon emission patterns in the Hohhot–Baotou–Ordos area. The Global Moran’s I measures overall spatial correlation, ranging from –1 to 1: values greater than 0 indicate clustering, values less than 0 indicate dispersion, and 0 represents no correlation. The Local Moran’s I further identifies spatial structures within counties: positive values correspond to ‘high–high’ or ‘low–low’ clusters, while negative values indicate ‘high–low’ or ‘low–high’ differences, reflecting intra-regional spatial heterogeneity.”

This supplementary explanation has been added to the revised manuscript on page 8, Section 3.3.3 Spatial Autocorrelation Analysis of Carbon Emissions, with the changes highlighted in red.

Comment 5: The same for the math details of the models and definitions of the six main parameters listed above used to rationalize the observed heterogeneities. Currently, literally zero details is presented about them in the main text. Moreover, their actual meaning is described first on page 24 out of 29, in the discussion section. It will make much more sense to shift these descriptions to the start of the modelling section, to give the reader a clear view regarding the meaning of these key parameters.

Response: Thank you for pointing out the issue regarding parameter definitions. We realized that the initial manuscript lacked clear explanations of the six indicator parameters. According to your suggestion, we have added supplementary descriptions in the revised version. The meanings of each indicator are as follows:

Spatial form index	Abbreviation	Meaning of representation
Class Area	CA	Urban Scale
Landscape Shape Index	LSI	Urban Complexity
Largest Patch Index	LPI	Centrality
Patch Cohesion Index	COHESION	Compactness
Patch Density	PD	Fragmentation
Interspersion Juxtaposition Index	IJI	Land-Use Adjacency

The revised text and table have been incorporated into the Abstract (page 1, lines 8–12) and Table 1 Urban Spatial Form Indicators and Their Descriptions (page 8) of the revised manuscript, with the changes highlighted in red.

Comment 6: The conclusions should be presented much more clearly. Also, some repetitions from the section with the results should at best be removed. One can easily deliver the same information in only half of the space, if one would want to and read the text critically and then optimize the presentation.

Response: Thank you for your suggestion. We acknowledge that the original conclusion section was indeed somewhat redundant, and we have removed repetitive content accordingly. In addition, we have revised overlapping descriptions in the results section. For example, we reanalyzed the statistical tables of Moran’s I and GTWR regression coefficients, and streamlined the sections on the spatiotemporal evolution of carbon emissions and urban spatial form.

The revised passages can be found in the updated manuscript on page 14 (paragraphs 1–4), page 16 (paragraphs 2), and pages 9–12 (Sections 4.1 Spatiotemporal Evolution Characteristics of Carbon Emissions and 4.2 Spatiotemporal Evolution of Urban Spatial Form). All modifications have been highlighted in red in the revised version.

Comment 7: Additionally, in the conclusions the readers often prefer VERY CLEAR---may be even numbered---4-5 statements regarding the novelty and the importance of the obtained results. After this, the authors are STRONGLY encouraged to critically compare and contrast their main results to the finding of the most related studies available in the literature (and described in the introduction, i presume/hope).

Response: We sincerely thank the reviewer for the constructive comments. We agree that the conclusion section in the original version was rather lengthy and repetitive, and the expression was not sufficiently clear. Following your advice, we have comprehensively revised and optimized the conclusion:

Redundant descriptions overlapping with the results section were removed, and the text was condensed to make the conclusions more concise.
In the opening paragraph of the conclusion, we summarized the innovations of the study. Afterwards, we presented five key conclusions and corresponding policy recommendations in a numbered format, making the content clearer and more straightforward.
In the conclusion section, we also compared and contrasted our findings with related studies mentioned in the introduction (e.g., Fang, Falahatkard, Shi), highlighting the uniqueness and academic contribution of this study at the county scale and from the perspective of urban spatial morphology.

The revised conclusion is presented in Section 6 Conclusions and Suggestions on pages 24–25 of the revised manuscript, with the changes highlighted in red.

Comment 8: Finally, the authors consider the spatial correlations, but what about the temporal ones? For instance, from the data of Fig. 3 it is easy to compute the so-called temporal averages and from them to forecast the properties of further growth of the CO2 level. The prediction of this growth is evidently of paramount importance, even more so than the temporal pattern of growth, possibly. The authors can therefore consult Ref. [https://doi.org/10.1088/1367-2630/aa7199] for the math details of the methods of such an analysis (the so-called time-averaged mean-squared displacement, TAMSD) for other temporal data. The authors are encouraged to compute such forecasts for their data too. A serious and deep discussion of the analysis of temporal growth of CO2 emission should certainly be presented in the revised version, with the detailed description of the relevant math methods mentioned above.

Response: We sincerely appreciate the reviewer’s valuable comments. We agree that temporal correlation is an important dimension in examining the evolution of carbon emissions. In the revised manuscript, we have incorporated a more in-depth discussion of temporal characteristics in the results section. Specifically, we emphasized interannual variation trends and provided statistical descriptions of change rates based on multi-year data to reveal the overall evolutionary trajectory of carbon emissions (see pages 9–10, Section 4.1 Spatiotemporal Evolution Characteristics of Carbon Emissions in the revised manuscript, with changes highlighted in red). This addition helps readers better understand the dynamic nature of carbon emissions in the study area.

Regarding the prediction of carbon emissions, after careful consideration, we believe it is not appropriate to include such analysis in this paper. The primary aim of our study is to explore the relationship between urban spatial morphology and the spatiotemporal heterogeneity of carbon emissions, with an emphasis on explaining the “why” and “how” of spatial distribution rather than providing quantitative forecasts of future emission levels. Predictive analysis typically requires the integration of additional socioeconomic variables (e.g., GDP, industrial structure, energy policies) and scenario assumptions. Without these, the results may involve high uncertainty and could weaken the credibility of the study’s core conclusions. Moreover, time series forecasting methods such as TAMSD are generally more suitable for longer temporal sequences. Although our dataset covers nine years (2013–2021), it may be insufficient for robust predictive modeling: the relatively small sample size could make results sensitive to outliers and lead to considerable prediction errors. Therefore, we chose to focus the temporal analysis on trend identification and rate of change, rather than introducing predictive models, in order to maintain the clarity of the research objectives and the robustness of the conclusions.

Thank you for pointing out the language issues. We have carefully revised the manuscript to improve clarity, grammar, and academic style.

Yours Sincerely,

Yun Xue

School of Architecture and Art Design, Inner Mongolia University of Science and Technology, Baotou 014010, China

Email: xueyun_cn@126.com

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

My concerns have been addressed.

Author Response

Dear Reviewer，

We believe this paper will contribute valuable insights to the field of yours, and we are very pleased and excited that it will be published in your esteemed journal.

Thank you once again for your efficient handling of our manuscript.

Yours Sincerely,

Yun Xue

School of Architecture and Art Design, Inner Mongolia University of Science and Technology, Baotou 014010, China

Email: xueyun_cn@126.com

Reviewer 4 Report

Comments and Suggestions for Authors

A number of points were covered during this revision. The point regarding the analysis of the time-series (incl. the proposed reference and the vital method of the TAMSD itself) was however not described satisfactorily. The authors should still address this issue in detail in the second revision. The time-series analysis is the most common method of analysis of time-equidistant data sets.

Author Response

Dear reviewer,

Thank you again for your suggestion to improve the quality of our submitted manuscript (article number: 3881890). Our point-to-point response to the questions raised by the reviewers is as follows：

Comment 1: A number of points were covered during this revision. The point regarding the analysis of the time-series (incl. the proposed reference and the vital method of the TAMSD itself) was however not described satisfactorily. The authors should still address this issue in detail in the second revision. The time-series analysis is the most common method of analysis of time-equidistant data sets.

Response: Thank you for your valuable suggestions. We agree with your comments and have made the following revisions:

We carefully studied and cited the references you provided, and we applied the time-averaged mean-square displacement (TAMSD) method combined with logarithmic regression analysis to predict the future growth characteristics of carbon emissions in 27 counties of Hohhot, Baotou, and Ordos (HBO). This approach has further improved the comprehensiveness of our analysis of carbon emission dynamics.
In the methodology section, we added an introduction to the TAMSD approach and explained the significance of logarithmic regression.
In the results, we generated a scatter plot of α values based on the regression outcomes, allowing readers to better interpret the data. The results show: The α values range from −0.53 to 1.78, with an average of 1.25. Among the 27 counties in HBO, 20 counties have α values greater than 1, while 7 counties have α values less than 1, including 2 with α < 0. For counties with α < 0, the coefficient of determination (R²) is below 0.5, indicating very poor goodness-of-fit. This suggests that the power-law model cannot adequately describe their TAMSD curves; hence, these cases are not shown in the figure. Overall, most counties in the HBO metropolitan area are projected to exhibit accelerated growth in the future, with carbon emissions expected to show active and highly volatile fluctuations. In contrast, a small number of counties are expected to experience a slowdown in growth, with carbon emissions remaining relatively stable.
We also provided comparative explanations of the results derived from this method in the abstract, discussion, and conclusion sections.

The revised passages can be found in the marked-up manuscript: Abstract (page 1, lines 16–18), Figure 5 (page 11), Section 4. Results (page 12, first paragraph), Section 5. Discussion (page 23, last sentence of the first paragraph), and Section 6. Conclusions and Suggestions (page 24-25，part (1)). The modifications are highlighted in blue.

We believe these additions have substantially improved the manuscript by addressing the temporal aspect of CO₂ emissions as requested, and we thank the reviewer again for pointing us toward this important dimension.

Yours Sincerely,

Yun Xue

School of Architecture and Art Design, Inner Mongolia University of Science and Technology, Baotou 014010, China

Email: xueyun_cn@126.com

Round 3

Reviewer 4 Report

Comments and Suggestions for Authors

The authors did a solid revision, with an extensive amount of changes being incorporated in the text to reply to the comments and the critique points of the referees. This version can thus be accepted for publication.

Author Response

Dear Reviewer，

We believe this paper will contribute valuable insights to the field of yours, and we are very pleased and excited that it will be published in your esteemed journal.

Thank you once again for your efficient handling of our manuscript.

Yours Sincerely,

Yun Xue

School of Architecture and Art Design, Inner Mongolia University of Science and Technology, Baotou 014010, China

Email: xueyun_cn@126.com