How Does the Spatial Structure of the Furniture Industry Shape Urban Residents’ Health? Evidence from China Labor-Force Dynamics Survey and POI Data

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Although the analysis based on the furniture sector is conducted thoroughly, the article lacks sufficient justification as to why this particular sector was chosen as a representative example for studying the relationship between industrial agglomeration and the health of residents. Although the authors mention the economic importance of the industry and its impact on the environment, no convincing arguments are presented to show that the furniture industry has characteristics that allow it to be treated as a model case in the broader context of industrial urbanization.

It would be advisable to supplement the text with a detailed justification for the choice of this industry: 
- Is it characterized by a particular spatial distribution? 
- Do the structure of the value chain, the intensity of emissions, 
- or the employment profile distinguish it from other industries? 

In its current form, narrowing the subject of the analysis to a single sector may limit the generalizability of the conclusions and their application in urban policy practice.

Comments on the Quality of English Language

Minor linguistic and stylistic flaws: The article was written in English, and at times it is apparent that the authors are not native speakers of the language, resulting in some awkward phrasing. Fortunately, this does not greatly affect comprehensibility, but it is worth noting a few examples. The very first sentence of the introduction contains a rather unidiomatic construction: “the current and increasing interest is all on how the spatial structures of industries... interplay to determine the general well-being.” The expression “is all on how” sounds awkward—it would be better to say, for example, “is focused on how.” There are several similar stylistic details (e.g., the use of the word “dispensation” in the context of the distribution of health resources is unusual). In some places, the sentences are very complex, which requires careful reading to grasp the meaning (especially in the literature section and in the policy discussion). At the editorial level, clarity could be improved by splitting some sentences or clarifying certain phrases. In places, I also noticed minor typos and formatting inconsistencies (e.g., different spelling of abbreviations, minor punctuation errors), which is typical for a “for peer review” version. However, these shortcomings do not significantly affect the content – the article is easy to understand and the argumentation is consistent. Linguistic and editorial corrections would be welcome before final publication, but they do not constitute a serious flaw in the scientific work.

Author Response

Comments 1:Although the analysis based on the furniture sector is conducted thoroughly, the article lacks sufficient justification as to why this particular sector was chosen as a representative example for studying the relationship between industrial agglomeration and the health of residents. Although the authors mention the economic importance of the industry and its impact on the environment, no convincing arguments are presented to show that the furniture industry has characteristics that allow it to be treated as a model case in the broader context of industrial urbanization.

It would be advisable to supplement the text with a detailed justification for the choice of this industry:

- Is it characterized by a particular spatial distribution?

- Do the structure of the value chain, the intensity of emissions, or the employment profile distinguish it from other industries?

In its current form, narrowing the subject of the analysis to a single sector may limit the generalizability of the conclusions and their application in urban policy practice.

Response 1: We sincerely thank the reviewers for their valuable comments, which emphasized the importance of providing a stronger rationale for selecting the furniture industry as the focus of this study. This clarification is crucial for assessing the generalizability of our findings and their applicability to urban policy practice. In response, we have both supplemented the manuscript with relevant findings from leading journals and our previous research on furniture industry agglomeration (Chen et al., 2024) and provided additional explanations based on the industry’s real-world characteristics. This addresses the question of what features make the furniture industry a representative case within the broader context of industrial urbanization.

First, as a consumer essential industry, furniture manufacturing and sales have broad geographic coverage. Constrained by high transportation costs, the industry exhibits a clear market-oriented layout, with distribution across towns and cities at all levels nationwide. At the same time, the industry demonstrates significant regional clustering, forming five major furniture industrial clusters in the Pearl River Delta, Yangtze River Delta, Bohai Rim, Northeast, and Central-West regions. This ensures representative coverage across different city types and creates a continuous gradient from high- to low-agglomeration areas, which helps isolate the effects of industrial spatial structure on health while controlling for city-level confounding factors. Second, the furniture industry has a complete value chain, covering upstream raw materials and components such as wood, hardware, and coatings; midstream design and manufacturing; and downstream sales, logistics, and after-sales services. This chain integrates capital-, technology-, and labor-intensive processes, providing a natural setting to examine the differential health impacts of specialized versus diversified agglomeration. Unlike heavy industrial parks located far from urban areas or relatively enclosed high-tech zones, the downstream and some midstream activities of the furniture industry, such as processing and assembly, are closely embedded within urban residential areas, forming a spatial pattern where production, sales, and living spaces are tightly intertwined. This proximity makes the environmental externalities of industrial activity and their health impacts more immediate and perceptible to residents. Finally, the furniture industry is a typical source of light industrial pollution, with emissions directly linked to public health. Manufacturing processes such as paint spraying and gluing release large amounts of air pollutants, primarily volatile organic compounds (VOCs) and particulate matter. VOCs are key precursors for PM2.5 and ozone formation, and compounds such as benzene and formaldehyde are recognized carcinogens directly associated with respiratory diseases and cancer risk. Therefore, the agglomeration patterns of the furniture industry and environmental regulations can directly and significantly influence local public health outcomes related to air pollution.

In summary, we have revised the introduction section in the updated manuscript. The revised text is as follows:

The furniture industry is not a typical traditional manufacturing sector. It spans the entire value chain from raw material processing and creative design to end retail services, and it has a distinct “manufacturing plus services” character. As a key livelihood-oriented industry, its sales activities are closely connected to urban living spaces, and its production processes directly affect local environmental quality. These features make the furniture sector a valuable context for studying how industrial activity influences residents’ health and the broader urban living environment. Over the past two decades, China’s furniture industry has grown at an average annual rate of more than 15 percent, forming major industrial clusters in the Pearl River Delta, Yangtze River Delta, Bohai Rim, Northeast China, and the central and western regions (Zhang, 2023). As a producer of everyday necessities, the industry’s market-oriented spatial layout has also led to a widespread presence across cities and towns of different sizes. This combination of wide geographic distribution and strong regional clustering promotes resource sharing, knowledge spillovers, and economies of scale, and it significantly shapes local environmental conditions, public service provision, and the well-being of community residents.

Partial references:

• Chen, Z., Su, L., Wu, K., et al. Spatial Cluster Identification and Spatial Entropy Decomposition of Furniture Industry Chain in the Pearl River Delta Region. Economic Geography, 2024, 44(12): 141-152. (In Chinses)

 

Comments 2: Minor linguistic and stylistic flaws: The article was written in English, and at times it is apparent that the authors are not native speakers of the language, resulting in some awkward phrasing. Fortunately, this does not greatly affect comprehensibility, but it is worth noting a few examples. The very first sentence of the introduction contains a rather unidiomatic construction: “the current and increasing interest is all on how the spatial structures of industries... interplay to determine the general well-being.” The expression “is all on how” sounds awkward—it would be better to say, for example, “is focused on how.” There are several similar stylistic details (e.g., the use of the word “dispensation” in the context of the distribution of health resources is unusual). In some places, the sentences are very complex, which requires careful reading to grasp the meaning (especially in the literature section and in the policy discussion). At the editorial level, clarity could be improved by splitting some sentences or clarifying certain phrases. In places, I also noticed minor typos and formatting inconsistencies (e.g., different spelling of abbreviations, minor punctuation errors), which is typical for a “for peer review” version. However, these shortcomings do not significantly affect the content – the article is easy to understand and the argumentation is consistent. Linguistic and editorial corrections would be welcome before final publication, but they do not constitute a serious flaw in the scientific work.

Response 2: We sincerely thank the reviewers for their careful evaluation and valuable comments on the language and expression in the manuscript. We fully acknowledge the issues you raised and apologize for any non-native phrasing. Based on your specific suggestions, we have revised the opening sentence of the introduction as follows: “Increasingly, attention is focused on how the spatial layout of industries, local climate conditions, and the distribution of health resources work together to shape the overall well-being of residents.”

We have carefully considered your comment regarding the term “dispensation” and consulted relevant academic usage. Where appropriate, we replaced it with more precise and widely accepted alternatives such as “distribution” or “allocation.” To address overly complex sentences in the literature review and policy discussion sections, we thoroughly reorganized and rewrote the relevant paragraphs. By splitting long sentences, adjusting word order, and simplifying subordinate clauses, we aimed to improve logical clarity and convey ideas more directly, enhancing overall readability. We also conducted a comprehensive review of the manuscript’s language, correcting awkward or unnatural phrasing to ensure smoother flow and adherence to academic English conventions.

We also fully recognize the importance of consistency in spelling, abbreviations, and punctuation, as these details are crucial for the manuscript’s professionalism and compliance with scholarly standards. Accordingly, we conducted a thorough check and standardized these elements throughout the revised manuscript to eliminate any language or formatting inconsistencies prior to publication. We sincerely appreciate your patience and guidance, which have encouraged us to maintain both scientific rigor and linguistic precision in our future work. Given the number and dispersed nature of the revisions, we have highlighted all changes in blue in the revised manuscript to maintain clarity, and we respectfully invite the reviewers to examine our modifications.

Reviewer 2 Report

Comments and Suggestions for Authors

The article “  How does the spatial structure of the furniture  enterprises shape urban resident’s health: Implications for Sustainable Urbanisation in China advances  knowledge on  integrated  urban planning . Its strength lies in use of novel methodological approaches to  advancing sustainable urban planning . The  following observations and propositions could assist  in improving the manuscript.

Title :  can be improved

General comments

The highlighted sections in the manuscript capture sections that  need some improvement

Specific comments are as hereunder

• Missing citation including line 62; 363; 420-422;49-50; 351-353
• Need to operationalise and/or clearly contextualise the use of some  terms such as  ecology and ecology restoration. The terms could have been used out of context ( line 145/154)
• Need to cite some of the “ established research practices) on line  286
• Where is “ BBB” in equation defined in  line 315?
• There is likelihood of several interpretation of subjective terms such “healthy” on  line  334/434. The authors need to clearly define / operationalise these terms in context of the study

 

Methods section

• Providing Georeferenced map of the area is necessary
• Most of material  section  in 4.3   ( from line 486) should be brought here  and not in the results section
• Need to provide some descriptive statistics

 

 

The article “  How does the spatial structure of the furniture  enterprises shape urban resident’s health: Implications for Sustainable Urbanisation in China advances  knowledge on  integrated  urban planning . Its strength lies in use of novel methodological approaches to  advancing sustainable urban planning . The  following observations and propositions could assist  in improving the manuscript.

Title :  can be improved

General comments

The highlighted sections in the manuscript capture sections that  need some improvement

Specific comments are as hereunder

• Missing citation including line 62; 363; 420-422;49-50; 351-353
• Need to operationalise and/or clearly contextualise the use of some  terms such as  ecology and ecology restoration. The terms could have been used out of context ( line 145/154)
• Need to cite some of the “ established research practices) on line  286
• Where is “ BBB” in equation defined in  line 315?
• There is likelihood of several interpretation of subjective terms such “healthy” on  line  334/434. The authors need to clearly define / operationalise these terms in context of the study

 

Methods section

• Providing Georeferenced map of the area is necessary
• Most of material  section  in 4.3   ( from line 486) should be brought here  and not in the results section
•  providing some descriptive statistics could enhance the manuscript

 

 

 

Comments for author File: Comments.pdf

Author Response

Comments 1: Title can be improved.

Response 1: We appreciate the reviewer’s suggestion regarding the title. After careful consideration, we have revised the manuscript title to: “How Does the Spatial Structure of the Furniture Industry Shape Urban Residents’ Health? Evidence From China Labor-force Dynamics Survey and POI Data”.

 

Comments 2: General comments: The highlighted sections in the manuscript capture sections that need some improvement.

Response 2: We sincerely thank the reviewers for their careful evaluation of this manuscript and for providing numerous valuable comments, as well as for annotating the manuscript to guide our revisions. We have addressed each of the reviewers’ suggestions in the revised manuscript. To facilitate review and due to space limitations in this response letter, we have not presented all changes here. Instead, all modifications are highlighted in blue within the revised manuscript, and we respectfully invite the reviewers to examine them.

 

Comments 3: Missing citation including line 62; 363; 420-422;49-50; 351-353. Need to cite some of the “established research practices” on line 286.

Response 3: We appreciate the reviewer’s valuable suggestions regarding the completeness and rigor of the manuscript’s citations. We have carefully addressed each recommendation highlighted in the review, supplementing the manuscript with necessary references and data sources. The specific modifications are as follows:

1. Supplementing key data sources and access paths: For the data source issues noted at lines 62 and 363, we have added the official websites of authoritative data platforms or clearly specified the data acquisition paths to ensure the traceability and reproducibility of the research data.
2. Strengthening methodological references: Around line 286, we have supplemented the processing procedures for the CLDS data with methodological references and concrete operational examples to enhance the transparency and rigor of the methods section.

Partial references:

• Li, Z, and Li, Y. Q. Environmental regulation and employment: Evidence from China's new Environmental Protection Law. Economic Analysis and Policy, 2024: 400-416.
• Qiao, X., Du, Q., and He, Y. Occupational mobility and its income effects in the context of the digital economy. Chinese Journal of Population Science, 2023, 1: 86-100. (In Chinses)

3. Strengthening the theoretical basis for control variables: In the “3.2.3 Other Variables” section (lines 351–353), we systematically supplemented the manuscript with literature from labor economics, health economics, and related fields to provide a solid theoretical foundation for the selection of control variables, particularly enhancing the discussion on how individual characteristics influence labor decisions and residents’ environmental outcomes.

Partial references:

• Cai, J. L., Peter C. Coyte, and Zhao, H. Z. Determinants of and socio-economic disparities in self-rated health in China. International Journal for Equity in Health, 2017, 16.(1): 7.
• Feng, Q. W., Ying, W., and Xia, X. B. Health inequalities for China’s low-income population: trends, subgroup differences, and influencing factors, 2010–2022. Frontiers in Public Health, 2025,13: 1569726.
• Wang, X. Z., and Liu, T. Home-made blues: Residential crowding and mental health in Beijing, China. Urban studies, 2023,60.(3): 461-482.

4. Updating references for general statements: For the background and macro-level discussions at lines 34–35, 40–43, and 49–50, we have added the latest authoritative references to ensure that the statements are well-supported.

References:

• Liu, X. Y., and Junghan Bae. Urbanization and industrialization impact of CO2 emissions in China. Journal of cleaner production, 2018, 172: 178-186.
• Benassi, Federico, and Antonio De Falco. Residential Segregation and Accessibility: Exploring Inequalities in Urban Resources Access Among Social Groups. Land, 2025, 14 (2): 429.
• He, Q, et al. Theoretical model of environmental justice and environmental inequality in China’s four major economic zones. Sustainability, 2019, 11(21): 5923.
• Luo J, Wang Y, Zhu L, et al. Structural Features of China's Industrial Pollution Inequality: A Spatial-Temporal Distribution Perspective. Frontiers in Environmental Science, 2022, 10: 846787.
• Guo X, Guo K, Kong L. Industrial agglomeration and corporate ESG performance: Empirical evidence from manufacturing and producer services. Sustainability, 2023, 15(16): 12445.
• Qin C, Lu D, Li Y. Industrial agglomeration, environmental regulation, and regional environmental performance: Direct and interactive effects. Managerial and Decision Economics, 2024, 45(8): 5527-5540.
• Wang L, Sun W, Zhou K, et al. Spatial analysis of built environment risk for respiratory health and its implication for urban planning: a case study of Shanghai. International journal of environmental research and public health, 2019, 16(8): 1455.
• Sun J, Lu C, Xie J. The impact of sustained exposure to air pollutant on the mental health: Evidence from China. Sustainability, 2022, 14(11): 6693.
• Ye Y, Ye S, Yu H. Can industrial collaborative agglomeration reduce haze pollution? City-level empirical evidence from China. International Journal of Environmental Research and Public Health, 2021, 18(4): 1566.
• Zhang, Z. Niche Fitness Measurement and Health Evaluation of Furniture Sectoral Innovation Ecosystem [Doctoral dissertation, Northeast Forestry University]. 2023,China Doctoral Dissertations Full-text Database.
• Chen, Z., Su, L., Wu, K., et al. Spatial Cluster Identification and Spatial Entropy Decomposition of Furniture Industry Chain in the Pearl River Delta Region. Economic Geography, 2024, 131-141. (In Chinses)
• Cheng B, Tian Y, Lu Y. Current Status, Problems and Countermeasures of Furniture Industry Cluster in China. Journal of Beijing Forestry University (Social), 2011, 10(2): 59-62. (In Chinses)
• Lee HC, Yang EH, Shin S, Moon SH, Song N, Ryoo JH. Correlation of commute time with the risk of subjective mental health problems: 6th Korean Working Conditions Survey (KWCS). Annals of occupational and environmental medicine. 2023, 35: e9.
• Ehsan A, Klaas HS, Bastianen A, Spini D. Social capital and health: A systematic review of systematic reviews. SSM-population health. 2019, 8: 100425.

 

Comments 4: Need to operationalise and/or clearly contextualise the use of some  terms such as ecology and ecology restoration. The terms could have been used out of context (line 145/154)；

Response 4: We sincerely appreciate your important comments regarding the use of terminology in the manuscript. The issues you highlighted at lines 145 and 154 concerning the context of “ecology” and “ecology restoration” are particularly critical. Upon review, we found that these were not deliberate attempts to treat these terms as operationalized technical concepts. Rather, they resulted from imprecise word choices during the translation and adaptation of the original Chinese draft into English, which led to ambiguous expressions and potential misunderstandings.

For instance, in the original Chinese text, the terms often referred more broadly to “environment” or “environmental governance,” but were directly rendered as “ecology.” Following your suggestions, we conducted a thorough review of the manuscript and revised all relevant expressions to more precise terms consistent with conventions in economics and public health research, such as “environmental quality,” “environmental improvement,” or “pollution control.” We greatly appreciate your careful review, which helped us correct these language-related oversights and substantially improve the accuracy of the manuscript’s English expression.

 

Comments 5: is it reduce ecological damage? (line 154)

Response 5: To systematically address your comments, we have added a discussion on the ecological effects of industrial agglomeration in the main text preceding Hypothesis H2. This section emphasizes the relationship between pollution characteristics and governance conditions. It notes that, in the short term, industrial agglomeration may exacerbate local pollution due to the spatial concentration of production activities, consistent with the expectations of the “pollution haven hypothesis.” However, in the long term, considering features of the furniture industry such as standardized pollution sources, classification as light manufacturing, and relatively concentrated emission points, combined with mechanisms associated with agglomeration economies—such as shared large-scale pollution control facilities, diffusion of green technologies, and cost pressures under strict environmental policies—industrial agglomeration is more likely to drive a transformation in environmental governance paradigms. This can reduce pollution per unit of output and ultimately achieve a net decrease in ecological damage.

In summary, we have revised the Literature Review and Hypothesis section in the updated manuscript. The revised text is as follows:

However, in the long run, the furniture industry, as a light manufacturing sector with relatively standardized pollution processes and clear governance pathways, is more likely to generate scale effects, coordinated governance benefits, and green technology gains under agglomeration conditions (Li et al., 2023). According to the Porter Hypothesis, agglomeration in industry stimulates green technological innovation through the concentration of rivalry, whereas shared pollution-control facilities may considerably reduce the number of abatement units. Here, the agglomeration of the furniture enterprises can theoretically increase technological innovation, lower the cost of transportation and information, and improve the economic and energy efficiency, which in the end will promote ecological restoration and the management of the environment in the urban environment. Besides, industrial agglomeration can also contribute to the fact individuals become more aware of environmental issues and, thus, encourage people to start engaging in the fight against pollution and drive businesses to invest more in green. Under an increasingly mature governance system and stricter environmental regulations, the agglomeration of the furniture industry is more likely to reduce per-unit industrial emissions and thereby improve overall urban environmental quality (Li et al., 2024).

Partial references:

• Cheng, Ke, et al. "Volatile organic compounds emission from Chinese wood furniture coating industry: Activity-based emission factor, speciation profiles, and provincial emission inventory." Aerosol and Air Quality Research 18.11 (2018): 2813-2825.
• Li L, Zhang D, Hu W, et al. Improving VOC control strategies in industrial parks based on emission behavior, environmental effects, and health risks: A case study through atmospheric measurement and emission inventory. Science of the Total Environment, 2023, 865: 161235.
• Li Z, Ding J, Tao T, et al. Novel Evaluation Method for Cleaner Production Audit in Industrial Parks: Case of a Park in Central China. Sustainability, 2024, 16(6): 2330.

 

Comments 6: Where is “ BBB” in equation defined in line 315?

Response 6: We sincerely thank you for your careful review of our manuscript and for pointing out the unclear definition of the symbols in Equation (2). Your observation is entirely correct. In the original manuscript, the symbol “BBB” used near line 315 in Equation (2) was not explicitly defined due to an oversight during translation and editing. We apologize for this error. In the revised manuscript, we have corrected this issue through careful review and translation. Equation (2) and all related symbols are now accurately defined and consistently presented in the text. We greatly appreciate your thoroughness and the time you dedicated, as your comments have significantly improved the clarity and rigor of our paper.

 

Comments 7: There is likelihood of several interpretation of subjective terms such “healthy” on line 334/434.The authors need to clearly define / operationalize these terms in context of the study.

Response 7: We sincerely appreciate your insightful comment. You are correct in noting that self-reported health measures may vary due to differences in individual reference frames, and clarifying the measurement baseline is essential for ensuring the credibility of the study’s findings. Self-reported health data indeed contain subjective components, with the baseline essentially reflecting each respondent’s internal reference system shaped by their perceptions, experiences, and cultural context. In response to your concern, we have added the following discussion in Section 3.2.2 of the revised manuscript: “The main dependent variable in this study is residents’ physical and mental health. Specifically, health is captured along two dimensions: physical health and psychological well-being. We measure these outcomes using self-rated health data from the CLDS survey. Although such measures are subjective and reflect individuals’ perceptions of their own health, they are widely used in health economics and epidemiology because they reliably predict objective health outcomes and offer strong feasibility and comparability in large-scale social surveys.”

In the revised manuscript, we note that the core health indicators used in this study are derived from self-reported data. Their advantage lies in capturing individuals’ overall perception of their own health, which itself is a key factor influencing health behaviors and well-being, while also being feasible and broadly comparable across groups in large-scale social surveys. Although inherently subjective, both single-item self-reported health (SRH) and the Kessler Psychological Distress Scale (K-6) are widely validated standardized tools. Through consistent question formats, graded response options, and application in large populations, they provide a relatively reliable measure of relative health status. The K-6 scores, in particular, are known to correlate with clinical diagnostic criteria, offering an external reference benchmark. To address potential measurement errors in self-reported data, this study conducted robustness checks (Section 4.2) by replacing the self-reported physical health variable with body mass index (BMI) as an objective physiological measure, confirming the robustness of the main findings.

 

Comments 8: Most of material section in 4.3 (from line 486) should be brought here and not in the results section.

Response 8: We sincerely appreciate your valuable suggestions regarding the manuscript’s structure. You correctly recommended moving the description of the heterogeneity analysis design and grouping criteria from Section 4.3 to the model specification section (e.g., Section 3.3), which is a highly professional and critical recommendation that we fully support. The methodology section (Section 3) should comprehensively present all analytical strategies, including the design logic of heterogeneity tests, the definition of grouping variables, and their operationalization, while the results section (Section 4) should focus on presenting and interpreting the empirical findings. In accordance with your advice, we have made the following structural adjustments to the manuscript:

1. We have added a methodological explanation of the heterogeneity analysis at the end of Section 3.3, "Model Specification."
2. We have streamlined Section 4.3, "Heterogeneous Analysis." After relocating the methodological content, this section now primarily presents the core regression results for each group and interprets the economic implications of the heterogeneity findings.

 

Comments 9: indenting each of the variable is advisable

Response 9: We sincerely appreciate your valuable suggestions regarding the manuscript’s formatting. You correctly noted that the presentation of the variable list is crucial for enhancing readability and adherence to standards. We fully agree and have revised and standardized the formatting in Section 3.3, "Model Specification," in accordance with your recommendations.

 

Comments 10: Is “pollutant fees” meant to refer to “polluter pay fees”?

Response 10: We sincerely appreciate your careful and important comment regarding terminology. Your concern about the term “pollutant fees” is entirely valid. This inaccuracy arose during the translation from Chinese to English, where we attempted to align it with contextually related terms such as “emission standards” and “taxes,” resulting in an overly literal translation. In the original Chinese text, “惩罚性税收” is a policy-generalized term. Our initial translation dissected it in an effort to find an equivalent English policy instrument, which led to the nonstandard and potentially confusing term “pollutant fees.” It does not correspond to the “polluter pays principle” nor to the standard concept of an “emission fee.”

Accordingly, we have revised the manuscript to correct this issue. The updated text is as follows: “On the one hand, environmental regulations, such as emission standards and punitive taxes, force high-polluting enterprises to either exit the market or upgrade their technologies (Luo et al., 2025).”

 

Comments 11: Providing a georeferenced map of the area is necessary.

Response 11: We are most grateful for your valuable suggestions regarding the visualization of this paper. In response, we have included a map illustrating the sample cities involved in this study and the distribution of furniture enterprises within them in Section 3.1 of the revised manuscript. We kindly request the reviewer to examine Fig. 1 in the revised version.

 

Comments 12: Providing some descriptive statistics could enhance the manuscript.

Response 12: We are most grateful for your valuable suggestions regarding this paper. In addition to presenting descriptive statistics for all variables in Table 1 of Section 3.3, we have supplemented these with a visual representation (Fig. 1) of the POI data for furniture enterprises in Chinese cities covered by this study, included in Section 3.1 of the revised manuscript. We kindly request the reviewer to examine the content of Section 3.1 in the revised version.

Reviewer 3 Report

Comments and Suggestions for Authors

This manuscript presents the relationship between furniture industry agglomeration and urban residents' health in China. The study's strengths lie in its innovative use of POI big data combined with a large-scale micro-survey (CLDS), its comprehensive analytical framework examining both physical and mental health, and its exploration of heterogeneous effects and underlying mechanisms. The topic aligns well with the journal's scope, contributing to the discourse on sustainable urbanization. However, the manuscript requires revisions before publication.

1. The paper uses the Location Quotient (LQ) based on POI counts. Please provide a more detailed justification for why LQ is superior to other potential measures (e.g., Ellison-Glaeser index) in this specific context, given the acknowledged data limitations. Furthermore, the formula for Aggregation1 (LQ) and Aggregation2 (Diversity Index) needs clearer explanation. For instance, in the LQ formula, what does m=3 represent? Is it the three value-chain segments (up/mid/downstream)? Clarifying how these segments are defined and aggregated into a single "intensity" measure is crucial for reproducibility.
2. While the use of terrain ruggedness as an instrumental variable (IV) is a good attempt, the exclusion restriction requires stronger theoretical and empirical justification. Why would terrain ruggedness, which influences economic activity and population distribution, not directly affect residents' health through channels other than industry agglomeration (e.g., by limiting access to healthcare or healthy food)? A more detailed discussion of this potential limitation and, if possible, robustness checks with alternative instruments (e.g., historical industrial patterns) would significantly strengthen the causal inference.
3. The mediation analysis, while valuable, is not fully transparent. The manuscript should explicitly state the empirical model used for testing mechanisms (e.g., a causal steps approach or bootstrapping). Presenting the direct and indirect effects with their significance levels, rather than just the coefficients from separate regressions, would provide a more standardized and interpretable assessment of the mediation pathways (environment, income, public health).
4. The literature review is comprehensive but could be better integrated with the study's own findings in the Discussion section. For example, how do the results on specialized vs. diversified agglomeration specifically confirm or challenge Marshallian vs. Jacobsian externalities in the context of health outcomes? Explicitly connecting back to the theoretical frameworks established in the introduction would strengthen the narrative.lThe authors should add more recently published related papers about residents' health in China as complementary references, such as: doi.org/10.1016/j.chieco.2024.102309.
5. The finding of a U-shaped relationship between agglomeration diversity (Aggregation2) and health is intriguing. However, the discussion in section 4.5 is somewhat speculative. Please provide more concrete examples or references to theories explaining why the negative effects of diversity eventually reverse. What specific "innovation network externalities" or "technological integration" thresholds are hypothesized? Linking this finding more firmly to existing economic geography literature would enhance its contribution.
6. The results for environmental regulation's moderating role are complex and somewhat contradictory (positive for mental health with intensity, negative for mental health with diversity). The current discussion is insufficient. The authors should delve deeper into interpreting these nuanced findings. What does it imply for policy if the same regulatory stringency can have opposite psychological effects depending on the industrial structure? This requires a more sophisticated discussion.
7. The limitations section is too brief. It should be expanded to discuss other potential confounders not controlled for, such as general urban population density (which correlates with both agglomeration and health outcomes) or other major local industrial pollutants unrelated to furniture. Also, the cross-sectional nature of the data severely limits the ability to infer long-term effects or rule out reverse causality (healthier people moving to areas with specific industries). This fundamental limitation deserves greater emphasis.

Author Response

Comments 1: The paper uses the Location Quotient (LQ) based on POI counts. Please provide a more detailed justification for why LQ is superior to other potential measures (e.g., Ellison-Glaeser index) in this specific context, given the acknowledged data limitations. Furthermore, the formula for Aggregation1 (LQ) and Aggregation2 (Diversity Index) needs clearer explanation. For instance, in the LQ formula, what does m=3 represent? Is it the three value-chain segments (up/mid/downstream)? Clarifying how these segments are defined and aggregated into a single "intensity" measure is crucial for reproducibility.

Response 1: We sincerely thank you for raising important questions regarding the measurement of industrial agglomeration. Your inquiries about the rationale for using the location quotient (LQ) method, the details of the formula, and the aggregation process of industry chain data are crucial for ensuring methodological transparency, the appropriateness of our approach, and the reproducibility of our results. We fully acknowledge your concerns and have extensively supplemented and clarified Section 3.2.1, "Identification of Industrial Agglomeration," in the revised manuscript. Our revisions directly address your two main questions: why LQ is the preferred choice under data constraints, and clarification of the formula symbols and industry chain data aggregation logic.

1. Detailed rationale for using the location quotient (LQ) over other indices

We have added a more comprehensive methodological explanation. The primary reason is that our study relies on a large but information-limited POI (Point of Interest) dataset. This dataset accurately identifies the geographic location and industry type of enterprises but generally lacks microeconomic details such as employment, output, or revenue required for conventional agglomeration measures.

Traditional indices, such as industry concentration ratios or the Herfindahl-Hirschman Index, heavily depend on firm-level size data (employment or output), which are unavailable in our POI dataset. The Ellison-Glaeser (EG) index is a superior measure that controls for firm size distribution and geographic unit area, distinguishing “natural” from “random” agglomeration. However, its calculation strictly relies on firm-level employment data, which our dataset cannot provide. Applying the EG index with only firm locations and types is neither feasible nor methodologically appropriate. Under these constraints, the location quotient (LQ) emerges as the most feasible and suitable option. LQ requires only firm counts, which the POI dataset directly provides, to effectively measure the relative geographic concentration of a given industry in a specific region compared to the national level. Although it does not control for firm size like the EG index, LQ is intuitive, requires minimal data, and produces robust results. It has been widely validated and accepted in “quantity-based” agglomeration studies using business registries, patent data, or POI datasets. Our choice of LQ represents a rigorous and reasonable balance between methodological sophistication and data reality.

2. Clarification of formula symbols and aggregation of industry chain data

We have added an explanation of how the agglomeration intensities of upstream, midstream, and downstream furniture enterprises are combined into a single overall intensity value using the entropy weight method. We have also included a description of the numerical characteristics of the agglomeration structure indicators.

We respectfully invite the reviewers to examine the revised manuscript.

 

Comments 2：While the use of terrain ruggedness as an instrumental variable (IV) is a good attempt, the exclusion restriction requires stronger theoretical and empirical justification. Why would terrain ruggedness, which influences economic activity and population distribution, not directly affect residents' health through channels other than industry agglomeration (e.g., by limiting access to healthcare or healthy food)? A more detailed discussion of this potential limitation and, if possible, robustness checks with alternative instruments (e.g., historical industrial patterns) would significantly strengthen the causal inference.

Response 2: We sincerely appreciate your valuable comments. The validity of instrumental variables is indeed a critical prerequisite for addressing endogeneity and achieving causal identification, and relying solely on terrain ruggedness as an instrument may not fully support the reasonableness of our endogeneity tests. In response, we have incorporated an additional historical instrument—whether the city had a railway in 1933—and provided a detailed explanation of its validity. Furthermore, Table 3 in the revised manuscript presents the empirical results for both instrumental variables. The corresponding textual revisions are as follows: “In addition, following existing studies^[65], this paper introduces a historical variable indicating whether the city had a railway in 1933 as an additional instrumental variable. At that time, given the poor road infrastructure in China, the opening of a railway had a significant impact on industrial agglomeration in cities, making this historical variable relevant to the current clustering of furniture firms. Moreover, since this instrumental variable predates the study period by approximately 70 years, it does not directly affect current residents’ health, thus satisfying the exogeneity requirement. The results of the endogeneity tests are presented in Table 3, with columns (8) to (11) reporting the estimates obtained using the instrumental variable under the two-stage least squares method.”

 

Comments 3：The mediation analysis, while valuable, is not fully transparent. The manuscript should explicitly state the empirical model used for testing mechanisms (e.g., a causal steps approach or bootstrapping). Presenting the direct and indirect effects with their significance levels, rather than just the coefficients from separate regressions, would provide a more standardized and interpretable assessment of the mediation pathways (environment, income, public health).

Response 3: We sincerely appreciate your valuable comments. In response to your suggestions regarding the mediation model, we have added the results of a Bootstrap analysis in the revised manuscript. The results confirm the robustness of our mechanism analysis and further illustrate the relative contributions of direct and indirect effects in the mediation process (please refer to Table 6 in the revised manuscript). The corresponding textual revisions are as follows: “In addition, this study employs the Bootstrap method to conduct robustness tests on the mediating effects of the three mechanism variables in the relationship between furniture firm agglomeration and residents’ physical and mental health, as well as to analyze the direct and indirect effects. The results show that, first, the significance of the mediating effects obtained from the Bootstrap test aligns with the mediating effect model, supporting the reliability of the mechanism analysis. Second, in terms of the magnitude of direct and indirect effects, all three mechanisms generally exhibit larger direct effects than indirect effects, indicating that furniture firm agglomeration influences residents’ health through multiple pathways.”

 

Comments 4：The literature review is comprehensive but could be better integrated with the study's own findings in the Discussion section. For example, how do the results on specialized vs. diversified agglomeration specifically confirm or challenge Marshallian vs. Jacobsian externalities in the context of health outcomes? Explicitly connecting back to the theoretical frameworks established in the introduction would strengthen the narrative. The authors should add more recently published related papers about residents' health in China as complementary references, such as: doi.org/10.1016/j.chieco.2024.102309

Response 4: We sincerely appreciate your valuable comments. You correctly noted that the literature review and discussion sections were not sufficiently integrated, particularly in linking the results of specialized and diversified agglomeration to the theoretical frameworks of Marshall and Jacobs externalities. You also suggested incorporating recent literature on Chinese residents’ health. We fully agree and have made the following revisions:

1.We added a discussion explicitly connecting our findings to mainstream Marshall and Jacobs theories, explaining how the health benefits of specialized agglomeration support Marshallian externalities, while the U-shaped effect of diversified agglomeration extends the explanatory power of Jacobs externalities in the health context.

2.We incorporated recent studies on Chinese residents’ health and its relationship with industry, environment, and urban development, including the suggested reference (doi:10.1016/j.chieco.2024.102309), integrating these findings naturally into the literature review and discussion sections.

   Partial references:

• Liao L, Kong S, Du M. The effect of clean heating policy on individual health: Evidence from China. China Economic Review. 2025;89:102309.
• Wu H, Li C. The impact of economic agglomeration on China’s urban public health. Frontiers in Public Health. 2024;12:1476339.
• Wang J, Luo X, Liang X, Ning C. New-type urbanization and regional public health: mechanisms and effects. Frontiers in Public Health. 2025 ;13:1513173.

3.We have reorganized the structure of the literature review and discussion sections to strengthen the theoretical logic, creating a clearer narrative flow from theory to methodology, results, and discussion.

We sincerely thank you again for your detailed and constructive feedback, which has further enhanced the theoretical rigor and academic contribution of this manuscript.

 

Comments 5：The finding of a U-shaped relationship between agglomeration diversity (Aggregation2) and health is intriguing. However, the discussion in section 4.5 is somewhat speculative. Please provide more concrete examples or references to theories explaining why the negative effects of diversity eventually reverse. What specific "innovation network externalities" or "technological integration" thresholds are hypothesized? Linking this finding more firmly to existing economic geography literature would enhance its contribution.

Response 5: We sincerely appreciate your insightful and constructive comments. You correctly noted that our initial discussion of the U-shaped relationship between diversified agglomeration (Aggregation2) and health was somewhat speculative and that the concept of the “innovation threshold” required a clearer theoretical definition. Your feedback provided an excellent opportunity to strengthen the theoretical contribution of our findings. In response, we have made substantial revisions to Section 4.5, Further Analysis: Agglomeration Structure and Policy Adjustment Effects, focusing on two key aspects to reinforce the theoretical foundation and reduce speculative statements:

1. Introducing the "related variety" theoretical framework to replace the vague notion of "innovation networks": We have incorporated the concept of related variety from evolutionary economic geography as the central theoretical framework for explaining the U-shaped relationship. This theory emphasizes that not all forms of diversification are beneficial; only when co-located industries share knowledge bases and technological capabilities that are complementary can knowledge spillovers, collaborative innovation, and adaptive capacity be maximized (Frenken et al., 2007; Boschma, 2017). This framework provides a clear definition of the “threshold”: it represents the critical point at which an industrial structure shifts from “unrelated variety” to “related variety.”
2. Applying related variety theory to the furniture industry to hypothesize qualitative changes around the U-shaped inflection point:

Before the threshold, industrial diversification manifests as “unrelated variety.” For example, a region hosting furniture manufacturing, metal processing, and textile industries involves highly disparate knowledge bases, production processes, and pollution control technologies. This diversity creates challenges in standardizing environmental regulations, sharing pollution control facilities, and facilitating cross-industry knowledge exchange. Consequently, coordination costs exceed innovation benefits, increasing the complexity of environmental governance and associated health risks.

After the threshold, diversification reaches a level where a related industrial ecosystem emerges. For instance, furniture manufacturing may coexist with smart home electronics, eco-friendly paint R&D, interior design, and modern logistics. These industries share overlapping knowledge in material science, ergonomics, and volatile organic compound (VOC) control. At this stage, cross-industry knowledge recombination becomes possible: smart home firms may introduce air quality monitoring technologies, eco-friendly paint developers provide upstream emission reduction solutions, and design firms enhance green product standards. This knowledge spillover fosters systemic green innovation and enables collaborative development of green supply chains or regional VOC management platforms, leading to improved environmental performance and health benefits.

Based on these revisions, we have clarified the theoretical rationale behind the U-shaped relationship and explicitly defined the innovation threshold. The corresponding revised manuscript section is as follows:

According to the theory of related variety, the health externalities of an industry depend not only on the number of sectors involved but also on the degree to which their underlying knowledge bases are connected (Frenken et al., 2007; Boschma, 2017). On the left side of the U-shaped curve, diversification in the furniture industry is likely to reflect “unrelated variety.” For example, furniture firms may coexist with other manufacturing activities that share little technological overlap with the sector. This type of structure can lead to inconsistent production standards, weak coordination in pollution-control technologies, and higher environmental regulatory costs. As a result, regional ecological “coordination costs” rise sharply, and the burden of structural complexity is ultimately shifted onto residents’ health.

We propose that this turning point represents a shift from unrelated to related variety within the local industrial ecosystem. Once this threshold is crossed, industries connected to furniture manufacturing, including environmental coating research, smart home design, and flexible logistics, are more likely to cluster together. An ecosystem built on shared knowledge bases can support cross-sector knowledge recombination and the integration of green technologies. Related industries also tend to adopt unified environmental standards and jointly invest in shared pollution-control facilities. In this setting, the Jacobs-type externalities generated by diversification, such as cross-industry knowledge spillovers and collaborative innovation, begin to outweigh coordination costs, allowing industrial diversity to translate into environmental benefits and sustainable health productivity.

   Partial references:

• Frenken K, Van Oort F, Verburg T. Related variety, unrelated variety and regional economic growth. Regional studies. 2007;41(5):685-97.
• Boschma R. Relatedness as driver of regional diversification: A research agenda. Regional Studies. 2017;51(3):351-64.

 

Comments 6：The results for environmental regulation's moderating role are complex and somewhat contradictory (positive for mental health with intensity, negative for mental health with diversity). The current discussion is insufficient. The authors should delve deeper into interpreting these nuanced findings. What does it imply for policy if the same regulatory stringency can have opposite psychological effects depending on the industrial structure? This requires a more sophisticated discussion.

Response 6: We sincerely appreciate your highly insightful comments. You correctly identified the complexity in interpreting the moderating effects of environmental regulation and urged us to move beyond a superficial description of apparent contradictions toward a deeper, theoretically grounded analysis. We fully agree that this seemingly “contradictory” finding reveals the multidimensional interaction between environmental regulation and industrial organization, representing one of the study’s key insights. We have deepened our discussion accordingly and revised the relevant sections in 4.5 Further Analysis and 5.1 Key Findings as follows:

1. Core Revision: Establishing a “Signal-Cost” Dual Explanation Framework

The original discussion did not sufficiently explain why the same environmental regulation enhances psychological well-being in specialized clusters but increases psychological burden in low-diversity clusters. We introduce an integrative theoretical perspective: the effects of environmental regulation depend not only on direct compliance costs but also on the social signals it conveys. The impact of these two channels on residents’ mental health is highly contingent on local industrial structure.

In specialized clusters, internal knowledge is homogeneous and technological paths are clear. In this context, strict environmental regulation primarily acts as a clear “quality upgrade” signal. It communicates to residents that local industries are enhancing competitiveness through green innovation and that the government is committed to improving the living environment. This signal effectively strengthens residents’ local identity and sense of security, yielding positive psychological benefits. Here, the “signal gain” outweighs potential economic cost concerns.

In low-diversity, loosely structured clusters, firms differ in technology standards and pollution types. Uniform and stringent environmental regulation in these settings is perceived mainly as an increase in already high coordination costs and compliance uncertainty. Firms may struggle to meet heterogeneous standards or pass compliance burdens onto community relations. For residents, such regulation does not provide clear expectations of industrial transformation; instead, it may be perceived as amplifying economic instability and governance challenges, triggering anxiety and uncertainty, thereby harming mental health. In this context, the “cost and uncertainty burden” effect dominates.

2. Policy Implications: From “One-Size-Fits-All” to Tailored Regulation

The policy implication is clear: one-size-fits-all environmental regulation can have opposing effects on psychological well-being. Policy design must move from a single-dimensional focus on regulatory intensity to a multi-dimensional approach that aligns regulatory instruments with industrial structure.

We translate this principle into a concrete policy design guideline: for mature clusters characterized by specialization, regulations should emphasize their “signal” function, such as implementing regional green brand certifications. For clusters at low levels of diversification, policy should prioritize reducing coordination costs, for instance by providing technical diagnostics and establishing shared pollution control platforms, while also engaging with the community to mitigate uncertainty.

 

Comments 7：The limitations section is too brief. It should be expanded to discuss other potential confounders not controlled for, such as general urban population density (which correlates with both agglomeration and health outcomes) or other major local industrial pollutants unrelated to furniture. Also, the cross-sectional nature of the data severely limits the ability to infer long-term effects or rule out reverse causality (healthier people moving to areas with specific industries). This fundamental limitation deserves greater emphasis.

Response 7: We sincerely appreciate your valuable and critical comments on the “Study Limitations” section. Your feedback prompted us to reflect more carefully and thoroughly on the boundaries of our research. We fully agree that a detailed examination of these limitations not only demonstrates academic rigor but also provides clearer guidance for future studies. Following your suggestions, we have revised and expanded the original 5.2 Study Limitations section to offer more specific content and deeper analysis. The revised text is as follows:

This study has several limitations. The health assessment data are based on respondents' self-reported evaluations, including the five-point physical health scale and the K-6 psychological distress scale from the CLDS, which may introduce social desirability and recall biases. Individuals often avoid extremely negative ratings such as "very unhealthy," and retrospective evaluations of chronic conditions may be distorted by memory errors. These systematic measurement biases may weaken the observed association between industrial agglomeration and health outcomes. Nonetheless, the validated survey instruments used have shown strong reliability in previous studies, and the robustness tests in this research incorporate more objective health indicators that produce consistent results. Second, the cross-sectional data structure constrains the ability to fully resolve endogeneity. Although terrain ruggedness is used as an instrumental variable to mitigate selection bias and the K-P F-statistics indicate the absence of weak instruments, unobserved time-varying confounders may still affect estimate consistency. Cross-sectional data cannot capture the long-term dynamic effects of industrial agglomeration policies or environmental regulation. Future research should combine panel tracking data to build a more robust causal chain linking industrial agglomeration and residents’ health. Second, although the models control for a range of variables, omitted variable bias may still exist. Some macro-level factors that affect both furniture industry agglomeration and residents’ health are not fully accounted for. In addition, the study focuses mainly on pollution from the furniture industry, but pollutants from heavy chemical, energy, and other non-furniture sectors also pose significant health risks. If the spatial distribution of these non-furniture pollution sources is correlated with furniture enterprise agglomeration, our estimates may be confounded.

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

After reviewing the authors' responses and making the necessary changes and additions, I can recommend the article for publication. However, a question arises: Would similar results be obtained if the analysis were performed not for a single sector, but for several or even a dozen sectors? This is the most important task for future research: How does industrial development affect the health of urban residents?

Reviewer 3 Report

Comments and Suggestions for Authors

Accept in present form.