Gender Disparity in Perceived Urban Green Space and Subjective Health and Well-Being in China: Implications for Sustainable Urban Greening

Round 1

Reviewer 1 Report

I find the authors have responded to my questions and criticisms.  From my standpoint, the paper is now acceptable for publication  

Author Response

The manuscript has been further revised and edited by a native English speaker to improve its readability.

Reviewer 2 Report

Thanks for re-submitting the works with some corrections. While I appreciate the authors persistence, and I agree they have some good data, the paper is yet ready for publication at this stage, in my opinion. I am pointing some issues;

1. Introduction still not covered the current literature review. I am not convinced the authors have read adequate recent studies on Greenspace perception and health. A dictionary definition does not justify how a field of study defining perceived greenspace. Neither the author clarified what is meant by perceived greenspace, as I previously comments, do these mean perceived access to greenspace, perception of greenspace quality? Also the definition is vague, “recognized through the senses”, what this means for greenspace? Check the following studies to identify and recognise the use of word ‘perceived’ in this field of greenspace and health study. I can add more, but these are just a few examples.

Grahn, P. and Stigsdotter, U.K., 2010. The relation between perceived sensory dimensions of urban green space and stress restoration. Landscape and urban planning, 94(3-4), pp.264-275.

Aggio, D., Smith, L., Fisher, A. and Hamer, M., 2015. Mothers' perceived proximity to green space is associated with TV viewing time in children: the Growing Up in Scotland study. Preventive Medicine, 70, pp.46-49.

Völker, S., Heiler, A., Pollmann, T., Claßen, T., Hornberg, C. and Kistemann, T., 2018. Do perceived walking distance to and use of urban blue spaces affect self-reported physical and mental health?. Urban Forestry & Urban Greening, 29, pp.1-9.

Dzhambov, A.M., Markevych, I., Tilov, B., Arabadzhiev, Z., Stoyanov, D., Gatseva, P. and Dimitrova, D.D., 2018. Lower noise annoyance associated with GIS-derived greenspace: pathways through perceived greenspace and residential noise. International journal of environmental research and public health, 15(7), p.1533.

2. Using geographical detector method is not yet convincing. I agree a method in one discipline can be applied in others with some variations, but the fundamentals need to be present in the application. Although the authors are right in the equation derivation, the term ‘geographical’ inherently mean spatial pattern. I am not sure why the authors want to use a spatially explicit method in a non-spatial data set. Despite I see some maps added. Why not use other statistical methods; simple regression method can also identify which factors are significantly associated with subjective health and well-being. Complex modelling can also be used for non-normal data, such as if the authors wanted they could have used structural equation modelling, to account for latent constructs.

My main issue is, I have never read a paper that used GDM in a non-spatial approach. Here I am listing some papers that used this method, including paper from sustainability and other MDPI journals.

Luo, L., Mei, K., Qu, L., Zhang, C., Chen, H., Wang, S., Di, D., Huang, H., Wang, Z., Xia, F. and Dahlgren, R.A., 2019. Assessment of the Geographical Detector Method for investigating heavy metal source apportionment in an urban watershed of Eastern China. Science of the Total Environment, 653, pp.714-722.

Bai, L., Jiang, L., Yang, D.Y. and Liu, Y.B., 2019. Quantifying the spatial heterogeneity influences of natural and socioeconomic factors and their interactions on air pollution using the geographical detector method: A case study of the Yangtze River Economic Belt, China. Journal of Cleaner Production, 232, pp.692-704.

Luo, W. and Liu, C.C., 2018. Innovative landslide susceptibility mapping supported by geomorphon and geographical detector methods. Landslides, 15(3), pp.465-474.

Zhao, Y., Deng, Q., Lin, Q., Zeng, C. and Zhong, C., 2020. Cadmium source identification in soils and high-risk regions predicted by geographical detector method. Environmental Pollution, p.114338.

Wang, Y., Wang, S., Li, G., Zhang, H., Jin, L., Su, Y. and Wu, K., 2017. Identifying the determinants of housing prices in China using spatial regression and the geographical detector technique. Applied Geography, 79, pp.26-36.

MDPI journals including sustainability:

Wu, R., Zhang, J., Bao, Y. and Zhang, F., 2016. Geographical detector model for influencing factors of industrial sector carbon dioxide emissions in Inner Mongolia, China. Sustainability, 8(2), p.149.

Xu, X., Zhao, Y., Zhang, X. and Xia, S., 2018. Identifying the impacts of social, economic, and environmental factors on population aging in the Yangtze River Delta using the geographical detector technique. Sustainability, 10(5), p.1528.

Zhou, L., Zhen, F., Wang, Y. and Xiong, L., 2019. Modeling the Spatial Formation Mechanism of Poverty-Stricken Counties in China by Using Geographical Detector. Sustainability, 11(17), p.4752.

Huang, J., Wang, J., Bo, Y., Xu, C., Hu, M. and Huang, D., 2014. Identification of health risks of hand, foot and mouth disease in China using the geographical detector technique. International journal of environmental research and public health, 11(3), pp.3407-3423.

Cao, Z., Liu, T., Li, X., Wang, J., Lin, H., Chen, L., Wu, Z. and Ma, W., 2017. Individual and interactive effects of socio-ecological factors on dengue fever at fine spatial scale: A geographical detector-based analysis. International journal of environmental research and public health, 14(7), p.795.

I would suggest authors provide evidence of 5 credible peer-review publications that applied GDM in a non-spatial pattern and contexts.

I believed this study should be further developed with a proper review of literature in greenspace and health domain. Then use more appropriate modelling approach. Adding a few lines of limitations do not justify using a sub-optimal modelling approach in a different field of study. I encourage the authors to think out of the box to see the value of their data and try to publish the results using a more suitable method.

Author Response

1. We appreciated the comments. The revised manuscript added explanation of the term “perception”. We see in the literature, the term is used usually without providing a definition. That means the most common sense definition is used. Our survey used this common sense definition. It refers to the overall perception of greenspace quality that includes perceived accessibility, size, authenticity, and usefulness of the greenspace.
2. Thanks for a long list of references. Yes, we understand the geographical detector method (GDM) was designed for detecting both spatial and non-spatial relationships, though the spatial relationship is especially targeted for. Our research intended to detect both kinds of relationships. We found only non-spatial relationship was present and believe it is important to report it. The method has also been used by other authors to report non-spatial relationships and several such reports are referenced in the paper. You are right that some other methods may do a similar job. However, we particularly preferred the GDM for the three modules of geographic detection, namely, factor detection, risk detection, and interactive detection for this research, though webelieve that using other methods would produce a similar result. Therefore, the GDM is an appropriate method for this study as it has been used in other studies with similar datasets (Ye et al.,2018; Zhan, et al., 2017; Zhan et al., 2015; Ding et al., 2014).

 

Ding Y., Cai J.M., Ren Z.P., Yang Z,S., 2014. Spatial disparities of economic growth rate of China's National-level ETDZs and their determinants based on geographical detector analysis. Progress in Geography, 33(05): 658—666.

Ye Y.S., Qi Q.W., Jiang L.L., Zhang A., 2018. Impact factors of grain output from farms in Heilongjiang reclamation area based on geographical detector. GEOGRAPHICAL RESEARCH, 37(1): 171—182.

Zhan D. S., Zhang W. Z, Dang Y. X., Qi W., Liu Q.Q., 2017. Urban livability perception of migrants in China and its effects on settlement intention. Progress in Geography, 36(10): 1250—1259.

Zhan, D.S., Zhang, W.Z., Yu, J.H., Meng, B., Dang, Y.X., 2015. Analysis of influencing mechanism of residents’ livability satisfaction in Beijing using geographical detector. Progress in Geography, 34(8), 966—975.

Reviewer 3 Report

The authors have not submitted any response to my comments. However, they improved description of methods and discussed critically their results. Minor revision of English and formatting is recommended.  

Author Response

The manuscript has been further revised and edited by a native English speaker to improve its readability.

Round 2

Reviewer 2 Report

Thanks to the authors for another update on the manuscript. I found the manuscript improved a bit in terms of context settings. 

The references they provided for GDM used in for non-spatial data, I checked those and none of the journals is indexed in the major and reliable scientific database (e.g., Scopus, Web of Science). I am sceptical about the quality of these studies and therefore, the validity of the use of this method in non-spatial data. While there may be some works that trying this method for non-spatial data, this would be heavily questioned, as I did. It is the authors' responsibility to make sure their method is credible and logically used in the context of the study. Apart from this critical issue, I am fine if the authors insist on using this method and do not want to run statistical regression model, which would eventually provide the same results (as mentioned by the authors). But running the regression models alongside with GDM would have provided more reliability to this study. 

Saying so, I would suggest the authors to at least provide correlation tables (for male and female split) for variables used in this study to make sure the people who do not fully aware of the GDM can understand in simple terms what is the correlations among the variables and how the relations may vary by gender groups. 

Finally, I know this seems quite frustrating when a reviewer asking to change the study method. But I hope the authors would see why I made such request and the logic behind using proper methods for an interesting study. Anyway, all the best. 

Author Response

Thanks for the suggestions. We added Table 2 to better explain the correlations.

Furthermore, Table 2 indicates that green space variables affected subjective health and well-being differently between the genders. The effects were also different between men and women. Green space variables affected men significantly in perceived subjective health but not as much among women. The gender differences were smaller in well-being.

Table 2. The correlation between green space variables and variables of subjective health and well-being.

Variable	Subjective health		Subjective well-being
	Male	Female	Male	Female
Water space charm Green coverage Number of parks Climate comfort City cleanliness	0.041*** 0.057*** 0.055*** 0.029** 0.053***	0.028* 0.012 0.022 0.017 0.018	0.048*** 0.022 0.057*** 0.054*** 0.056***	0.049*** 0.016 0.055*** 0.047*** 0.008

* = significant at 0.1 ; ** = significant at 0.05 ; *** = significant at 0.01 .

This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.

Round 1

Reviewer 1 Report

Overall, this study asks interesting questions, ones relevant to the journal.  The study’s strength lies in the large sample size (amazingly high survey response rate).  I have few substantive criticisms: 

First, what is meant by “perception” of green space?  I would define perception as the ability sense or be aware of the existence of something, or a way of understanding an aspect of the world.  Neither of these seems to be exactly what the authors mean.

Second, are the differences reported (e.g., well-being averages of 3.40 vs. 3.55 in Fig. 2) significant?  Even if they are, the strength of the effect seems relatively small. 

Finally, the Discussion needs elaboration.  For example, what is the meaning of the U-shaped responses?  The paragraph beginning “This study explored green space perception among urban Chinese residents…”  seems to mostly repeat the prior paragraph.  It “may be more important for cities to increase the number of parks rather than the total areas of parks, in particular for female residents,” but surely these two interact in a complex fashion; one large park vs. many small parks (but totaling the same area) wouldn’t seem to have the same importance to health and well-being.

Minor issues:

Introduction:  “ethnic minorities in Britain are at greater risk of COVID-19 deaths because of their poor social and economic conditions, including overcrowded housing with 11 times less green space to access as compared to the ethnic majority population.”  This is a large logical over-reach; is there really a causal connection between green space and risk of COVID as the sentence seems to imply?

M&M:  Risk detection: presumably the variances both fall under the root

Results

First, “Most respondents were college-educated urban technical professionals, with an annual family income between CNY5,000—15,000 (US$700—2200) and owning an apartment.”  Does this limit the generality of the conclusions?

Table 3:  perhaps I don’t fully understand the metric, but why are there interaction factors for each factor (e.g., water space charm – water space charm).  Also, the values in this table (and Table 4) are all quite small; what are the bounds for this metric?  They are also all fairly similar.  Does this have meaning and relevance to the interpretation?

Reviewer 2 Report

Thanks for inviting me to review the manuscript " Gender Disparity in Perceived Urban Green Space and Subjective Health and Well-being in China: Implications for Sustainable Urban Greening ". The study explored the associations between perceived greenspace and subjective health outcomes using Geographic Detector model. While the study has some interesting data, but I think the study lacks many dimensions of perception of green space and inappropriate use of GDM approach. I am listing a few major concerns,

Issue#1: The introduction does not set the study well. It does not provide detailed about the variables used in greenspace perception studies, and what is the state of the art regarding how to measure the perception of green space and perceived use of green space. The term perception is also broadly defined, it does not indicate if this study focused on how respondent perceive the availability or accessibility green spaces, or if the respondents perceived the use of green space? So the introduction needs a lot more clarification regarding the novelty of the paper, also what problem is exactly been addressed. As far as I know, the gender difference in greenspace use is researched in many existing studies. So more evidence of these studies will be needed to set what gap the current study tried to fill. Currently, I am not sure what exactly this study tried to address, that the existing literature do not provide any evidence?

Issue#2: Study Design: Perception of green space or perceived use of greenspace is mostly dependent on the quality of green space, proximity to green space and perceived safety (Jorgensen et al., 2002; Hillsdon et al., 2006; Jones et al., 2009). The authors discussed the safety issue in the introduction, but None of these key factors considered in this study. Previously several studies indicated the quality of green space is a key to the actual use of green space. Therefore, The green coverage, numbers of parks, climate comfort and others do influence the perceived presence of green space but they do not fully explain the use of green spaces. Therefore, they are unable to explain the health benefit obtained by actually using green spaces. 

Jorgensen, A., Hitchmough, J. and Calvert, T., 2002. Woodland spaces and edges: their impact on perception of safety and preference. Landscape and urban planning, 60(3), pp.135-150.

Hillsdon, M., Panter, J., Foster, C. and Jones, A., 2006. The relationship between access and quality of urban green space with population physical activity. Public health, 120(12), pp.1127-1132.

Jones, A., Hillsdon, M. and Coombes, E., 2009. Greenspace access, use, and physical activity: Understanding the effects of area deprivation. Preventive medicine, 49(6), pp.500-505.

Issue#3: I have a major concern about the Geographic Detector models. First, it appears the authors did not fully use the spatial modelling concepts appropriately considering the spatial pattern of the variables. GDM depend on spatial autocorrelation and pattern of spatial stratified heterogeneity. I do not see any use of spatial patterns in the modelling. The equations are written incorrect form, but the use of spatial patterns are incorrect. In particular, Factor detector “Compares the accumulated dispersion variance of each sub‐region with the dispersion variance of the entire study region; the smaller the ratio, the stronger the disease contribution of the stratum.” Check the Wang et al. (2010) paper for main theory. In this equation  "n" is sub-regions and σ2 is variance in study regions. In my understanding, the theory is not for sample size and variance of each respondent. It is the geographic sample and spatial variance. So I, do not think the method properly applied in this study.

Wang, J.F., Li, X.H., Christakos, G., Liao, Y.L., Zhang, T., Gu, X. and Zheng, X.Y., 2010. Geographical detectors‐based health risk assessment and its application in the neural tube defects study of the Heshun Region, China. International Journal of Geographical Information Science, 24(1), pp.107-127.

 It requires a lot of clarifications and modifications in their modelling approach. It also requires further justification for the selection of variables and the use of appropriate modelling tool.

Reviewer 3 Report

The paper could be interesting if it could be better written. In my opinion there are too few information about selection of groups of respondents. It is key problem in survey studies and analyses of obtained materials. Reader does not know what was criteria of selection of persons. Both women and men should be similar in terms of age, education, health, wealth etc. There could be various groups of people but according to these factors but distribution, proportions of particular groups need to be equal between two genders. 

The beginning of results show some differences between men and women. They should be provided in material and methods. The selection should be stratified-sampling. 

Aims of the study are not clear. None of them relate to differences between two genders. It seems that division of people in two genders is useless and it is not supported neither by introduction, nor results, discussion and further conclusions.

I recommend to strongly improve description of assumption of this study and resubmit it again.