Mapping Ecosystem Service Supply–Demand Bundles for an Integrated Analysis of Tradeoffs in an Urban Agglomeration of China

Round 1

Reviewer 1 Report

Please see the attached report

Comments for author File: Comments.pdf

Author Response

Comment 1:

This study mapped ecosystem service (ES) supply-demand bundles to comprehensively analyse the ES tradeoffs in the Yangtze River Delta by using cluster analysis, correlation analysis, hotspots analysis, and principal component analysis I enjoyed reading the paper. I have some minor comments. My specific comments are as follows:

Authors’ Response:

Thank you for these positive comments and helpful suggestions.  We have revised the manuscript based on your suggestions.

 

Comment 2:

Introduction：The description of the background is fine but I missed the description of the literature concerning research gaps. I suggest adding one paragraph to describe the research gaps with reference to the literature.

Authors’ Response:

Thank you for pointing out this problem.  Actually, we have some sentences to illustrate the research gaps, for example, lines 44-45: “Few studies have analyzed ES bundles by considering both ES supply and ES demand”.  Lines 52-55: “However, the focus of existing research has been on identifying the different social values of ES bundles [14,17].  Few researchers have analyzed ES tradeoffs in an integrated manner from ES supply-demand bundle perspective.” To make the research gap clearer, we have added a sentence to summarize it as follows:

“Thus, the knowledge gap of this research was to comprehensively analyze ES tradeoffs by considering both ES supply and demand.” (Lines 55-56)

 

Comment 3:

Line 79: it should be km²

.Authors’ Response:

Revised as suggested. (Line 80)

 

Comment 4:

The description of the data used was not organized. I suggest adding a separate section to describe the data and its sources.

Authors’ Response:

Thank you for these excellent suggestions, we have added a separate section to describe the data sources as follows in lines 93-107:

“2.2. Data collection and processing

The food provisioning service data (including grain, meat, and aquatic) are ob-tained from the Statistical Year Book.  The NPP data are derived from the MODIS 8-day synthetic data (MOD17A3H) product with a spatial resolution of 500m for the period 2001-2020.  ET is derived from the Modis 8-day synthetic data (MODIS16A2) product with a spatial resolution of 500m.  Soil data with 1km spatial resolution was obtained from the World Soil Database (http://www.fao.org/soils-portal/data-hub).  The population data with 1km spatial resolution for 2000-2020 was from Landscan (https://landscan.ornl.gov/).  Meteorological data (including rainfall, temperature, wind speed, and relative humidity) were obtained from the China Meteorological Centre (http://cdc.cma.gov.cn).  We obtained the LULC map (30m) of 2020 from the Data Center of Resource and Environmental Science (http://www.resdc.cn).  The Soil Hydrology Groups data for this study were obtained from the official Earth Data web-site (https://earthdata.nasa.gov/) [32].  Air quality data was obtained from The World Air Quality Index Project Team (https://aqicn.org/city).”

 

Comment 5:

The method section is much longer. Some parts are not necessary. I recommend shortening this section by removing unnecessary parts.

Authors’ Response:

Thank you for this suggestion, we have deleted some unnecessary sentences and moved Tables 2/3/4/5 to the Supplementary Files.

 

Comment 6:

The description of the discussion is fine. I just recommend to add the findings of other similar studies to compare with your results. And also describe the significance of your results compared to other studies. How it would contribute to relevant policy?

Authors’ Response:

Actually, we have compared our results with that of other similar studies.  We have highlighted them in the discussion section. For example:

Lines387-393: “This is consistent with the findings of most ES tradeoff studies [36,37].  According to Bennett, Peterson and Gordon [3], ES supply tradeoffs may be related to interactions between ES, or maybe due to responses to the same driver of change.  In the YRD, given the apparently different land-use conditions (Fig.10), the tradeoffs between food production services and other services is mainly due to the fact that they both respond to the drivers of land-use change.”

Lines 396-399: “This is consistent with the results of a similar study in the Barcelona metropolitan region [16]. The possible reason for this synergistic relationship is that the demand for these services is strongly associated with population density and economic development. Interestingly, we also found significant tradeoffs between ES demands”

Line 406-407: “These results could explain why ES supply-demand mismatches widely exist [38-40].”

 

The significance of our results compared to other studies can be found in lines 423-428: “ES supply-demand bundle studies also differ from the widely studied ES sup-ply-demand mismatch.  Most ES supply-demand mismatch studies focus mainly on paired ES supply and demand [43], which is not conducive to identifying multiple ES supply-demand relationships.  In contrast, ES supply-demand bundles can help iden-tify not only the relationship between one ES supply and the demand of other ES types, but also the repetitive and consistent ES supply-demand relationship across landscapes”

 

The contribution of our results to relevant policy can be found in lines 461-462: “Our identified ES supply-demand bundles can work as a scientific basis for this zoning.” And lines 467-468: “In addition, our results support the “land sharing” strategy for urban development from the perspective of ES supply-demand relationships.”

Reviewer 2 Report

1. This paper uses Geographic Information System technology to analyze the spatial autocorrelation of ecosystem services with the Yangtze River Delta as the study area. The demonstrative model used in this paper yields meaningful results. Please clarify the following doubts.

2. Certain terms are not well defined. CC in equation (1), GDP in Line 86, NPP in Line 107, LUCC in Line 114, Kc in Line 116, UHI in Line 123, SCS in Line 130. Please add relevant definitions.

3. What does the measurement unit of Accessibility in Table 5 mean by km?

4. Is Lforest in Line 156 ????????

5. It is suggested that in the conclusion section of this paper, what other research content can be improved as suggestions for follow-up research?

Author Response

Comment 1:

This paper uses Geographic Information System technology to analyze the spatial autocorrelation of ecosystem services with the Yangtze River Delta as the study area. The demonstrative model used in this paper yields meaningful results. Please clarify the following doubts.

Authors’ Response:

Thank you for these positive comments and helpful suggestions.  We have revised the manuscript based on your suggestions.

 

Comment 2:

Certain terms are not well defined. CC in equation (1), GDP in Line 86, NPP in Line 107, LUCC in Line 114, Kc in Line 116, UHI in Line 123, SCS in Line 130. Please add relevant definitions.

Authors’ Response:

Thank you for pointing out this problem.  We have revised as suggested.

 

Comment 3:

What does the measurement unit of Accessibility in Table 5 mean by km?

.Authors’ Response:

It refers to the Euclidian distances to recreation sites (the medium to very high capacity recreation areas (i.e., Recreation capacity equal to or higher than 0.33) assuming that inhabitants want to reach these areas and not low capacity areas (recreation capacity lower than 0.33 mostly corresponds to artificial land covers). To make it clearer, we have added several sentences as follows:

“Outdoor recreation demand values were obtained by a cross-tabulation matrix be-tween a reclassified raster of Euclidian distances to recreation sites and the population density grid (Baro et al., 2016) with the assumption that all inhabitants in the case study area have similar desires in terms of outdoor recreational opportunities, but their level of fulfillment depends on proximity to recreation sites. The recreation sites refer to the medium to very high capacity recreation areas (i.e., Recreation capacity equal to or higher than 0.33) assuming that inhabitants want to reach these areas and not low capacity areas (recreation capacity lower than 0.33 mostly corresponds to artificial land covers (Baro et al., 2016).  The classification of population density and dis-tance to recreation sites was referenced (Baro et al., 2016).”

 

Comment 4:

Is Lforest in Line 156 ????????

Authors’ Response:

Yes, Lforest in Line 156 is ???????. We have changed it to the right form

 

Comment 5:

It is suggested that in the conclusion section of this paper, what other research content can be improved as suggestions for follow-up research?

Authors’ Response:

Thank you for this excellent suggestion, we have added a sentence to indicate what other research content can be improved as suggestions for follow-up research in lines 503-504 as follows:

“Future research could further help develop balanced strategies on how to improve ES synergies while reduce tradeoffs by considering ES supply and demand.”

Reviewer 3 Report

Dear Authors. The paper was statistically strong. With some further teasing out the analysis and implications it can become stronger. A few constructive comments:

Needs more details about the term tradeoff. What are the common tradeoffs and what are the implications of one versus the other. You can expand briefly on the work of Mouchet, et al. for example.

Define or characterise all key terms. For example “urban agglomeration”. Don’t assume the readership has the background to comprehend all.

It would be useful if you could clearly describe your findings on the question you pose: “how can different bundles be explained by social ecological determinants?”

Reference the Statistical Year Book (Line 106).

Negative correlations could be expanded upon as to reason’s why they are interesting. Up to this section the correlation reporting has basically described/reported with little analytic discussion as to the results.

It would be useful to add to the analysis on how not all ES demand has a tradeoff with ES supply, and some ES demand shows synergy with ES supply.

All the best with this and future research endeavours.

Author Response

Comment 1:

Dear Authors. The paper was statistically strong. With some further teasing out the analysis and implications it can become stronger. A few constructive comments.

Authors’ Response:

Thank you for these positive comments and helpful suggestions.  We have revised the manuscript based on your suggestions.

 

Comment 2:

Needs more details about the term tradeoff. What are the common tradeoffs and what are the implications of one versus the other. You can expand briefly on the work of Mouchet, et al. for example.

Authors’ Response:

We agree that the term “tradeoff” should be explained in detail.  Actually, we have provided many details about this term.  For example, In lines 28-30, we explained what are the common tradeoffs as “ES tradeoffs indicate one ES type decreases with the increase in another ES type, while ES synergy indicates two ES types increase or decrease at the same time [3]”.  We also expanded the work of Mouchet, et al., for example, in lines 34-38:” Mouchet, et al. [8] proposed a more comprehensive typology of ES tradeoffs that in-cludes three aspects: (1) tradeoffs in the simultaneous provision of ES; (2) tradeoffs between ES supply and ES demand; and (3) tradeoffs between different ES demands of stakeholders.  This new typology considered both ES supply and ES demand and is more useful for transdisciplinary landscape planning and management.”  In lines 232-237, “We followed a unified framework proposed by Mouchet, Lamarque, Martín-López, Crouzat, Gos, Byczek and Lavorel [8] to analyze ES tradeoffs (Fig.2.).  In this frame-work, we analyze three broad types of ES tradeoffs considering both ES supply and ES demand.  For the supply-supply tradeoff, we analyzed how one ES supply correlates with the other.  For the supply-demand tradeoff, we analyzed whether ES supply and ES demand are spatially matched.”

 

Comment 3:

Define or characterise all key terms. For example “urban agglomeration”. Don’t assume the readership has the background to comprehend all.

Authors’ Response:

Thank you for this excellent suggestions.  Urban agglomeration is a highly developed spatial form of integrated cities. It occurs when the relationships among cities shift from mainly competition to both competition and cooperation.  As you suggested, we have define or characterise all key terms in the revised version manuscript (lines 58-59).

 

Comment 4:

It would be useful if you could clearly describe your findings on the question you pose: “how can different bundles be explained by social ecological determinants?”

Authors’ Response:

We agree that it is good to clearly describe our findings on the question “how can different bundles be explained by social ecological determinants?” Actually, we have done this in the submitted version of this manuscript as follows (Lines 385-394):

“The human-environmental characteristics of the clusters differ significantly from one another (Fig.10).  In terms of socioeconomic characteristics, Clusters 4 and 2 have a significantly higher total population, GDP per capita, and urban land area, compared to Cluster1and 3.  For climatic indicators, Cluster 4 and Cluster 3 have higher temperatures than the other two clusters, while Cluster 1 has lower average annual precipitation than the other three clusters.  For vegetation type indicators, the area of farmland area in Cluster 1 is significantly higher than that of the other three clusters, while the forestland and grassland area is higher in Cluster 3 than that of the other three clusters.  In addition, the PM2.5 concentrations in clusters 1 and 2 are significantly higher than those in clusters 3 and 4.”

 

Comment 5:

Reference the Statistical Year Book (Line 106).

Authors’ Response:

Revised as suggested.

 

Comment 6:

Negative correlations could be expanded upon as to reason’s why they are interesting. Up to this section the correlation reporting has basically described/reported with little analytic discussion as to the results.

Authors’ Response:

Yes, in the result section, we only described our results.  But the analytic discussion about the results can be found in our discussion section.

 

Comment 7:

It would be useful to add to the analysis on how not all ES demand has a tradeoff with ES supply, and some ES demand shows synergy with ES supply.

Authors’ Response:

Thank you for this excellent suggestion, we have further added the explanation about “how not all ES demand has a tradeoff with ES supply, and some ES demand shows synergy with ES supply”  as follows (lines 429-435):

“Regions with high food production capacity also happen to be the more developed industrial areas so there is a synergy between grain supply capacity and the air pollu-tion.  As CO2 emissions are higher in the developed coastal areas and the fisheries are also more developed in the coastal areas so there is a synergy between fish production and CO2 emissions.  The high temperature areas happen to be the best vegetated areas in the Yangtze River Delta, so there is a synergy between the demand for urban cooling and the supply capacity for flood risk mitigation.”