Improved human greenspace exposure equality during 21^st century urbanization

Domain: earth_science
Published: 2023-10-13
Editorial outcome: major_revision

Abstract
Greenspace plays a crucial role in urban ecosystems and has been recognized as a key factor in promoting sustainable and healthy city development. Recent studies have revealed a growing concern about urban greenspace exposure inequality; however, the extent to which urbanization affects human exposure to greenspace and associated inequalities over time remains unclear. Here, we incorporate a Landsat-based 30-meter time-series greenspace mapping and a population-weighted exposure framework to quantify the changes in human exposure to greenspace and associated equality (rather than equity) for 1028 global cities from 2000 to 2018. Results show a substantial increase in physical greenspace coverage and an improvement in human exposure to urban greenspace, leading to a reduction in greenspace exposure inequality over the past two decades. Nevertheless, we observe a contrast in the rate of reduction in greenspace exposure inequality between cities in the Global South and North, with a faster rate of reduction in the Global South, nearly four times that of the Global North. These findings provide valuable insights into the impact of urbanization on urban nature and environmental inequality change and can help inform future city greening efforts.

Introduction
Urban areas are the hub of human society, characterized by agglomeration, industrialization, and modernization1. Currently, over half of the world’s population resides in urban areas and this number is expected to increase to 68% by 20502. While urbanization has brought about many social and economic benefits, such as improved access to public infrastructure, sanitation, and education services3, it has also resulted in severe environmental degradation, including deforestation4, habitat loss5, air and noise pollution6, and increased fossil fuel consumption and CO2 emissions7. This has led to environmental injustice, with some communities bearing a disproportionate share of the negative impact of urbanization8,9. Achieving equality in urban environmental sustainability is crucial, as it will help address the imbalance between social, environmental, and economic developments and reduce society’s vulnerability to risks10. This is the focus of the 11th Sustainable Development Goal of creating inclusive, resilient, and sustainable cities and human settlements11.

Greenspace, as a key component of urban nature, offers a wide range of ecosystem services and health benefits, making it a useful proxy for evaluating urban environmental sustainability12. Providing universal access to green and public space has been acknowledged as an important pathway in the pursuit of sustainable and healthy development goals. Using greenspace supply metrics such as greenspace total or per capita supply, previous studies have measured the greenspace supply at both the city and global scales13,14. Human exposure to greenspace, measured as the averaged amount of greenspace coverage within people’s nearby environment expressed as a percentage15,16, has also been quantified in sampled cities17–19. Recent research has used high-resolution satellite imagery and population data, combined with environmental exposure models, to reveal the disparities in greenspace exposure between cities in the Global North and Global South. Urban residents in Global South cities (i.e., cities of developing countries, such as the Chinese, Indian, and Middle Eastern cities) have only one-third the exposure to greenspace compared to those in Global North cities (i.e., cities of developed countries, such as United States, European, and Australian cities), but experience twice the level of inequality17. However, existing studies on greenspace exposure and inequality are constrained to data from individual baseline years that cannot capture the impacts of long-term urban development20,21 and the gradual temporal evolution of human-nature interactions such as urban warming and greenspace management strategies22,23, limiting our understanding of the impact of urban development on greenspace supply, human exposure, and inequality over time.

The impacts of urbanization on greenspace exposure are of two kinds:24,25 the replacement of green land cover with built land cover, and the enrichment of built land cover with designed green land. This replacement often occurs during early-stage urban development via rapid urban expansion, transforming naturally vegetated areas into artificial impervious surfaces and ultimately resulting in a subsequent decrease in greenspace coverage. Historically, there is typically a process of greenspace destruction as agricultural and natural green land is replaced by impervious surfaces and then a later process of re-greening via environmental improvements. Urban expansion through comprehensive spatial planning (as opposed to incremental unplanned sprawl), tends to ‘reprovision’ greenspace by design from the outset, but these spaces themselves tend to evolve (decrease and increase in quantity and quality) over time according to income, land value and environmental preferences26,27. In later periods, a highly urbanized city generates its own ecology, with factors such as urban warming and CO2 fertilization leading to an extension of the growing season and increased greenspace growth24,28,29. City management practices, such as planting street trees and creating vertical gardens, can also increase the overall greenspace supply in an urban environment developed over time under a legal environmental planning regime30,31. The growth of the urban population modifies the interactions between the population and the urban green environment, leading to changes in human exposure to greenspace16–18,32. Greenspace is a ‘superior good’, with demand increasing as the prosperity of citizens increases33. In contrast to a crowding effect of population growth, leading to reduced greenspace exposure, an income effect tends to increase greenspace supply, diversity, and aesthetics in wealthier cities. The two effects (destruction and construction of greenspace) can also occur periodically and are not always strictly chronological. Different parts of cities developed under different political and economic conditions often display different patterns of greenspace, for example, highly planned systems of public local parks in the post-World War II cities; intensive street-tree planting during the first half of the 20th century; private greenspaces in ancient city quarters, very little greenspace in a period of informal urban expansion34–37.

While both destructive and constructive processes are well understood in the context of particular cities and professional urban management activities38,39, limited studies have investigated the net change in greenspace supply and human exposure to greenspace across a global sample of cities for a comparable time, to identify general trends. Such trends are the net outcomes of the destructive and constructive processes often at work simultaneously in the evolution of city fabric. Inequality in greenspace exposure is an increasing concern as it can be translated into adverse effects in mental and physical health18,19. Individual studies show how greenspace provision and the joint effects of greenspace provision and spatial configuration control greenspace exposure inequality17. However, the drivers of changing greenspace exposure inequality over time remain unclear and limit our understanding of the relationship between greenspace and population distributions in determining and attempting to promote greenspace exposure equality in the future projection.

Here we generate a global urban greenspace dataset for 1028 large cities (i.e., urban area > 100 km2) using 30-m-resolution Landsat satellite data from 2000 to 2018, and validate its accuracy using the 1-m-resolution National Agriculture Imagery Program (NAIP) aerial data and 10-m Sentinel-2 satellite observations. Specifically, we adopted a broad definition of greenspace to refer to land that is partly or completely covered with grass, trees, shrubs, and other vegetation32. That is, green-covered land within the curtilage of an urban boundary, since we are focusing on cities in this study. We further leveraged the 30-m-resolution satellite-derived greenspace coverage map (i.e., the vertical projection area of greenspace within a 30 × 30 m2 pixel) and 100-m population data, together with the population-weighted exposure model, to assess the spatiotemporal trends of urban greenspace exposure, inequality, and the associated drivers over the first two decades of 21st century. The equality term in this study is to quantify whether individual people share the even greenspace resource40. To this end, we included cities with different urban development stages, classified as Global North and Global South cities, by answering the following four questions. (1) What has been the fate of urban greenspace supply under global urbanization during the first two decades of the current century? (2) Has human exposure to greenspace and the inequality issue improved or worsened in this period? (3) What are the relative contributions of greenspace provision and population growth to changes in exposure and the associated inequality? (4) What can be learned from the start of the century to guide city greening in the decades ahead?

Results
Spatiotemporal pattern of physical greenspace coverage
We used the 30-m-resolution Landsat-derived fractional greenspace mapping to quantify the temporal change of physical greenspace coverage for 1028 global cities from 2000 to 2018 (see Methods). Results reveal a contrasting pattern in the direction of temporal change in greenspace coverage between Global North and South (Fig. 1a). Global North cities show a prominently increasing trend in greenspace coverage, including Europe, North America, Russia, Australia, and a few cities of East Asia. Global South cities have a decreasing trend, most of which are spatially clustered in East Asia, Southeast Asia, Africa, and Latin America. Global North cities experienced a higher trend in the absolute magnitude of greenspace exposure level (2.58 × 0.001 yr−1) than Global South cities (−2.51 × 0.001 yr−1), resulting in an overall slightly positive change trend for all global cities (0.22 × 0.001 yr−1) (Table 1). By continent, European cities have the largest level of change trend for greenspace exposure (4.41 × 0.001 yr−1), which is twice that of Asian (−2.11 × 0.001 yr−1) and Australian and Oceanian (2.16 × 0.001 yr−1) cities. Cities in South America (−0.44 × 0.001 yr−1) and Africa (0.51 × 0.001 yr−1) experience the lowest temporal trend level, which is approximately half of that of the North American cities (0.95 × 0.001 yr−1). Interestingly, we find a turning point around the year 2011 in the temporal trajectory of annual greenspace coverage for Global North and Global South cities (Fig. 1b). Before this turning point, Global North cities have a slight reduction in annual greenspace coverage, while Global South cities are sharply losing greenspace coverage in this period. After 2011, both Global North and Global South cities experienced an increasing annual greenspace coverage, with a larger magnitude observed for Global North cities. Continents show different directions and magnitudes in the temporal trajectories of annual greenspace coverage (Supplementary Fig. 1a). Our sensitivity analysis regarding the greenspace mapping with the spectral unmixing-based threshold classifications (see Methods) show very similar temporal evolution patterns of greenspace coverage (Supplementary Figs. 2 and 3, and Supplementary Tables 1–3).

Spatiotemporal patterns of greenspace exposure and associated inequality
To adjust gross greenspace supply to reflect assumed demand, we used population distribution maps and a population-weighted exposure framework to quantify the temporal change of human greenspace exposure and inequality. Like greenspace coverage, human exposure to greenspace shows contrasting temporal change trends between Global North and Global South cities (Fig. 1c). While the global cities have an increasing greenspace exposure (1.79 × 0.001 yr−1) (Table 1), Global North cities experience a much higher temporal change magnitude (3.10 × 0.001 yr−1) than Global South cities (0.44 × 0.001 yr−1). In North American, European, African, Australian, and Oceanian cities, population distribution amplifies the share of greenspace coverage, with a greater temporal change magnitude in human greenspace exposure compared to physical greenspace coverage. An important finding is that despite a negative trend of greenspace coverage in Global South and Asian cities, people in these cities experienced a slightly positive change in greenspace exposure in the first two decades of the 21st century. Annual continental-level greenspace exposure has similar temporal trajectory patterns as greenspace coverage but shows larger variabilities across continents (Supplementary Fig. 4). Sensitivity analysis of the spectral unmixing-based threshold classifications shows that these findings are consistent for different greenspace mapping methods (Supplementary Figs. 5 and 6, and Supplementary Tables 1–3). Our results show a dominantly decreasing trend in greenspace exposure inequality (measured by Gini index) for global cities, regardless of Global North or South, together with a decreasing annual continental-level average over time (Fig. 2). Global South cities experienced a higher change level of greenspace exposure inequality (−3.54 × 0.001 yr−1), almost four times that of Global North cities (−0.94 × 0.001 yr−1) (Table 1). The changing trends of the Gini index of greenspace exposure for North American, South American, European, African, Asian, Australian, and Oceanian cities are in the range of (−0.85)–(−3.80) × 0.001 yr−1, with a mean value of −2.22 × 0.001 yr−1 for global cities (Supplementary Fig. 7). These conclusions are consistent with our sensitivity analysis of different greenspace mapping approaches (Supplementary Figs. 8 and 9, and Supplementary Tables 1–3). To further verify these Gini-based results, we adopted the other two widely used economic inequality metrics (i.e., Atkinson and Theil) to measure the inequality of greenspace exposure, which show very consistent results (Supplementary Figs. 10 and 11 and Supplementary Tables 4 and 5).

Drivers of changing inequality in greenspace exposure
We proposed a Venn conceptual model to examine the drivers of temporal changing trends in greenspace exposure inequality. Taking Gini as an example, results reveal that greenspace coverage, as a measure of greenspace supply, has dominantly promoted the improvement (i.e., negative magnitude) in the temporal change of greenspace exposure inequality over first two decades of the 21st century. The absolute contribution of greenspace coverage to the reduction in the Gini index is larger in Global South cities than Global North cities (Fig. 3 and Supplementary Fig. 12). We also find that these greenspace coverage-induced improvement effects are loosening over time during this period. Compared to greenspace coverage, the population distribution and its combined effects with greenspace coverage, have slighter contributions to the temporal change of Gini. In addition to results based on the Gini index, our sensitivity analysis using the Atkinson and Theil metrics also show very similar attribution results (Supplementary Figs. 13 and 14). We also incorporated the comparative attribution approach41 to verify the robustness of the proposed Venn conceptual model, similar results show that the contribution from greenspace dominates the overall change in the Gini index compared to population growth (Supplementary Fig. S15). To dive into the controls of greenspace coverage on the temporal change of the Gini index, we selected two cities with contrasting greenspace coverage change patterns as pilots, Clermont-Ferrand city, France, in the Global North and Jining city, China, in the Global South. Clermont-Ferrand city has a net-balanced greenspace coverage change from 2000 to 2011 but a substantial increase from 2011 to 2018 (Fig. 4a, c). Greenspace coverage in Jining City shows a notable reduction from 2000 to 2011 and recovers around half of the previous level from 2011 to 2018 (Fig. 4e, g). Results reveal that the regulations of greenspace coverage on greenspace exposure inequality have different pathways. When greenspace provision increases, the opportunities of being exposed to urban greenspace resources will be improved for individual urban residents, and thereby reducing the greenspace exposure inequality (Fig. 4d, h). By contrast, if greenspace provision decreases, or remains stable in the face of population growth, urban residents initially being highly exposed to greenspace might lose greenspace exposure, resulting into a more balanced greenspace share (at a lower exposure level) (Fig. 4b and f). These regulating pathways are supported by a reduction in the coefficient of variation (CV) which can directly measure the relative dispersion of greenspace share among resident individuals within the city.

Methods
Research design
We generated two-decade time-series greenspace maps from the 30-m-resolution satellite data for global large cities using the linear spectral unmixing approach. We further combined the long-term fine-scale greenspace and population mappings with a population-weighted exposure framework to explore temporal changes in greenspace coverage, human greenspace exposure, and the associated inequality. To decipher the drivers controlling the inequality in human exposure to greenspace over time, we proposed a Venn conceptual model to quantify the individual and joint contributions from greenspace and population for the past two decades. The full workflow of the research design is shown in Supplementary Fig. 16.

Global urban areas
We selected 1028 major urban areas globally as our study targets. The boundaries of these urban areas were extracted from the latest global urban boundaries (GUB) product60. As the high-quality year-by-year GUB datasets are lacking, we chose the GUB data in 2018 with a geographic area larger than 100 km2 as the urban boundary to allow for 1) the exploration of the urban expansion impacts on greenness change and 2) the collection of sufficient samples for measuring human greenspace exposure and inequality17. It is noted that we refer to these 1028 urban areas as ‘cities’ throughout the manuscript.

Landsat satellite imagery
We used 19-year (2000–2018) Landsat surface reflectance products from three satellite sensors (i.e., Landsat-5, Landsat-7, and Landsat-8) with a 30-m spatial solution to quantify the spatial distribution and temporal dynamics of greenspace. Landsat provides the longest high-quality temporal record of global surface reflectance data together with the pixel-level quality assurance (QA) layer indicating cloud, cloud shadow, snow, and ice conditions61. We used the surface reflectance products of three visible (i.e., blue, green, and red) and one near-infrared band. To minimize the uncertainty caused by Landsat-7 scan line off failure62, we primarily focused on the use of Landsat-5 and Landsat-8 Collection 2/Tier 1 Level-2 products, with data availabilities of 12-year Landsat-5 (2000–2011), 2-year Landsat-7 (2012-2013), and 5-year Landsat-8 (2014–2018).

Several data pre-processing steps are conducted to ensure high-quality inputs for greenspace mapping. We first harmonized Landsat-5 and Landsat-7 data using Landsat-8 data as a baseline to generate consistent time-series data that removes potential impacts due to the difference in spectral settings across satellite sensors63. With the QA bitmask layer, we excluded the pixels that were contaminated by cloud cover, shadow, and snow. We further calculated the normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) to quantify the spectral characteristics of green vegetation and water bodies, respectively. Finally, we adopted a maximum value composite approach to generate the annual greenest vegetation green metrics by selecting the pixel-based maximum NDVI values from the cloud-free time series within a one-year cycle. In addition to the maximum NDVI value, we also recorded the corresponding NDWI and spectral reflectance of blue, green, red, and near-infrared bands for the following fractional greenspace coverage mapping.

Population data
We used the WorldPop dataset from 2000 to 2018 to map the population’s spatially explicit distribution and temporal dynamics. WorldPop provides a global annual update of demographic datasets (e.g., population density, age and sex structures, and urban growth) at a 100-m spatial resolution using a random forest regression tree-based mapping approach64. We chose the WorldPop population density dataset for its advantages of high spatial resolution, annual update frequency, and global coverage over alternatives such as Gridded Population of the World (GPW)65, LandScan66, and High-Resolution Settlement Layer (HRSL)67.

Greenspace coverage
We adopted the linear spectral unmixing model to map fractional greenspace coverage from the composited Landsat surface reflectance-NDVI-NDWI time series, which can capture subpixel greenspace signals32. The linear spectral unmixing model assumes that one pixel’s spectral signature (including reflectance and its derivative index) is a linearly weighted sum of a few spectrally pure endmembers and their fraction covers within pixel68.

Ri = \sum_{k=1}^{n} fi,k \cdot Ck + \epsilon i (1)

where Ri represents the spectral signatures of pixel i, including spectral reflectance of three visible (i.e., blue, green, red) and one near-infrared bands, NDVI, and NDWI, Ck represents the spectral signature of the kth endmember, \epsilon i is the unmodeled residual in the kth pixel, n is the total number of endmembers, fi,k is the fraction of kth endmember within pixel i, which is usually calculated from the least-squares method with the following physical constraints:

\sum_{k=1}^{n} fi,k = 1 and fi,k \ge 0, k=1, (\dots), n (2)

We selected vegetation, impervious areas, and water as the three endmembers (n=3). To collect pure spectra of endmembers and minimize their annual variations, in addition to Eq. (2), we included four more physical constraints for three endmembers: (1) vegetation endmembers should have an NDVI value > 0.8, (2) impervious endmembers should have an NDVI value < 0.2, (3) water endmembers should have an NDWI value > 0, and (4) three endmembers should be temporally stable over the past two decades, namely, constraints (1–3) should be satisfied for the endmembers for each year from 2000 to 2018.

With the endmember spectra and the associated physical constraints, we first calculated the pixel-level fractional greenspace coverage from Eq. (1). To remove the impacts of residue cloud contaminations, we conducted a pixel-level data smooth to generate the high-quality fractional greenspace coverage time series by using the Savitzky–Golay (SG) filtering approach69. We reprojected the 30-m fractional greenspace coverage using the nearest neighbor resampling approach and aggregated it to 100-m resolution to ensure the derived greenspace map is spatially consistent with the 100-m population data. Then, we calculated the city-level physical greenspace coverage rate by overlapping the 100-m resampled pixel-level fractional greenspace coverage with the city boundary and averaging all greenspace coverages of pixels within the city. To further explore the residual uncertainty in the spectral unmixing process, we proposed a spectral unmixing-based threshold classification approach for the sensitivity analysis of physical greenspace coverage mapping. We first generated a greenspace binary map by classifying the 100-m pixel-level fractional greenspace coverage into greenspace (i.e., fractional greenspace coverage \ge threshold) or non-greenspace (i.e., fractional greenspace coverage < threshold) components using a threshold approach70. We adopted the thresholds of 0.3, 0.4, and 0.5 for this classification. Then, we aggregated the greenspace binary map to 100 m for the calculation of city-level physical greenspace coverage.

To validate the accuracy of the Landsat-derived city-level physical greenspace coverage, we used the classification maps of 1-m resolution National Agricultural Imagery Program (NAIP) aerial imagery for 2003–2015 and 10-m resolution Sentinel-2 satellite for 2016-2018 as benchmarks, following the approach in Chen et al. 17. This task includes three steps. First, we generated the annual composite from Sentinel-2 and NAIP datasets. For Sentinel-2 data, we applied a maximum value composite approach to generate the yearly greenest vegetation green metric across 1028 urban cities like Landsat data. Since NAIP only collected aerial imagery during the agricultural growing season in the sampling regions of the continental United States, we first chose the summer NAIP data (i.e., June—September) of the sampled United States cities as candidates. We then excluded these NAIP candidate data whose observation dates are notably different from the peak growing season by visually comparing them with the corresponding Sentinel-2 and Landsat images. Consequently, a total of 639 United States cities with 19,580 NAIP images were selected (Supplementary Table 6). Second, we generated the vegetation classification maps from the fine-resolution NAIP and Sentinel-2 imageries using a random forest machine-learning approach. To minimize the impacts of inter-annual variations, we selected training samples of vegetation and non-vegetation pixels from the NAIP and Sentinel-2 imagery for each year to calibrate the random forest algorithm in Google Earth Engine with 15 decision trees while keeping other parameters as default. With the annually calibrated random forest models, we classify the NAIP and Sentinel-2 images into vegetation and non-vegetation binary maps. Third, we calculated city-level greenspace fractions from the binary vegetation maps as references to evaluate the accuracy of Landsat-derived greenspace coverage. The overall consistency results with high Pearson’s correlation coefficients (Supplementary Fig. 17) supported the feasibility and acceptable accuracy of using Landsat-derived physical greenspace coverage to measure urban greenspace provision.

Greenspace exposure
We adopted the population-weighted exposure framework16–18,32, which can model the spatial interactions between greenspace and population, to quantify the probability and level of human exposure to greenspace according to Eq. (3):

GEd = \frac{\sum_{i=1}^{M} Pi \times Gdi}{\sum_{i=1}^{M} Pi} (3)

where Pi represents the population of pixel i, Gdi represents the fractional greenspace coverage of pixel i that considers both the central and nearby green environment with a buffer size of d (500 m is used in this study), M is the total pixel number within the city, and GEd is the population-weighted greenspace exposure at a city level.

Greenspace exposure inequality
We used three commonly used economic metrics (including the Gini coefficient, Atkinson, and Theil indices) to measure the inequality in human exposure to greenspace for global cities following the framework and method of Chen et al. 17. The calculations of these three metrics are provided in the Supplementary materials. Three metrics range between 0 and 1, where 0 indicates absolute equality and 1 indicates absolute inequality, and increasing value means a larger level of inequality.

Temporal trend analysis
We used the non-parametric Mann–Kendall statistic and Theil–Sen slope estimator approaches, which are insensitive to data distribution and outliers71, to calculate the magnitude and direction of the city-level monotonic trends of physical greenspace coverage, human greenspace exposure, and greenspace exposure inequality. We adopted a significance level of 0.05 to assess the significance of time series trends.

Attribution of changing inequality in greenspace exposure
We proposed a Venn conceptual model to quantify the contributions of greenspace and population to the temporal changes of greenspace exposure inequality. This model originates from the widely used variation partitioning approach that attributes the variations of a response result (i.e., outcome) into different explanatory variables. As shown in Fig. 3, the change of greenspace exposure inequality measured by the Gini index can be decomposed into three parts: individual greenspace provision (region I in Fig. 3a), individual population change (region III in Fig. 3a), and joint greenspace and population impacts (region II in Fig. 3a). When greenspace exposure inequality changes from year i to i+1, the individual contribution of greenspace provision (green regions I+II in Fig. 3a) can be modeled as:

I+II = \Delta Gini green,i = Gini(gi+1, pi) - Gini(gi, pi) (4)

where \Delta Gini green,i denotes the contribution of greenspace provision to the overall changes of greenspace exposure inequality measured by the Gini index in year i (i=2001, …, 2018), Gini(gi+1, pi) is the Gini index with greenspace coverage gi+1 in year i+1 and population pi in year i, Gini(gi, pi) is the Gini index with greenspace coverage gi in year i and population pi in year i.

Similarly, the individual contribution of population distribution to the overall greenspace exposure inequality (orange regions I+III in Fig. 3a) can be modeled as:

II+III = \Delta Gini pop,i = Gini(gi, pi+1) - Gini(gi, pi) (5)

where \Delta Gini pop,i denotes the contribution of the population to the overall changes of the Gini index in year i (i=2001, …, 2018), Gini(gi, pi+1) is the Gini index with greenspace coverage gi in year i and population pi+1 in year i+1.

The joint contribution of greenspace provision and population change (regions I+II+III in Fig. 3a) can be modeled as:

I+II+III = \Delta Gini all,i = Gini(gi+1, pi+1) - Gini(gi, pi) (6)

where \Delta Gini all,i denotes the contribution of greenspace provision and population to the overall changes of the Gini index in year i (i=2001, …, 2018), Gini(gi+1, pi+1) is the Gini index with greenspace coverage gi+1 and population pi+1 in year i+1.

By solving Eqs. (4–6), we can quantify the contributions of greenspace provision, population distribution, and joint greenspace and population to the change of greenspace exposure inequality.

In addition to the Venn conceptual model, we adopted another empirical approach41 to quantify the comparative contributions of greenspace and population to the change of human greenspace exposure inequality, with four major steps: 1) calculation of the monotonic trend of greenspace exposure inequality \beta expo by varying greenspace coverage and population from 2000 to 2018; 2) calculation of greenspace coverage \beta green (or population growth, \beta pop) contribution with the monotonic trend analysis by varying greenspace coverage (or population) while keeping a fixed population (or greenspace coverage) at the baseline year 2000; 3) calculation of population growth, \beta pop (or greenspace coverage \beta green) contribution by subtracting greenspace (population) contribution from the overall trend of greenspace exposure inequality, i.e., \beta pop = \beta expo - \beta green (or \beta green = \beta expo - \beta pop); 4) calculation of the comparative contribution (CC) as: CC = (|\beta pop| - |\beta green|) / |\beta expo|, where |·| represents the absolute function.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "KEY RESULTS\nThe key result of the paper is the identification of a different trend in the evolution of inequality in green space exposure depending on the stage of urban development. All cities show a decrease in urban green space exposure inequality but at the origin lie different mechanisms. Cities in the global south have overall a slight decrease in urban green exposure due to rapid urban expansion, while cities in the global north show an increase in urban green exposure.\nVALIDITY\nThe approach taken by the authors is detailed enough to lead to broad conclusions on such a large geographical extent. Nevertheless, I would advise the authors to comment on some of the shortcomings in their analysis and to suggest opportunities for future research in the discussion section.\nSIGNIFICANCE\nI believe this research is significant in the field since it looks at urban greening and green space exposure inequality from a global perspective. This allows one to take conclusions that transcend local observations.\nDATA AND METHODOLOGY\nGiven the pace of urban development, I believe the Landsat time series is most suitable for an analysis like this. Additionally, spectral unmixing is a suitable method for the assessment of urban green presence because it achieves good results without the need for a location and time-specific reference dataset.\nANALYTICAL APPROACH\nThe analytical approach in the paper is good however it does not lie in my expertise to give detailed feedback on the quality of the venn conceptual model and the inequality measures that are used in the paper.\nSUGGESTED IMPROVEMENTS\nNevertheless, I have some specific remarks/questions for the authors that I believe should be addressed in the paper:\n- I would include a section on future research and possible weakness of the analysis. Which nuances of green space provision are being missed in the current analysis?\n- Why did you decide to apply a harmonization of the surface reflectance Landsat products rather than using the L2 products?\n- Why did you opt for nearest neighbor resampling to map the Landsat green fractions to the population data? Since we are decreasing the resolution I believe that taking the average of the fraction of the pixels that overlap most would give a better idea of the green space fraction in a 100 m tile. (line 552)\n- Equation 3 shows how the city-wide green space exposure was calculated but it is unclear how this links to the greenspace exposure parameter that is used for the calculation of the Gini coefficient (g). From reference 18 I understand that the population per tile is linked to a vegetation fraction but I was wondering if the buffer distance to include nearby green environment was also used to calculate this fraction? Or does the fraction only relate to the green coverage in the tile itself?\n- For the validation: it is not entirely clear how the validation maps were constructed. In the text, it says that a training set was constructed (with fraction per pixel is assume) from the “annual vegetation green metric”. Does this mean that these metrics were used to manually annotate a training set? Or were only “fully green” pixels chosen to train a binary classifier? I believe it is the latter but please reformulate the text to make this clear to the reader.\n- In figure 15 in the supplemental material is says that the NAIP and Sentinel-2 imageries are “linear unmixed”. I believe this is a mistake?\n- Additionally in line 581 it says, “decision tress parameter of 15”. Which parameter is this? The number of decision trees in the random forest? What was the depth of the trees, the number of samples per leaf, etc.? It would be interesting to include these parameters in the supplemental material.", "reviewer_2": "Recommendation: Major revisions\nThis paper quantifies the changes in greenness and human exposure to green space in 1028 cities around the world between the years 2000 and 2018. It also analyzes inequality in green space exposure and differences amongst the study cities, noting a difference in changes to exposure inequality between cities in the Global North vs the Global South. Although the paper needs major revisions, once these are addressed, it will offer a timely and novel contribution to the literature and will be of interest to the readers of Nature Communications.\nMajor Comments:\n• The authors do not define what they mean by “greenspace” or what it is they have actually measured in their analysis. They also refer to “greenery” without defining this term. This is problematic because different disciplines understand “greenspace” and “greenery” to mean different things and because this research is measuring a specific type and scale of greenspace as discussed in my next comment.\n• I am not convinced that the NDVI threshold of 0.8 is the best choice in this study. A lower threshold might better capture actual greenness exposure, although I realize this comes with a greater risk of classification error. Although the sensitivity analyses show similar trends, they are not the same and the absolute greenspace coverage and exposure levels are markedly different at different NDVI thresholds. The fact that the authors constrained vegetation endmembers to NDVI values above 0.8 is a limitation of the paper and needs to be reflected in the framing of the paper. NDVI values above 0.8 in urban areas typically represent closed-canopy forests such as would be found in a remnant woodland or highly treed park. This dataset does not capture street trees, smaller parks, or most grassy areas. Using lower NDVI thresholds helps to reduce this limitation, but the 30-m resolution of Landsat imagery means that the research is mostly capturing larger green spaces such as parks and woodland areas, rather than distributed greenness such as backyards and street trees. This is not clear in the introduction or general paper framing. In some disciplines, “greenspace” is understood to include distributed greening which is mostly not captured in this analysis.\n• Although the authors are careful to use the word “equality” rather than “equity” throughout the paper (with the exception of “equity” in line 45) they need to clearly explain in the introduction and discussion that they are not measuring equity and cannot do so with their methods. For example, although greenspace may be more evenly distributed across a city, this does not mean that those who are most under-resourced have equal (or greater, depending on local definitions of equity) exposure to green space as more privileged populations, or even that under-resourced populations weren’t displaced during the creation of green spaces. I appreciate that the authors were careful with their language but the distinction between equity and equality needs to be clearer as Nature Communications is a multi-disciplinary journal and many readers will not understand the distinction. If it is not made clear, readers may come away with the perception that green space is more equitably distributed in cities across the world, when there is no evidence in this paper to support that conclusion.\n• On a related note, the authors have not clearly defined “the disparity issue” they introduce in objective 2 (line 100). What is the disparity issue and are they measuring it in this paper?\n• Lines 257-263: The implications of this research are overstated here. A global study such as this cannot identify drivers of change at the scale at which urban planners and developers operate and thus cannot meaningfully inform local-scale urban planning, as suggested by this section. The study provides a helpful analysis of broad trends in urban greening across many cities and its impact should be understood as such. It is not a study that is highly policy-relevant, as most urban greening decisions occur at a municipal or regional scale.\n• Where are the limitations of this paper clearly laid out? They need to be clearly included somewhere, ideally in the discussion section.\nMinor Comments:\n• This paper is well-written but includes grammatical errors throughout. I have noted some below but will leave it to the authors to complete a thorough review to correct errors.\n• My preference is to separate “greenspace” into two words (\"green space\") but this is just a minor suggestion. The authors have used both modes in the manuscript. Please choose one and be consistent.\n• Line 28: Change “between” to “from” or change “–“ to “and.”\n• Line 31: Change “contrasting difference” to “contrast.”\n• Lines 43-44: This sentence needs citations (“This has led to environment injustice, with some communities bearing a disproportionate share of the negative impact of urbanization”).\n• Line 52: “environment” should be “environmental”.\n• Lines 68-70: Sentence needs revision for grammar/clarity.\n• Lines 87-88: The beginning of this sentence needs citations (i.e. What are these particular cities? Provide some examples via citations).\n• Figure 1: Please explain the colour scale more clearly in a and c.\n• Line 132: This should be framed as assumed demand rather than demand. Green space is not universally considered to be positive.\n• Lines 138-141: This sentence is confusing due to what appear to be grammatical errors. Please revise for clarity.\n• Lines 190-191: I suggest softening this sentence. The patterns and effects of urbanization are not consistent around the world.\n• Lines 197-198: Sentence needs citation.\n• Lines 221-222: The study does not measure why Global North cities have become greener. The authors need to soften this sentence with the word “may” and they need to add citations.\n• Lines 222-224: Please provide some examples of these cities.\n• Lines 232-233: This does not indicate a benefit of urbanization but simply an increase in greening, perhaps because of and perhaps despite urbanization.\n• Methods: I question whether the satellite data should be referred to as high-resolution in this case, given the scale at which urban areas show green space and other land cover changes. I would prefer to see the resolution specified in the methods rather than the arbitrary “high-resolution” framing, although I acknowledge that others have framed it as such.\n• The methodology is sound and enough detail has been provided to reproduce results.\n- Lorien Nesbitt", "reviewer_3": "The manuscript describes a global assessment of urban greenspace coverage, exposure and inequality. Within a time series of almost 20 years, the authors try to observe general trends in the change of coverage of greenspace and its exposure to the urban population The authors employed a remote sensing methodology to generate a population-weighted greenspace cover in each study area, using Landsat data across the entire time frame. Their results show that there is a general increase in greenspace coverage within the studied cities, and a significant contrast between global north and global south cities. Additionally, significant differences between the greenspace exposure were found between these two groups of cities. This research sheds light on the impact of urbanization on greenspace availability and highlights the need for more equitable distribution of urban nature.\nHowever, the manuscript has several flaws that need to be addressed before it can be considered publication worthy. The major issues include the analysis of greenspace inequality drivers in the methods section, the structure of the results section, and the use of ambiguous and confusing terms throughout the manuscript.\nSpecific comments from the different sections of the manuscript are as follows:\nIntroduction:\n• Line 43: environmental, instead of environment.\n• Line 56: Greenspace privilege sounds very much like greenspace access. Why not use a single term for this?\n• Authors keep jumping from different concepts related to greenspace, which is confusing. This is seen throughout the introduction section. For example, line 56 and 57 authors talk about greenspace access. And then the succeeding lines talk about greenspace exposure, without linking them. While these concepts are related, they are also distinct from each other, and failing to clearly differentiate between them can make it difficult for readers to follow the authors' arguments.\n• Lines 62 to 64: Please expand on this research gap as it is a critical component of the study. Why single year or short time period studies are not sufficient? Do such studies already reveal some trend? Why is it so important to have multiyear assessments? Providing relevant literature to support this framing of the research gap would also be beneficial.\n• Line 74: What do you mean by “mature” urban environment?\n• Lines 76 to 77: Not sure whether this is always the case. Does this not depend on the type of greenspace too? For example, larger parks or green belts within cities can provide more exposure than smaller green areas.\n• Line 80: This conflicts with the statement made in line 68 about it being often chronological.\n• Line 87 to 88: Unclear how is this linked to the research gap stated in the following lines.\n• Line 99: Would you agree Greenspace stock or Greenspace inventory would be a better term than supply in this context?\n• The background information regarding research questions 3 and 4 have not been sufficiently introduced. These two questions could benefit from more explicit and detailed explanation. Please integrate them into the relevant part of introduction section\nMethods:\n• Line 487: Citation for the GUB product required.\n• Line 489: Please simplify the sentence. If I understood correctly, the filtering criteria is the area of cities should be more than 100km2?\n• How do you account for the change in urban boundary for each year of the time series?\n• Is there a cloud screening criterion as well? For instance, were images with less than 30% cloud cover used?\n• Please provide specific information regarding the Landsat images used to generate annual metrics for this study. It would be important to know whether the images were daily, 10-day composites or monthly, as well as the month or season during which the images were used.\n• Line 511: Similar to the comment above, the maximum value for within which time period was used (daily, 10-day, monthly etc)?\n• Line 542: It seems the NDVI threshold might be too high to capture all types of vegetation for identifying the vegetation end member in the spectral mixed model. Sparse vegetation such as grass or shrubs typically have NDVI values between 0.3 to 0.6, and by setting the threshold at 0.8, the authors may be excluding significant amounts of non-tree vegetation from their analysis. Additionally, this high threshold may also bias the results towards larger and healthier trees, potentially overlooking important information about smaller or less healthy vegetation.\n• As a suggestion, it would be helpful for readers to have a visual aid that illustrates the entire mapping process. Therefore, it would be beneficial if the authors could create a flow chart that outlines the steps taken to map the greenspace coverage.\n• I have serious reservations about the choice of analysis for investigation the drivers of greenspace inequality. What motivated the authors use this Venn framework for the analysis over other methods? What is the theoretical background behind this? Has this been used is similar studies? If yes, where are the references? If not, why specifically this was used for this dataset. How do you check the robustness of the results from the analysis? There are several open questions unanswered here.\nResults and Discussions:\n• The structuring of this whole section makes it difficult to read. Please consider reorganizing the whole section. Include subheadings that breakup into smaller, more manageable sections. Additionally, follow a similar flow to the methods section, as this would enhance the readability of the manuscript.\n• These sections contain significant amount of ambiguous language, which can make it challenging to follow the authors' arguments. The authors are encouraged to clarify their language to make their points more precise and understandable.\n• What is this factor of 0.001?\n• Line 117: Does largest temporal trend mean highest magnitude of change during the time period? Please be concise.\n• Line 132: Unclear how greenspace supply reflects demand.\n• Line 135: Should it not be Fig 1c or 1d?\n• A clear explanation of the turning point year of 2011 in the discussion section is missing. The authors should consider providing more context and potential reasons for any significant changes or trends observed in the data around this time."}, "author_response": "Response Letter\n> We appreciate three reviewers for their time spent assessing this manuscript and\nfor their constructive comments and suggestions, which are very helpful in\nimproving our manuscript.\n> Please find our point-by-point responses below to reviewers’ specific comments.\nReviewer #1 (Remarks to the Author):\nKEY RESULTS\nThe key result of the paper is the identification of a different trend in the evolution\nof inequality in green space exposure depending on the stage of urban\ndevelopment. All cities show a decrease in urban green space exposure inequality\nbut at the origin lie different mechanisms. Cities in the global south have overall a\nslight decrease in urban green exposure due to rapid urban expansion, while cities\nin the global north show an increase in urban green exposure.\n> Thank you very much for the detailed review and summary of our key findings.\nVALIDITY\nThe approach taken by the authors is detailed enough to lead to broad conclusions\non such a large geographical extent. Nevertheless, I would advise the authors to\ncomment on some of the shortcomings in their analysis and to suggest\nopportunities for future research in the discussion section.\n> Thanks for the good suggestion. We have added a new paragraph to\nacknowledge limitations and address prospects from our study in the Discussion\nof the revised manuscript (Pages 14-16, Lines 303-341), which is duplicated as\nfollows.\n“There are also some levels of limitations that should be acknowledged in this study.\nFirst, although the integration of 30-m resolution Landsat satellite with a spectral\nunmixing approach can help resolve the sub-pixel greenspace mapping, our\nmethod is still unable to explicitly identify certain greenspace types in small and\nfragmented patches at the Landsat’s resolution, such as street plantation, lawns,\nand pocket gardens and parks, given the heterogeneous landscape of cities.\nTherefore, satellites with a higher spatial resolution such as 10-m Sentinel-2, 3-m\nPlanetScope, and sub-meter WorldView imageries can be incorporated to offer\nadvanced observational opportunities for urban greenspace mapping, but one of\nthe key challenges is how to develop robust spatiotemporal reconstruction\nalgorithms for generating high-quality historical archives. Second, the spatial\ndistributions of greenspace and population footprints within each year of this study\nare static without modeling spatiotemporal interactions between greenspace and\nhumans dynamically. On the one hand, people living in cities are spatially moving\nin their daily routines, being exposed to different nearby green environments\nbeyond those near to their place of residence. On the other hand, seasonal\nchanges of urban greenspace that reflect different phenological phases will\ninfluence the greenspace availability over time. Therefore, a promising open topic\nfor follow-up research is to integrate a human mobility dataset with dynamic\ngreenspace observations to enable a spatially and temporally explicit human-\ngreenspace interaction framework and moving towards real-time greenspace\nexposure assessment. Third, a global sample of 1028 cities are adopted to\ninvestigate urban greening and greenspace exposure inequality across a large\ngeographical spectrum, allows us to make conclusions that transcend the local\nobservations that limit many greenspace studies. We acknowledge, however, that\nthe area criterion of 100 km2 excludes small and medium-sized cities, home to\nmultiple millions. It will be of interest to compare our results for spatially large cities\nwith a replicated study of smaller cities, since cities of different sizes use space\ndistinctly and have different socio-economic functions and greenspace scales with\ncity size in predictable ways 59. These unique processes, in turn, influence the\ngreenspace supply-demand relationship and associated temporal development\ntrajectory, further underscoring the importance in this analysis. One future research\ndirection is to explore the associations of a wider spectrum of urban characteristics\n(e.g., size, shape, density, function, and socio-economic structure) with\ngreenspace, and measure specific human-centric exposures. Clearly another\nimportant factor in assessing the degree and distribution of access or exposure to\ngreenspace is demographic structure such as gender, age, income, and race. In\nthe present study, greenspace exposure differences across population groups are\nnot considered, since their measurement, and a technical consideration of\nbetween-group equity is beyond the scope of our methods in this study. We\ntherefore talk in terms of equality, rather than equity of exposure and our\nmeasurements relate to human population counts abstracted from specific\npopulation sub-groups. A deeper understanding of greenspace exposure and\ndemographic structure such as gender, age, income, and race, is becoming\nsocially and politically urgent 18, 60, which will help identify ‘hotspot’ areas with less\ngreenspace that disproportionately affect certain vulnerable populations, and\npotentially help quantify the variations in health outcomes.”\nReferences:\n[18] Chen, B., Wu, S., Song, Y., Webster, C., Xu, B., & Gong, P. (2022). Contrasting\ninequality in human exposure to greenspace between cities of Global North\nand Global South. Nature Communications, 13(1), 4636.\n[59] Fuller, R. A., & Gaston, K. J. (2009). The scaling of green space coverage in\nEuropean cities. Biology Letters, 5(3), 352-355.\n[60] Lu, Y., Chen, L., Liu, X., Yang, Y., Sullivan, W. C., Xu, W., ... & Jiang, B. (2021).\nGreen spaces mitigate racial disparity of health: A higher ratio of green spaces\nindicates a lower racial disparity in SARS-CoV-2 infection rates in the\nUSA. Environment International, 152, 106465.\nSIGNIFICANCE\nI believe this research is significant in the field since it looks at urban greening and\ngreen space exposure inequality from a global perspective. This allows one to take\nconclusions that transcend local observations.\n> Thank you for your positive comments on the significance of this study.\nDATA AND METHODOLOGY\nGiven the pace of urban development, I believe the Landsat time series is most\nsuitable for an analysis like this. Additionally, spectral unmixing is a suitable method\nfor the assessment of urban green presence because it achieves good results\nwithout the need for a location and time-specific reference dataset.\n> We appreciate your positive feedback and agreement on the methodology we\nhave employed for urban greenspace mapping, with the integration of Landsat time\nseries and spectral unmixing approach.\nANALYTICAL APPROACH\nThe analytical approach in the paper is good however it does not lie in my expertise\nto give detailed feedback on the quality of the venn conceptual model and the\ninequality measures that are used in the paper.\n> Thanks for your good points. We have added more clarifications and additional\nmethod comparisons to better elaborate and verify the analytical approaches (i.e.,\ninequality measures and Venn model) we used in this study.\nOn the one hand, the three inequality measures adopted in this study (i.e., Gini,\nAtkinson, and Theil), are widely used metrics for quantifying environment exposure\ninequality, such as urban heat stress (Chakraborty et al. 2019), greenspace (Chen\net al. 2022), air pollution (Jbaily et al. 2022), and water scarcity (Ma et al. 2020).In\nthe context of greenspace exposure inequality assessment from this study, the Gini\nindex is calculated by the Lorenz curve framework and quantifies the degree to\nwhich the distribution of greenspace exposure deviates from an ideally equal\ndistribution. The Atkinson index is welfare-based inequality metric and measures\nthe proportion of total greenspace in a society that must be foregone to have more\nequitable distribution of greenspace among individuals in that society. A key\nattribute of the Atkinson index is its capacity of being decomposed into within and\nbetween-group inequality. The Theil index is a generalized entropy measure that\nquantifies the degree of inequality in the allocation of green spaces over space.\nA full description on these inequality metrics can be found in Afonso et al. (2015).\nThe Venn conceptual model originates from the widely used variation partitioning\napproach that attributes the variations of a response result (i.e., outcome) into\ndifferent explanatory variables (Borcard et al. 1992; Delgado-Baquerizo et al. 2020;\nLegendre et al. 2012; Peres-Neto et al. 2006). For two explanatory variables (X\n1\nand X ), as shown in Fig. R1, the model can be divided into four components\n2\n(Borcard et al. 1992; Legendre et al. 2012):\n[a] = R2(Y| [X , X ]) - R2(Y|X ) (R1)\n1 2 2\n[b] = R2(Y|X ) + R2(Y|X ) - R2(Y| [X , X ]) (R2)\n1 2 1 2\n[c] = R2(Y| [X , X ]) - R2(Y|X ) (R3)\n1 2 1\n[d] = 1- R2(Y| [X , X ]) (R4)\n1 2\nwhere the notation (Y|X) refers to the analysis (regression or canonical redundancy\nanalysis) of result Y by explanatory variable X and R2(Y|X) is the variation (R2) of\nthat analysis.\n[redacted]\nFig. R1. The Venn conceptual diagram of variation partitioning results for two\nexplanatory variables X and X . This diagram is revised from Legendre et al.\n1 2\n(2012).\nSimilarly, we used the Venn conceptual model, which has been adopted as a factor\nseparation approach for attributing the drivers of heat exposure (Broadbent et al.\n2020), to quantify the individual and joint contributions of greenspace coverage and\npopulation distribution in explaining the change of greenspace exposure inequality\nmeasured by the Gini index. As shown in Fig. 3, we assume the impact from\ngreenspace on the Gini change encompasses two parts: the individual effect\n(region I, equivalent to [a] in Fig. R1) and the joint effect of both greenspace and\npopulation (region II, equivalent to [b] in Fig. R1). The total effects of greenspace\n( ) can be calculated by varying the greenspace coverage while keeping a\nⅠ Ⅱ\nfixed population.\n𝐶 +\n(R5)\nⅠ Ⅱ\n𝐶 + = ∆𝐺𝑖𝑛𝑖𝑔𝑟𝑒𝑒𝑛,𝑖 = 𝐺𝑖𝑛𝑖(𝑔𝑖+1,𝑝𝑖) − 𝐺𝑖𝑛𝑖(𝑔𝑖,𝑝𝑖)\nwhere and are greenspace coverage at years i+1 and i, respectively.\nis population at year i. and is the Gini index and its change\n𝑔𝑖+1 𝑔𝑖 𝑝𝑖\ninduced by greenspace coverage between year i and i+1.\n𝐺𝑖𝑛𝑖 ∆𝐺𝑖𝑛𝑖𝑔𝑟𝑒𝑒𝑛,𝑖\nSimilarly, the total effects of population on the Gini change ( ) can be divided\nⅠ Ⅱ\ninto two parts: the individual effect (region III, equivalent to [c] in Fig. R1) and the\n𝐶𝐼 + 𝐼\njoint effect of both greenspace and population (region II, equivalent to [b] in Fig.\nR1). This impact can be calculated by varying the population distribution while\nkeeping a fixed greenspace coverage.\n(R6)\nⅠ ⅠⅡ\n𝐶𝐼 + = ∆𝐺𝑖𝑛𝑖𝑝𝑜𝑝,𝑖 = 𝐺𝑖𝑛𝑖(𝑔𝑖,𝑝𝑖+1) − 𝐺𝑖𝑛𝑖(𝑔𝑖,𝑝𝑖)\nwhere is population at year i+1. is the Gini change caused by\npopulation between year i and i+1.\n𝑝𝑖+1 ∆𝐺𝑖𝑛𝑖𝑝𝑜𝑝,𝑖\nThus, the total effects of greenspace and population on the Gini change ( )\nⅠ Ⅱ Ⅱ\ncan be quantified by varying both population distribution and greenspace coverage.\n𝐶 + + 𝐼\n(R7)\nⅠ Ⅱ ⅠⅡ\n𝐶 + + = ∆𝐺𝑖𝑛𝑖𝑎𝑙𝑙,𝑖 = 𝐺𝑖𝑛𝑖(𝑔𝑖+1,𝑝𝑖+1) − 𝐺𝑖𝑛𝑖(𝑔𝑖,𝑝𝑖)\nwhere is the Gini change caused by both greenspace and population.\n∆𝐺𝑖𝑛𝑖𝑎𝑙𝑙,𝑖\nBy solving Eqs. (R5-7), we can quantify individual and joint contributions from\ngreenspace and population to the Gini change for each year i (i = 2000, 2001, …,\n2017, 2018).\n(R8)\nⅠ Ⅰ Ⅱ ⅠⅡ Ⅰ ⅠⅡ\n(R9)\n𝐶 ⅠⅡ = 𝐶 Ⅰ+ Ⅱ+ ⅠⅡ − 𝐶𝐼Ⅰ + Ⅱ\n(R10)\n𝐶 Ⅰ = 𝐶 Ⅰ + Ⅱ+ − Ⅰ 𝐶 ⅠⅡ + Ⅰ Ⅱ ⅠⅡ\n𝐶𝐼 = 𝐶 + +𝐶𝐼 + − 𝐶 + +\nIn addition to our Venn conceptual model, we adopted another approach\nproposed by Tuholske et al. (2020), to empirically quantify the comparative\ncontributions of greenspace and population to the change of greenspace exposure\ninequality (i.e., the Gini change). This approach assumes the total inequality trend\ncan be linearly decomposed into the contributions of greenspace coverage and\n𝛽𝑒𝑥𝑝𝑜\npopulation distribution (i.e., ), the calculation of which,\n𝛽𝑔𝑟𝑒𝑒𝑛\nincludes four major steps:\n𝛽𝑝𝑜𝑝 𝛽𝑒𝑥𝑝𝑜 = 𝛽𝑔𝑟𝑒𝑒𝑛 +𝛽𝑝𝑜𝑝\n(Step-1) Calculate the city-level monotonic trend of greenspace exposure\ninequality with varying greenspace coverage and population distribution\nbetween 2000 and 2018.\n𝛽𝑒𝑥𝑝𝑜\n(Step-2) Calculate the share of greenspace exposure inequality from greenspace\ncoverage by varying greenspace coverage across year and fixed\npopulation at baseline year 2000.\n𝛽𝑔𝑟𝑒𝑒𝑛\n(Step-3) Calculate the share of greenspace exposure inequality trend from\npopulation growth by subtracting greenspace contribution from the total trend\nof greenspace exposure inequality, i.e., . In steps. (2-3), we\n𝛽𝑝𝑜𝑝\ncan also first calculate the share of greenspace exposure inequality from\n𝛽𝑝𝑜𝑝 = 𝛽𝑒𝑥𝑝𝑜 −𝛽𝑔𝑟𝑒𝑒𝑛\npopulation growth and derive the share from greenspace coverage\nbased on the linear model assumption, i.e., .\n𝛽𝑝𝑜𝑝 𝛽𝑔𝑟𝑒𝑒𝑛\n(Step-4) Normalize the relative contributions as comparative contribution (CC): CC\n𝛽𝑔𝑟𝑒𝑒𝑛 = 𝛽𝑒𝑥𝑝𝑜 −𝛽𝑝𝑜𝑝\n= , where | · | represents the absolute function.\n(|𝛽𝑝𝑜𝑝|−|𝛽𝑔𝑟𝑒𝑒𝑛|) ÷|𝛽𝑒𝑥𝑝𝑜|\nResults show that greenspace dominates the overall change in the Gini index\nbased on the comparative contribution (CC) metric compared to population growth\n(Fig. S15). We have included the description and attribution results of Tuholske et\nal.’s approach in the revised manuscript, which are duplicated as follows.\n(Page 37, Lines 765-775): “In addition to the Venn conceptual model, we adopted\nanother empirical approach 42 to quantify the comparative contributions of\ngreenspace and population to the change of human greenspace exposure\ninequality, with four major steps: 1) calculation of the monotonic trend of\ngreenspace exposure inequality by varying greenspace coverage and\npopulation between 2000 and 2018; 2) calculation of greenspace coverage\n𝛽𝑒𝑥𝑝𝑜\n(or population growth, ) contribution with the monotonic trend analysis by\n𝛽𝑔𝑟𝑒𝑒𝑛\nvarying greenspace coverage (or population) while keeping a fixed population (or\n𝛽𝑝𝑜𝑝\ngreenspace coverage) at baseline year 2000; 3) calculation of population growth,\n(or greenspace coverage ) contribution by subtracting greenspace\n(population) contribution from the overall trend of greenspace exposure inequality,\n𝛽𝑝𝑜𝑝 𝛽𝑔𝑟𝑒𝑒𝑛\ni.e., (or ); 4) calculation of the\ncomparative contribution (CC) as: CC = , where | · |\n𝛽𝑝𝑜𝑝 = 𝛽𝑒𝑥𝑝𝑜 −𝛽𝑔𝑟𝑒𝑒𝑛 𝛽𝑔𝑟𝑒𝑒𝑛 = 𝛽𝑒𝑥𝑝𝑜 −𝛽𝑝𝑜𝑝\nrepresents the absolute function. ”\n(|𝛽𝑝𝑜𝑝|−|𝛽𝑔𝑟𝑒𝑒𝑛|) ÷|𝛽𝑒𝑥𝑝𝑜|\n(Pages 9-10, Lines 195-200): “In addition to results based on the Gini index, our\nsensitivity analysis using the Atkinson and Theil metrics also show very similar\nattribution results (Supplementary Figs. 13-14). We also incorporated the\ncomparative attribution approach 42 to verify the robustness of the proposed Venn\nconceptual model, similar results show that the contribution from greenspace\ndominates the overall change in the Gini index compared to population growth\n(Supplementary Fig. S15).”\nFig. 3. The Venn conceptual model of quantifying individual and joint\ncontributions from greenspace and population to the overall change in the\nGini index. Green circle (regions I and Ⅱ) denotes the Gini index change ΔGini\ngreen,i\ninduced by the change of greenspace provision from g to g with a fixed\ni i+1\npopulation pi for year i (i = 2001,…, 2018). Orange circle (regions IIand III) denotes\nthe Gini index change ΔGinipop,i induced by the population change from p to p\ni i+1\nwith a fixed greenspace g for year i. These two circles (regions I, Ⅱ, and Ⅲ) denote\ni\nthe overall change of Gini index ΔGini induced by both greenspace and\nall,i\npopulation change from (g, p) to (g , p ) for year i. The overlapped region II\ni i i+1 i+1\ndenotes the joint effects of greenspace and population changes.\nFig. S15. Comparative contributions of greenspace and population to the\ntemporal change in the Gini index using the empirical approach proposed by\nTuholske et al. (2020). a-b. Spatial patterns of population versus greenspace for\nthe overall change of the Gini index using the comparative contribution (CC) metric:\nCC = . c-d. City statistics from greenspace and\npopulation to the temporal change in greenspace exposure inequality. a and c. The\n(|βpop|−|βgreen|) ÷|βexpo|\noverall trend and the share of greenspace are first calculated and\nthen the share of population is quantified: . b and d. The\n𝛽𝑒𝑥𝑝𝑜 𝛽𝑔𝑟𝑒𝑒𝑛\noverall trend and the share of population are first calculated and then\n𝛽𝑝𝑜𝑝 = 𝛽𝑒𝑥𝑝𝑜 −𝛽𝑔𝑟𝑒𝑒𝑛\nthe share of greenspace is quantified: .\n𝛽𝑒𝑥𝑝𝑜 𝛽𝑝𝑜𝑝\n𝛽𝑔𝑟𝑒𝑒𝑛 = 𝛽𝑒𝑥𝑝𝑜 −𝛽𝑝𝑜𝑝\nReferences:\n[1] Afonso, H., LaFleur, M., & Alarcón, D. (2015). Inequality measurement:\nDevelopment issues no. 2. Department of Economic and Social Affairs.\n[2] Borcard, D., Legendre, P., & Drapeau, P. (1992). Partialling out the spatial\ncomponent of ecological variation. Ecology, 73(3), 1045-1055.\n[3] Broadbent, A. M., Krayenhoff, E. S., & Georgescu, M. (2020). The motley drivers\nof heat and cold exposure in 21st century US cities. Proceedings of the\nNational Academy of Sciences, 117(35), 21108-21117.\n[4] Chakraborty, T., Hsu, A., Manya, D., & Sheriff, G. (2019). Disproportionately\nhigher exposure to urban heat in lower-income neighborhoods: a multi-city\nperspective. Environmental Research Letters, 14(10), 105003.\n[5] Chen, B., Wu, S., Song, Y., Webster, C., Xu, B., & Gong, P. (2022). Contrasting\ninequality in human exposure to greenspace between cities of Global North\nand Global South. Nature Communications, 13(1), 4636.\n[6] Delgado-Baquerizo, M., Reich, P. B., Bardgett, R. D., Eldridge, D. J., Lambers,\nH., Wardle, D. A., ... & Fierer, N. (2020). The influence of soil age on ecosystem\nstructure and function across biomes. Nature Communications, 11(1), 4721.\n[7] Jbaily, A., Zhou, X., Liu, J., Lee, T. H., Kamareddine, L., Verguet, S., & Dominici,\nF. (2022). Air pollution exposure disparities across US population and income\ngroups. Nature, 601(7892), 228-233.\n[8] Legendre, P., Borcard, D., & Roberts, D. W. (2012). Variation partitioning\ninvolving orthogonal spatial eigenfunction submodels. Ecology, 93(5), 1234-\n1240.\n[9] Peres-Neto, P. R., Legendre, P., Dray, S., & Borcard, D. (2006). Variation\npartitioning of species data matrices: estimation and comparison of\nfractions. Ecology, 87(10), 2614-2625..\n[10] Ma, T., Sun, S., Fu, G., Hall, J. W., Ni, Y., He, L., ... & Zhou, C. (2020). Pollution\nexacerbates China’s water scarcity and its regional inequality. Nature\nCommunications, 11(1), 650.\n[11] Tuholske, C., Caylor, K., Funk, C., Verdin, A., Sweeney, S., Grace, K., ... &\nEvans, T. (2021). Global urban population exposure to extreme\nheat. Proceedings of the National Academy of Sciences, 118(41),\ne2024792118.\nSUGGESTED IMPROVEMENTS\nNevertheless, I have some specific remarks/questions for the authors that I believe\nshould be addressed in the paper:\n- I would include a section on future research and possible weakness of the\nanalysis. Which nuances of green space provision are being missed in the current\nanalysis?\n> Thanks for your good suggestion. Our study used a broad concept of greenspace\ncoverage (i.e., the fraction of greenspace area within a satellite pixel) to quantify\nthe general pattern of global urban greenspace and the associated indicator of\nhuman exposure to greenspace. Although the fine-resolution Landsat footprint (30\nx 30 m) and spectral unmixing approaches can help resolve the sub-pixel greenspace mapping issue, our\nmethod might miss specific greenspace types in small and fragmented patches, such as street plantation, lawns, and small gardens and parks, given the heterogeneous landscape of cities. Following your suggestion,\nwe have added a new paragraph to acknowledge these limitations and address\nfuture prospects from our study in the Discussion section of the revised manuscript\n(Pages 14-16, Lines 303-341), which is duplicated as follows.\n“There are also some levels of limitations that should be acknowledged in this study.\nFirst, although the integration of 30-m resolution Landsat satellite with a spectral\nunmixing approach can help resolve the sub-pixel greenspace mapping, our\nmethod is still unable to explicitly identify certain greenspace types in small and\nfragmented patches at the Landsat’s resolution, such as street plantation, lawns,\nand pocket gardens and parks, given the heterogeneous landscape of cities.\nTherefore, satellites with a higher spatial resolution such as 10-m Sentinel-2, 3-m\nPlanetScope, and sub-meter WorldView imageries can be incorporated to offer\nadvanced observational opportunities for urban greenspace mapping, but one of\nthe key challenges is how to develop robust spatiotemporal reconstruction\nalgorithms for generating high-quality historical archives. Second, the spatial\ndistributions of greenspace and population footprints within each year of this study\nare static without modeling spatiotemporal interactions between greenspace and\nhumans dynamically. On the one hand, people living in cities are spatially moving\nin their daily routines, being exposed to different nearby green environments\nbeyond those near to their place of residence. On the other hand, seasonal\nchanges of urban greenspace that reflect different phenological phases will\ninfluence the greenspace availability over time. Therefore, a promising open topic\nfor follow-up research is to integrate a human mobility dataset with dynamic\ngreenspace observations to enable a spatially and temporally explicit human-\ngreenspace interaction framework and moving towards real-time greenspace\nexposure assessment. Third, a global sample of 1028 cities are adopted to\ninvestigate urban greening and greenspace exposure inequality across a large\ngeographical spectrum, allows us to make conclusions that transcend the local\nobservations that limit many greenspace studies. We acknowledge, however, that\nthe area criterion of 100 km2 excludes small and medium-sized cities, home to\nmultiple millions. It will be of interest to compare our results for spatially large cities\nwith a replicated study of smaller cities, since cities of different sizes use space\ndistinctly and have different socio-economic functions and greenspace scales with\ncity size in predictable ways 59. These unique processes, in turn, influence the\ngreenspace supply-demand relationship and associated temporal development\ntrajectory, further underscoring the importance in this analysis. One future research\ndirection is to explore the associations of a wider spectrum of urban characteristics\n(e.g., size, shape, density, function, and socio-economic structure) with\ngreenspace, and measure specific human-centric exposures. Clearly another\nimportant factor in assessing the degree and distribution of access or exposure to\ngreenspace is demographic structure such as gender, age, income, and race. In\nthe present study, greenspace exposure differences across population groups are\nnot considered, since their measurement, and a technical consideration of\nbetween-group equity is beyond the scope of our methods in this study. We\ntherefore talk in terms of equality, rather than equity of exposure and our\nmeasurements relate to human population counts abstracted from specific\npopulation sub-groups. A deeper understanding of greenspace exposure and\ndemographic structure such as gender, age, income, and race, is becoming\nsocially and politically urgent 18, 60, which will help identify ‘hotspot’ areas with less\ngreenspace that disproportionately affect certain vulnerable populations, and\npotentially help quantify the variations in health outcomes.”\n- Why did you decide to apply a harmonization of the surface reflectance Landsat\nproducts rather than using the L2 products?\n> Thanks for your question. We have already used Landsat Level 2 products in the\nmanuscript, including Landsat-5, Landsat-7, and Landsat-8 Level 2, Collection 2,\nTier 1 products. We conducted the surface reflectance harmonization among\nLandsat-5, Landsat-7, and Landsat-8 sensors following Roy et al.’s (2016)\napproach to minimize the greenspace estimation bias from spectral characteristic\ndifferences across Landsat satellites (https://developers.google.com/earth-\nengine/tutorials/community/landsat-etm-to-oli-harmonization). We have included\nthe data information in the method and data availability sections (Page 30, Lines\n616-620), which are duplicated as follows.\n“To minimize the uncertainty caused by Landsat-7 scan line off failure 63, we\nprimarily focused on the use of Landsat-5 and Landsat-8 Collection 2/Tier 1 Level-\n2 products, with data availabilities of 12-year Landsat-5 (2000-2011), 2-year\nLandsat-7 (2012-2013), and 5-year Landsat-8 (2014-2018).”\n“Landsat-5 Collection 2/Tier1 Level-2 surface reflectance product is available at:\nhttps://developers.google.com/earth-\nengine/datasets/catalog/LANDSAT_LT05_C02_T1_L2\nLandsat-7 Collection 2/Tier1 Level-2 surface reflectance product is available at:\nhttps://developers.google.com/earth-\nengine/datasets/catalog/LANDSAT_LE07_C02_T1_L2\nLandsat-8 Collection 2/Tier1 Level-2 surface reflectance product is available at:\nhttps://developers.google.com/earth-\nengine/datasets/catalog/LANDSAT_LC08_C02_T1_L2”\nReference:\n[1] Roy, D. P., Kovalskyy, V., Zhang, H. K., Vermote, E. F., Yan, L., Kumar, S. S., &\nEgorov, A. (2016). Characterization of Landsat-7 to Landsat-8 reflective\nwavelength and normalized difference vegetation index continuity. Remote\nSensing of Environment, 185, 57-70.\n- Why did you opt for nearest neighbor resampling to map the Landsat green\nfractions to the population data? Since we are decreasing the resolution I believe\nthat taking the average of the fraction of the pixels that overlap most would give a\nbetter idea of the green space fraction in a 100 m tile. (line 552)\n> Thanks for your question. Yes, the nearest neighbor resampling is used for\nreprojection, and we do use the averaging to reduce the resolution from 30 m to\n100 m.\nAs the Landsat greenspace fractions and WorldPop population datasets come from\ndifferent sources, two steps are needed to ensure their spatial consistency: (1) data\nreprojection and (2) spatial resolution aggregation. We first reprojected the\nLandsat-derived greenspace fractions to the WorldPop population data using the\ndefault nearest neighbor resampling approach in Google Earth Engine platform\n(https://developers.google.com/earth-engine/guides/resample) and then\naggregated the 30-m reprojected greenspace data to 100-m grid (using the\naverage of greenspace fractions of the pixels) to ensure its spatially consistency\nwith the 100-m WorldPop data set. We have reworded this sentence to avoid\nconfusions in the revised manuscript (Page 32, Lines 667-670), which is duplicated\nas follows.\n“We reprojected the 30-m fractional greenspace coverage using the nearest\nneighbor resampling approach and aggregated it to 100-m resolution to ensure the\nderived greenspace map is spatially consistent with the 100-m population data.”\n- Equation 3 shows how the city-wide green space exposure was calculated but it\nis unclear how this links to the greenspace exposure parameter that is used for the\ncalculation of the Gini coefficient (g). From reference 18 I understand that the\npopulation per tile is linked to a vegetation fraction but I was wondering if the buffer\ndistance to include nearby green environment was also used to calculate this\nfraction? Or does the fraction only relate to the green coverage in the tile itself?\n> Thanks for your good questions. The Gini index is calculated from the Lorenz\ncurve framework with individual shares of greenspace coverage (Fig. R2),\nrather than directly from the city-level population-weighted greenspace exposure\nshown in Eq. 3. As shown in Fig. R2, Gini is defined as the ratio of the area that\nlies between the line of equality (i.e., 45-degree line) and the Lorenz curve (i.e.,\ncumulative share of greenspace exposure ranked by residents that are exposed\nfrom lowest to highest greenspace) (region A) over the total area under the line\nof equality (region A plus region B) (Chen et al. 2022; Song et al. 2021):\n(R11)\n𝑆𝐴\n𝐺wh𝑖𝑛e𝑖re=\n𝑆\n𝐴a\n+n\n𝑆\nd𝐵 represent the areas of regions A and B, respectively.\nSince b\ns\no𝐴th x- an 𝑠𝐵d y-axis in Fig. R2b scale from 0 to 1, we have S\nA\n+ S\nB\n= 0.5.\nThus, the Gini index can be formulated as:\n(R12)\n𝑆𝐴 0.5−𝑆𝐵\n𝐺Th𝑖𝑛e𝑖 k=e y0 .t5o= solv0.e5 E=q. 1(R−1 22) ×is𝑆 t 𝐵 he area calculation of region B that is bounded\nby the cumulative share of greenspace exposure and the cumulative share of\nresidents from lowest to highest greenspace exposure. To this end, we first\nused the numerical integration approach to calculate the area of trapezoid B\ni\nthat contributed by the ith resident, and then summed each trapezoid area\nacross all residents. The area of trapezoid B (Area ) is calculated as follows:\ni Bi\n(R13)\n1 ∑\ni\n𝑗\n-\n=\n1\n1𝑔𝑗\n∑𝑖\n𝑗=1𝑔𝑗 1\n𝐴w𝑟h𝑒e𝑎r𝐵e𝑖 = 2is × th(e∑ 𝑛 𝑗g=1re𝑔e𝑗n+s∑p 𝑛 𝑗a=1c𝑔e𝑗 )th×at𝑛 exposed to the jth resident and n is the total\nresiden𝑔t 𝑗number.\nThus, the area of region B is calculated as\n(R14)\n𝑛 𝑛 1 ∑\ni\n𝑗\n-\n=\n1\n1𝑔𝑗\n∑𝑖\n𝑗=1𝑔𝑗 1\n𝑆B𝐵y =su b∑s𝑖=ti1tu𝑆t𝐵in𝑖g= E∑q𝑖.= (1R21 ×4)( i∑n 𝑛 𝑗t=o1 𝑔E𝑗q+. (∑R 𝑛 𝑗=112𝑔𝑗)), ×we𝑛 finally derive the Gini index using\nthe following formula.\n(R15)\n𝑛 𝑖−1 𝑛 𝑖\n∑𝑖=1∑𝑗=1𝑔𝑗 + ∑𝑖=1∑𝑗=1𝑔𝑗\n𝑛\n𝐺𝑖𝑛𝑖 = 1− 𝑛 × ∑𝑗=1𝑔𝑗\nFig. R2. Illustrative diagram of Gini index for inequality assessments of\ngreenspace exposure. The Gini index is defined as the ratio of the area that\nlies between the line of equality and the Lorenz curve (region A) over the total\narea under the line of equality (region A plus region B), where Lorenz curve\nplots the proportion of the greenspace exposure (y-axis) that is cumulatively\nshared by the residents (x-axis). B indicates the contribution of ith residents to\ni\nthe accumulated greenspace exposure and is estimated by the trapezoid area\nas shown in the left panel, where g represents the greenspace that is exposed\ni\nto the ith resident, and N represents the resident number. Y-axis shows the\ncumulative share of greenspace exposure; X-axis shows the cumulative share\nof residents from lowest to highest greenspace exposure.\nYes, we have considered the impacts of nearby green environments on\nhuman exposure to greenspace by using a buffer analysis, with the widely\nused 500-m catchment buffer size. Therefore, the buffered area including nearby\ngreen environment will be used to calculate the averaged green fraction for\nconducting human exposure to greenspace assessment. Moreover, our previous\nsensitivity analysis in terms of different buffer distances (i.e., 500m, 1km, 1.5km,\nand 2km) shows that a 500-m buffer distance is appropriate for measuring\ngreenspace exposure and the inequality across global cities (Chen et al. 2022).\nTo clarify this issue, we have included the buffer size information for the calculation of\ngreenspace exposure inequality in the Supplementary information section of the\nrevised manuscript (Page 2, Lines 5-8).\n“To consider the impacts of nearby green environments on the inequality measures,\nwe conducted a buffer analysis on greenspace coverage with a size of 500 m using\nthe image convolution algorithm with the “convolve” function in Google Earth\nEngine.”\nReferences:\n[1] Chen, B., Wu, S., Song, Y., Webster, C., Xu, B., & Gong, P. (2022). Contrasting\ninequality in human exposure to greenspace between cities of Global North\nand Global South. Nature Communications, 13(1), 4636.\n[2] Song, Y., Chen, B., Ho, H. C., Kwan, M. P., Liu, D., Wang, F., ... & Song, Y.\n(2021). Observed inequality in urban greenspace exposure in\nChina. Environment International, 156, 106778.\n- For the validation: it is not entirely clear how the validation maps were constructed.\nIn the text, it says that a training set was constructed (with fraction per pixel is\nassume) from the “annual vegetation green metric”. Does this mean that these\nmetrics were used to manually annotate a training set? Or were only “fully green”\npixels chosen to train a binary classifier? I believe it is the latter but please\nreformulate the text to make this clear to the reader.\n> Thanks for your good questions. We validated the Landsat-derived greenspace\ncoverage mapping using the vegetation and non-vegetation classification maps of\n1-m NAIP and 10-m Sentinel-2 imagery. This validation includes three steps. First,\nwe collected the training samples of vegetation and non-vegetation pixels from the\nfine-resolution NAIP and Sentinel-2 maps for each year to train the random forest\nalgorithm. Second, with the calibrated random forest algorithm, we classified the\nSentinel-2 and NAIP images into the vegetation and non-vegetation binary\nclassification maps. Third, the city-level greenspace fractions calculated from the\nbinary classification maps are used as references to validate the accuracy of\nLandsat-derived greenspace coverage. We have reworded the validation process\nin the revised manuscript (Pages 33-34, Lines 694-702), which is duplicated as\nfollows.\n“Second, we generated the vegetation classification maps from the fine-resolution\nNAIP and Sentinel-2 imageries using a random forest machine learning approach.\nTo minimize the impacts of inter-annual variations, we selected training samples of\nvegetation and non-vegetation pixels from the NAIP and Sentinel-2 imagery for\neach year to calibrate the random forest algorithm in Google Earth Engine with 15\ndecision trees while keeping other parameters as default. With the annually\ncalibrated random forest models, we classify the NAIP and Sentinel-2 images into\nvegetation and non-vegetation binary maps. Third, we calculated city-level\ngreenspace fractions from the binary vegetation maps as references to evaluate\nthe accuracy of Landsat-derived greenspace coverage.”\n- In figure 15 in the supplemental material is says that the NAIP and Sentinel-2\nimageries are “linear unmixed”. I believe this is a mistake?\n> Thanks for spotting this typo. We extracted the validation reference of the NAIP\nand Sentinel-2 by classifying the annual maximum-value composited imageries\ninto vegetation and non-vegetation types, and then calculating the city-level\ngreenspace fraction for comparison. To make it clear, we have reworded the figure\ncaption of Supplementary Fig. 17, which is duplicated as follows.\n“Supplementary Fig. 17. Comparison of physical greenspace coverage (GC)\nderived from 1-m NAIP and 10-m Sentinel-2 imageries (x-axis) and Landsat\nimagery (y-axis) across global 1028 cities. a. NAIP vs. Landsat in 2003-2015.\nb-d. Sentinel-2 vs. Landsat in 2016-2018. The GCs from NAIP and Sentinel-2\nimageries are extracted from the vegetation and non-vegetation classification\nmapping with a random forest approach and then aggregated to city-level mean\nfor comparison. Linear regression was used to measure their correlation with\nPearson’s r coefficient.”\n- Additionally in line 581 it says, “decision tress parameter of 15”. Which parameter\nis this? The number of decision trees in the random forest? What was the depth of\nthe trees, the number of samples per leaf, etc.? It would be interesting to include\nthese parameters in the supplemental material.\n> Thanks for your good questions. Your interpretation is right. The decision tress\nparameter refers to the number of decision trees in the random forest.\nAs the classification analysis was conducted on the Google Earth Engine (GEE)\ncloud-computing platform, we adopted the default random forest function\n“ee.Classifier.smileRandomForest” to generate the vegetation and non-vegetation\nclassification maps from NAIP and Sentinel-2. This default function provides six\nparameters (https://developers.google.com/earth-engine/apidocs/ee-classifier-\nsmilerandomforest) as follows:\nNumberOfTrees: The number of decision trees to create.\nVariablesPerSplit: The number of variables per split, default: Null.\nMinLeafPopulation: Nodes whose training set contains at least these many points,\ndefault: 1.\nbagFraction: The fraction of input to bag per tree, default: 0.5.\nMaxNodes: The maximum number of leaf nodes in each tree, default: Null.\nSeed: The randomization seed,default: 0.\nWe only changed the NumberOfTrees parameter holding the other parameters at\ndefault when using this function. To avoid confusion, we have reworded the use of\nrandom forest machine learning approach in the methodology section (Page 33,\nLines 696-699), which is duplicated as follows.\n“To minimize the impacts of inter-annual variations, we selected training samples\nof vegetation and non-vegetation pixels from the NAIP and Sentinel-2 imagery for\neach year to calibrate the random forest algorithm in Google Earth Engine with 15\ndecision trees while keeping other parameters as default.”\nReviewer #2 (Remarks to the Author):\nRecommendation: Major revisions\nThis paper quantifies the changes in greenness and human exposure to green\nspace in 1028 cities around the world between the years 2000 and 2018. It also\nanalyzes inequality in green space exposure and differences amongst the study\ncities, noting a difference in changes to exposure inequality between cities in the\nGlobal North vs the Global South. Although the paper needs major revisions, once\nthese are addressed, it will offer a timely and novel contribution to the literature and\nwill be of interest to the readers of Nature Communications.\n> Thank you for your detailed assessment and positive comments.\nMajor Comments:\n• The authors do not define what they mean by “greenspace” or what it is they have\nactually measured in their analysis. They also refer to “greenery” without defining\nthis term. This is problematic because different disciplines understand “greenspace”\nand “greenery” to mean different things and because this research is measuring a\nspecific type and scale of greenspace as discussed in my next comment.\n> Thanks for your insightful comment. We agree that greenspace has different\ndefinitions for different research communities. Narrowly, it is regarded as outdoor\nplaces with significant amounts of vegetation that exist mainly in semi-natural areas\nand managed parks and gardens and roadside locations (Jim and Chen, 2003).\nBroadly, greenspace refers to land that is partly or completely covered with grass,\ntrees, shrubs, and other vegetation (Chen et al. 2022a, 2022b; Cameron and\nHitchmough, 2016; James et al. 2009). In this study, we used the broad definition\nof greenspace throughout the manuscript. Following your suggestion, we have\nreplaced the term “greenery” as “greenspace” for a consistent concept.\nWe also have included our greenspace definition in the methodology section in the\nrevised manuscript (Page 6, Lines 111-113), which is duplicated as follows.\n“Specifically, we adopted a broad definition of greenspace to refer to land that is\npartly or completely covered with grass, trees, shrubs, and other vegetation 33. That\nis, green-covered land within the curtilage of an urban boundary, since we are\nfocusing on cities in this study.”\nReferences:\n[1] Cameron, R., Hitchmough, J. (2016). Environmental horticulture: science and\nmanagement of green landscapes. CAB International, Boston: MA.\n[2] Chen, B., Tu, Y., Wu, S., Song, Y., Jin, Y., Webster, C., ... & Gong, P. (2022a).\nBeyond green environments: Multi-scale difference in human exposure to\ngreenspace in China. Environment International, 166, 107348.\n[3] Chen, B., & Webster, C. (2022b). Eight Reflections on Quantitative Studies of\nUrban Green Space: A Mapping-Monitoring-Modeling-Management (4M)\nPerspective. Landscape Architecture Frontiers 10(3): 66-77.\n[4] James, P., Tzoulas, K., Adams, M. D., Barber, A., Box, J., Breuste, J., ... &\nThompson, C. W. (2009). Towards an integrated understanding of green space\nin the European built environment. Urban Forestry & Urban Greening, 8(2), 65-\n75.\n[5] Jim, C. Y., & Chen, S. S. (2003). Comprehensive greenspace planning based\non landscape ecology principles in compact Nanjing city, China. Landscape\nand Urban Planning, 65(3), 95-116.\n• I am not convinced that the NDVI threshold of 0.8 is the best choice in this study.\nA lower threshold might better capture actual greenness exposure, although I\nrealize this comes with a greater risk of classification error. Although the sensitivity\nanalyses show similar trends, they are not the same and the absolute greenspace\ncoverage and exposure levels are markedly different at different NDVI thresholds.\nThe fact that the authors constrained vegetation endmembers to NDVI values\nabove 0.8 is a limitation of the paper and needs to be reflected in the framing of\nthe paper. NDVI values above 0.8 in urban areas typically represent closed-canopy\nforests such as would be found in a remnant woodland or highly treed park. This\ndataset does not capture street trees, smaller parks, or most grassy areas. Using\nlower NDVI thresholds helps to reduce this limitation, but the 30-m resolution of\nLandsat imagery means that the research is mostly capturing larger green spaces\nsuch as parks and woodland areas, rather than distributed greenness such as\nbackyards and street trees. This is not clear in the introduction or general paper\nframing. In some disciplines, “greenspace” is understood to include distributed\ngreening which is mostly not captured in this analysis.\n> Thanks for your good questions on our methodology regarding greenspace\ncoverage extraction. We employed the widely used linear spectral unmixing model\n(Haase et al. 2019) to map sub-pixel greenspace coverage from Landsat, which\nassumes that one pixel’s spectral signature is a linearly weighted sum of a few\nspectrally pure endmembers and their fractional covers within the target pixel：\n(R16)\n𝑛\n𝑅𝑖 = ∑𝑘=1𝑓𝑖𝑘 ∙𝐶𝑖𝑘 +𝜀𝑖\nwhere represents the spectral signatures of pixel i, represents the\nspectral signature of the kth endmember, is the unmodeled residual in\n𝑅𝑖 𝐶𝑖𝑘\nthe ith pixel, n is the total number of endmembers, is the fraction of kth\n𝜀𝑖\nendmember within pixel i. We selected vegetation (k=1), impervious area (k=2),\n𝑓𝑖𝑘\nand water (k=3) as three endmembers (n = 3) for Eq. (R16) to calculate the\nvegetation fraction (f ) as greenspace coverage.\ni1\nThe accuracy of the linear spectral unmixing model highly relies on the purity of the\nselected endmembers (Dennison and Roberts, 2003; Somers et al. 2011).\nConsidering the lack of spectral library of vegetation endmember, we thus use a\nhigh NDVI threshold (i.e., >0.80) to enable the selection of pure vegetation pixels\nfor endmember spectra extraction. According to linear spectral unmixing theory,\nthe NDVI saturates at closed canopy cover, i.e., pixels above high NDVI threshold\nmost likely represent pure vegetation (Fernández-Guisuraga et al. 2020; Schug et\nal. 2020).To quantify the impacts of NDVI threshold on the result of greenspace\ncoverage extraction, we conducted the sensitivity analysis using different NDVI\nthresholds of 0.74 - 0.86 with an 0.2 interval holding the other thresholds\n(impervious area and water) constant for greenspace coverage extraction for 2000-\n2018. Our results show that the NDVI threshold of 0.80 is very comparable with the\nother thresholds in extracting city-level absolute greenspace coverage (Fig. R3).\nNote that the maximum NDVI threshold of 0.86 is used in the sensitivity analysis\nbecause of the limited Landsat’s spectral response and the constraint of temporally\nstable endmember over the past two decades (i.e., we require endmember pixels\nshould have a NDVI value over 0.86 for each year within 2000-2018).\nFig. R3. Sensitivity analysis of the NDVI threshold of pure vegetation\nendmember selection for extracting city-level physical greenspace\ncoverage (GC) across global 1028 cities in 2000-2018. a-f. NDVI threshold\nof 0.80 vs. a 0.74. b. 0.76. c. 0.78. d. 0.82. e. 0.84. f.0.86. Each green circle\nrepresents city-level physical GC in a specific year. Linear regression was used\nto measure their correlation with Pearson’s r coefficient.\nAs the linear spectral unmixing approach aims to capture sub-pixel greenspace\ninformation, our methodology can quantify and incorporate fine-patched\ngreenspace types, such as street trees, shrubs, small parks, and grassy areas (Fig.\nR4).\nFig. R4. City examples of Landsat data for capturing fine-patched\ngreenspace types. a and b. St. Louis city, Missouri, United States. c and d.\nKansas city, Missouri, United States. a and c. 1-m resolution National\nAgricultural Imagery Program (NAIP) aerial imagery for 2020. b and d. 30-m\nresolution Landsat-derived greenspace for 2020 (USDA Farm Production and\nConservation - Business Center, Geospatial Enterprise Operations)\nNevertheless, we acknowledge that the 30-m resolution Landsat might miss some\nsmall greenspace patches, which require the use of higher-resolution satellite data\n(e.g., 10-m Sentinel-2 and 3-m PlanetScope imagery). We have discussed our\nmethodology limitation for fine-scale greenspace mapping in the revised\nmanuscript (Pages 14-15, Lines 303-312), which is duplicated as follows.\n“There are also some levels of limitations that should be acknowledged in this study.\nFirst, although the integration of 30-m resolution Landsat satellite with a spectral\nunmixing approach can help resolve the sub-pixel greenspace mapping, our\nmethod is still unable to explicitly identify certain greenspace types in small and\nfragmented patches at the Landsat’s resolution, such as street plantation, lawns,\nand pocket gardens and parks, given the heterogeneous landscape of cities.\nTherefore, satellites with a higher spatial resolution such as 10-m Sentinel-2, 3-m\nPlanetScope, and sub-meter WorldView imageries can be incorporated to offer\nadvanced observational opportunities for urban greenspace mapping, but one of\nthe key challenges is how to develop robust spatiotemporal reconstruction\nalgorithms for generating high-quality historical archives.”\nReferences:\n[1] Dennison, P. E., & Roberts, D. A. (2003). Endmember selection for multiple\nendmember spectral mixture analysis using endmember average\nRMSE. Remote Sensing of Environment, 87(2-3), 123-135.\n[2] Fernández-Guisuraga, J. M., Calvo, L., & Suárez-Seoane, S. (2020).\nComparison of pixel unmixing models in the evaluation of post-fire forest\nresilience based on temporal series of satellite imagery at moderate and very\nhigh spatial resolution. ISPRS Journal of Photogrammetry and Remote\nSensing, 164, 217-228.\n[3] Haase, D., Jänicke, C., & Wellmann, T. (2019). Front and back yard green\nanalysis with subpixel vegetation fractions from earth observation data in a\ncity. Landscape and Urban Planning, 182, 44-54.\n[4] Schug, F., Frantz, D., Okujeni, A., van Der Linden, S., & Hostert, P. (2020).\nMapping urban-rural gradients of settlements and vegetation at national scale\nusing Sentinel-2 spectral-temporal metrics and regression-based unmixing\nwith synthetic training data. Remote Sensing of Environment, 246, 111810.\n[5] Somers, B., Asner, G. P., Tits, L., & Coppin, P. (2011). Endmember variability in\nspectral mixture analysis: A review. Remote Sensing of Environment, 115(7),\n1603-1616.\n• Although the authors are careful to use the word “equality” rather than “equity”\nthroughout the paper (with the exception of “equity” in line 45) they need to clearly\nexplain in the introduction and discussion that they are not measuring equity and\ncannot do so with their methods. For example, although greenspace may be more\nevenly distributed across a city, this does not mean that those who are most under-\nresourced have equal (or greater, depending on local definitions of equity) \nexposure to green space as more privileged populations, or even that under-\nresourced populations weren’t displaced during the creation of green spaces.\nI appreciate that the authors were careful with their language but the distinction\nbetween equity and equality needs to be clearer as Nature Communications is a\nmulti-disciplinary journal and many readers will not understand the distinction. If it\nis not made clear, readers may come away with the perception that green space is\nmore equitably distributed in cities across the world, when there is no evidence in\nthis paper to support that conclusion.\n> Thanks for your insightful comment and good suggestions. Our study is primarily\nfocused on quantifying whether everyone equally shares the same greenspace\nresource abstracted from income, ethic, or other group status (Song et al. 2021;\nChen et al. 2022; Wu and Kim. 2021), while greenspace equity refers more about\nthe social and environmental justice of the greenspace access, which involves\nmore complex components, such as distributional injustice (i.e., geographic\nallocation of greenspace resources), procedural justice (i.e., the equality of the\ninstitutional decision-making processes), and interactional injustice (i.e., the\ninteractions that people have on greenspace in an anthropological dimension)\n(Nesbitt et al. 2018, 2019; Williams et al. 2020).\nTo avoid confusion, we have replaced the word “equity” in original line 45 with\n“equality”. We have also defined the scope of measuring greenspace equality in\nthe introduction section (Page 6, Lines 111-113) and discuss our approach for\nquantifying greenspace equity from the future perspective (Pages 15-16, Lines\n332-341) in the revised manuscript, which is duplicated as follows.\n(Page 6, Lines 111-113): “Specifically, we adopted a broad definition of greenspace\nto refer to land that is partly or completely covered with grass, trees, shrubs, and\nother vegetation 33. That is, green-covered land within the curtilage of an urban\nboundary, since we are focusing on cities in this study.”\n(Pages 15-16, Lines 332-341): “Clearly another important factor in assessing the\ndegree and distribution of access or exposure to greenspace is demographic\nstructure such as gender, age, income, and race. In the present study, greenspace\nexposure differences across population groups are not considered, since their\nmeasurement, and a technical consideration of between-group equity is beyond\nthe scope of our methods in this study. We therefore talk in terms of equality, rather\nthan equity of exposure and our measurements relate to human population counts\nabstracted from specific population sub-groups. A deeper understanding of\ngreenspace exposure and demographic structure such as gender, age, income,\nand race, is becoming socially and politically urgent (Chen et al. 2022; Lu et al.\n2021), which will help identify ‘hotspot’ areas with less greenspace that\ndisproportionately affect certain vulnerable populations, and potentially help\nquantify the variations in health outcomes.”\nReferences:\n[1] Chen, B., Wu, S., Song, Y., Webster, C., Xu, B., & Gong, P. (2022). Contrasting\ninequality in human exposure to greenspace between cities of Global North\nand Global South. Nature Communications, 13(1), 4636.\n[2] Song, Y., Chen, B., Ho, H. C., Kwan, M. P., Liu, D., Wang, F., ... & Song, Y.\n(2021). Observed inequality in urban greenspace exposure in\nChina. Environment International, 156, 106778.\n[3] Nesbitt, L., Meitner, M. J., Sheppard, S. R., & Girling, C. (2018). The dimensions\nof urban green equity: A framework for analysis. Urban Forestry & Urban\nGreening, 34, 240-248.\n[4] Nesbitt, L., Meitner, M. J., Girling, C., Sheppard, S. R., & Lu, Y. (2019). Who has\naccess to urban vegetation? A spatial analysis of distributional green equity in\n[5] Williams, T. G., Logan, T. M., Zuo, C. T., Liberman, K. D., & Guikema, S. D.\n(2020). Parks and safety: a comparative study of green space access and\ninequity in five US cities. Landscape and Urban Planning, 201, 103841.\n[6] Wu, L., & Kim, S. K. (2021). Exploring the equality of accessing urban green\nspaces: A comparative study of 341 Chinese cities. Ecological Indicators, 121,\n107080.\n• On a related note, the authors have not clearly defined “the disparity issue” they\nintroduce in objective 2 (line 100). What is the disparity issue and are they\nmeasuring it in this paper?\n> Thanks for your good question. As we explained above, our target in this study\nis to measure equality of human exposure to greenspace rather than intergroup\nequity. The term “disparity issue” is more related to racial, ethnic, and\nsocioeconomic groups. We have replaced this term as “inequality issue”\nthroughout the manuscript, making it clear that the inequality measured is for total\nurban populations undifferentiated by sub-group characteristics.\n• Lines 257-263: The implications of this research are overstated here. A global\nstudy such as this cannot identify drivers of change at the scale at which urban\nplanners and developers operate and thus cannot meaningfully inform local-scale\nurban planning, as suggested by this section. The study provides a helpful analysis\nof broad trends in urban greening across many cities and its impact should be\nunderstood as such. It is not a study that is highly policy-relevant, as most urban\ngreening decisions occur at a municipal or regional scale.\n> Thanks for your good comments. This study provides a global-scale pattern of\nhuman greenspace exposure and the associated time-series changes that can\nbenefit large-scale urban planning strategies such as greening actions and\nprioritized practices in a broad, historic, and even forward-looking perspective.\nSuch global awareness might influence the design of local urban planning\nstandards and implementation of municipal or regional greening.\nAccording to your suggestion, we also acknowledged the balance between global\nvision and local action, and highlighted local-scale practices require multi-\ndimensional contexts of greening history, greenspace supply status quo, prioritized\nvulnerable hotspots and the underlying socio-economic factors, and discussed\nrelated points in the revised manuscript (Page 14, Lines 290-296), which is\nduplicated as follows.\n“The study tells a broadly positive story of the opening decades of the ‘urban\ncentury’, and our analysis of trends, and subsequent studies of positive outliers in\nthose trends, will help cities achieve better net outcomes when planning for\nbalanced changes in urban greenspace loss and construction by incorporating\nmultidimensional contexts of greening history, greenspace supply status quo,\nprioritized vulnerable hotspots and the underlying socio-economic factors. The\npower of big-picture global narrative studies to influence policy-makers, national\ndebates and lobbyists, should also not be underestimated.”\n• Where are the limitations of this paper clearly laid out? They need to be clearly\nincluded somewhere, ideally in the discussion section.\n> Thanks for your good suggestions. We have added a new paragraph to\nacknowledge limitations and address prospects in the Discussion of the revised\nmanuscript (Pages 14-16, Lines 303-341), which is duplicated as follows.\n“There are also some levels of limitations that should be acknowledged in this study.\nFirst, although the integration of 30-m resolution Landsat satellite with a spectral\nunmixing approach can help resolve the sub-pixel greenspace mapping, our\nmethod is still unable to explicitly identify certain greenspace types in small and\nfragmented patches at the Landsat’s resolution, such as street plantation, lawns,\nand pocket gardens and parks, given the heterogeneous landscape of cities.\nTherefore, satellites with a higher spatial resolution such as 10-m Sentinel-2, 3-m\nPlanetScope, and sub-meter WorldView imageries can be incorporated to offer\nadvanced observational opportunities for urban greenspace mapping, but one of\nthe key challenges is how to develop robust spatiotemporal reconstruction\nalgorithms for generating high-quality historical archives. Second, the spatial\ndistributions of greenspace and population footprints within each year of this study\nare static without modeling spatiotemporal interactions between greenspace and\nhumans dynamically. On the one hand, people living in cities are spatially moving\nin their daily routines, being exposed to different nearby green environments\nbeyond those near to their place of residence. On the other hand, seasonal\nchanges of urban greenspace that reflect different phenological phases will\ninfluence the greenspace availability over time. Therefore, a promising open topic\nfor follow-up research is to integrate a human mobility dataset with dynamic\ngreenspace observations to enable a spatially and temporally explicit human-\ngreenspace interaction framework and moving towards real-time greenspace\nexposure assessment. Third, a global sample of 1028 cities are adopted to\ninvestigate urban greening and greenspace exposure inequality across a large\ngeographical spectrum, allows us to make conclusions that transcend the local\nobservations that limit many greenspace studies. We acknowledge, however, that\nthe area criterion of 100 km2 excludes small and medium-sized cities, home to\nmultiple millions. It will be of interest to compare our results for spatially large cities\nwith a replicated study of smaller cities, since cities of different sizes use space\ndistinctly and have different socio-economic functions and greenspace scales with\ncity size in predictable ways (Fuller and Gaston, 2009). These unique processes,\nin turn, influence the greenspace supply-demand relationship and associated\ntemporal development trajectory, further underscoring the importance in this\nanalysis. One future research direction is to explore the associations of a wider\nspectrum of urban characteristics (e.g., size, shape, density, function, and socio-\neconomic structure) with greenspace, and measure specific human-centric\nexposures. Clearly another important factor in assessing the degree and\ndistribution of access or exposure to greenspace is demographic structure such as\ngender, age, income, and race. In the present study, greenspace exposure\ndifferences across population groups are not considered, since their measurement,\nand a technical consideration of between-group equity is beyond the scope of our\nmethods in this study. We therefore talk in terms of equality, rather than equity of\nexposure and our measurements relate to human population counts abstracted\nfrom specific population sub-groups. A deeper understanding of greenspace\nexposure and demographic structure such as gender, age, income, and race, is\nbecoming socially and politically urgent (Chen et al. 2022; Lu et al. 2021), which\nwill help identify ‘hotspot’ areas with less greenspace that disproportionately affect\ncertain vulnerable populations, and potentially help quantify the variations in health\noutcomes.”\nMinor Comments:\n• This paper is well-written but includes grammatical errors throughout. I have\nnoted some below but will leave it to the authors to complete a thorough review to\ncorrect errors.\n> Thanks for spotting the following errors, we have revised accordingly and double-\nchecked the language throughout the manuscript.\n• My preference is to separate “greenspace” into two words (\"green space\") but\nthis is just a minor suggestion. The authors have used both modes in the\nmanuscript. Please choose one and be consistent.\n> Thanks for your good suggestion. We have made it consistent as “greenspace”.\n• Line 28: Change “between” to “from” or change “–“ to “and.”\n> Revised. Now it reads,\n“Here, we incorporate a Landsat-based 30-meter time-series greenspace mapping\nand a population-weighted exposure framework to quantify the changes in human\nexposure to greenspace for 1028 global cities between 2000 and 2018.”.\n• Line 31: Change “contrasting difference” to “contrast.”\n> Revised accordingly (Page 2, Lines 30-33). Now it reads:\n“Nevertheless, we observe a contrast in the rate of reduction in greenspace\nexposure inequality between cities in the Global South and North, with a faster rate\nof reduction in the Global South, nearly four times that of the Global North.”\n• Lines 43-44: This sentence needs citations (“This has led to environment injustice,\nwith some communities bearing a disproportionate share of the negative impact of\nurbanization”).\n> Thanks for your good suggestion. We have added the following references to\nsupport this sentence (Page 3, Line 42-43). Now it reads:\n“This has led to environmental injustice, with some communities bearing a\ndisproportionate share of the negative impact of urbanization 8-9”\nReferences:\n[8] Han, C., Xu, R., Gao, C. X., Yu, W., Zhang, Y., Han, K., ... & Li, S. (2021).\nSocioeconomic disparity in the association between long-term exposure to\nPM2. 5 and mortality in 2640 Chinese counties. Environment International, 146,\n106241.\n[9] Mitchell, B. C., & Chakraborty, J. (2015). Landscapes of thermal inequity:\ndisproportionate exposure to urban heat in the three largest US cities.\nEnvironmental Research Letters, 10(11), 115005.\n• Line 52: “environment” should be “environmental”.\n> Revised accordingly (Page 3, Line 50-51). Now it reads:\n“Greenspace, as the key component of urban nature, offers a wide range of\necosystem services and health benefits, making it an ideal lens for evaluating\nurban environmental sustainability 12.”\n• Lines 68-70: Sentence needs revision for grammar/clarity.\n> We have revised this sentence (Page 4, Line 70-72). Now it reads.\n“This replacement often occurs during early-stage urban development via rapid\nurban expansion, transforming naturally vegetated areas into artificial impervious\nsurfaces and ultimately resulting in a subsequent decrease in greenspace\ncoverage.”\n• Lines 87-88: The beginning of this sentence needs citations (i.e. What are these\nparticular cities? Provide some examples via citations).\n> Thanks for your good suggestion. We have included the following references to\nsupport this sentence. Two example cities are：Shanghai with urbanization-induced\ngreenspace lose (Wu et al. 2019) and most European cities with urbanization-\ninduced greening (Xu et al. 2022) (Page 5, Line 96-101). Now it reads.\n“While both destructive and constructive processes are well understood in the\ncontext of particular cities and professional urban management activities 39-40, no\nstudy has investigated the net change in greenspace supply and human exposure\nto greenspace across a global sample of cities for a comparable time, to identify\ngeneral trends, which may be regarded as net outcomes of the destructive and\nconstructive processes often at work simultaneously in the evolution of city fabric.”\nReferences:\n[39] Xu, F., Yan, J., Heremans, S., & Somers, B. (2022). Pan-European urban\ngreen space dynamics: A view from space between 1990 and 2015.\nLandscape and Urban Planning, 226, 104477.\n[40] Wu, Z., Chen, R., Meadows, M. E., Sengupta, D., & Xu, D. (2019). Changing\nurban green spaces in Shanghai: Trends, drivers and policy implications. Land\nUse Policy, 87, 104080.\n• Figure 1: Please explain the colour scale more clearly in a and c.\n> Thanks for your good suggestion. In Fig. 1a and c, the city-level temporal change\ntrends are divided into four qualitative levels, where cool (dark and slight green)\nand warm colors (yellow and red) refer to the increasing and decreasing trends of\ngreenspace coverage/exposure, respectively. We have included these\nexplanations into the caption of Fig. 1. Now it reads.\n“Fig. 1. Temporal changes of physical greenspace coverage (GC) and\ngreenspace exposure (GE) for global 1028 cities from 2000-2018. a. City-level\ntemporal trend of GC changes. b. Annual GC dynamics for Global North and Global\nSouth cities. c. City-level temporal trend of GE changes. d. Annual GE dynamics\nfor Global North and Global South cities. In a and c, the city-level temporal\nchange trends are divided into four qualitative levels, where cool (dark and\nslight green) and warm colors (yellow and red) refer to increasing and\ndecreasing greenspace trends, respectively. The non-parametric Theil–Sen\nslope estimator approach is used to determine the long-term trends of both GC\nand GE. The non-parametric Mann-Kendall is used to evaluate the significance of\nthese detected temporal trends. Large bubble sizes represent a statistically\nsignificant level of 0.05 (p-value <0.05) and small bubble sizes represent a non-\nsignificant trend with p-value >0.05.”\n• Line 132: This should be framed as assumed demand rather than demand. Green\nspace is not universally considered to be positive.\n> Thanks. We have replaced the word “demand” with “assumed demand”\naccordingly (Page 8, Line 155-157).\n“To adjust gross greenspace supply to reflect assumed demand, we used\npopulation distribution maps and a population-weighted exposure framework to\nquantify the temporal change of human greenspace exposure and inequality.”\n• Lines 138-141: This sentence is confusing due to what appear to be grammatical\nerrors. Please revise for clarity.\n> We have revised this sentence (Page 8, Line 161-164) and now it reads.\n\"In North American, European, African, Australian, and Oceanian cities, population\ndistribution amplifies the share of greenspace coverage, with a greater temporal\nchange magnitude in human greenspace exposure compared to physical\ngreenspace coverage.\"\n• Lines 190-191: I suggest softening this sentence. The patterns and effects of\nurbanization are not consistent around the world.\n> Thanks for your good suggestion. We have rewritten this sentence (Page 11,\nLines 218-220) and now it reads.\n“Global urbanization might promote increasing population growth and social-\neconomic prosperity but also might enhance environmental degradation, which\nsubstantially challenges urban sustainability 43-44.”\n• Lines 197-198: Sentence needs citation.\n> We have added the following two references to support this sentence (Page 11,\nLines 225-226). Now it reads.\n“We note, however, this is not entirely true, due to greenspace being a superior\neconomic good, the demand for which rises with income 46-47.”\nReferences:\n[46] Fruth, E., Kvistad, M., Marshall, J., Pfeifer, L., Rau, L., Sagebiel, J., ... &\nWiniarski, B. (2019). Economic valuation of street-level urban greening: A case\nstudy from an evolving mixed-use area in Berlin. Land Use Policy, 89, 104237.\n[47] Xiao, Y., Li, Z., & Webster, C. (2016). Estimating the mediating effect of\nprivately-supplied green space on the relationship between urban public green\nspace and property value: Evidence from Shanghai, China. Land Use Policy,\n54, 439-447.\n• Lines 221-222: The study does not measure why Global North cities have become\ngreener. The authors need to soften this sentence with the word “may” and they\nneed to add citations.\n> Thanks for your good suggestion. Following your comments, we have reworded\nthis sentence (Page 12, Lines 249-251) and added the related reference\n(Anguelovski et al. 2022). Now it reads.\n“Global North cities that are highly urbanized have become greener over the past\ntwo decades, the likely cause being more green real estate building and green\nurban planning and city management by both private and public developers and\nagencies 52.”\nReference:\n[52] Anguelovski, I., Connolly, J. J., Cole, H., Garcia-Lamarca, M., Triguero-Mas,\nM., Baró, F., ... & Minaya, J. M. (2022). Green gentrification in European and\nNorth American cities. Nature Communications, 13(1), 3816.\n• Lines 222-224: Please provide some examples of these cities.\n> Thanks for your good points. As shown in Fig. 1a, we can find some cities in\nNorth America, Europe, and Russia with a brown color indicating negative trend of\ngreenspace coverage. For clarity, we presented two city examples with different\ndecreasing patterns of greenspace coverage (Fig. R5). Greenspace coverage in\nthe Fargo, United States deceased between 2000-2021 and then turned to an\nincrease, with an overall negative trend of greenspace coverage in 2000-2018.\nGreenspace coverage in Kaliningrad, Russia shows a lasting negative trend in\n2000-2018 despite a slight increase between 2000-2003.\nFig. 1. Temporal changes of physical greenspace coverage (GC) and\ngreenspace exposure (GE) for global 1028 cities from 2000-2018. a. City-level\ntemporal trend ofGC changes. b. Annual GC dynamics for Global North and Global\nSouth cities. c. City-level temporal trend of GE changes. d. Annual GE dynamics\nfor Global North and Global South cities. In a and c, the city-level temporal change\ntrends are divided into four qualitative levels, where cool (dark and slight green)\nand warm colors (yellow and red) refer to increasing and decreasing greenspace\ntrends, respectively. The non-parametric Theil–Sen slope estimator approach is\nused to determine the long-term trends of both GC and GE. The non-parametric\nMann-Kendall is used to evaluate the significance of these detected temporal\ntrends. Large bubble sizes represent a statistically significant level of 0.05 (p-value\n<0.05) and small bubble sizes represent a non-significant trend with p-value>0.05.\nFig. R5. City examples in the Global North experience greenspace loss over the\npast two decades in a Fargo city, North Dakota, United States, and b Kaliningrad\ncity, Kaliningrad Oblast, and Russia.\nWe now have revised this sentence (Pages 12, Line 251-253). Now it reads.\n“On the other hand, cities with lower levels of urbanization in the Global North are\nstill experiencing vegetation cover loss (Fig. 1a).”\n• Lines 232-233: This does not indicate a benefit of urbanization but simply an\nincrease in greening, perhaps because of and perhaps despite urbanization.\n> Thanks for your good suggestion. We have rephase this sentence (Page 12,\nLines 262-263). Now it reads.\n“Prior to this, cities globally, were experiencing a decline in greenspace coverage,\nbut after 2011, the trend reversed turning to a positive increase, indicating a net\ngreenspace provisioning.”\n• Methods: I question whether the satellite data should be referred to as high-\nresolution in this case, given the scale at which urban areas show green space and\nother land cover changes. I would prefer to see the resolution specified in the\nmethods rather than the arbitrary “high-resolution” framing, although I\nacknowledge that others have framed it as such.\n> Thanks for pointing out this issue. We have replaced the term “high-resolution”\nas “30-m-resolution” in the revised manuscript (Page 29, Lines 593-594). Now it\nreads.\n“We generated two-decade time-series greenspace maps from the 30-m-resolution\nsatellite data for global large cities using the linear spectral unmixing approach.”\n• The methodology is sound and enough detail has been provided to reproduce\nresults.\n> Thanks for your nice evaluation on our methodology section.\nReviewer #3 (Remarks to the Author):\nThe manuscript describes a global assessment of urban greenspace coverage,\nexposure and inequality. Within a time series of almost 20 years, the authors try to\nobserve general trends in the change of coverage of greenspace and its exposure\nto the urban population The authors employed a remote sensing methodology to\ngenerate a population-weighted greenspace cover in each study area, using\nLandsat data across the entire time frame. Their results show that there is a general\nincrease in greenspace coverage within the studied cities, and a significant\ncontrast between global north and global south cities. Additionally, significant\ndifferences between the greenspace exposure were found between these two\ngroups of cities. This research sheds light on the impact of urbanization on\ngreenspace availability and highlights the need for more equitable distribution of\nurban nature. However, the manuscript has several flaws that need to be\naddressed before it can be considered publication worthy. The major issues include\nthe analysis of greenspace inequality drivers in the methods section, the structure\nof the results section, and the use of ambiguous and confusing terms throughout\nthe manuscript.\n> Thanks for your detailed assessment and comments. According to your\nsuggestions, we have carefully revised our manuscript, including the explanation\nof Venn-based attribution model (i.e., variation partitioning approach), the\nreorganization of result section, and the removal of confusing expressions and\nterms.\nSpecific comments from the different sections of the manuscript are as follows:\n> Thank you. Please see our point-by-point responses below.\nIntroduction:\n• Line 43: environmental, instead of environment.\n> Thanks, we revised it accordingly (Page 3, Lines 42-43). Now it reads.\n“This has led to environmental injustice, with some communities bearing a\ndisproportionate share of the negative impact of urbanization 8-9”\n• Line 56: Greenspace privilege sounds very much like greenspace access. Why\nnot use a single term for this?\n> Thanks for this good suggestion. We here used the term “Greenspace privilege”\nto represent human exposure to greenspace. We have rewritten this sentence\n(Page 3, Lines 55-57). Now it reads.\n“Human exposure to greenspace, measured as the averaged amount of\ngreenspace coverage within people’s nearby environment expressed as a\npercentage 18, has also been quantified in sampled cities 19-20”\n• Authors keep jumping from different concepts related to greenspace, which is\nconfusing. This is seen throughout the introduction section. For example, line 56\nand 57 authors talk about greenspace access. And then the succeeding lines talk\nabout greenspace exposure, without linking them. While these concepts are\nrelated, they are also distinct from each other, and failing to clearly differentiate\nbetween them can make it difficult for readers to follow the authors' arguments.\n> Thanks for pointing out this issue. Greenspace access typically estimates\nproximity to urban greenspace. Exposure metrics derive from remote sensing\nimagery, such as the Normalized Difference Vegetation Index (NDVI), measures\nsurrounding greenness. Unlike accessibility that usually expressed as a distance-\nvalue, exposure is generally quantified as the proportional value of greenspace\nwithin a specified area, for example, a buffer area around a residence used in this\nstudy (Jarvis et al. 2020; Reid et al. 2018).\nTo avoid confusion, we used the term “greenspace exposure” throughout the\nmanuscript to indicate the possibility of human sharing greenspace.\nReferences:\n[1] Jarvis, I., Gergel, S., Koehoorn, M., & van den Bosch, M. (2020). Greenspace\naccess does not correspond to nature exposure: Measures of urban natural\nspace with implications for health research. Landscape and Urban Planning,\n194, 103686.\n[2] Reid, C. E., Kubzansky, L. D., Li, J., Shmool, J. L., & Clougherty, J. E. (2018).\nIt's not easy assessing greenness: a comparison of NDVI datasets and\nneighborhood types and their associations with self-rated health in New York\nCity. Health & Place, 54, 92-101.\n• Lines 62 to 64: Please expand on this research gap as it is a critical component\nof the study. Why single year or short time period studies are not sufficient? Do\nsuch studies already reveal some trend? Why is it so important to have multiyear\nassessments? Providing relevant literature to support this framing of the research\ngap would also be beneficial.\n> Thanks for your good questions and suggestions. The temporal impacts of\nurbanization on greenspace are twofold. First, the urban sprawl or expansion\nprocess that might cause green space loss through land cover conversion is a long-\nterm phenomenon (Chen et al. 2020; Gao and Neill, 2020). Second, the positive\neffects of urbanization on greenspace through the effects of fertilization, irrigation,\nwarmer temperatures, greater tropospheric CO concentrations, higher\n2\natmospheric nitrogen deposition, as well as greenspace management activities are\nrelatively slow temporal evolution processes (Du et al. 2019; Mattijssen et al. 2017).\nThe single-year or short time period (e.g., 5 years) studies reflect greenspace\npatterns over baseline year (Chen et al. 2022; He et al. 2023) but cannot fully\ncapture the temporal details of urbanization’s impacts on greenspace and attribute\nthe associated causality.\nWe have rephased this sentence (Page 4, Lines 62-66). Now it reads.\n“However, existing studies on greenspace exposure and inequality are constrained\nto data from individual baseline years that cannot capture long-term urbanization\nprocess 21-22 and gradual temporal evolution of human-nature interactions such as\nurban warming and green-space management strategy impacts 23-24, limiting our\nunderstanding of the impact of urban development on greenspace supply, human\nexposure, and inequality over time.”\nReferences:\n[1] Chen, B., Wu, S., Song, Y., Webster, C., Xu, B., & Gong, P. (2022). Contrasting\ninequality in human exposure to greenspace between cities of Global North\nand Global South. Nature Communications, 13(1), 4636.\n[2] Chen, G., Li, X., Liu, X., Chen, Y., Liang, X., Leng, J., ... & Huang, K. (2020).\nGlobal projections of future urban land expansion under shared socioeconomic\npathways. Nature Communications, 11(1), 537.\n[3] Du, J., Fu, Q., Fang, S., Wu, J., He, P., & Quan, Z. (2019). Effects of rapid\nurbanization on vegetation cover in the metropolises of China over the last four\ndecades. Ecological Indicators, 107, 105458.\n[4] Gao, J., & O’Neill, B. C. (2020). Mapping global urban land for the 21st century\nwith data-driven simulations and Shared Socioeconomic Pathways. Nature\nCommunications, 11(1), 2302.\n[5] Han, Y., He, J., Liu, D., Zhao, H., & Huang, J. (2023). Inequality in urban green\nprovision: A comparative study of large cities throughout the world. Sustainable\nCities and Society, 89, 104229.\n[6] Mattijssen, T. J. M., Van der Jagt, A. P. N., Buijs, A. E., Elands, B. H. M., Erlwein,\nS., & Lafortezza, R. (2017). The long-term prospects of citizens managing\nurban green space: From place making to place-keeping?. Urban Forestry &\nUrban Greening, 26, 78-84.\n• Line 74: What do you mean by “mature” urban environment?\n> Thanks. We here refer to well-developed urban environment. Revised\naccordingly (Pages 4-5, Lines 80-83). Now it reads.\n“City management practices, such as planting street trees and creating vertical\ngardens, can also increase the overall greenspace supply in an urban environment\ndeveloped over time under a legally strong environmental planning regime 31-32”\n• Lines 76 to 77: Not sure whether this is always the case. Does this not depend\non the type of greenspace too? For example, larger parks or green belts within\ncities can provide more exposure than smaller green areas.\n> Thanks for this good question. Yes, different green landscapes regarding types,\nsizes, and compositions will demonstrate distinct green exposure values,\nunderlying the ‘luxury effects’. Wealthier neighborhoods can invest more in the\nmaintenance and management efforts of urban greenspace, which in turn\nincreases greenspace supply, species, planting diversity, and atheistic landscape\nqualities (Avolio et al. 2018;Leong et al. 2018; Zhang et al. 2022).\nAccording to your suggestion, we have revised this sentence (Page 5, Lines 85-\n88), which is duplicated as below.\n“As well as a crowding effect of population growth, leading to reduced greenspace\nexposure, an income effect tends to increase greenspace supply, diversity, and\naesthetics in wealthier cities, since greenspace is a ‘superior good’, with demand\nincreasing as the prosperity of citizens increases 34.”\nReferences:\n[1] Avolio, M. L., Pataki, D. E., Trammell, T. L., & Endter‐Wada, J. (2018).\nBiodiverse cities: the nursery industry, homeowners, and neighborhood differences\ndrive urban tree composition. Ecological Monographs, 88(2), 259-276.\n[2] Leong, M., Dunn, R. R., & Trautwein, M. D. (2018). Biodiversity and\nsocioeconomics in the city: a review of the luxury effect. Biology Letters, 14(5),\n20180082.\n[3] Zhang, H. L., Cubino, J. P., Nizamani, M. M., Harris, A. J., Cheng, X. L., Da,\nL., ... & Wang, H. F. (2022). Wealth and land use drive the distribution of urban\ngreen space in the tropical coastal city of Haikou, China. Urban Forestry & Urban\nGreening, 71, 127554.\n• Line 80: This conflicts with the statement made in line 68 about it being often\nchronological.\n> Thanks for pointing out this issue. We have revised this sentence on the\ngreenspace loss in early-stage urban development (Page 4, Lines 70-78). Now it\nreads.\n“This replacement often occurs during early-stage urban development via rapid\nurban expansion, transforming naturally vegetated areas into artificial impervious\nsurfaces and ultimately resulting in a subsequent decrease in greenspace\ncoverage. Historically, there is typically a process of greenspace destruction as\nagricultural and natural green land is replaced by impervious surfaces and then a\nlater process of re-greening via environmental improvements. Urban expansion\nthrough comprehensive spatial planning (as opposed to incremental unplanned\nsprawl), tends to ‘reprovision’ greenspace by design from the outset, but these\nspaces themselves tend to evolve (decrease and increase in quantity and quality)\nover time according to income, land value and environmental preferences 27-28.”\n• Line 87 to 88: Unclear how is this linked to the research gap stated in the following\nlines.\n> Thanks for your good comment. The destructive and constructive processes\nimpact the net change of greenspace supply and human exposure. We have\nrevised this sentence (Page 5, Line 96-101). Now it reads.\n“While both destructive and constructive processes are well understood in the\ncontext of particular cities and professional urban management activities 39-40, no\nstudy has investigated the net change in greenspace supply and human exposure\nto greenspace across a global sample of cities for a comparable time, to identify\ngeneral trends, which may be regarded as net outcomes of the destructive and\nconstructive processes often at work simultaneously in the evolution of city fabric.”\n• Line 99: Would you agree Greenspace stock or Greenspace inventory would be\na better term than supply in this context?\n> Thanks for your insightful suggestion. Greenspace stock refers to the amount of\ngreen space that exists within urban areas and can be measured in terms of the\ntotal area of green space per capita or as a percentage of the total land area of the\nurban area (Huang et al. 2021a, 2021b). Greenspace inventory refers to the\ncharacteristics and features of the urban green space, such as the types of\nvegetation, the age and condition of trees, the presence of wetlands or streams,\nas well as the availability of amenities like benches, playgrounds, or walking paths\n(Feltynowski et al. 2018; Vogt and Fischer, 2017). Compared to greenspace stock\nand greenspace inventory, greenspace supply has more implications for urban\ngreenspace planning because it reflects more about the greenspace provision and\nhuman needs in a supply-demand perspective (Boulton et al. 2018; Liu et al. 2020).\nReferences:\n[1] Boulton, C., Dedekorkut-Howes, A., & Byrne, J. (2018). Factors shaping urban\ngreenspace provision: A systematic review of the literature. Landscape and\nUrban Planning, 178, 82-101.\n[2] Feltynowski, M., Kronenberg, J., Bergier, T., Kabisch, N., Łaszkiewicz, E., &\nStrohbach, M. W. (2018). Challenges of urban green space management in\nthe face of using inadequate data. Urban Forestry & Urban Greening, 31, 56-\n66.\n[3] Huang, Y., Lin, T., Zhang, G., Zhu, Y., Zeng, Z., & Ye, H. (2021). Spatial patterns\nof urban green space and its actual utilization status in China based on big\ndata analysis. Big Earth Data, 5(3), 391-409.\n[4] Huang, Y., Lin, T., Xue, X., Zhang, G., Liu, Y., Zeng, Z., ... & Sui, J. (2021).\nSpatial patterns and inequity of urban green space supply in China. Ecological\nIndicators, 132, 108275.\n[5] Liu, H., Remme, R. P., Hamel, P., Nong, H., & Ren, H. (2020). Supply and\ndemand assessment of urban recreation service and its implication for\ngreenspace planning-A case study on Guangzhou. Landscape and Urban\nPlanning, 203, 103898.\n[6] Vogt, J. M., & Fischer, B. C. (2017). A protocol for citizen science monitoring of\nrecently-planted urban trees. Urban Forests, Ecosystem Services and\nManagement; Blum, J., Ed, 153-186.\n• The background information regarding research questions 3 and 4 have not been\nsufficiently introduced. These two questions could benefit from more explicit and\ndetailed explanation. Please integrate them into the relevant part of introduction\nsection\n> Thanks for your good comments. Answering questions 3 and 4 can help us learn\nthe spatiotemporal patterns and the drivers of greenspace and then improve our\nunderstanding of future projection of urban greenspace. We have included the\nbackground information on these two questions in the revised manuscript (Pages\n5-6, Lines 101-107), which is duplicated as follows.\n“Inequality in greenspace exposure is an increasing concern as it can be\ntranslated into adverse effects in mental and physical health 19-20. Individual studies\nshow how greenspace provision and the joint effects of greenspace provision and\nspatial configuration control greenspace exposure inequality 18. However, the\ndrivers of changing greenspace exposure inequality over time remain elusive and\nlimit our understanding of the relationship between greenspace and population\ndistributions in determining and attempting to promote greenspace exposure\nequality in the future projection.”\nMethods:\n• Line 487: Citation for the GUB product required.\n> Thanks for pointing out this issue. We have included the reference (Li et al. 2020)\n(Page 29, Lines 603-604). Now it reads:\n“The boundaries of these urban areas were extracted from the latest global urban\nboundaries (GUB) product 59.”\nReference:\n[59] Li, X., Gong, P., Zhou, Y., Wang, J., Bai, Y., Chen, B., ... & Zhu, Z. (2020).\nMapping global urban boundaries from the global artificial impervious area\n(GAIA) data. Environmental Research Letters, 15(9), 094044.\n• Line 489: Please simplify the sentence. If I understood correctly, the filtering\ncriteria is the area of cities should be more than 100km2?\n> Thanks. Yes, we have now revised this sentence (Page 29, Lines 604-608). Now\nit reads.\n“As the high-quality year-by-year GUB datasets are lacking, we chose the GUB\ndata in 2018 with a geographic area large than 100 km2 as urban boundary to allow\nfor: 1) the exploration of the urban expansion impacts on greenness change and\n2) the collection of sufficient samples for measuring human greenspace exposure\nand inequality 18.”\n• How do you account for the change in urban boundary for each year of the time\nseries?\n> Thanks for your good question. We used the global urban boundaries product for\n2018 (i.e., GUB_2018) to extract greenspace time series of urban extent for two\nreasons. On one hand, as urban population is primarily resided in urban areas (Fig.\nR6a), the differences between city-level greenspace exposure and the associated\ninequality extracted using the actual urban boundary and the bigger GUB_2018\nused in this study are minor. Using the 5-year interval global urban boundaries\nproduct for 2005 and 2010, comparison results show close relationships between\ngreenspace exposure and Gini levels from actual urban boundaries (i.e.,\nGUB_2005 and 2010) and GUB_2018 for year 2005 and 2010 (Fig. R6b-e). On\nthe other hand, the high-quality year-by-year urban boundary product with temporal\nconsistency is still lacking for global urban greenspace analysis.\nWe explained this reason in the revised manuscript (Page 29, Lines 604-608),\nwhich is duplicated as follows.\n“As the high-quality year-by-year GUB datasets are lacking, we chose the GUB\ndata in 2018 with a geographic area large than 100 km2 as urban boundary to allow\nfor: 1) the exploration of the urban expansion impacts on greenness change and\n2) the collection of sufficient samples for measuring human greenspace exposure\nand inequality 18.”\nFig. R6. Minor difference from the dynamic urban boundary for the city-level\ngreenspace exposure estimation compared to the global urban boundary\nproduct for 2018. a. Scheme diagram of the difference induced from different\nurban boundaries (true urban boundary vs. GUB_2018 used in this study) for\nmodeling human-greenspace interactions. b-e. Close relationships between city-\nlevel greenspace exposure and inequality extracted from different urban\nboundaries. b and c refer to GUB_2005 vs. GUB_2018. c and e refer to GUB_2010\nvs. GUB_2018.\n• Is there a cloud screening criterion as well? For instance, were images with less\nthan 30% cloud cover used?\n> Thanks for your question. We used the pixel-level quality assurance (QA) layer\ndata (i.e., the QA_PIXEL band in the Landsat surface reflectance product as\nshown in https://developers.google.com/earth-\nengine/datasets/catalog/LANDSAT_LC08_C02_T1_L2#bands) instead of image-\nlevel cloud screening to remove pixels that are contaminated by clouds, cloud\nshadow, and snow. Moreover, we used the maximum value composite approach\nto generate the annual greenest vegetation green metrics, which can further\nminimize the cloud cover impacts. We have specified these two points in the\noriginal manuscript (Page 30, Lines 624-630), which are duplicated as follows.\n“With the QA bitmask layer, we excluded the pixels that were contaminated by\ncloud cover, shadow, and snow. We further calculated the normalized difference\nvegetation index (NDVI) and normalized difference water index (NDWI) to quantify\nthe spectral characteristics of green vegetation and water body, respectively.\nFinally, we adopted a maximum value composite approach to generate the annual\ngreenest vegetation green metrics by selecting the pixel-based maximum NDVI\nvalues from the cloud-free time series within a one-year cycle.”\n• Please provide specific information regarding the Landsat images used to\ngenerate annual metrics for this study. It would be important to know whether the\nimages were daily, 10-day composites or monthly, as well as the month or season\nduring which the images were used.\n> Thanks for your question. We generated the annual greenspace metrics from the\nsurface reflectance products of three visible (i.e., blue, green, and red), one\nnear-infrared bands, the associated normalized difference vegetation index\n(NDVI) and normalized difference water index (NDWI) with three steps: (1) cloud\nremoval using the pixel-level quality assurance (QA) layer data, (2) annual\ngreenspace metric composition using the maximum value composite approach; (3)\ngreenspace coverage extraction using the linear spectral unmixing method. As\nLandsat satellites have a 16-day temporal resolution, repeatedly observing the\nsame place on Earth with a 16-day revisit cycle, we composed Landsat satellite\nimages in a one-year cycle as the annual greenspace metric. We have clarified\nthese points in the revised manuscript (Page 30, Lines 627-630), which is\nduplicated as follows.\n“Finally, we adopted a maximum value composite approach to generate the annual\ngreenest vegetation green metrics by selecting the pixel-based maximum NDVI\nvalues from the cloud-free time series within a one-year cycle.”\n• Line 511: Similar to the comment above, the maximum value for within which time\nperiod was used (daily, 10-day, monthly etc)?\n> Thanks for your question. We used the pixel-level maximum NDVI value within a\none-year cycle as the greenspace metric. We have rewritten this sentence for\nclarity (Page 30, Lines 627-630), which is duplicated as follows.\n“Finally, we adopted a maximum value composite approach to generate the annual\ngreenest vegetation green metrics by selecting the pixel-based maximum NDVI\nvalues from the cloud-free time series within a one-year cycle.”\n• Line 542: It seems the NDVI threshold might be too high to capture all types of\nvegetation for identifying the vegetation end member in the spectral mixed model.\nSparse vegetation such as grass or shrubs typically have NDVI values between\n0.3 to 0.6, and by setting the threshold at 0.8, the authors may be excluding\nsignificant amounts of non-tree vegetation from their analysis. Additionally, this high\nthreshold may also bias the results towards larger and healthier trees, potentially\noverlooking important information about smaller or less healthy vegetation.\n> Thanks for your good question. We employed the widely used linear spectral\nunmixing model (Haase et al. 2019) to map sub-pixel greenspace coverage from\nLandsat, which assumes that one pixel’s spectral signature is a linearly weighted\nsum of a few spectrally pure endmembers and their fractional covers within the\ntarget pixel：\n(R17)\n𝑛\n𝑅𝑖 = ∑𝑘=1𝑓𝑖𝑘 ∙𝐶𝑖𝑘 +𝜀𝑖\nwhere represents the spectral signatures of pixel i, represents the\nspectral signature of the kth endmember, is the unmodeled residual in\n𝑅𝑖 𝐶𝑖𝑘\nthe ith pixel, n is the total number of endmembers, is the fraction of kth\n𝜀𝑖\nendmember within pixel i. We selected vegetation (k=1), impervious area (k=2),\n𝑓𝑖𝑘\nand water (k=3) as three endmembers (n = 3) for Eq. (R17) to calculate the\nvegetation fraction (f ) as greenspace coverage.\ni1\nThe accuracy of the linear spectral unmixing model relies on the purity of the\nselected endmembers (Dennison and Roberts, 2003; Somers et al. 2011).\nConsidering the lack of spectral library of vegetation endmember, we thus use a\nhigh NDVI threshold (i.e., >0.80) to enable the selection of pure vegetation pixels\nfor endmember spectra extraction. According to linear spectral unmixing theory,\nthe NDVI saturates at closed canopy cover, i.e., pixels above high NDVI threshold\nmost likely represent pure vegetation (Fernández-Guisuraga et al. 2020; Schug et\nal. 2020).To quantify the impacts of NDVI threshold on the result of greenspace\ncoverage extraction, we conducted a sensitivity analysis using different NDVI\nthresholds of 0.74-0.86 with an 0.2 interval holding the other thresholds\n(impervious area and water) constant for greenspace coverage extraction for 2000-\n2018. Our results show that the NDVI threshold of 0.80 is very comparable with the\nother thresholds in extracting city-level absolute greenspace coverage (Fig. R6).\nNote that the maximum NDVI threshold of 0.86 is used here because of the limited\nLandsat’s spectral response and the constraint of temporally stable endmember\nover the past two decades (i.e., we require endmember pixels should have a NDVI\nvalue over 0.86 for each year within 2000-2018).\nFig. R6. Sensitivity analysis of the NDVI threshold of pure vegetation\nendmember selection for extracting city-level physical greenspace\ncoverage (GC) across global 1028 cities in 2000-2018. a-f. NDVI threshold\nof 0.80 vs. a 0.74. b. 0.76. c. 0.78. d. 0.82. e. 0.84. f.0.86. Each green circle\nrepresents city-level physical GC in a specific year. Linear regression was used\nto measure their correlation with Pearson’s r coefficient.\nAs the linear spectral unmixing approach calculates sub-pixel greenspace\ninformation, it to some extent can detect fine-patched greenspace types,\nsuch as street trees, shrubs, small parks, and grassy areas. Nevertheless, we acknowledge\nthat the 30-m resolution Landsat might miss some small greenspace patches,\nwhich require the use of higher-resolution satellite data (e.g., 10-m Sentinel-2 and\n3-m PlanetScope imagery). We have discussed our methodology limitation for fine-\nscale greenspace mapping in the revised manuscript (Pages 14-15, Lines 303-\n312), which is duplicated as follows.\n“There are also some levels of limitations that should be acknowledged in this study.\nFirst, although the integration of 30-m resolution Landsat satellite with a spectral\nunmixing approach can help resolve the sub-pixel greenspace mapping, our\nmethod is still unable to explicitly identify certain greenspace types in small and\nfragmented patches at the Landsat’s resolution, such as street plantation, lawns,\nand pocket gardens and parks, given the heterogeneous landscape of cities.\nTherefore, satellites with a higher spatial resolution such as 10-m Sentinel-2, 3-m\nPlanetScope, and sub-meter WorldView imageries can be incorporated to offer\nadvanced observational opportunities for urban greenspace mapping, but one of\nthe key challenges is how to develop robust spatiotemporal reconstruction\nalgorithms for generating high-quality historical archives.”\nReferences:\n[1] Dennison, P. E., & Roberts, D. A. (2003). Endmember selection for multiple\nendmember spectral mixture analysis using endmember average\nRMSE. Remote Sensing of Environment, 87(2-3), 123-135.\n[2] Fernández-Guisuraga, J. M., Calvo, L., & Suárez-Seoane, S. (2020).\nComparison of pixel unmixing models in the evaluation of post-fire forest\nresilience based on temporal series of satellite imagery at moderate and very\nhigh spatial resolution. ISPRS Journal of Photogrammetry and Remote\nSensing, 164, 217-228.\n[3] Haase, D., Jänicke, C., & Wellmann, T. (2019). Front and back yard green\nanalysis with subpixel vegetation fractions from earth observation data in a\ncity. Landscape and Urban Planning, 182, 44-54.\n[4] Schug, F., Frantz, D., Okujeni, A., van Der Linden, S., & Hostert, P. (2020).\nMapping urban-rural gradients of settlements and vegetation at national scale\nusing Sentinel-2 spectral-temporal metrics and regression-based unmixing\nwith synthetic training data. Remote Sensing of Environment, 246, 111810.\n[5] Somers, B., Asner, G. P., Tits, L., & Coppin, P. (2011). Endmember variability in\nspectral mixture analysis: A review. Remote Sensing of Environment, 115(7),\n1603-1616.\n• As a suggestion, it would be helpful for readers to have a visual aid that illustrates\nthe entire mapping process. Therefore, it would be beneficial if the authors could\ncreate a flow chart that outlines the steps taken to map the greenspace coverage.\n> Thanks for your good question. We have added the flowchart outlining the entire\nresearch design in the method summary section (Page 29, Lines 599-600), which\nis shown as follows.\nFig. S16. Flowchart of the research design in this study with five major steps.\na. Data preparations of global city boundary and population datasets. b.\nGreenspace mapping from long-term Landsat satellite imagery with the linear\nspectral unmixing approach. c. human exposure to greenspace with population-\nweighted exposure model and inequality assessment. d. spatiotemporal analysis\nof physical greenspace coverage, human exposure, and inequality over global\n1028 cities. e. Driver attribution of temporal change in greenspace exposure\ninequality over past two decades by accounting for individual greenspace and\npopulation effects, and their interaction effects.\n• I have serious reservations about the choice of analysis for investigation the\ndrivers of greenspace inequality. What motivated the authors use this Venn\nframework for the analysis over other methods? What is the theoretical background\nbehind this? Has this been used is similar studies? If yes, where are the references?\nIf not, why specifically this was used for this dataset. How do you check the\nrobustness of the results from the analysis? There are several open questions\nunanswered here.\n> Thanks for your good question. Venn diagram model originates from the widely\nused variation partitioning approach that attributes the variations of a response\nresult into different explanatory variables (Borcard et al. 1992; Delgado-Baquerizo\net al. 2020; Legendre et al. 2012; Peres-Neto et al. 2006). For two explanatory\nvariables (X and X ), the model can be divided into four components (Fig. R7;\n1 2\nBorcard et al. 1992; Legendre et al. 2012):\n[a] = R2(Y| [X , X ]) - R2(Y|X ) (R18)\n1 2 2\n[b] = R2(Y|X ) + R2(Y|X ) - R2(Y| [X , X ]) (R19)\n1 2 1 2\n[c] = R2(Y| [X , X ]) - R2(Y|X ) (R20)\n1 2 1\n[d] = 1- R2(Y| [X , X ]) (R21)\n1 2\nwhere the notation (Y|X) refers to the analysis (regression or canonical redundancy\nanalysis) of result Y by explanatory variable X and R2(Y|X) is the variation (R2) of\nthat analysis.\n[redacted]\nFig. R7. The Venn conceptual diagram of variation partitioning results for two\nexplanatory variables X and X . This diagram is revised from Legendre et al.\n1 2\n(2012).\nSimilarly, we used the Venn conceptual model, which has been adopted as a factor\nseparation approach for attributing the drivers of heat exposure (Broadbent et al.\n2020), to quantify the individual and joint contributions of greenspace coverage and\npopulation growth in explaining the change of greenspace exposure inequality\nmeasured by the Gini index. As shown in Fig. 3, we assumed the impact of\ngreenspace on the Gini change includes two parts: individual effect (region I,\nequivalent to [a] in Fig. R7) and joint effects from greenspace and population\n(region II, equivalent to [b] in Fig. R7). The total effects of greenspace ( ) can\nⅠ Ⅱ\nbe calculated by varying the greenspace coverage while keeping a fixed population.\n𝐶 +\n(R22)\nⅠ Ⅱ\n𝐶 + = ∆𝐺𝑖𝑛𝑖𝑔𝑟𝑒𝑒𝑛,𝑖 = 𝐺𝑖𝑛𝑖(𝑔𝑖+1,𝑝𝑖) − 𝐺𝑖𝑛𝑖(𝑔𝑖,𝑝𝑖)\nwhere and are greenspace coverage at years i+1 and i, respectively.\nis population at year i. and is the Gini index and its change\n𝑔𝑖+1 𝑔𝑖 𝑝𝑖\ninduced by greenspace coverage between year i and i+1.\n𝐺𝑖𝑛𝑖 ∆𝐺𝑖𝑛𝑖𝑔𝑟𝑒𝑒𝑛,𝑖\nSimilarly, the total effects of population on the Gini change ( ) can be divided\nⅠ Ⅱ\ninto two parts: individual effect (region III, equivalent to [c] in Fig. R7) and the joint\n𝐶𝐼 + 𝐼\neffects of both greenspace and population (region II, equivalent to [b] in Fig. R7).\nThis impact can be calculated by varying the population distribution while keeping\na fixed greenspace coverage.\n(R23)\nⅠ ⅠⅡ\n𝐶𝐼 + = ∆𝐺𝑖𝑛𝑖𝑝𝑜𝑝,𝑖 = 𝐺𝑖𝑛𝑖(𝑔𝑖,𝑝𝑖+1) − 𝐺𝑖𝑛𝑖(𝑔𝑖,𝑝𝑖)\nwhere is population at year i+1. is the Gini change caused by\npopulation between year i and i+1.\n𝑝𝑖+1 ∆𝐺𝑖𝑛𝑖𝑝𝑜𝑝,𝑖\nThus, the total effects of greenspace and population on the Gini change ( )\nⅠ Ⅱ Ⅱ\ncan be quantified by varying both population distribution and greenspace coverage.\n𝐶 + + 𝐼\n(R24)\nⅠ Ⅱ ⅠⅡ\n𝐶 + + = ∆𝐺𝑖𝑛𝑖𝑎𝑙𝑙,𝑖 = 𝐺𝑖𝑛𝑖(𝑔𝑖+1,𝑝𝑖+1) − 𝐺𝑖𝑛𝑖(𝑔𝑖,𝑝𝑖)\nwhere is the Gini change caused by both greenspace and population.\n∆𝐺𝑖𝑛𝑖𝑎𝑙𝑙,𝑖\nBy solving Eqs. (R22-24), we can quantify individual and shared contributions from\ngreenspace and population to the Gini change for each year i (i = 2000, 2001,…,\n2017, 2018).\n(R25)\nⅠ Ⅰ Ⅱ ⅠⅡ Ⅰ ⅠⅡ\n(R26)\n𝐶 ⅠⅡ = 𝐶 Ⅰ+ Ⅱ+ ⅠⅡ − 𝐶𝐼Ⅰ + Ⅱ\n(R27)\n𝐶 Ⅰ = 𝐶 Ⅰ + Ⅱ+ − Ⅰ 𝐶 ⅠⅡ + Ⅰ Ⅱ ⅠⅡ\n𝐶𝐼 = 𝐶 + +𝐶𝐼 + − 𝐶 + +\nIn addition to this Venn conceptual model, we also adopted another approach\nproposed by Tuholske et al. (2020), to empirically quantify the comparative\ncontributions of greenspace and population to the change of greenspace exposure\ninequality (i.e., the Gini change). This approach assumes that the total inequality\ntrend can be linearly decomposed into the contributions of greenspace\ncoverage and population distribution (i.e., ),\n𝛽𝑒𝑥𝑝𝑜\nwhich calculation includes four major steps:\n𝛽𝑔𝑟𝑒𝑒𝑛 𝛽𝑝𝑜𝑝 𝛽𝑒𝑥𝑝𝑜 = 𝛽𝑔𝑟𝑒𝑒𝑛 +𝛽𝑝𝑜𝑝\n(1) Calculate the city-level monotonic trend of greenspace exposure inequality\nwith varying greenspace coverage and population distribution between 2000\nand 2018.\n𝛽𝑒𝑥𝑝𝑜\n(2) Calculate the share of greenspace exposure inequality from greenspace\ncoverage by varying greenspace coverage across year and fixed\npopulation at baseline year 2000.\n𝛽𝑔𝑟𝑒𝑒𝑛\n(3) Calculate the share of greenspace exposure inequality trend from population\ngrowth by subtracting greenspace contribution from the total trend of\ngreenspace exposure inequality, i.e., . In stpes. (2-3), we\n𝛽𝑝𝑜𝑝\ncan also first calculate the share of greenspace exposure inequality from\n𝛽𝑝𝑜𝑝 = 𝛽𝑒𝑥𝑝𝑜 −𝛽𝑔𝑟𝑒𝑒𝑛\npopulation growth and derive the share from greenspace coverage\nbased on the linear model assumption, i.e., .\n𝛽𝑝𝑜𝑝 𝛽𝑔𝑟𝑒𝑒𝑛\n(4) Normalize the relative contributions as comparative contribution (CC): CC =\n𝛽𝑔𝑟𝑒𝑒𝑛 = 𝛽𝑒𝑥𝑝𝑜 −𝛽𝑝𝑜𝑝\n, where | · | represents the absolute function.\n(|𝛽𝑝𝑜𝑝|−|𝛽𝑔𝑟𝑒𝑒𝑛|) ÷|𝛽𝑒𝑥𝑝𝑜|\nResults show that greenspace dominates the overall change in the Gini index\nbased on the comparative contribution (CC) metric compared to population growth\n(Fig. S15). We have included the description and attribution results of Tuholske et\nal. (2020) approach in the revised manuscript, which are duplicated as follows.\n(Page 37, Lines 765-775): “In addition to the Venn conceptual model, we adopted\nanother empirical approach 42 to quantify the comparative contributions of\ngreenspace and population to the change of human greenspace exposure\ninequality, with four major steps: 1) calculation of the monotonic trend of\ngreenspace exposure inequality by varying greenspace coverage and\npopulation between 2000 and 2018; 2) calculation of greenspace coverage\n𝛽𝑒𝑥𝑝𝑜\n(or population growth, ) contribution with the monotonic trend analysis by\n𝛽𝑔𝑟𝑒𝑒𝑛\nvarying greenspace coverage (or population) while keeping a fixed population (or\n𝛽𝑝𝑜𝑝\ngreenspace coverage) at baseline year 2000; 3) calculation of population growth,\n(or greenspace coverage ) contribution by subtracting greenspace\n(population) contribution from the overall trend of greenspace exposure inequality,\n𝛽𝑝𝑜𝑝 𝛽𝑔𝑟𝑒𝑒𝑛\ni.e., (or ); 4) calculation of the\ncomparative contribution (CC) as: CC = , where | · |\n𝛽𝑝𝑜𝑝 = 𝛽𝑒𝑥𝑝𝑜 −𝛽𝑔𝑟𝑒𝑒𝑛 𝛽𝑔𝑟𝑒𝑒𝑛 = 𝛽𝑒𝑥𝑝𝑜 −𝛽𝑝𝑜𝑝\nrepresents the absolute function. ”\n(|𝛽𝑝𝑜𝑝|−|𝛽𝑔𝑟𝑒𝑒𝑛|) ÷|𝛽𝑒𝑥𝑝𝑜|\n(Pages 9-10, Lines 195-200): “In addition to results based on the Gini index, our\nsensitivity analysis using the Atkinson and Theil metrics also show very similar\nattribution results (Supplementary Figs. 13-14). We also incorporated the\ncomparative attribution approach 42 to verify the robustness of the proposed Venn\nconceptual model, similar results show that the contribution from greenspace\ndominates the overall change in the Gini index compared to population growth\n(Supplementary Fig. S15).”\nFig. 3. The Venn conceptual model of quantifying individual and joint\ncontributions from greenspace and population to the overall change in the\nGini index. Green circle (regions I and Ⅱ) denotes the Gini index change ΔGini\ngreen,i\ninduced by the change of greenspace provision from g to g with a fixed\ni i+1\npopulation pi for year i (i = 2001,…, 2018). Orange circle (regions IIand III) denotes\nthe Gini index change ΔGinipop,i induced by the population change from p to p\ni i+1\nwith a fixed greenspace g for year i. These two circles (regions I, Ⅱ, and Ⅲ) denote\ni\nthe overall change of Gini index ΔGini induced by both greenspace and\nall,i\npopulation change from (g, p) to (g , p ) for year i. The overlapped region II\ni i i+1 i+1\ndenotes the joint effects of greenspace and population changes.\nFig. S15. Comparative contributions of greenspace and population for the\ntemporal change in the Gini index using the empirical approach proposed by\nTuholske et al. (2020). a-b. Spatial patterns of population versus greenspace for\nthe overall change of the Gini index using the comparative contribution (CC) metric:\nCC = . c-d. City statistics from greenspace and\npopulation to the temporal change in greenspace exposure inequality. a and c. The\n(|βpop|−|βgreen|) ÷|βexpo|\noverall trend and the share of greenspace are first calculated and\nthen the share of population is quantified: . b and d. The\n𝛽𝑒𝑥𝑝𝑜 𝛽𝑔𝑟𝑒𝑒𝑛\noverall trend and the share of population are first calculated and then\n𝛽𝑝𝑜𝑝 = 𝛽𝑒𝑥𝑝𝑜 −𝛽𝑔𝑟𝑒𝑒𝑛\nthe share of greenspace is quantified: .\n𝛽𝑒𝑥𝑝𝑜 𝛽𝑝𝑜𝑝\n𝛽𝑔𝑟𝑒𝑒𝑛 = 𝛽𝑒𝑥𝑝𝑜 −𝛽𝑝𝑜𝑝\nReferences:\n[1] Borcard, D., Legendre, P., & Drapeau, P. (1992). Partialling out the spatial\ncomponent of ecological variation. Ecology, 73(3), 1045-1055.\n[2] Broadbent, A. M., Krayenhoff, E. S., & Georgescu, M. (2020). The motley drivers\nof heat and cold exposure in 21st century US cities. Proceedings of the\nNational Academy of Sciences, 117(35), 21108-21117.\n[3] Delgado-Baquerizo, M., Reich, P. B., Bardgett, R. D., Eldridge, D. J., Lambers,\nH., Wardle, D. A., ... & Fierer, N. (2020). The influence of soil age on ecosystem\nstructure and function across biomes. Nature Communications, 11(1), 4721.\n[4] Legendre, P., Borcard, D., & Roberts, D. W. (2012). Variation partitioning\ninvolving orthogonal spatial eigenfunction submodels. Ecology, 93(5), 1234-\n1240.\n[5] Peres-Neto, P. R., Legendre, P., Dray, S., & Borcard, D. (2006). Variation\npartitioning of species data matrices: estimation and comparison of\nfractions. Ecology, 87(10), 2614-2625.\n[6] Tuholske, C., Caylor, K., Funk, C., Verdin, A., Sweeney, S., Grace, K., ... &\nEvans, T. (2021). Global urban population exposure to extreme\nheat. Proceedings of the National Academy of Sciences, 118(41),\ne2024792118.\nResults and Discussions:\n• The structuring of this whole section makes it difficult to read. Please consider\nreorganizing the whole section. Include subheadings that breakup into smaller,\nmore manageable sections. Additionally, follow a similar flow to the methods\nsection, as this would enhance the readability of the manuscript.\n> Thanks for your good question. Following your suggestion, we have separated\nthe result section into three subsections with subheadings: (1) Spatiotemporal\npatterns of physical greenspace coverage; (2) Spatiotemporal patterns of\ngreenspace exposure and associated inequality; and (3) Drivers of changing\ninequality in greenspace exposure.\nSimilarly, we also divided the method section into nine subsections with\nsubheadings:\n(1) Research design\n(2) Global urban areas\n(3) Landsat satellite imagery\n(4) Population data\n(5) Greenspace coverage\n(6) Greenspace exposure\n(7) Greenspace exposure inequality\n(8) Temporal trend analysis\n(9) Attribution of changing inequality in greenspace exposure\nBesides the subheadings, we also set the first sentence of each paragraph to\nexpress the central clause to improve the readability of the manuscript.\n• These sections contain significant amount of ambiguous language, which can\nmake it challenging to follow the authors' arguments. The authors are encouraged\nto clarify their language to make their points more precise and understandable.\n> Thanks for your good question. We have simplified and polished the language of\neach sentence in the result section and the key message of each paragraph can\nbe summarized: (1) Spatiotemporal pattern of physical greenspace coverage; (2)\nSpatiotemporal patterns of greenspace exposure and associated inequality; and\n(3) Drivers of changing inequality in greenspace exposure. We have included these\nsubheadings to improve the readability of the result section. For each section, we\nalso polish the language with precise and concise expressions and words.\n• What is this factor of 0.001?\n> Thanks. It’s the magnitude level of temporal trends of greenspace coverage,\nhuman exposure, and the associated inequality. As greenspace coverage has a\nsmall range of 0-1, its magnitude level of temporal trend is relatively small.\n• Line 117: Does largest temporal trend mean highest magnitude of change during\nthe time period? Please be concise.\n> Thanks. Your understanding is right. We have revised this sentence (Page 7,\nLines 138-140). Now it reads:\n“By continent, European cities have the largest level of change trend for\ngreenspace exposure (4.41 × 0.001 yr-1), which is twice that of Asian (-2.11 × 0.001\nyr-1) and Australian and Oceanian (2.16 × 0.001 yr-1) cities.”\n• Line 132: Unclear how greenspace supply reflects demand.\n> Thanks for your good question. Physical greenspace coverage reflects the\ncapacity of urban greenspace to supply humans’ need of greenspace environment\nthat are used. Supply is measured as stock (detectable from satellite data).\nDemand is assumed by adding information about the proximity of humans, on the\nassumption that humans will use proximal green space (for a range of uses). Based\non your and the 2nd reviewer’s comment, we have replaced “demand” as “assumed\ndemand” (Page 8, Lines 155-157). Now it reads:\n“To adjust gross greenspace supply to reflect assumed demand, we used\npopulation distribution maps and a population-weighted exposure framework to\nquantify the temporal change of human greenspace exposure and inequality.”\n• Line 135: Should it not be Fig 1c or 1d?\n> Thank you for pointing out this issue. It should be Fig. 1c. We have corrected\nthis figure citation in the revised manuscript (Page 8, Lines 158-159).\n• A clear explanation of the turning point year of 2011 in the discussion section is\nmissing. The authors should consider providing more context and potential reasons\nfor any significant changes or trends observed in the data around this time.\n> Thanks for your good question. The turning point year of 2011 might be explained\nby two reasons (Zhang et al. 2021). First, the global urban population exceeded\nthe rural population around year 2010. Second, both satellite observations and\nland-surface model simulations reveal that several natural and anthropogenic\nfactors, including CO fertilization, climate change, land-use change, and nitrogen\n2\ndeposition, caused a net global vegetation greening since 2010 (Liu et al. 2022;\nPiao et al. 2020), which simultaneously results into an increase in urban\ngreenspace. We included these two potential explanations in the revised\nmanuscript (Page 12, Lines 257-261), which is duplicated as follows.\n“Interestingly, a turning point in the trend of greenspace provision and exposure\nwas observed around the year 2011, when the world’s urban population exceeded\nthe rural population 57 and natural and anthropogenic activities plausibly related to\na net global vegetation greening (e.g., CO fertilization, climate change, land-use\n2\nchange, and nitrogen deposition) have been enhanced 48”\nReferences:\n[1] Liu, Y., Chai, Y., Yue, Y., Huang, Y., Yang, Y., Zhu, B., … & Ullah, W. (2022).\nEffects of global greening phenomenon on water sustainability. Catena, 208,\n105732.\n[2] Piao, S., Wang, X., Park, T., Chen, C., Lian, X. U., He, Y., ... & Myneni, R. B.\n(2020). Characteristics, drivers and feedbacks of global greening. Nature\nReviews Earth & Environment, 1(1), 14-27.\n[3] Zhang, X., Brandt, M., Tong, X., Ciais, P., Yue, Y., Xiao, X., ... & Fensholt, R.\n(2022). A large but transient carbon sink from urbanization and rural\ndepopulation in China. Nature Sustainability, 5(4), 321-328.\nREVIEWERS' COMMENTS\nReviewer #1 (Remarks to the Author):\nThank you for answering all my questions in great detail. I am satisfied with the provided answers and\nfind the paper fit for publication.\nReviewer #2 (Remarks to the Author):\nThe authors have carefully and comprehensively addressed my earlier comments and I am satisfied\nwith their responses and associated revisions to the manuscript. In particular, I believe the addition of\na paragraph describing the study limitations improves the quality of the paper.\nSome minor grammatical and spelling errors remain in the manuscript, especially in areas where new\ntext has been added (e.g., lines 368-371), that the authors should fix prior to publication. I leave it to\nthe authors and editorial team to identify and correct these errors.\nI recommend accepting this paper, contingent on a copy edit to correct minor errors and ensure clear\ncommunication of the findings and their implications.\nReviewer #4 (Remarks to the Author):\nDear authorship,\nI greatly appreciate your careful revision of your valuable research. Very well done in general.\nSome tiny aspects slipped out of sight, however:\nIn the letter to Reviewer 3 (pg. 32 in Letter to Reviewers) you explain so nicely the definition of\ngreenspace exposure. This is really important to understand why you undertook this study. And you\nadd to References Jarvis et al.; Reid et al. - however in the text this paragraph is not found. For this\nreason, questions remain what you understand by the terminology greenspace exposure. Beyond, the\nreference from the letter is not found in the manuscript. Please add.\nLanguage:\n(1) throughout the text make sure you say\neither between 2000 and 2011\nor from 2000 to 2011\na mixture is not possible.\n(2) decide which spelling you choose\n- greenspace\n- green space\nplease be consistent throughout the text.\nI would advise you to use less self-citation. This is not a good style in the highly esteemed research\ncommunity.\nStill, greatly appreciated to read your research and all the well-done amendments.\nResponse Letter\n> We appreciate the editor and three reviewers’ assessment of our manuscript.\nThe detailed comments and constructive suggestions have greatly improved our\nstudy. We have well addressed all raised points and revised the manuscript\ncarefully.\n> Please find our point-by-point response below.\nReviewer #1 (Remarks to the Author):\nThank you for answering all my questions in great detail. I am satisfied with the\nprovided answers and find the paper fit for publication.\n> Thank you.\nReviewer #2 (Remarks to the Author):\nThe authors have carefully and comprehensively addressed my earlier comments\nand I am satisfied with their responses and associated revisions to the manuscript.\nIn particular, I believe the addition of a paragraph describing the study limitations\nimproves the quality of the paper.\nSome minor grammatical and spelling errors remain in the manuscript, especially\nin areas where new text has been added (e.g., lines 368-371), that the authors\nshould fix prior to publication. I leave it to the authors and editorial team to identify\nand correct these errors.\nI recommend accepting this paper, contingent on a copy edit to correct minor errors\nand ensure clear communication of the findings and their implications.\n> Thank you for your comments and suggestions in the first round, which greatly\nhelp improve our manuscript.\nBased on your and Editor’s suggestions on grammatical and spelling issues, we\nhave carefully revised the manuscript language to better present the findings and\nimplications from this study.\nReviewer #3 (Remarks to the Author):\nDear authorship,\nI greatly appreciate your careful revision of your valuable research. Very well done\nin general. Some tiny aspects slipped out of sight, however:\nIn the letter to Reviewer 3 (pg. 32 in Letter to Reviewers) you explain so nicely the\ndefinition of greenspace exposure. This is really important to understand why you\nundertook this study. And you add to References Jarvis et al.; Reid et al. - however\nin the text this paragraph is not found. For this reason, questions remain what you\nunderstand by the terminology greenspace exposure. Beyond, the reference from\nthe letter is not found in the manuscript. Please add.\n> Thank you for your detailed assessment of our revised manuscript. Following\nyour suggestions, we included the definition of greenspace exposure in the revised\nmanuscript and added the related references from Jarvis et al. 2020 and Reid et\nal. 2018 as refs 18-19, which is depicted as follows:\n“Human exposure to greenspace, measured as the averaged amount of\ngreenspace coverage within people’s nearby environment expressed as a\npercentage 18-19, has also been quantified in sampled cities 20-22.”\nLanguage:\n(1) throughout the text make sure you say either between 2000 and 2011 or from\n2000 to 2011; a mixture is not possible.\n> Thank you for spotting this. We have used the consistent expressions “from xx\nto yy” for year ranges in the revised manuscript.\n(2) decide which spelling you choose\n- greenspace\n- green space\nPlease be consistent throughout the text.\n> Thank you for pointing this out. We now used the consistent “greenspace” in the\nrevised manuscript.\nI would advise you to use less self-citation. This is not a good style in the highly\nesteemed research community.\n> Thank you. Actually, the included literature from our previous studies are all highly\nrelevant to the present one. To address this issue, we have excluded the three\nfollowing references from our manuscript.\nto urban greenspace using multi-source big data. Science of the Total\nEnvironment 634, 1315-1325 (2018).\nexposure to green environments? A comparative study of 290 Chinese cities.\nJournal of Cleaner Production 246, 119018 (2020).\nof host cities. Landscape and Urban Planning 230, 104615 (2023).\nStill, greatly appreciated to read your research and all the well-done amendments.\n> Thank you again for your positive comment on our revised manuscript."}]

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