Introduction

In the context of escalating urbanization, over 56.9% of the world’s population now inhabits urban areas, a trend with profound implications for urban ecosystems (UN DESA 2022a). Urban Green Spaces (UGS) have emerged as a vital element in this landscape, offering significant benefits in terms of air quality improvement, climate change mitigation, and public health enhancement. The primary content and objectives of the United Nations Sustainable Development Goal 11 (SDG 11) are to achieve sustainability in cities and human settlements. SDG 11 focuses on making cities more inclusive, safe, resilient, and sustainable. Among its targets, Goal 11.6 emphasizes reducing the environmental impact of cities, which includes improving air quality and waste management. By promoting sustainable urban planning and management, SDG 11.6 contributes indirectly to reducing levels of PM2.5 and other pollutants (UN DESA 2018; Zhou et al. 2023). The significance of UGS in urban settings has been increasingly recognized for its role in promoting healthy living environments, as highlighted in various studies (Schuyler 1988; WHO 2016; He et al. 2022).

However, the sustainability of UGS is under threat from urban sprawl, population growth, and environmental pollution. The encroachment of urban development on green spaces, a particularly acute problem in Asian cities, not only diminishes ecological diversity but also poses severe public health risks. The World Health Organization has underscored the detrimental impact of air pollutants like PM2.5, emphasizing the crucial role of UGS in countering these health hazards (Zhan et al. 2022).

Additionally, the current state of UGS is often a reflection of historical land use patterns, known as legacy effects. This concept has been substantiated through various urban studies, indicating that past land use practices significantly influence contemporary green space configurations (Clarke et al. 2013; Troy et al. 2007; Larondelle and Strohbach 2017). Understanding these effects is essential for the effective planning and management of urban green spaces.

Our study delves into the dynamics driving the evolution of UGS in China’s urban landscapes. We analyze the situation in 291 Chinese cities, categorized into different urban clusters based on the United Nations’ definition of “urban agglomeration.” This term encompasses populations living in contiguous urbanized areas, including those with close economic and social ties to cities, without being confined to traditional administrative boundaries (UN DESA 2018, 2019). By adopting this broader perspective, we aim to offer a nuanced understanding of the factors influencing the growth and distribution of UGS amidst the challenges of rapid urbanization.

Therefore, this study analyzes the growth rate of urban green spaces (GRUGS), built-up area, population, and PM2.5 factors in 291 cities with a population greater than 300,000 in China from 2000 to 2020 to investigate the driving mechanisms and factors affecting urban green space growth. It provides a research reference for the construction of green space cities in China.

Methods

Datasets and pre-processing

Based on the WUP database (https://population.un.org/wup/), the growth rate of urban green spaces (GRUGS), land use, and PM2.5 data obtained, we finally identified 291 cities above the county level with more than 300,000 population in China as study area (UN DESA 2018). We divided these cities into the following urban agglomerations (Fig. 1), Beibu Gulf, Beijing-Tianjin-Hebei, Central Guizhou, Chengdu-Chongqing, Greater Bay Area, Guanzhong Plain, Harbin-Changchun, Hu-Bao-E-Yu, Middle Reaches of Yangtze River, Mid-Southern Liaoning, Ningxia Yellow River, Shandong Peninsula, West Coast of Taiwan Strait, Yangtze River Delta, Zhongyuan. Since the number of urban agglomerations in Central Guizhou, Chengdu-Chongqing, Guanzhong Plain, Harbin-Changchun, Hu-Bao-E-Yu, Ningxia Yellow River, and Shandong Peninsula and other urban agglomerations are too few data points, which makes the data impossible for prediction analysis, so they are excluded from the prediction results.

In this study, four types of data from 291 cities of different sizes in China were used: (1) Chinese GRUGS data from 2000 to 2020; (2) Chinese urban boundary data; (3) Chinese urban population data; and (4) Chinese urban PM2.5 data. Cities in this study are areas with a population of more than 300,000. GRUGS data are from the global Greenland growth data person released by Sun et al. 2020, and China urban boundary data are from the China built-up area data and urban boundary released by Sun et al. 2021 (Jiang et al. 2022; Sun et al. 2021). According to a database released by the United Nations World Urbanization Outlook (WUP) 2018 (https://population.un.org/wup/), the database aggregates the global urban agglomeration population of more than 300,000 people from 1950 to 2035. China’s urban population data are from WUP and China Statistical Yearbook. China urban PM2.5 data are from global PM2.5 data published by Aerosol Optical Depth (AOD) (Aaron et al. 2021). See Table 1 for details.

Fig. 1
figure 1

Distribution of sample cities within urban agglomerations

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Table 1 List of data sources with details of providers and how to access the data
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ArcGIS is a geographic information system software developed by ESRI in the United States and is one of the most widely used software in the field of geographic information systems in the world. Model Builder is a graphical and visual modeling tool provided by ArcGIS software for building geoprocessing workflows and scripts. It is a tool for spatial analysis, geostatistical analysis, and other spatial processing and analysis. In this experiment, we use the model builder of ArcGIS software (Model Builder) to make a cropped model. By iterating the cropping elements (vector frames of cities of different scales), the GRUGS and PM2.5 image files are cropped according to the range of the vector frames of cities of different scales. A folder is created at the specified location, and the dynamic storage and storage of cropped image files are realized by acquiring variable names. Finally, batch cropping of PM2.5 images based on the ArcGIS model builder is realized, and the average value of PM2.5 in 291 cities in China in 2000, 2005, 2010, 2015, and 2020 is finally obtained.

LCR, PGR, LCRPGR calculation

To further explore the growth rate of urban green space (GRURS) received the influence of urban built-up areas and population. The driving mechanism was explored by calculating the land consumption rate (LCR), population growth rate (PGR), and the ratio of land consumption rate to the population growth rate (LCRPGR) (Eqs. (1)–(3)).

LCR, PGR, LCRPGR calculation formula:

$$\varvec{L}\varvec{C}\varvec{R}=\frac{\mathbf{L}\mathbf{N}\left(\frac{{\mathbf{U}\mathbf{r}\mathbf{b}}_{\left(\mathbf{t}+\mathbf{N}\right)}}{{\mathbf{U}\mathbf{r}\mathbf{b}}_{\mathbf{t}}}\right)}{\varvec{y}}$$
(1)
$$\varvec{P}\varvec{G}\varvec{R}=\frac{\mathbf{L}\mathbf{N}\left(\frac{{\mathbf{P}\mathbf{o}\mathbf{p}}_{\left(\mathbf{t}+\mathbf{N}\right)}}{{\mathbf{P}\mathbf{o}\mathbf{p}}_{\mathbf{t}}}\right)}{\varvec{y}}$$
(2)
$$\varvec{L}\varvec{C}\varvec{R}\varvec{P}\varvec{G}\varvec{R}=\frac{\mathbf{L}\mathbf{C}\mathbf{R}}{\varvec{P}\varvec{G}\varvec{R}}$$
(3)

Where Urbt and Urb(t+N) is the areal extent of the UBA at the initial reference year t and the final year t + N; Popt and Pop(t+N) input the total population of the spatial unit at the initial reference year and the final reference year, y is the number of years between t and t + N.

Statistical analysis

We used multiple regression to determine the combined and individual effects of the following potential growth rate of urban green spaces predictors: Urban built-up area, LCR, PGR, LCRPGR, and PM2.5. We performed different multiple regression analyses with current and previous data to find out the legacy effect. We performed multiple regression analyses in R 4.0.4 (https://cran.r-project.org/bin/windows/base/). Since the population data were not significant in our model, we removed the population totals from the model and used PGR.

Results

Trends in green space growth in different urban agglomerations with populations greater than 300,000 from 2000 to 2020

In our study, the number of Chinese urban agglomerations (UA) and their distribution cities were compiled out of Beibu Gulf (33), Beijing-Tianjin-Hebeiurban (18), Central Guizhou (7), Chengdu-Chongqing (2), Greater Bay Area (15), Guanzhong Plain (10), Harbin-Changchun (24), Hu-Bao-E-Yu (7), Middle Reaches of Yangtze River (55), Mid-Southern Liaoning (19), Ningxia Yellow River (2), Shandong Peninsula (1), West Coast of Taiwan Strait (22), Yangtze River Delta (45), Zhongyuan (34), Central Guizhou (7), Chengdu-Chongqing (2), Guanzhong Plain (10), Harbin-Changchun (24), Hu-Bao-E-Yu (7), Ningxia Yellow River (2), and Shandong Peninsula (1) UA have a small number of cities and are not included in our study because some data are missing. The top three UA with the highest GRUGS in China from 2000 to 2020 are Greater Bay Area (24.94 ± 9.79), Mid-Southern Liaoning (14.62 ± 9.83), and Middle Reaches of Yangtze River (12.36 ± 9.42). The lowest UA is the Yangtze River Delta (9.16 ± 7.88). (Figs. 2, 3, 4) and (Table 2)

Fig. 2
figure 2

Distribution of the number of cities in different urban agglomerations with a population greater than 300,000 in China

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Table 2 The growth rate of urban green spaces (GRUGS) means and standard deviation for different urban agglomerations in China from 2000 to 2020
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Fig. 3
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The growth rate of urban green spaces (GRUGS) means and standard deviation for different urban agglomerations in China from 2000 to 2020

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Fig. 4
figure 4

The growth rate of urban green space (GRUGS) of different urban agglomerations in China

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Growth rate of urban green spaces (GRUGS) driving mechanism

In the Beibu Gulf, GRUGS was positively correlated with urban built-up areas in 2005 (β coefficient = 0.001**), 2010 (β coefficient = 0.001**), 2015 (β coefficient = 5.056 × 10−4*) (Table 3). In Beijing-Tianjin-Hebei, GRUGS was positively correlated with urban built-up areas in 2005 (β coefficient = 1.77 × 10−4**), 2015 (β coefficient = 1.10 × 10−4* (Table 4). In the Greater Bay Area, GRUGS was positively correlated with the urban built-up area (β coefficient = 1.48 × 10−4**) and LCR (β coefficient = 2.491**) in 2015 (Table 5). In 2015, GRUGS was negatively correlated with population growth rate (β coefficient = -0.114***) and LCRPGR (β coefficient = -0.038***). GRUGS was negatively correlated with the population growth rate (β coefficient = -4.383*) in 2020. In Middle Reaches of the Yangtze River, GRUGS was negatively correlated with PGR in 2010 (β coefficient = -1.933*), in 2020 (β coefficient = -2.595*), and with PM2.5 in 2010 (β coefficient = -0.003*) (Table 6). In the Mid-Southern Liaoning, GRUGS was negatively correlated with LCR in 2015 (β coefficient = -0.428*), in 2020 (β coefficient = -1.672*), and with PGR in 2015 (β coefficient = -4.635**). GRUGS was positive with LCRPGR in 2015 (β coefficient = -0.004*) (Table 7). In the West Coast of the Taiwan Strait, GRUGS was negatively correlated with LCR in 2015 (β coefficient =-2.980*) (Table 8). In the Yangtze River Delta, GRUGS was positively correlated with urban built-up area in 2005 (β coefficient = 2.590 × 10−4***), in 2010 (β coefficient = 2.153 × 10−4***), in 2015 (β coefficient = 1.605 × 10−5**), in 2020 (β coefficient = 1.340 × 10−4**) and with PM2.5 in 2005 (β coefficient = 0.005*) (Table 9). In the Zhongyuan, GRUGS was not significant with all factors (Table 10).

Table 3 Analysis of the factors influencing the GRUGS in Beibu Gulf of China. Multiple regressions of GRUGS with the urban built-up area, urban built-up area, LCR, PGR, LCRPGR, and PM2.5 of different periods (Current and previous data)
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Table 4 Analysis of the factors influencing the GRUGS in Beijing-Tianjin-Hebei of China. Multiple regressions of GRUGS with the urban built-up area, urban built-up area, LCR, PGR, LCRPGR, and PM2.5 of different periods (Current and previous data)
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Table 5 Analysis of the factors influencing the GRUGS in the Greater Bay Area of China. Multiple regressions of GRUGS with the urban built-up area, urban built-up area, LCR, PGR, LCRPGR, and PM2.5 of different periods (Current and previous data)
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Table 6 Analysis of the factors influencing the GRUGS in the Middle Reaches of the Yangtze River of China. Multiple regressions of GRUGS with the urban built-up area, urban built-up area, LCR, PGR, LCRPGR, and PM2.5 of different periods (Current and previous data)
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Table 7 Analysis of the factors influencing the GRUGS in Mid-Southern Liaoning of China. Multiple regressions of GRUGS with the urban built-up area, urban built-up area, LCR, PGR, LCRPGR, and PM2.5 of different periods (Current and previous data)
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Table 8 Analysis of the factors influencing the GRUGS in the West Coast of Taiwan Strait of China. Multiple regressions of GRUGS with the urban built-up area, urban built-up area, LCR, PGR, LCRPGR, and PM2.5 of different periods (Current and previous data)
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Table 9 Analysis of the factors influencing the GRUGS in the Yangtze River Delta of China. Multiple regressions of GRUGS with the urban built-up area, urban built-up area, LCR, PGR, LCRPGR, and PM2.5 of different periods (Current and previous data)
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Table 10 Analysis of the factors influencing the GRUGS in the Yangtze River Delta of China. Multiple regressions of GRUGS with the urban built-up area, urban built-up area, LCR, PGR, LCRPGR, and PM2.5 of different periods (Current and previous data)
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Discussion

Analysis of the spatial pattern of the growth rate of urban green spaces (GRUGS) in Chinese urban agglomerations

The transformation of urban green spaces in China’s Greater Bay Area from 2000 to 2020 is a product of several interrelated factors. Influenced by evolving urban development patterns, policy changes, and infrastructural advancements, this region exemplifies a shift towards more organic and sustainable urban clusters. This growth in green spaces aligns with broader trends in China’s urban planning that balance urbanization with ecological preservation and efficient labor market integration (Xie et al. 2022; Wu et al. 2021; Wang et al. 2022; Hui et al. 2020; Yu et al. 2020).

In the Mid-Southern Liaoning region, encompassing industrial cities like Shenyang and Dalian, there has been a notable increase in urban green spaces. This development, amidst its industrial landscape, showcases efforts to harmonize rapid urbanization with environmental sustainability. The variability in green space growth indicates the dynamic nature of urban planning and industrial growth in the region. These changes reflect the region’s response to environmental challenges through measures like ecological protection zoning and improved land use (Zhang et al. 2022).

The Middle Reaches of the Yangtze River, particularly in cities like Wuhan, demonstrate a significant commitment to integrating green spaces within urban landscapes. The region’s Green Urban Spaces (GRUGS) value, with its considerable variability, highlights the complex interaction between urban growth and environmental sustainability. This underscores the importance of continuous monitoring and adaptive urban planning to ensure a harmonious balance between urban expansion and ecological preservation (Hu et al. 2023).

The Yangtze River Delta, which includes major cities like Shanghai, presents a different scenario. It registers a lower GRUGS value, suggesting a potential shortfall in urban green development. Despite being one of China’s most economically advanced regions, the need to enhance focus on integrating green spaces into the urban fabric is evident. The lower variability in green space development might indicate a consistent yet insufficient approach to fulfilling the ecological and recreational needs of its dense urban population (Wang et al. 2023; Yang et al. 2022).

Overall, the variations in urban green space development across China’s major urban areas are influenced by a blend of factors, including urban planning policies, economic development, and regional environmental priorities. The high variability observed in most regions suggests dynamic changes in urban green spaces over the past two decades. These insights are crucial for urban planners and environmental policymakers to understand the impact of urbanization on ecological sustainability and to guide future green space development strategies (Zou and Wang 2021).

The driving mechanism of GRUGS at different UA in China

In the Beibu Gulf region, the study reveals a significant positive correlation between GRUGS and urban built-up areas over the years 2005, 2010, and 2015. This indicates a consistent effort to integrate green spaces in tandem with urban expansion. The β coefficients for 2005 and 2010 show a strong and steady relationship, suggesting a proactive approach in combining urban development with environmental sustainability. However, the data from 2015 indicate a decrease in the rate of green space development relative to urban growth, pointing to a potential shift in urban planning priorities. This trend, if not addressed, could lead to challenges such as reduced biodiversity and a lower quality of life in urban areas, necessitating balanced and sustainable urban development strategies that prioritize green spaces (Juntti et al. 2021; Muhamad et al. 2021).

In the Beijing-Tianjin-Hebei region, a positive correlation between GRUGS and urban built-up areas is evident, especially in 2005 and 2015. The β coefficients for these years reflect that the expansion of urban areas was accompanied by an increase in green spaces. The high level of statistical significance in 2005 indicates a strong and reliable relationship between urban expansion and green space development during this period. However, by 2015, while the correlation remains statistically significant, its strength has diminished. This suggests that while the region initially showed a strong commitment to integrating green spaces within urban development, there has been a relative decline in focusing on green space development in recent years. This trend indicates the need for renewed strategies to ensure that green space development keeps pace with rapid urban expansion in this economically and politically significant region (Claassens et al. 2020; Tóth 2023).

The 2015 study data from the Greater Bay Area shows a significant positive correlation between GRUGS and both urban built-up areas and Land Cover Ratio (LCR). The β coefficient values indicate a high level of statistical significance, suggesting a conscious and effective integration of green spaces in line with the region’s urban expansion and land cover changes. This strong correlation, especially with LCR, demonstrates a strategic response to overall land use changes, reflecting a successful approach to sustainable urban planning. The findings underscore the region’s commitment to balancing rapid urbanization with ecological sustainability, ensuring that the development of green spaces keeps pace with urban growth and enhances the quality of life and environmental health in this dynamic region (Wang et al. 2023; Chen et al. 2023).

Data from 2015 to 2020 across different regions reveal a consistent negative correlation between GRUGS and factors like population growth rate and LCRPGR. In 2015, areas with rapid population growth exhibited a significant decrease in green urban spaces, a trend that persisted into 2020. This is particularly evident in the Middle Reaches of the Yangtze River in both 2010 and 2020, where a negative correlation with PM2.5 levels was also observed in 2010. These findings highlight the challenge of maintaining or expanding green spaces in regions experiencing significant urban and population growth. It underscores the need for integrated urban planning strategies that prioritize ecological sustainability alongside developmental needs (Medeiros and van der Zwet 2020).

The Mid-Southern Liaoning region displays complex trends in the relationship between GRUGS and urban factors over 2015 and 2020. A consistent negative correlation exists between GRUGS and Land Cover Ratio in both years, suggesting a decline in green spaces as urban land cover increases. Additionally, a significant negative correlation between GRUGS and Population Growth Rate was observed in 2015, indicating that areas with higher population growth tended to have reduced green spaces. Interestingly, in 2015, there was also a positive correlation with LCRPGR, albeit with a small magnitude. These findings point to the challenges faced in balancing urban development with ecological sustainability in this region, highlighting the need for comprehensive urban planning strategies that prioritize the development and maintenance of green spaces amidst changing land cover and demographic dynamics (Sturiale and Scuderi 2019).

The 2015 data for the West Coast of the Taiwan Strait shows a significant negative correlation between GRUGS and Land Cover Ratio. This suggests that as the land cover changed, likely due to urbanization or industrial development, there was a corresponding decrease in green urban spaces. The strength of the β coefficient and its statistical significance emphasize the challenge of maintaining or developing green spaces amid changing land cover. This trend highlights the importance of considering ecological sustainability in urban planning, particularly in areas undergoing rapid transformation. It calls for strategies that prioritize the preservation and enhancement of green spaces to balance the ecological impacts of urban and industrial expansion in this region.

In the Yangtze River Delta, a positive correlation between GRUGS and urban built-up areas is observed consistently over 15 years, along with a correlation with PM2.5 levels in 2005. The β coefficients for the correlation with urban built-up areas show a decreasing trend over the years. This indicates that while urban areas expanded, there was a concurrent increase in green urban spaces, albeit at a gradually reducing rate. The diminishing strength of the correlation over time suggests a shift in urban planning dynamics or resource allocation. Initially, there may have been significant efforts to incorporate green spaces, but later stages might have seen a relative decline in these efforts. The positive correlation with PM2.5 in 2005 could reflect initiatives to mitigate environmental pollution through green areas. These trends highlight the region’s commitment to integrating green spaces in urban planning, though the decreasing correlation strength over time suggests the need for sustained or enhanced efforts to maintain this balance amidst rapid urban development.

Analysis of Green Urban Spaces (GRUGS) Trends across Chinese regions

The analysis of Green Urban Spaces (GRUGS) across various regions in China over different years reveals significant trends in how urban development, population growth, land cover changes, and environmental factors interact with the presence and development of green spaces. In the Yangtze River Delta and the Greater Bay Area, a notable positive correlation between the Growth Rate of Urban Green Spaces (GRUGS) and urban expansion highlights a commendable effort to incorporate green spaces into urban development. Specifically, from 2005 to 2020, the Yangtze River Delta demonstrated this positive trend, albeit with a diminishing strength over time, suggesting a gradual decline in the rate of green space development relative to urban growth. Similarly, in 2015, the Greater Bay Area showed a positive correlation of GRUGS with both urban built-up areas and Land Cover Ratio (LCR), further indicating a conscious integration of green spaces. However, the observed decline in the rate of green space development, particularly in the Yangtze River Delta, points to an area needing more focused attention. It suggests that while there is an initial commitment to including green spaces in urban planning, maintaining this momentum and ensuring that the development of green spaces keeps pace with rapid urban expansion remains a crucial and ongoing challenge (Benton-Short et al. 2021).

The contrasting trends observed in various regions, specifically along the West Coast of the Taiwan Strait, Mid-Southern Liaoning, and the Middle Reaches of the Yangtze River, offer a revealing insight into the complex dynamics of urban development. Between 2010 and 2020, these areas exhibited negative correlations between the Growth Rate of Urban Green Spaces (GRUGS) and factors such as population growth and Land Consumption Rate (LCR). This pattern indicates significant challenges in sustaining or expanding green spaces amidst the pressures of rapid urbanization and demographic shifts. In these regions, the rapid pace of urban expansion and the increasing demands of a growing population appear to be outpacing the development of green spaces. This trend is concerning as it suggests a potential compromise in environmental quality and urban livability. The negative correlation in these specific areas underscores the need for more focused and innovative urban planning strategies that can effectively integrate green space development even in the face of intense urban and demographic pressures (Ramaiah and Avtar 2019).

The study highlights the intricate and varied relationship between urban development, ecological sustainability, and the integration of green spaces across different regions. In areas where a positive correlation is observed, it reflects the success of urban planning initiatives that have effectively incorporated green spaces, demonstrating a commitment to ecological sustainability alongside urban growth. Conversely, regions showing negative correlations reveal the challenges in maintaining a balance between urban expansion and environmental preservation, indicating areas where urban growth may be occurring at the expense of green spaces. These findings underscore the critical importance of adopting tailored urban planning strategies. Such strategies should not only prioritize the development of new green spaces but also focus on the maintenance and enhancement of existing ones, particularly in regions experiencing rapid urbanization and transformation. This approach is essential for ensuring sustainable urban development that harmonizes the needs of urban growth with ecological and environmental health (Jiang et al. 2023).

The analysis emphasizes the imperative for continuous evaluation and adaptation in urban planning, highlighting how regions with successful integration of green spaces in urban growth serve as models of sustainable development. In contrast, areas with negative correlations between urban expansion and green space integration necessitate a reassessment of planning strategies, calling for innovative approaches to ensure ecological sustainability is not compromised by urban and demographic expansion. This dynamic underscores the need for a responsive urban planning framework, one that is adaptable and informed by the successes and challenges of different regions, to maintain a balance between urban development and environmental preservation, ensuring both progress and ecological health are sustained in tandem (Girma et al. 2019; Cobbinah and Nyame 2021).

Policy recommendations

The following policy recommendations are tailored to address the identified trends and challenges:

  • In regions like the Yangtze River Delta, where a positive correlation between GRUGS and urban expansion is observed but with a diminishing strength, policies should focus on sustaining and increasing the rate of green space development. This could involve setting minimum green space requirements for new developments and incentivizing the preservation and expansion of existing green spaces.

  • For areas like the West Coast of the Taiwan Strait, Mid-Southern Liaoning, and the Middle Reaches of the Yangtze River, where negative correlations between GRUGS and factors such as population growth and Land Consumption Rate (LCR) are noted, innovative urban planning is needed. Policies should encourage vertical greening, rooftop gardens, and the integration of green spaces in high-density urban areas to counteract the limited availability of land.

  • In regions experiencing rapid urban expansion, policies should mandate a balanced approach to development. This involves integrating green spaces as a fundamental part of urban planning rather than as an afterthought. Urban planners should ensure that new developments include adequate green spaces to offset the environmental impact of urbanization.

  • Continuous monitoring of the relationship between urban expansion and GRUGS is essential. Policymakers should implement adaptive management strategies that can respond to changing urban dynamics and ensure that the integration of green spaces remains a priority.

  • Increase public awareness about the importance of green spaces for environmental and personal wellbeing. Encourage community involvement in the planning and maintenance of green spaces to ensure that they meet the needs and preferences of local residents.

  • Invest in ongoing research to better understand the complex dynamics between urban development and green space. Utilize data-driven approaches to inform policy decisions and urban planning strategies.

  • Facilitate the exchange of best practices and lessons learned between regions. Regions with successful green space integration can serve as models, while those facing challenges can benefit from collaborative problem-solving and shared experiences.

By implementing these recommendations, policymakers can address the specific challenges identified in our study, ensuring a more sustainable and environmentally-friendly approach to urban development in China and other developing countries. These strategies emphasize the importance of maintaining a balance between urban growth and the preservation and enhancement of green spaces.

Conclusion

The analysis of Green Urban Spaces (GRUGS) across Chinese regions from 2000 to 2020 highlights a complex interplay between urban development, population growth, and ecological sustainability. Regions like the Greater Bay Area, Mid-Southern Liaoning, and the Middle Reaches of the Yangtze River, which have the highest GRUGS values, demonstrate effective integration of green spaces into their urban landscapes, suggesting a strong commitment to environmental preservation. In contrast, the Yangtze River Delta, with the lowest GRUGS, indicates a need for enhanced urban green development. The varying trends, including both positive and negative correlations between GRUGS and factors like urban built-up areas, population growth, and Land Cover Ratio, underline the challenges and successes in different areas. This comprehensive analysis underscores the importance of tailored urban planning and continued efforts to balance urban expansion with ecological sustainability, ensuring the health and well-being of urban environments and their inhabitants.