Emergy Based Decoupling Analysis of Ecosystem Services on Urbanization: A Case of Shanghai, China

In order to respond to rapid urbanization, understanding the relationships between urbanization and ecosystem services (ESs) is of practical importance to move toward sustainable urban development. In this study, an emergy-GIS based method is proposed to evaluate ESs. Spatiotemporal emergy values of water retention (WR), air purification (AP), carbon sequestration (CS), soil conservation (SC), and biodiversity conservation (BC) were quantified and relationships among these ESs were analyzed by taking China’s largest city, Shanghai, as a case. The decoupling analysis was conducted to study the relationship between urbanization and ESs. Results show that the total value of regulating ESs had declined by 8.24% from 2005 to 2010. Chongming had the largest value of ESs, followed by Pudong. There is a synergetic relationship among AP, CS, and SC, while a tradeoff appears between WR and other services. Irregular “U” shape relationships between the decrease of ESs and urbanization indicators were observed. Results from decoupling analysis show that ESs experienced weak decoupling from urbanization in most districts. Finally, policy implications were raised based on the study results.


Introduction
The world is experiencing unprecedented urban growth. According to a report from the United Nations [1], over 4 billion population lived in cities in 2015, accounting for 54% of the world's total population. Furthermore, it is projected that six out of 10 people will live in cities by 2030. The urbanization rate has risen more sharply in developing countries [2]. In China, accompanying with rapid economic growth since the reform and opening policies enacted in 1978, the urbanization rate increased from 17.92% in 1978 to 58.52% in 2017, while the urban area had a   Energies 2020, 13, 6139 5 of 25

Data Sources
Given the interdisciplinary nature of this study, the required data should come from various sources, including governmental documents, statistical yearbooks, and research papers. Such data can be categorized into biophysical and socioeconomic types. The land cover data at a spatial resolution of 30 m × 30 m were provided by the Data Center for Resources and Environmental Sciences at the Chinese Academy of Sciences (http://www.resdc.cn). Precipitation data, the net primary productivity (NPP), sunshine duration, and wind speed data, both at 1 km × 1 km spatial resolution, were supplied by the National Earth System Science Data Sharing Infrastructure of China (http://www.geodata.cn). Evapotranspiration data at a spatial resolution of 1 km × 1 km from MOD16A3 were supplied by NASA-USGA (http://files.ntsg.umt.edu/data/NTSG_Products/MOD16/). Social and economic data, including spatial distribution of population and GDP, both at 1 km × 1 km spatial resolution, were obtained from the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (http://www.resdc.cn). Runoff coefficients and species density (indigenous to China and endangered) were obtained from Ouyang et al. [11]. Data on ecosystem capacity to purify pollutants were from Wang et al. [19], Liu and Yang [63], and Zhang et al. [64]. Data of biomass in ecosystems were from Bai et al. [32]. Data related to soil conservation, including rainfall erosivity factors, soil erodibility factors, topographic factors, and cover-management factors, were from Teng [65]. The period of 2005-2010 was chosen as the study period due to data availability.

Emergy-GIS Based Evaluation on ESs
Emergy measures the contributions from both nature and humans to production based upon the environmental work required to support a system's dynamics [66]. By focusing on nature's investment, the complete role of the natural system as a source, sink, and regulator can be identified when conducting emergy analysis [67]. Unit Emergy Values (UEVs), which is the equivalent solar emergy (sej) input to generate a unit of output, convert all flows and stocks into emergy so that the distinctions between qualities of resources can be enabled. The total annual emergy input to the biosphere is defined as a geobiosphere emergy baseline (GEB). The updated 12.00 × 10 24 sej/yr value was adopted as the GEB for this study [66]. Integrating the GIS tool into emergy analysis can uncover the spatiotemporal dynamic changes of ESs. When adopting an emergy-GIS-based method to account for ESs, the following procedures should be taken.

Identification of the Study Boundaries and Related ESs Provided by Local Ecosystems
Since the purpose of this study is to evaluate urban ESs and analyze the relationship between urbanization and ESs, the boundary of this study is set as the administrative region of Shanghai city. The landscape was classified into 6 categories, including forest land, grassland, crop land, water area, buildup land, and unused land. Bai et al. [32] identified (1) water retention; (2) water purification; (3) carbon sequestration; (4) soil conservation, and; (5) biodiversity conservation as the priority ESs in Shanghai. Beyond these ESs, this study also takes air purification into consideration due to the severe ambient air pollution in Shanghai [68]. The related ESs provided by local ecosystems are referred to in Table 2.

An Emergy-GIS-Based Framework to Evaluate Ecosystem Services
An emergy flow diagram can reflect various flows and stocks of the studied system. Figure 2 shows the emergy flow diagram of Shanghai urban ESs. The renewable inputs, the role of the ecosystem and the urban system, and the main ecological processes among them are illustrated. After drawing this emergy flow diagram, the emergy based equations are raised to quantify ESs into emergy by considering the related ecological processes. The related UEVs and their sources in this study refer to Table A5 (Appendix A). Finally, the spatial emergy values of ESs are assigned and mapped by using GIS.

An Emergy-GIS-Based Framework to Evaluate Ecosystem Services
An emergy flow diagram can reflect various flows and stocks of the studied system. Figure 2 shows the emergy flow diagram of Shanghai urban ESs. The renewable inputs, the role of the ecosystem and the urban system, and the main ecological processes among them are illustrated. After drawing this emergy flow diagram, the emergy based equations are raised to quantify ESs into emergy by considering the related ecological processes. The related UEVs and their sources in this study refer to Table A5 (Appendix A). Finally, the spatial emergy values of ESs are assigned and mapped by using GIS. (1) Water retention Water retention refers to the ecosystem's ability to intercept or store water resources from natural precipitation [32]. It is crucial to keep an adequate freshwater supply in Shanghai so that local citizens can benefit. The equation proposed by Jia et al. [69] is adopted to account emergy of water retention service, as shown in Equation (1): is the emergy of water retention, P is natural precipitation, ET is local evapotranspiration, A is the area of the ecosystem as defined by land cover, UEVwater is the UEV value of water. (1) Water retention Water retention refers to the ecosystem's ability to intercept or store water resources from natural precipitation [32]. It is crucial to keep an adequate freshwater supply in Shanghai so that local citizens can benefit. The equation proposed by Jia et al. [69] is adopted to account emergy of water retention service, as shown in Equation (1): E wr is the emergy of water retention, P is natural precipitation, ET is local evapotranspiration, A is the area of the ecosystem as defined by land cover, UEV water is the UEV value of water.
(2) Air, water, and soil purification Due to the purification ability of the local ecosystem, the adverse impacts of emissions on the environment and public health can be reduced. In this study, we adopt the accounting framework proposed by Yang et al. [46] to quantify these services. The reduced impacts (air, water, and soil purification services provided by urban ecosystems) are quantified by integrating Disability Adjusted Life Years (DALYs) and Potentially Disappeared Fraction (PDF) into emergy [46,70,71]. DALYs can be considered as a measurement of the gap between current health status and an ideal health situation where the entire population lives to an advanced age, free of disease and disability [72]. Potentially Disappeared Fraction (PDF) measures emissions' impacts on ecosystem quality which can be considered as the fraction of species with a high probability of no occurrence in a region due to unfavorable conditions [46,73]. The following equations quantify emissions' impacts into emergy.
Max(R i ) = Max (Sum (sunlight, deep heat, tidal energy), wind energy, wave energy, rain (chemical potential energy), runoff (geopotential and chemical potential energy)) (4) where E mHH is the emergy required to reduce harmful effects on public health (sej); M i is the capacity of local ecosystem to purify the i-th pollutant (kg/yr),  [60]). E mEQ is the emergy required to reduce emissions' impact on ecosystem quality (sej). PDF i indicates the potential fraction of species affected by the i-th emission (PDF × ha × yr × kg −1 ), Table A2 (Appendix A) lists the detailed values of DALY and PDF. E Bio is the emergy of stored biological resource per unit area [49], which equals to MAX(R i ) [46]. E mE is the sum of E mHH and E mEQ , which denotes the total emergy required to reduce all the emissions' impacts. According to the State Forestry Administration of the People's Republic of China [74], water pollutant absorbed by local ecosystems can be calculated as follows: where M W is the i-th pollutant absorbed by water area (kg/yr); Q i is emission amount of pollutant i (kg/yr); c input,i is the concentration of pollutant i in water inlet (%) and c output,i is the concentration of pollutant i in the water outlet (%). This study ignores water pollutant purification services since the data related to water pollutant concentrations, including DALYs and PDF parameters, are lacking.
Additionally, water quality monitoring is beyond the scope of this study.
(3) Carbon sequestration In order to respond to global climate change, it is of great importance to increase the carbon sink. Especially, Shanghai is considered as the most vulnerable Chinese city facing climate change due to its low-lying character [32]. The following equations can account for carbon sequestration of ecosystems into emergy [46].
E mNPP = MAX(R i ) = Max (Sum (sunlight, deep heat, tidal energy), wind energy, wave energy, rain (chemical potential energy), runoff (geopotential and chemical potential energy)) where E mCS is the emergy of carbon sequestration, B i is the amount of biomass in ecosystem classified by landscape i, T is the turnover time of biomass (one year estimated from Odum [39]), A i is the area of the related ecosystem of land use type i, S is the area of the studied city. The amount of carbon sequestration is estimated as half of the biomass, and UEV Bio is the unit emergy value of biomass [46].

(4) Soil conservation
Soil erosion is a national dilemma in China. In particular, the Yangtze River Basin suffers the most [32]. Located in the Yangtze River Delta, Shanghai is also suffering from soil erosion. In this study, we assess the soil conservation service based on the Revised Universal Soil Loss Equation (RUSLE) [75], shown in Equations (10) and (11).
where SC is the soil retention capacity (t ha −1 a −1 ), R represents the rainfall erosivity factor (MJ mm ha −1 h −1 a −1 ), K is the soil erodibility factor (t ha h ha −1 MJ −1 mm −1 ), LS is the slope-length and steepness factor, C is the cover-management factor, and P is the conservation practices factor. Table A4 (See Appendix A) lists the values of these parameters. UEV soil is the UEV of soil.

(5) Biodiversity conservation
Maintaining biodiversity is crucial for the sustainable productivity of land due to its core role to provide ecosystem functions and services [76,77]. Equation (12) can account for the emergy required by biodiversity conservation [46].
where E bc represents the emergy required by biodiversity conservation (sej); N 1 is the species density in the study area (No./ha); S is the area of the study system (ha); GEB is the geobiosphere emergy baseline (GEB) (sej/yr); T is the average turnover time of species (yr) (3 million years); N 0 is the number of global species (8.7 million species [78]). Only the value of 2010 is considered in this study due to the lack of data for other years.

Trade-Off and Synergy among ESs
Studying the relationship among multiple ESs is of particular importance to identify win-win outcomes for ESs management [79]. Two interaction relationships of ESs have been identified, i.e., trade-off and synergy. Trade-off indicates that the provision of one ES is reduced as a result of another increased ES, while synergy reflects that multiple ESs are enhanced simultaneously [80]. This study quantifies the different values of ESs between 2005 and 2010 to investigate the relationships, i.e., ES i, 2010 − ES i, 2005 . Grid-scaled ESs data are extracted to conduct this calculation. Scatter diagrams are employed to demonstrate the relationships between two ESs. A point that appeared in the first or third quadrant indicates that the ESs are increased or decreased simultaneously, which can be classified as synergy. While a point that appears in the second or fourth quadrant, means that one ES is reduced as a result of another increased ES, which can be classified as trade-offs.

Relationship between Urbanization Indicators and ESs
Previous studies consider the total GDP of one city as the main indicator to reflect its urbanization level [16,18,21,81]. In this study, the GDP value of the manufacturing industry was adopted due to its great impact on regulating ESs. Other indicators, including population and the built-up land area, are also considered as key factors indicating urbanization. Since various relationships may exist between urbanization indicators and the total ESs, such as linear, logarithm, exponential, power law, and polynomial, the curve estimation method is adopted to determine the relationship [16,19]. We acknowledge that the regression analysis does not establish the causal relationship, but may uncover Energies 2020, 13, 6139 9 of 25 the dissimilarity or similarity relationship between the variables [43]. Thus, the Tapio decoupling method is employed to study the relationships between urbanization and ESs since it is more applicable to communicate [54]. The traditional Tapio decoupling theory focuses on the undesired output, such as CO 2 and pollutants, while the ES is considered as the desired output in this study. Therefore, the decrease in ESs is adopted. Following Tapio [54], the urbanization elasticity of ESs can be calculated by using Equation (13). The district-level data were extracted to conduct this analysis.

Urbanization elasticity of ESs
where ∆TES is the decreased value of the total ES during the study period; UI refers to the changed value of corresponding urbanization indicators, i.e., population, the built-up land, and the GDP of manufacturing industry during the study period. Finally, the degrees of coupling and decoupling of ES influenced by urbanization can be identified according to Figure 3.
Energies 2020, 13, x FOR PEER REVIEW 9 of 26 relationships may exist between urbanization indicators and the total ESs, such as linear, logarithm, exponential, power law, and polynomial, the curve estimation method is adopted to determine the relationship [16,19]. We acknowledge that the regression analysis does not establish the causal relationship, but may uncover the dissimilarity or similarity relationship between the variables [43]. Thus, the Tapio decoupling method is employed to study the relationships between urbanization and ESs since it is more applicable to communicate [54]. The traditional Tapio decoupling theory focuses on the undesired output, such as CO2 and pollutants, while the ES is considered as the desired output in this study. Therefore, the decrease in ESs is adopted. Following Tapio [54], the urbanization elasticity of ESs can be calculated by using Equation (13). The district-level data were extracted to conduct this analysis.
Urbanization elasticity of ESs = % △ % △ where ΔTES is the decreased value of the total ES during the study period; △ refers to the changed value of corresponding urbanization indicators, i.e., population, the built-up land, and the GDP of manufacturing industry during the study period. Finally, the degrees of coupling and decoupling of ES influenced by urbanization can be identified according to Figure 3.  Table 3 shows the changes in land use and land cover (LULC) in Shanghai. During the study period, crop land accounted for the largest proportion, followed by built-up land and water area.  Figure 4 shows the LULC at the district level, and Figure 5 illustrates the contributions from main districts to land-use changes. For all districts, both built-up land and unused land increased or remained unchanged. Pudong had the most significant change in land use, which contributed 116.68% of total forest land increase, 33.12% of total crop land decrease, 29.95% of total buildup land increase, and 43.04% of the total unused land increase.  Table 3 shows the changes in land use and land cover (LULC) in Shanghai. During the study period, crop land accounted for the largest proportion, followed by built-up land and water area.  Figure 4 shows the LULC at the district level, and Figure 5 illustrates the contributions from main districts to land-use changes. For all districts, both built-up land and unused land increased or remained unchanged. Pudong had the most significant change in land use, which contributed 116.68% of total forest land increase, 33.12% of total crop land decrease, 29.95% of total buildup land increase, and 43.04% of the total unused land increase. Jiading had the second-largest decrease in crop land and increase in built-up land, accounting for 16.24% of total crop land decrease and 18.66% of total built-up land increase, respectively. The largest decrease in forest land occurred in Minhang, which contributed 8.63% of the total forest land decrease. Finally, the maximum water area reduction occurred in Qingpu.                 in h a n g C h a n g n in g J in g a n X u h u i S o n g ji a n g F e n g x ia n J in s h a n H u a n g p u in h a n g C h a n g n in g J in g a n X u h u i S o n g ji a n g F e n g x ia n J in s h a n H u a n g p u       Figure 14D-F demonstrated the trade-off relationships between WR and other ESs. Correlations among different ESs at the grid level are listed in Table 4. The results show that SC and AP had the most correlated relationship, followed by SC and CS. The correlation relationships between WR and others were weak, while negative correlation relationships between WR and AP, WR and SC were observed. Besides, the biodiversity conservation and the total ES were largely correlated in 2010 (Pearson correlation coefficient = 0.979). This result is not surprising since biodiversity plays a core role in producing ESs [32].  Figure 14 shows the relationships between various ESs. The most points in Figure 14A-C appear in the down-left quadrant, indicating the synergy between these ESs. Figure 14D-F demonstrated the trade-off relationships between WR and other ESs. Correlations among different ESs at the grid level are listed in Table 4. The results show that SC and AP had the most correlated relationship, followed by SC and CS. The correlation relationships between WR and others were weak, while negative correlation relationships between WR and AP, WR and SC were observed. Besides, the biodiversity conservation and the total ES were largely correlated in 2010 (Pearson correlation coefficient = 0.979). This result is not surprising since biodiversity plays a core role in producing ESs [32].

The Impacts of Urbanization on ESs
The spatial changes in grid-scaled GDP and population from 2005 to 2010 are shown in Figure 15. Table 5 lists the values of urbanization indicators in 2005 and 2010 at the district level. Relationships between the total ES and the urbanization indicators at the district level were explored by using the curve estimations ( Figure 16). Energies 2020, 13, x FOR PEER REVIEW 17 of 26  Table 5. District-level urbanization indicators in Shanghai.  The results from the curve estimations show that the increase of urbanization indicators and the decrease of ESs can be characterized by a cubic polynomial, and the irregular "U" shape relationship between the decrease of ESs and the increase of urbanization indicators are observed. In the beginning, with the increase of built-up land and GDP of the manufacturing industry, the decrease of ESs experienced an upward trend. The turning point is observed when the increase in built-up land and GDP of the manufacturing industry reach 2.00 × 10 3 and 1.50 × 10 3 , respectively. The decrease The results from the curve estimations show that the increase of urbanization indicators and the decrease of ESs can be characterized by a cubic polynomial, and the irregular "U" shape relationship between the decrease of ESs and the increase of urbanization indicators are observed. In the beginning, with the increase of built-up land and GDP of the manufacturing industry, the decrease of ESs experienced an upward trend. The turning point is observed when the increase in built-up land and GDP of the manufacturing industry reach 2.00 × 10 3 and 1.50 × 10 3 , respectively. The decrease of ESs remains steady before the increase in the built-up land and GDP of the manufacturing industry reach 5.00 × 10 3 and 3.00 × 10 3 . After these points, the decrease of ESs experienced a rapid upward trend. Finally, an overt linear relationship between the decrease of ESs and the increase of population appears. Figure 17 shows the values of urbanization elasticity of ESs from decoupling analysis at the district level. All elasticity values except the population elasticity in Changning range from 0 to 0.8, indicating the weak decoupling of ESs decrease from urbanization. The value of population elasticity in Changning equals 1.1201, which reflects the expansive coupling of ESs decrease from population growth. Jingan district shows a strong negative decoupling of ESs decrease from population growth, mainly because the population in Jingan declined. According to the decoupling theory, when the two parameters are larger than 0, a larger value of elasticity indicates decreased ESs, reflecting the higher pressure of urbanization on the ecosystem. Besides, when the elasticity value is close to 0.8, it indicates the potential trend toward expansive coupling. Putuo had the largest value of the buildup land elasticity of ESs, with a figure of 0.4454. Banshan had the largest value of GDP elasticity of ESs, with a figure of 0.6052. Changning had the largest value of population elasticity of ESs, indicating the potential expansive coupling trends of ESs decreasing from these urbanization indicators. Finally, the values of urbanization elasticity of ESs in Yangpu, Zhabei, Hongkou, Jingan, Xuhui, and Huangpu are equal to 0, which is mainly because the values of ESs in these districts remain unchanged during the study period.

Research Limitations and Future Prospective
Due to a lack of sufficient data, this study did not quantify all the ESs, but only those core ESs identified by previous studies. Beyond the considered ESs, the worldwide significant overuses of ESs, such as phosphorus and nutrient cycles, should be also considered in future studies. In addition, this study only quantified the biodiversity conservation for the year 2010, leading to a lack of a dynamic picture of biodiversity conservation. The year 2010 was chosen as the last year of this study because social and economic data (including spatial distributions of GDP and population) are not available for more recent years. Finally, although we proposed this framework to evaluate water pollutant purification services, such a value was not quantified due to the lack of basic data. Further studies can complement these issues when the relevant data are available.

Research Limitations and Future Prospective
Due to a lack of sufficient data, this study did not quantify all the ESs, but only those core ESs identified by previous studies. Beyond the considered ESs, the worldwide significant overuses of ESs, such as phosphorus and nutrient cycles, should be also considered in future studies. In addition, this study only quantified the biodiversity conservation for the year 2010, leading to a lack of a dynamic picture of biodiversity conservation. The year 2010 was chosen as the last year of this study because social and economic data (including spatial distributions of GDP and population) are not available for more recent years. Finally, although we proposed this framework to evaluate water pollutant purification services, such a value was not quantified due to the lack of basic data. Further studies can complement these issues when the relevant data are available.
There are several research directions for future studies. Firstly, the impacts of urbanization on ESs demanded by the socioeconomic system and the driving factors can be further studied so that more appropriate recommendations can be raised. Additionally, Shanghai is the most advanced city in China and may have pressure on the broader ecosystem beyond its administrative boundaries, indicating that the teleconnection effect should be taken into consideration in a future study. Finally, it is crucial to further investigate the relationships among fundamental supporting ESs, intermediate ESs, final ESs, and ecosystem structure and functions so that a more complete picture of the ESs process can be uncovered.

Conclusions
China's rapid urbanization and economic growth have led to the great change of ecosystem functions. Understanding the relationship between urbanization and ecosystem services is of critical importance to achieving China's ecological civilization targets and the UN's SDGs. Under such a circumstance, this study proposes an emergy-GIS-based framework to evaluate the ESs with consideration of the contribution of Shanghai, one of the most economically advanced and populous cities in China and the world. Then, the tradeoffs among different kinds of ESs and the relationships between urbanization indicators and ESs were explored. Finally, a decoupling analysis was conducted to identify the decoupling state of ESs from urbanization.
The results reflect that the area of crop land decreased by 10.26% during the study period, while the area of forest land, unused land, and built-up land increased by 4.31%, 36.88%, and 21.72%, respectively. Shanghai's total ecosystem service value declined by 8.24% from 3.45 × 10 20   There are several research directions for future studies. Firstly, the impacts of urbanization on ESs demanded by the socioeconomic system and the driving factors can be further studied so that more appropriate recommendations can be raised. Additionally, Shanghai is the most advanced city in China and may have pressure on the broader ecosystem beyond its administrative boundaries, indicating that the teleconnection effect should be taken into consideration in a future study. Finally, it is crucial to further investigate the relationships among fundamental supporting ESs, intermediate ESs, final ESs, and ecosystem structure and functions so that a more complete picture of the ESs process can be uncovered.

Conclusions
China's rapid urbanization and economic growth have led to the great change of ecosystem functions. Understanding the relationship between urbanization and ecosystem services is of critical importance to achieving China's ecological civilization targets and the UN's SDGs. Under such a circumstance, this study proposes an emergy-GIS-based framework to evaluate the ESs with consideration of the contribution of Shanghai, one of the most economically advanced and populous cities in China and the world. Then, the tradeoffs among different kinds of ESs and the relationships between urbanization indicators and ESs were explored. Finally, a decoupling analysis was conducted to identify the decoupling state of ESs from urbanization.
The results reflect that the area of crop land decreased by 10.26% during the study period, while the area of forest land, unused land, and built-up land increased by 4.31%, 36.88%, and 21.72%, respectively. Shanghai's total ecosystem service value declined by 8.24% from 3.45 × 10 20 sej in 2005 to 3.16 × 10 20 sej in 2010, mainly contributed by the AP decrease from 2.01 × 10 20 sej in 2005 to 1.76 × 10 20 sej in 2010. ES of the crop land system contributed the most to the total ES. At the district level, Chongming had the highest value of ES, followed by Pudong and Fengxian. The irregular "U" shape relationships between the decreases of ESs and the increases of urbanization indicators in Shanghai were observed. Synergy relationships among AP, CS, and SC exist, while tradeoff between WR and others can be observed. Finally, most districts experienced the weak decoupling of ESs decrease from urbanization. Results from such a systematic framework can help provide insightful policy implications to move toward sustainable urbanization. To improve the relationships between various ESs and urbanization, we propose the following policy recommendations to the city of Shanghai and other cities facing similar challenges.
Firstly, urban planners should fully consider all the relevant ES information into their urban plans so that sustainable urban policies can be made. Detailed data should include natural hydrologic and ecological processes, spatial patterns, and dynamic changes of ESs and the synergies and tradeoff Energies 2020, 13, 6139 20 of 25 relationships among various ESs. For example, our results reflect that the top priority should be given to Chongming and Pudong due to their dominating roles in producing ESs, while Minhang, Jiading, Baoshan, and Pudong also deserve considerable attention due to their decreased ESs during the study period. Spatial heterogeneity in different districts requires more region-specific mitigation policies.
Secondly, a nature-based solution [82] should be carefully employed. Detailed actions include planned ecological redline areas [32], tree-planting campaigns, expanding urban forest and urban parks [83], and the establishment of ecological corridors. Besides, compact use of the built-up land and optimized land planning is effective to overcome the sprawling expansion of the built-up land. Moreover, actions should be taken to compensate for the loss of ESs caused by the crop land decrease.
Finally, it is necessary to adopt this framework to build up an ESs evaluation database covering different regions and cities so that different stakeholders can share related knowledge and information. Such a database can also help decision-makers to dynamically monitor local ecosystems and prepare more appropriate urban policies so that cities can move toward sustainable urban development.

Conflicts of Interest:
The authors declare no conflict of interest.