Quantification and driving force analysis of ecosystem services supply, demand and balance in China

Science of the Total Environment 652 (2019) 1375–1386

Contents lists available at ScienceDirect

Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Quantification and driving force analysis of ecosystem services supply, demand and balance in China Xue Wu a, Shiliang Liu a,⁎, Shuang Zhao a, Xiaoyun Hou a, Jingwei Xu a, Shikui Dong a, Guohua Liu b a b

State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• ES had strong spatiotemporal heterogeneity on national, provincial and city scales. • Forest proportion was the most important driving force of ES supply. • Different land cover conversion strategies should be implemented for ES balance.

a r t i c l e

i n f o

Article history: Received 2 September 2018 Received in revised form 23 October 2018 Accepted 24 October 2018 Available online 26 October 2018 Editor: Deyi Hou Keywords: Ecosystem services supply and demand LULC matrix Time-varying analysis Socio-economic factors Driving force analysis

a b s t r a c t Spatially quantifying ecosystem services (ES) supply, demand and balance dynamics and exploring their relations with socio-economic factors are very significant for regional sustainability. In this study, land use/land cover (LULC) matrix model was used to quantify the relevant capacity of the ES supply, demand and balance in China. Also, we explored the spatial-temporal characteristics of ES at three scales (national, provincial and city scale). The results revealed that the ES supply, demand and balance in China had strong spatial heterogeneity and showed different time-varying characteristics on different scales. For the provinces with ES deficit, linear optimization model was then applied to achieve the theoretical ES balance through land cover conversion. For the provinces with negative regulating ES, farmland should be significantly reduced while desert and grassland should be converted to farmland and forest for the provinces with negative provisioning ES. In addition, the key driving factors of ES dynamics were selected through ordination analysis of 109 cities at city scale. The results showed that forest proportion was the most important influencing factor of ecosystem services supply while ES supply management can be carried out by adjusting the output values of agriculture, forestry and animal husbandry. On the other hand, ES demand can be adjusted by per capita GDP, energy consumption per unit of GDP and permanent population. The results can provide targeted information with ES management and this method can be applied at a smaller scale considering data availability. This study provides a convenient and propagable method for ES quantification and a quantitative support for regional ES management decisions. © 2018 Elsevier B.V. All rights reserved.

⁎ Corresponding author at: School of Environment, Beijing Normal University, Beijing 100875, China. E-mail address: [email protected] (S. Liu).

https://doi.org/10.1016/j.scitotenv.2018.10.329 0048-9697/© 2018 Elsevier B.V. All rights reserved.

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1. Introduction Ecosystem services (ES) are defined as the natural resources and goods provided by the natural ecosystem components which can be directly used by human beings, or as the direct beneficiaries (Burkhard et al., 2012; Redhead et al., 2018). With the deepening understanding of ES concept, the ES research focus gradually shifted from the ecosystem structure and process to the relationships between ES and human well-being (Daily, 1997; Xiao et al., 2016). For human beings, the acquisition of human welfare including economic and social welfare depends on the consumption of ES which would lead to the imbalance between ES supply and demand. The imbalance would cause ecosystem degradation, which in turn affects the sustainable supply of ES (Kumar, 2010). In 2016, 17 Sustainable Development Goals were put forward by the United Nations and the 12th goal aiming to promote responsible consumption and production can be achieved by reducing consumption and improving ecosystem service (United Nations, 2016). Therefore, the ecosystem management considering both supply and demand is necessary for sustainable development to keep ES consumption within a responsible range (Tao et al., 2018; Xie et al., 2010). In order to support the ecosystem management, assessment methods focusing on quantifying ES and identifying the driving factors of ES supply and demand are urgently needed (Rapport and Singh, 2006; Burkhard et al., 2012; Redhead et al., 2018). For ES assessment, land use is one of the important influencing factors of ES as the changes have complex impacts on ecosystem patterns and processes (Tolessa et al., 2017; Wu et al., 2018). Human beings often adopt the methods changing the pattern and function of land use to meet the increasing ES demand due to social development, which would feed back into the ES and change their supply (Fu et al., 2013; Fu and Forsius, 2015; Wu et al., 2017). For example, the landscape pattern change accompanying agriculture decreases the supply of pollination service (Nicholson et al., 2017). But in contrast, the implementation of ecological restoration projects can effectively improve the level of ES supply and promote regional sustainable development (Wu et al., 2018). For land use planning, Bai et al. (2018) delineated “ecological redline” of Shanghai, which reduced the tradeoff between regional urban construction and ecological protection (Bai et al., 2018). Thus, it is necessary to explore the comprehensive impacts of human activities on ES supply and demand from the land use change perspective. Combining with expert estimation, Burkhard et al. (2009) proposed a land use/land cover (LULC) matrix model to semi-quantify the relevant capacity of ES supply and demand, which has been applied in many regions (Nedkov and Burkhard, 2012; Stoll et al., 2015; Cai et al., 2017). For example, based on the improved LULC matrix approach, Tao et al. (2018) assessed the dynamics of ES supply and demand in the regions with rapid urbanization. The result showed that cities had a significant and direct impact on ES supply and demand (Tao et al., 2018). However, these studies are conducted on a single scale and lack in-depth analysis. Consequently, both ES demand and supply are also impacted by human beings. On the one hand, the demand pattern of ES can be significantly influenced by the changes of socio-economic factors through altering people's desires (Paetzold et al., 2010; Escobedo et al., 2011; Ouyang et al., 2016; Wilkerson et al., 2018). On the other hand, ES supply is indirectly impacted by land use changes which are mainly caused by population growth, policy control and economic driving (Kim et al., 2016; Cheng et al., 2009; Liu et al., 2009). Wilkerson et al. (2018) described the interaction pathways between ES and socio-economic in urban ecosystem by constructing conceptual models. The results showed that socio-economic factors affected the ES supply by impacting the quantity and quality of ecological facilities and ecological management measures. Meanwhile, socio-economic factors impacted ES demand (Wilkerson et al., 2018). From the above, we can draw a conclusion that socio-economic factors would drive ES supply and demand change. However, the relationships between ES and socio-

economic factors are still not clear, which can't satisfy the needs of ES management (Wilkerson et al., 2018). In the past decade, an increasing number of studies considered that ES should be taken into land use management, especially in the developing countries with dramatical land use change (Li et al., 2010; Polasky et al., 2011). Nedkov et al. defined the contribution rate of different land use types on flood regulation and selected the areas requiring the ES (Nedkov and Burkhard, 2012). Sahle et al. provided planners and decision makers with a spatially explicit manner of ES supply and demand, which provided scientific support for combating local climate change (Sahle et al., 2018). Schröter et al. established seven strategies taking sustainability as primary objective, which could be used in ES assessment, governance and management (Schroeter et al., 2017). In addition, many policy simulation systems have been developed for land use management optimization aiming at improving ES, such as the Computable General Equilibrium model and System Dynamics model (Jin et al., 2017). The improvement of ES needs a synthetic promoting action, not only for the rational utilization of land resources, but also for the development of society and economy (Jin et al., 2017). It has been verified that ES assessment could facilitate conceptual changes of policy makers, which was an essential factor of policy development (Posner et al., 2016). In China, the last decade witnessed the sharply increasing trend of ecosystem service research (Jiang, 2017). The main focuses include monetary valuation, quantitative assessment and policy evaluation in which many controversies still exist, such as ignoring ecological principles and lacking of convincing pricing method (Burkhard et al., 2009). In addition, current studies have paid more attention to the quantification of ES, while lacked the exploration of the driving forces of ES change (Jiang, 2017). Our study quantified and mapped ES supply, demand and balance in China based on LULC matrix approach. On this basis, the time-varying trends and the change rates of various ES balances were explored. More importantly, we took representative cites as samples and selected the socio-economic drivers of ES supply and demand through ordination analysis. Our main objectives included: 1) exploring the spatial and temporal characteristics of ES supply, demand and balance on different scales; 2) calculating land cover conversion area to achieve the ES balance; 3) screening out the key socio-economic driving forces of ecosystem supply and demand. The results of this study not only enriched the research on the spatial-temporal analysis of ES in China, but also provided a more integrative support for ES management to understand the human-ES relationship. 2. Methods 2.1. Data collection This study applied the land use/land cover (LULC) matrix approach to assess ES supply, demand and balance in China. On this basis, we conducted in-depth analysis on different scales and selected the key factors affecting the ES balance by ordination analysis. In this study, land use/ land cover data were provided by Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC) (http:// www.resdc.cn). The socio-economic data were collected from regional statistical yearbook for 2011 (http://data.cnki.net). The descriptions of the data sources were displayed in Table 1. 2.2. Data analysis To explore the spatial and temporal characteristics of ES supply, demand and balance and screen out the key socio-economic driving force of ecosystem supply and demand, we performed the following data analysis and the main research contents and framework were shown in Fig. 1.

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Table 1 Data description. Data

Description

1. Land cover 2. Socio-economic data

Land cover was classified into 7 types: farmland, forest, grassland, wetland, settlement, desert and bare land. Describe the output value of various industries, infrastructure and energy consumption status.

Resolution

2.2.1. Quantifying and mapping ecosystem service supply, demand and balance This study adjusted and applied the LULC matrix approach involving 44 land cover types and 22 ecosystem service types to ES assessment in China (Burkhard et al., 2009, 2012, 2015). In this study, we used the LULC classification system which land surface is divided into 7 classes with 100 m resolution. Due to the differences between the land cover classification systems, we integrated Burkhard's LULC matrices by consolidating similar land cover types and took the average values as the final value (keeping one decimal place). Ecosystem services supply matrix identified the capabilities to provide particular services of different land cover types (Fig. 2a) while demand matrix reflected the relative demand for ES from people's actual benefit demand for particular land cover (Fig. 2b). In these two matrices, the supply capability and relative demand were assessed on a scale ranging from 0 to 5. By calculating the difference between supply matrix and demand matrix, we can get the balance matrix which indicated particular ES deficit or surplus for different land cover types (Fig. 1c). The difference scale ranged from −5 (demand exceed supply, deficit) to 5 (supply exceed demand, surplus). In addition, 22 involved ES types were divided into 3 categories including provisioning services (11 types), regulating services (9 types) and cultural services (2 types). Based on the supply, demand and balance matrices, the ES of different land cover types can be quantified. And we can get the spatial distribution and deficit/surplus situation of particular ES category by combining with LULC map. In addition, we took 2010 as an example and calculated the averages of various ES balance in different cities. On this basis, we revealed the spatial distribution of various ES balances on city scale. In the LULC matrix model, the explanatory variables of ES supply were land cover and ecological integrity which was seen as the basis

Basic data

1) Exploring spatial (S) and temporal (T) characteristics of ES

100 m City level

Time period 1995, 2000, 2005, 2010 2011

of ES supply and directly impacted by ecosystem structures and processes. The explanatory variables of ES demand were population, economy and human benefits containing social, economic and personal well-being (Burkhard et al., 2012). 2.2.2. Changes of various ES value and sensitivity test We calculated the ecosystem service supply, demand, balance and total value of 1995, 2000, 2005 and 2010 through multiplying the area of 7 kinds of land cover by the corresponding quantitative ES value. Formulae (1) and (2) were used to calculate the values of 3 ecosystem service categories and total ecosystem service. ES j ¼

7 X

Si  VESi ði ¼ 1; 2; 3…::7Þ

ES value ¼

3 X

ES j ð j ¼ 1; 2; 3Þ

where i is the land cover types (7 types); j is the ecosystem service categories (provisioning, regulating and cultural services); and S is the area of land cover; VES is the quantitative value of ecosystem service category. The coefficient of sensitivity (CS) was further calculated to analyze the sensitivity of the total ecosystem service value to the adjustment of particular ecosystem service value. The CS reflected the dependence of total ecosystem service value to quantitative value and evaluated the rationality of the research method. If CS ≥ 1, total ecosystem service value had high sensitivity to quantitative value. By contrast, if CS b 1, total ecosystem service value was not sensitive to quantitative value,

National scale

S: Quantifying and mapping ES supply, demand and balance of 2010 T: Counting the changes of various ES value from 1995 to 2010

Provincial scale

T: Presenting time-varying trends of 3 categories ES of various provinces from 1995 to 2010

City scale

S: Describing the spatial distribution characteristics of various cities by three-dimensional heat map T: Calculating the change rate of various cities from 1995 to 2010

Socio-economic data

ð2Þ

j¼1

Land cover data

the LULC matrix

ð1Þ

i¼1

2) Land cover optimization

Calculating land cover conversion area to achieve the ES balance by linear optimization model

3) Driving force analysis

Screening out key driving factors of ES supply and demand by ordination analysis

Fig. 1. Research and analysis procedures.

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Fig. 2. Assessment matrices illustrating the supply (a), demand (b) and balance (c) for ES of different land cover types.

which revealed the quantification method was reasonable. Formula (3) was used to calculate CS.    V −V =V   ESj ESi  ESi  CS ¼   VC jk −VC ik =VC ik 

ð3Þ

where k is the land cover type; VESi and VESj are the original and adjusted total ecosystem service values; VCj and VCi are the original and adjusted quantitative values, adjustment quantity = ±50%. 2.2.3. Linear optimization model to achieve the regional ES balance As land cover is an important influence factor of ES supply and demand, its conversion can be used to theoretically adjust the regional ES balance. To achieve the ES balance at provincial scale, we calculated the land cover conversion area by the linear optimization model which is a mathematical method to get the extreme optimal values of a linear objective function under linear constraints. The linear optimization model can provide a scientific basis for making optimal decisions aiming at making rational use of limited resources and this model has been widely used in different fields (Rao et al., 2018, Kumar et al., 2016). In our study, we conducted linear optimization analysis at the provinces with current negative ES balance and its objective was to make the negative ES balance to 0. So, we set the maximum value of the ES balance to 0 as the objective of the linear optimization model. The certain constraints were as follows: 1) except the negative ES balance category, the other ES balance categories should be maintained in surplus after land cover conversion; 2) the total area of all land cover types after conversion should be remained unchanged for a certain province; 3) the area of each land cover type after its conversion should be kept non-negative.

2.2.4. Time-varying trends and change rates of various ecosystem service balances To describe the variations of ecosystem service balance, we made the statistics of time-varying trends at provincial scale and change rates at city scale. For the time-varying trends, we calculated the average values of provisioning service, regulating service and cultural service balance of 1995, 2000, 2005 and 2010. The three values were displayed in a three-dimensional coordinate systemin which X-axis was regulating services, Y-axis was cultural services, Z-axis was provisioning services. To analyze the ecosystem service change, we calculated the change rates (slope) of ecosystem service balance at city scale by formula (4). Positive slope values indicated that the averages of ES balance evolved to a higher level from 1995 to 2010, which showed an optimization trend of ecosystem service balance. Conversely, negative values indicated that the averages of ES balance evolved to a lower level, showing a deteriorated trend.

Slope ¼

n

Pn j¼1

P P j  balance j − nj¼1 j nj¼1 balance j  2 P n n  nj¼1 j2 − ∑ j¼1 j

ð4Þ

where Slope is the change rate of ES balance; n is the phase of balance data where n = 4; balancej is the average of ES balance in year j. 2.2.5. Driving force analysis of ES supply and demand Ordination analysis in CANOCO software was used to select the key factors affecting the ES supply and demand in China. The sample library contained 109 cities which belonged to provincial capitals and five national urban agglomerations including the Yangtze River Delta, Pearl

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River Delta, Beijing-Tianjin-Hebei, Middle Reaches of the Yangtze River and Chengdu-Chongqing urban agglomeration. For ES supply, we calculated the total quantities of 22 ES supply of each sample city, constructing a 22 × 109 ES supply matrix. Correspondingly, we selected ten socio-economic factors affecting ES supply (shown in Fig. 7a) to construct a 10 × 109 supply factor matrix. Based on the two above matrices, ordination analysis of ES supply driving factors can be carried out. Similarly, we calculated the total quantities of 22 ES demand of each sample city to construct a 22 × 109 ES demand matrix; and selected nine socio-economic factors affecting ES demand (shown in Fig. 7b) to construct a 9 × 109 demand factor matrix. Also, the ordination analysis of ES demand driving factors can be carried out. We standardized the range of all factors and statistic values of ES supply and demand to 0–1. The specific steps of data processing included: 1) model selection, depending on the lengths of gradient value of detrended correspondence analysis (DCA); 2) ordination analysis, from which can obtain the influence degree and correlation on ES supply and demand of each factor; 3) Monte Carlo permutation test, reflecting whether the factors were significantly correlated with the ES supply and demand. 3. Results 3.1. Sensitivity test for land cover-based method As shown in Table 2, the coefficient of sensitivity (CS) values of all land cover types were b1, which indicated that although the assigned values in the method were empirical, the quantitative values of this study were reasonable and the results were credible. Among different land cover types, the CS values of grassland was the largest. The reason was maybe that the area ratio of grassland was the largest and grassland management should be paid more attention in the future. 3.2. Spatial and temporal characteristics of ES 3.2.1. Quantitative maps and value changes of various ES at national scale Based on the LULC matrices and land cover data, we quantified and mapped the supply, demand and balance of 4 categories of ES, including provisioning services, regulating services, cultural services and total ES for 1995, 2000, 2005 and 2010. The quantitative maps of 2010 were shown in Fig. 3 as examples and the distribution characteristics of different ES supply and demand were as follows. Obviously, there existed great spatial variations for different ESs in China. In terms of the ES supply, Southeast and Northeast China were the high-supply areas for all 4 categories of ES. On the contrary, Northwest China including Xinjiang, western Gansu and Inner Mongolia province was the low-supply areas, especially the Tarim Basin (Xinjiang) and Alashan Desert (Inner Mongolia). Moreover, provisioning and regulating services supply of the North China Plain occupied an intermediate position while cultural services supply was low. For the ES demand, the demand center of provisioning, regulating and cultural services were Beijing, Pearl River and Yangtze River Deltas due to the high

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settlement density. In addition, the high-demand areas of regulating services and total ES were distributed in the North China Plain, Sichuan Basin and Northeast China. For provisioning services balance, the deficit areas were concentrated in northwest China, including Xinjiang, western Gansu and Inner Mongolia. The surplus areas were distributed in southeast and northeast China. For regulating services balance, the deficit areas were concentrated in the North China Plain, Sichuan Basin and northeast China, which was corresponding to the high-demand areas of regulating services. To test the spatial heterogeneity of ES balance, we conducted Global Moran's I analysis on provisioning services, regulating services, cultural services and total ecosystem services balances in 2010. Global Moran's I analysis was widely used to analyze whether the clustered pattern of spatial data was the result of random distribution according to whether the z-score was N1.65. If the z-score was b1.65, the spatial data was randomly distributed. If the z-score was N1.65, the spatial data had spatial heterogeneity. In this study, z-scores of all four ES balances were far N1.65 (Provisioning services balance: z-score = 468.48; Regulating services balance: z-score = 491.62; Cultural services balance: z-score = 439.55; Total ES balance: z-score = 493.62), which indicated there was a b1% likelihood that the clustered patterns of ES balances were the result of random chance and the spatial data had significant spatial heterogeneity. The statistics results of ecosystem service supply, demand, balance and total values of 1995, 2000, 2005 and 2010 were shown in Fig. 4, which displayed the temporal characteristics of various ES at national scale. For the values of ES supply, total ES increased at first and then decreased; provisioning services increased; regulating services decreased and cultural services remained stable. For the amount of ES demand, provisioning services increased sharply; regulating services also increased and the growth rate peaked at 1995–2000; cultural services increased a little and the overall trend of total ES increased. For the amount of ES balance, the surplus of total ES showed a decreasing tendency during 1995 to 2010; the surplus of provisioning and regulating services decreased, in which regulating services drastically declined between 1995 and 2000; for cultural services, the surplus changed little. 3.2.2. Time-varying trends of 3 categories ES of various provinces The time-varying trends of various ES balance at provincial scale were shown in Fig. 5. From 1995 to 2010, the provinces as a whole showed a significant aggregation trend, especially from 2005 to 2010. Moreover, we divided all provinces into four types according to the positive and negative values of provisioning and regulating services balances (cultural services balance of all provinces ≥0). For different types, the time-varying trend characteristics were as follows. The provinces with blue mark had positive provisioning and cultural services balances and negative regulating services balance. This kind of provinces were converging in the direction of higher cultural services balance, relatively stable provisioning services balance and lower regulating services balance, which will lead to a higher deficit in regulating services. Among these provinces, Shanghai became a new type province in 2005, whose provisioning services balance reduced to minus (green

Table 2 Changes in ecosystem service and sensitivity coefficient. Land cover types Years 1995 2000 2005 2010

Farmland Change of VES/% CS Change of VES/% CS Change of VES/% CS Change of VES/% CS

Adjustment quantity: ±50%.

/ / / / / / / /

Forest

Grassland

Wetland

Settlement

Desert

±32.36% 0.622 ±32.14% 0.618 ±32.32% 0.621 ±32.41% 0.623

±16.46% 0.817 ±16.71% 0.829 ±16.73% 0.83 ±16.76% 0.832

±2.07% 0.096 ±2.11% 0.098 ±2.12% 0.099 ±2.13% 0.099

±2.17% 0.046 ±2.25% 0.048 ±2.47% 0.052 ±2.6% 0.055

±1.28% 0.350 ±1.29% 0.354 ±1.3% 0.356 ±1.3% 0.356

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Fig. 3. Quantitative maps showing the supply, demand and balance of provisioning services, regulating services, cultural services and total ecosystem services of 2010.

mark). From 2005 to 2010, Shanghai moved in the direction of lower provisioning services balance and higher cultural services balance. The provinces marked in yellow had minus provisioning services balance and plus regulating and cultural services balances, including Xinjiang and Qinghai. For this type province, the regulating services balance declined drastically and the provisioning services balance declined slightly from 2005 to 2010. Meanwhile, cultural services balance as a whole didn't fluctuate much. In addition, the provinces marked in purple showed a significant aggregation trend with higher cultural services balance. 3.2.3. Spatial distribution characteristics and change rate of various cities The cities with strong provisioning services deficits included Shanghai and cities located in the Pearl River delta, such as Dongguan,

Shenzhen and Zhongshan. 27.15% of the total cities had deficits in regulating services balance and the cities with the highest deficits were concentrated in Anhui and Henan provinces, including Haozhou, Luohe, Zhoukou, Shangqiu and Fuyang. In Southwest China, Chengdu and surrounding cities formed the regulating services depression, including Deyang, Guangan, Meishan and Zigong. Contrary to the regulating services, the cities located Sichuan province had high surpluses in cultural services. And eight of top ten cities for cultural services surplus located in Sichuan province. For total ES, the cities with the highest deficits were concentrated in Yangtze River Delta, Henan and Shandong province, such as Shanghai, Jiaxing, Shangqiu, Luohe and Zhoukou. The cities with the highest surplus were Daxinganling, Shennongjia forest regions and surrounding cities. In addition, the North China Plain and Sichuan Basin were in equilibrium because of high-density farmland. Spatial

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Fig. 4. The changes of ecosystem service supply, demand, balance and total value of 1995, 2000, 2005 and 2010 (×108).

distribution map based on city scale statistics was shown in the Supplementary Fig. 1. Apart from that, the deficit or surplus status of ecosystem services in various cities was related to local socio-economic differences and ecological conditions. The reasons were that the ES demand was high in human-dominated land cover types with higher population number and more ES consuming activities; and ES supply was high in more near-natural land cover types with high ecological integrity. In China, the cities with strong ES deficits mostly located in the Pearl River delta, North China Plain and Yangtze River Delta, which were the hotspots for population growth, economic development and agricultural development. The change rates of various ES balances from 1995 to 2010 were shown in Fig. 6. For provisioning services balance, the cities with negative change rate accounted for 74% of the total; and cities with improved provisioning services balance were scattered in west and northeast China. For regulating services balance, 75% of cities had deteriorating trends. Among them, the Beijing-TianjinHebei region and Pearl River delta were the most degraded areas. Cultural services balance improved in 98% of cities and the degree of improvement in east China was obviously higher than that in west China. For total ES balance, 20% of cities improved and 80% of cities deteriorated. The cities with the most significant improvement were Jincheng, Jiyuan, Guangyuan and Harbin. On the contrary, the cities with the most significant deterioration

concentrated in the Pearl River delta, including Dongguan, Zhongshan, Shenzhen and Foshan. 3.3. Guiding role of land cover conversion for sustaining ES balance As cultural services balance of all provinces was surplus and only eight provinces (Anhui, Henan, Jiangsu, Shandong, Tianjin, Shanghai, Qinghai and Xinjiang) had negative provisioning or regulating service balance, we calculated the land cover conversion area by using the linear optimization model to achieve the ES balance in these provinces. In addition, all six types of land cover were brought into land cover conversion in Qinghai and Xinjiang provinces while five types, except desert, were considered in the other six provinces. As shown in Table 3, Anhui, Henan and Shandong provinces should significantly reduce farmland area, slightly reduce grassland and increase settlement, forest and wetland area at the same time. For Jiangsu province, forest should be converted into settlement and wetland. For Tianjin province, farmland and wetland area should be reduced while forest, settlement and grassland were increased. For Shanghai, farmland and settlement should be converted into forest, grassland and wetland. Qinghai and Xinjiang provinces should reduce desert and grassland area, increase forest, farmland and settlement area. The results can provide guidance for regional land cover conversion but only in theoretical aspect. In practical land cover design, local socio-economic factors should be

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Fig. 5. The time-varying trends of provisioning services, regulating services and cultural services balance in various provinces (For marks, P was provision services; R was regulation services; C was cultural services).

considered besides ES balance. Apart from this, the method can be applied to similar situations and lower administrative levels.

3.4. Driving factors of ES supply and demand based on ordination analysis The results of model selection indicated that redundancy analysis model (RDA) should be selected to do ordination analysis, because the length of gradient values of DCA was b3 (Lepx and Smilauer, 2003). For ES supply, length of gradient = 1.6806 and the results of RDA were as follows. The constrained inertia was 13.1602, which indicated the cumulative interpretation ratio was 17.5604/22 = 79.82%. As shown in Table 3, the first two axes carried 94.97% of the model's total interpretation, so the ordination analysis was performed on the first two axes. Similar with ES supply, RDA should be selected to make ordination analysis because the length of gradient value of DCA was 1.4503 and the constrained inertia was 9.8511, which indicated the cumulative interpretation ratio was 14.2516/22 = 64.78%. The first two axes which

accounted 97.71% of the model's total interpretation were performed, for ordination analysis (Table 4). By redundancy analysis and Monte Carlo permutation test, we can get the redundancy plot (Fig. 7) and correlation information. In Fig. 7, circles represented the sample cities; arrows represented the driving factors. The length of arrow represented the correlation degree between driving factors and ES distribution of sample cities which the longer arrow is, the higher correlation. The angle between arrow and axis indicated the correlation between driving factors and axis which the smaller angle is, the higher correlation. Through Monte Carlo permutation test, driving factors having significant impact on ecosystem services supply and demand can be identified (shown in the Supplementary Tables 2 & 3). For ES supply (Fig. 7a), there were four driving factors with significant impact, which were forest proportion (S9), animal husbandry output value (S3), agricultural output value (S1) and forestry output value (S2) in descending order. These four factors were all negatively correlated with RDA Axis 1 and Forestry output value was the key factor of the smallest

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Fig. 6. Change rates of provisioning services, regulating services and cultural services balance in various cities from 1995 to 2010.

angle. While RDA Axis 2 was negatively correlated with agricultural and animal Husbandry output value; positively correlated with forestry output value and forest proportion. The correlations between RDA Axis 2 and Agricultural output value, Animal Husbandry output value, Forest proportion were essentially the same. For ES demand (Fig. 7b), all driving factors had significant impact and the total retail sales of consumer goods (D3) had the greatest impact. RDA Axis 1 was positively correlated with per capita GDP and negatively correlated with other driving factors and permanent population was the key factor of RDA Axis 1. RDA Axis 2 was positively correlated with energy consumption per unit of GDP (D1) and negatively correlated with other driving factors. In addition, per capita GDP and energy consumption per unit of GDP were the key factors of RDA Axis 2.

4. Discussion 4.1. Comparison between LULC matrix approach with InVEST model method Based on LULC matrix approach, this study quantified and exhibited China's ecosystem service supply, demand and balance on different scales. For ecosystem service quantification, model assessment method is the most common and widely used method. Representatively, Ouyang et al. (2016) quantified seven ES using InVEST and other biophysical models, which is the first national ecosystem service assessment in China (Ouyang et al., 2016). Based on the model calculation results of various ES, Ouyang et al. (2016) overlaid local benefited population to get the final quantitative results aiming to response that

Table 3 Conversion area of different land cover in eight provinces (km2). Land cover types

Anhui

Henan

Jiangsu

Shandong

Tianjin

Shanghai

Qinghai

Xinjiang

Farmland Forest Grassland Wetland Settlement Desert

−2781 737 −464 132 2377 –

−4447 1915 −523 610 2445 –

1240 −3647 39 361 2007 –

−4502 3312 −883 342 1731 –

−261 286 10 −109 74 –

−137 164 15 9 −52 –

12,150 10,796 −27,826 5891 9673 −10,683

20,155 24,677 −26,836 16,038 19,940 −53,975

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Table 4 Eigenvalues and interpretation ratios of RDA for ES supply and demand. Type

Statistical item

1. Constrained axes 2. Eigenvalues Supply 3. Loading ratio of model interpretation 1. Constrained axes 2. Eigenvalues Demand 3. Loading ratio of model interpretation

Statistical results Axis1 10.864

Axis2 1.634

Axis3 Axis4 Axis5 0.482 0.179 0.002

82.55% 12.42% 3.66% 1.36% 0.02% Axis1 7.781

Axis2 1.844

Axis3 Axis4 Axis5 0.136 0.089 0.001

78.99% 18.72% 1.38% 0.90% 0.01%

human beings are the direct beneficiaries of ES (Burkhard et al., 2012). The results are used to select the areas with high ES supply efficiency, which has been incorporated into a national ecosystem management policy, Ecosystem Function Conservation Areas. The similarities between model assessment method and LULC matrix approach are as follows. To begin with, they both rely on LULC data which is the most important input data of InVEST model. Furthermore, the studies using these two methods can take the factor of population into account for ES assessment in different ways. Based on model calculation results, Ouyang et al. (2016) take the benefited population as an evaluation factor while LULC matrix approach reflects population influence in ES demand intensity for different land use types (2009). Compared the quantitative maps of two methods, the spatial distribution of the areas with high ES supply value is similar, although LULC matrix approach used in this study is a kind of semi-quantitative method compared with model calculation approach. The differences between the above two methods are that: (1) model assessment method needs more input data as parameters. Taking habitat quality and rarity service in InVEST model as an example, it needs LULC data, threat impact distance, relative threat impact weights, threat maps, habitat suitability and other information. Because of the strong demand of basic data, model assessment method faces the restrictions of application and further research. By contrast, LULC matrix approach with only LULS data requirement is a quick and valid method. Because of its convenience, this approach can be used more widely and more flexibly (Tao et al., 2018). (2) model assessment method takes the natural ecosystem as the assessment subject, which leads to that only ES supply can be calculated and demand can't be quantified. Different with model assessment method, LULC matrix approach can provide a more comprehensive assessment, including ES supply, demand and balance.

On the other hand, the matrix scores reflecting the relevant capacities of ES supply and demand are based on expert knowledge and they can be adjusted according to local actual situation to obtain more suitable scores. Apart from that, just like the application of Ouyang's research in identifying the key regions where important services originate (Ouyang et al., 2016), the results of our study has great application potential for ES protection and improvement through policy design. 4.2. Implications of time-varying analysis In this study, we comprehensively analyzed the time-varying characteristics of different ES on three scales: the total quantity change on national scale, the time-varying trends on provincial scale and the change rates on city scale. Through the total quantity change tendencies in different phases, we could find that 1995–2000 had unique characteristics compared with other phases. In this phase, demand and supply of provisioning services increased simultaneously which resulted a slight of balance decrease. The increase reasons of demand included that forest with less demand decreased while farmland and settlement with more demand increased. The increase reason of supply was the area ratios of farmland and grassland increased which offset the decrease of forest with high provisioning services supply. Contrary to the relatively stable of provisioning services balance, regulating services balance fell sharply from 1995 to 2000 because of the land cover change, especially the increase of farmland. In terms of total ES, the supply showed a little increase while the demand increased significantly, which resulted in a substantial reduction in the surplus. According to the time-varying trends from 1995 to 2010, we can judge the ES status of various provinces. Comprehensively considering the change rates, we can get the affiliated cities' improvement or deterioration situation for different ES types, which can be used to screen the hotspot administrative district needing specific management. Taking Yunnan province having 16 affiliated cities as an example, the time-varying characteristics were as follows. Provisioning services in Yunnan province declined a little from 1995 to 2010, and there were 11 cities have different degrees of decline in provisioning services. The city with the most dramatic decline was Lincang, followed by Nujiang, Kunming, Yuxi, and Puer city. In contrast, provisioning services in the remaining 5 cities improved, including Qujing, Dehong, Dali, Honghe and Diqing city. For Yunnan province, regulating services improved slowly after a significant decline from 1995 to 2000. In this period, there were only 3 cities' regulating services improved, including Dehong, Diqing and Honghe. Others deteriorated, among which the

Fig. 7. Driving factors of ES supply (a) and demand (b) based on ordination analysis (RDA).

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cities with more drastic deterioration included Lincang, Chuxiong, Baoshan and Kunming. In addition, cultural services of Yunnan province went up quickly from 2005 to 2010 while the all the affiliated cities' cultural services improved. Among them, the cities of Zhaotong, Qujing, Dehong and Wenshan improved greatly.

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permanent population. From the whole point of view, the quantification and time-varying analysis enriched the research on spatial-temporal analysis of ES supply, demand and balance in China; the driving force analysis provided a targeted and propagable support for ES management.

4.3. The advantages of the approach to select socio-economic factors Acknowledgments An increasing number of researches confirmed that socio-economic factors are important impact factors of ES supply and demand (Burkhard et al., 2012; Wolff et al., 2017; Wilkerson et al., 2018). Taking the 109 cities as samples, we identified the driving factors of ES supply and demand respectively based on ordination analysis method and socio-economic data at city level. The results can provide directional information with policy-makers. Previous studies on the correlation between ES and socio-economic factors tended to conceptual model and scenario analysis. Wilkerson et al. (2018) put forward a conceptual model and built three major pathways to describe the interactions between socio-economic factors, green spaces and ES (Wilkerson et al., 2018). By setting four policy scenarios, Minin et al. explored the tradeoffs of ES, land use, economic development and biodiversity, which aimed to identify priority areas (Minin et al., 2017). Through the interviews with stakeholders, Villegas-Palacio et al. established a comprehensive evaluation system for scenario simulation regarding ecological, society and economy as basic considerations (VillegasPalacio et al., 2016). The above methods can draw a preliminary qualitative conclusion that socio-economic factors have influence on ES. However, they lacked quantitative calculations and couldn't point out the specific types of socio-economic factors, which can't provide targeted guidance in policy application. In our study, we used available statistical data to quantify the driving effect of different factors on supply and demand changes, which can provide targeted information with policy making. The results showed that forest proportion (S9) has the greatest influence on ES supply of various cities. Carrying out forest conservation policy and “Grain for Green” project aiming to improve forest proportion was necessary for improving ES supply. For ES demand, total retail sales of consumer goods was the most influential factor and ES demand can be adjusted by per capita GDP, energy consumption per unit of GDP and permanent population. The results of our study were targeted and can provide clearly guiding effect on ES management. Apart from that, the ordination analysis can be implemented at lower administrative levels to get driving factors with land use heterogeneity. The reason was that the data used was relatively easy to obtain, which made the method higher operability and suitability. 5. Conclusions Quantification and driving force analysis of ES with high convenience and data availability can provide important contributions to the formulation and implementation of various management measures. In this study, we quantified and mapped ES supply, demand and balance of China based on LULC matrix approach, which was proved to be reasonable and credible by sensitivity test. On the basis of quantification, we explored the time-varying trends and change rates of ES on national, provincial and city scales, which brought a multi-level description of ecosystem service characteristics. Linear optimization model was applied in the provinces with negative ES to calculate the land cover conversion area to achieve ES balance. In addition, we identified the driving factors of both ES supply and demand based on ordination analysis method, which can provide targeted information with ecosystem service management. The results showed that increasing forest proportion was the most effective method to improve ES supply and the output value of agriculture, forestry and animal husbandry were important adjustment factors of supply management. For ES demand, it can be regulated by per capita GDP, energy consumption per unit of GDP and

The work was supported by National Key Research and Development Project, China (No. 2016YFC0502103) and National Natural Science Foundation of China, China (No. 41571173). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2018.10.329.

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