889

Sensors and Materials, Vol. 35, No. 3 (2023) 889–900
MYU Tokyo
S & M 3216

Spatial Patterns and Intensityof Ecosystem Service Values in Beijing
Qingli Liu,1,2* Honghao Ding,3 Chao Gao,1,2 and Tingting Wang1,2
1Beijing Institute Surveying and Mapping, Beijing 100038, China

2Beijing Key Laboratory of Urban Spatial Information Engineering, Beijing 100038, China

3College of Arts, College of Landscape Architecture, Fujian Agriculture and Forestry University,

Fuzhou 350100, China(Received October 29, 2022; accepted February 14, 2023)

Keywords:

ecosystem service, ecosystem service values, land use, urban planning, Beijing

Ecosystem services (ES) are the benefits that humans receive from nature. The payments for
ES provide financial incentives to urban managers to conserve natural ecosystems. The spatial
pattern and intensity of ES values have become leading principles of urban planning. Althoughmost ES evaluations of urban regions are at the city scale, urban land planning is typically madeat the landscape level. The applications of remote sensing data provide a great opportunity forquantifying the ES in a finer analytical unit. In this study, we investigated the spatial patterns of
ES in Beijing by calculating the economic value of each ES based on the landscape type. We
compared the ES intensities observed in different administrative districts and land use types,
and we obtained the following results: (1) The ES values in Beijing are distributed in a spatial
pattern with low value at the center and high values in the surrounding area, with the highest
value in Miyun District and the lowest value in Dongcheng District. The difference in ES
intensity between above two districts is 4.087 × 104 yuan per hectare. (2) The ES type withhigher value in Beijing are hydrology regulation and climate regulation services. (3) The land
use type of forest is the main source of ES values, accounting for 67.2% of the total value. This
study provides a spatial clarification of ES value in Beijing, which could supply a scientificreference to support urban optimization and resource allocation.

1.

Introduction

Ecosystem services (ES) refer to the benefit that humans obtain directly or indirectly fromecosystems.(1) ES are resources and the environmental foundation for human survival anddevelopment.(2–4) Rapid urbanization is having adverse effects on biodiversity and ES in urban
regions.(5,6) With the expected future climate change and the diversity of socioeconomic
pathways, the ES of urban regions will face great challenges.(7) Therefore, it is meaningful toquantify the total value of current urban ES, which will contribute to specifying the protection
priority of districts and the overall trends in ES.
*Corresponding author: e-mail: 277930961@qq.com

https://doi.org/10.18494/SAM4208

ISSN 0914-4935 © MYU K.K.
https://myukk.org/

890

Sensors and Materials, Vol. 35, No. 3 (2023)

Ecosystems produce wide ranges of goods and services including provision, regulation,
support, and cultural services.(8) Provision services are the basic ES directly provided byecosystems (e.g., food, water, fibers, and timber). Regulation services are the benefits obtained
from the regulation of ecosystem processes, including climate regulation, water regulation, andpest and disease control. Support services are indirect ES because they are necessary to maintainthe provision, regulation, and cultural services. Typical support services include soil formation,
nutrient cycling, and photosynthesis. Cultural services are the mental benefits that people obtainfrom ecosystems, such as aesthetic values, recreation, ecotourism, and cultural diversity.(9) In an
urban ecosystem, support services are still the foundation and source of other services. Provision
services account for a small proportion of ES, whereas regulation and cultural services accountfor a large proportion, both playing an important role in improving human wellbeing.(10,11) Urban
ES are the bridge between urban systems and ecosystems. They affect the input of useful
substances and energy into socioeconomic systems and the transformation of waste fromsocioeconomic systems to nature.(12)The ES value is an economic measure of the benefits provided by an ecosystem to humans.(13)The ES value is increasingly focused on urban regions where human populations are densest.(14)The evaluation of the ES value is of great significance for ecological protection and economic
development. Recently, research on the value of urban ES has primarily focused on multiscenario projections and the exploration of related influencing factors, such as ecologicalrisk,(15,16) urbanization intensity,(17) urban development strategy,(18) and landscape patterns.(19)Some studies have incorporated ES values into the planning and construction of urban ecologicalpatterns.(20) The market value theory is the basis for conducting research on the ES value. There
are three main market-based approaches to valuing ES: real market, alternative market, andhypothetical market approach. The value equivalent method is the most feasible approach withthe greatest adaptability at various spatial scales. Correctly estimating and evaluating the valueof ES are conducive to a correct understanding of the importance of ecosystems to humanbeings. However, most studies have been conducted at city or regional scale,(21–23) with fewerstudies on quantifying ES values at the landscape scale inside cities. The assessment of ES
values through remote sensing has increased substantially over the last few decades.(24) The newtechnologies and ES evaluation will be beneficial for integrating natural, technological, andsocioeconomic systems to carry out the urban ecological infrastructure planning and to further
improve human wellbeing.(25)In this study, we calculated ES values in the Beijing metropolis, which is a rapidly urbanizing
region. Data of landscape types were derived from a national geoinformation survey. This study
aims to (1) quantify the ES values associated with different land use types and (2) compare the
ES intensities observed in different administrative districts. This study will provide some useful
guidelines for urban geospatial governance based on remote sensing applications in Beijing andsimilar cities.

Sensors and Materials, Vol. 35, No. 3 (2023)

2.

Data and Methods

2.1

Study area

891

The total area of Beijing is 16410 km2, with vegetation accounting for 79.6% of the total area
(Fig. 1). The total area of cultivated land is 1374.92 km2, accounting for 11% of the total area,
garden land covers an area of 1562.44 km2, and the area of grassland is about 886.83 km2, about
7% of the total area. The distribution of green spaces in Beijing shows strong spatial
heterogeneity. For example, forests are mainly distributed in northwestern suburbs, such as
Huairou Distinct and Mentougou District. The cultivated land is mostly distributed in southwest
suburbans, such as Daxing District. The garden land and grassland are more evenly distributed.
Water is an important part of the ecosystem and has extremely high ES values. The abundant
water resources and water reservoirs in Miyun District and other districts ensure the normal
operation of the urban ecosystem.
2.2

Quantitative methods for the ES value

Four categories of ES were selected: provision, regulation, support, and cultural services.
These ES types were further subdivided into 11 service functions: food production, raw material
production, water supply, gas regulation, climate regulation, environmental purification,hydrology regulation, soil conservation, nutrient cycle maintenance, biodiversity, and aesthetic
landscape. The quantification of ES values was based on the land use data in Beijing from the2015 national geoinformation survey (https://www.bism.cn/zxfw/xzfw/wjxz/). The data used inthis study were derived from Beijing Statistical Yearbook 2015, China Price Yearbook 2015, andNational Agricultural Product Cost and Benefit Data Compilation 2015. The assessment
adopted the service value per unit area of different terrestrial ecosystems in China based on theequivalent factor per unit area (Table 1). (13) The ES value per unit area in Beijing was calculatedusing these equivalent factors.

Fig. 1.

(Color online) Distribution map of green space and water body types in Beijing.

892

Sensors and Materials, Vol. 35, No. 3 (2023)

Table 1
ES value equivalents per unit area.
Ecosystem classification
Primary
Secondaryclassification classification
Dry land
Arable land
Paddy field
Coniferous
Conifers
WoodlandBroadleaf
Shrub
Grassland
Grassland
Bushes
Meadow
Wetland
Wetland
Water
Water system

Provision services
Food
Raw material Water
production production supply
0.85
0.40
0.02
1.36
0.09
−2.63
0.22
0.52
0.27
0.31
0.71
0.37
0.29
0.66
0.34
0.19
0.43
0.22
0.10
0.14
0.08
0.38
0.56
0.31
0.22
0.33
0.18
0.51
0.50
2.59
0.80
0.23
8.29

Ecosystem classification
Primary
Secondaryclassification classification
Dry land
Arable land
Paddy field
Coniferous
Conifers
WoodlandBroadleaf
Shrub
Grassland
Grassland
Bushes
Meadow
Wetland
Wetland
Water
Water system

Soilconservation
1.03
0.01
2.06
2.86
2.65
1.72
0.62
2.40
1.39
2.31
0.93

Regulation services
Gas
Climate Environment Hydrology
regulation regulation purification regulation
0.67
0.36
0.10
0.27
1.11
0.57
0.17
2.72
1.70
5.07
1.49
3.34
2.35
7.03
1.99
3.51
2.17
6.50
1.93
4.74
1.41
4.23
1.28
3.35
0.51
1.34
0.44
0.98
1.97
5.21
1.72
3.82
1.14
3.02
1.00
2.21
1.90
3.60
3.60
24.23
0.77
2.29
5.55
102.24

Support services
Nutrient cycle
Biodiversity
maintenance
0.12
0.13
0.19
0.21
0.16
1.88
0.22
2.60
0.20
2.41
0.13
1.57
0.05
0.56
0.18
2.18
0.11
1.27
0.18
7.87
0.07
2.55

Cultural services
Aesthetic landscape
0.06
0.09
0.82
1.14
1.06
0.69
0.25
0.96
0.56
4.73
1.89

Taking the net profit of food production per unit area of a farmland ecosystem as a standard
equivalent factor of the ES value, we calculated the grain output of the farmland ecosystem on
the basis of three major grain products, namely, rice, wheat, and corn. The equation is as follows.
D = Sr × Fr + S w × Fw + Sc × Fc ,(1)

where D is the ES value of the standard equivalent factor (yuan/ha); Sr , Sw, and Sc are the ratios
of sown areas of rice, wheat, and corn to the total sown crop area in Beijing; and Fr , Fw, and Fc
are the average net profits per unit area of rice, wheat, and corn in that year (yuan/ha),respectively.
Using National Agricultural Product Cost and Benefit Data Compilation 2015, Beijing
Statistical Yearbook 2015, and the above formulas, we found that the net profit of grain
production per unit area of farmland ecosystem in Beijing was 1249.20 yuan/ha in 2014 and839.29 yuan/ha in 2013. The average of 1044.24 yuan/ha for 2013 and 2014 was taken as the Dvalue (the ES value of the standard equivalent factor) in our accounting.
Using the standard equivalent factor calculated above, the ES values per unit area were
calculated as shown in Table 2.

Sensors and Materials, Vol. 35, No. 3 (2023)

893

Table 2
ES value per unit area (unit: yuan/ha).
Ecosystem classification
Primary
Secondaryclassification classification
Dry land
Arable land
Paddy field
Coniferous
Conifers
WoodlandBroadleaf
Shrub
Grassland
Grassland
Bushes
Meadow
Wetland
Wetland
Water
Water system

Provision services
Food
Raw material Water
production production
supply887.60417.7020.88
1420.1793.98−2746.35
229.73543.00281.94323.71
741.41386.37302.83689.20355.04
198.41449.02229.73104.42
146.1983.54
396.81584.77323.71229.73344.60187.96532.56522.122704.58
835.39240.188656.75

Ecosystem classification
Primary
Secondaryclassification classification
Dry land
Arable land
Paddy field
Coniferous
Conifers
WoodlandBroadleaf
Shrub
Grassland
Grassland
Bushes
Meadow
Wetland
Wetland
Water
Water system

Soilconservation
1075.5710.44
2151.132986.532767.241796.09647.432506.18
1451.49
2412.19971.14

Regulation services
Gas
Climate Environment
regulation regulation purification
699.64375.93104.421159.11595.22177.521775.215294.30
1555.922453.967341.01
2078.04
2266.00
6787.56
2015.38
1472.38
4417.141336.63532.561399.28459.472057.155440.49
1796.091190.433153.60
1044.24
1984.06
3759.263759.26804.062391.315795.53

Support services
Nutrient cycle
Biodiversity
maintenance125.31135.75198.41
219.29167.08
1963.17229.732715.02208.852516.62135.751639.4652.21
584.77187.962276.44114.871326.18
187.968218.1773.10
2662.813.

Results

3.1

Spatial patterns of ES values in Beijing

Cultural services
Aesthetic
landscape
62.65
93.98
856.281190.43
1106.89720.53261.061002.47584.774939.26
1973.61

Hydrology
regulation
281.942840.33
3487.763665.284949.703498.201023.363989.00
2307.77
25301.94
106763.10

Total value
Total
4187.404062.09
18305.53
24111.50
23965.31
15893.33
5294.3020561.09
11935.66
54321.36
131166.99

The total ES value in Beijing is 664.256 billion yuan, with provision, regulation, support, andcultural services respectively accounting for 43.809, 457.353, 135.565, and 27.528 billion yuan
(Table 3). Specifically, Miyun District has the highest total ES value of 119.569 billion yuan,
followed by Huairou District with a total ES value of 99.583 billion yuan, then Yanqing Distinct,
Fangshan Distinct, and Mentougou District with total ES values of 89.072, 78.227, and 62.804
billion yuan, respectively (Fig. 2).
From the perspective of ES types, the value of provision services is highest in Miyun District
with a total value of 7.9584 billion yuan, with hydrology regulating services accounting for thelargest proportion. Miyun District has the largest water conservancy project in north China,
which is an important water source for the capital. Huairou District (6.1791 billion yuan) andYanqing District (5.804 billion yuan) have the next highest values of provision services. Miyun
District also has the highest value of regulation services with a total value of 86.0422 billion
yuan, with Huairou District and Yanqing District providing regulation services of 67.1605 and894

Sensors and Materials, Vol. 35, No. 3 (2023)

Table 3
ES values of administrative districts in Beijing (unit: 100 million yuan).
Provision services
Regulation services
Urban district
Food
Raw material
Gas
Climate Environment
Water supply
production
production
regulation regulation purification
Dongcheng
0.05
0.09
0.20
0.30
0.90
0.36
Xicheng
0.08
0.11
0.43
0.35
1.04
0.54
Chaoyang
1.34
2.05
3.21
6.76
19.34
7.20
Fengtai
1.01
1.62
2.10
5.30
15.17
5.36
Shijingshan
0.30
0.57
0.54
1.88
5.56
1.78
Haidian
1.88
3.01
3.66
9.89
28.32
9.82
Mentougou
8.26
17.44
10.56
57.52
170.3752.18
Fangshan
13.43
21.86
14.83
70.26
199.71
62.88
Dongzhou
6.80
7.13
10.12
21.49
55.53
21.35
Shunyi
7.75
8.52
8.13
25.61
66.27
23.07
Changping
7.72
13.52
10.54
44.33
127.27
40.97
Daxing
8.62
8.17
5.02
22.99
55.80
18.15
Huairou
15.19
27.77
18.84
90.18
261.7680.96
Pinggu
7.67
11.12
9.22
35.86
99.02
32.73
Miyun
17.53
26.61
35.44
85.86
243.78
86.91
Yanqing
16.04
25.64
16.36
81.10
230.63
71.53
Total
113.66175.22149.21559.681580.48515.77
Urban district
Dongcheng
Xicheng
Chaoyang
Fengtai
Shijingshan
HaidianMentougou
FangshanDongzhou
ShunyiChangping
DaxingHuairou
PingguMiyun
Yanqing
Total

Support services
Nutrient cycle
Soil conservation
maintenance
0.37
0.03
0.43
0.03
8.24
0.64
6.49
0.50
2.30
0.18
12.08
0.94
70.16
5.33
86.34
6.73
26.85
2.22
32.12
2.64
54.27
4.17
29.43
2.51
110.47
8.53
44.17
3.45
105.398.21
100.12
7.84
689.2253.95

Hydrology
regulation
2.52
5.39
41.15
26.44
6.52
46.08
143.74
195.67128.94103.96137.9163.32
238.70118.10443.87215.301917.60

Cultural services

Total value

Biodiversity

Aesthetic landscape

Total

0.37
0.47
7.85
6.04
2.13
11.23
64.15
76.51
22.97
26.55
49.27
21.86
99.34
39.09
97.28
87.37
612.48

0.18
0.24
3.66
2.78
0.96
5.14
28.33
34.03
10.78
12.11
22.00
9.82
44.09
17.54
44.80
38.80
275.285.38
9.11
101.4472.80
22.70
132.05628.04782.26314.19316.74511.96245.69995.83417.971195.69
890.726642.56

59.8561 billion yuan, respectively. Huairou District has the highest value of support services of
21.8342 billion, followed by Miyun District (21.0881 billion yuan) and Yanqing District (19.5323
billion yuan). Similarly, Miyun District provides 4.4801 billion yuan of cultural services,compared with 4.4091 and 3.8799 billion yuan for Huairou Distinct and Yanqing District,respectively.

Sensors and Materials, Vol. 35, No. 3 (2023)

895

Fig. 2. (Color online) Total ES value of administrative districts of Beijing.

3.2

Intensity of ES values in Beijing

The intensity of ES values represents the potential of ES in different districts of Beijing(Table 4). Miyun District has the highest intensity of ES values with an average ES value of537100 yuan/ha. The intensity of ES values is 469200 yuan/ha in Huairou District, followed by
Yanqing District, Pinggu District, and Mentougou District with values of 446400, 440800, and433600 yuan/ha, respectively (Fig. 3).
The highest intensity of provision services is 35700 yuan/ha in Miyun District, comparedwith 29500 and 29100 yuan/ha in Pinggu District and Huairou District, respectively. The highest
intensities of regulation services are 386500, 316500, and 301300 yuan/ha in Miyun District,Huairou District, and Pinggu District, respectively. Support services have the highest intensity
in Huairou District with a value of 102900 yuan/ha, with the value of 97900 and 96400 yuan/hain Yanqing Distinct and Mentougou District, respectively. The intensity of cultural services isrelatively high in Huairou Distinct (20800 yuan/ha), Miyun Distinct (20100 yuan/ha), andMentougou Distinct (19600 yuan/ha).

896

Sensors and Materials, Vol. 35, No. 3 (2023)

Table 4
Average ES values per unit area of each district in Beijing (unit: 104 yuan/ha).
Urban district
Dongcheng
Xicheng
Chaoyang
Fengtai
Shijingshan
HaidianMentougou
FangshanDongzhou
ShunyiChangping
DaxingHuairou
PingguMiyun
Yanqing
Urban district
Dongcheng
Xicheng
Chaoyang
Fengtai
Shijingshan
HaidianMentougou
FangshanDongzhou
ShunyiChangping
DaxingHuairou
PingguMiyun
Yanqing

3.3

Food
production
0.13
0.16
0.29
0.33
0.35
0.44
0.57
0.67
0.75
0.77
0.57
0.83
0.72
0.81
0.79
0.80

Provision services
Raw material
Water supply
production
0.22
0.48
0.21
0.86
0.44
0.69
0.53
0.69
0.68
0.64
0.70
0.85
1.20
0.73
1.10
0.74
0.79
1.12
0.84
0.81
1.01
0.78
0.79
0.48
1.31
0.89
1.17
0.97
1.20
1.59
1.28
0.82

Soil conservation
0.88
0.84
1.77
2.12
2.73
2.80
4.84
4.33
2.96
3.18
4.04
2.84
5.21
4.66
4.73
5.02

Support services
Nutrient cycle
maintenance
0.07
0.06
0.14
0.16
0.21
0.22
0.37
0.34
0.25
0.26
0.31
0.24
0.40
0.36
0.37
0.39

Gas
regulation
0.73
0.69
1.46
1.73
2.23
2.30
3.97
3.52
2.37
2.54
3.30
2.22
4.25
3.78
3.86
4.06

Regulation services
ClimateEnvironment
regulation
purification
2.16
0.86
2.06
1.06
4.16
1.55
4.96
1.75
6.59
2.11
6.58
2.28
11.76
3.60
10.01
3.15
6.13
2.36
6.56
2.28
9.48
3.05
5.38
1.75
12.33
3.81
10.44
3.45
10.95
3.90
11.56
3.58

Hydrology
regulation
6.00
10.66
8.86
8.65
7.73
10.70
9.92
9.81
14.24
10.29
10.27
6.11
11.25
12.46
19.94
10.79

Cultural services
Biodiversity

Aesthetic landscape

0.88
0.92
1.69
1.98
2.52
2.61
4.43
3.83
2.54
2.63
3.67
2.11
4.68
4.12
4.37
4.38

0.42
0.48
0.79
0.91
1.14
1.19
1.96
1.71
1.19
1.20
1.64
0.95
2.08
1.85
2.01
1.94

ES values for land use types

The ES values are analyzed for different land use types (Table 5). Broad-leaved shrub forests
provide the highest ES value in Beijing with an average of 153.206 billion yuan. Broad-leaved
forests account for a total value of 131.875 billion yuan. The next highest ES values are 98.407
billion yuan for rivers, 67.128 billion yuan for orchards, and 58.150 billion yuan for mixed
coniferous and broad-leaved forests, respectively. In total, Beijing’s forest contributes the highest
ES value, with a total value of 446.577 billion yuan. Water bodies have a total ES value of100.042 billion yuan, gardens have a total value of 80.339 billion yuan, and grassland has a total
value of 22.908 billion yuan (Fig. 4).

Sensors and Materials, Vol. 35, No. 3 (2023)

Fig. 3.

897(Color online) Intensities of ES values in different districts of Beijing (unit: 104 yuan/ha).

Table 5
ES values of various land types in Beijing (unit: 100 million yuan).
Primary class
Arable land

Second class
Dry land
Paddy field
Arbor

Bush
Woodland

Mixed arbor and shrub forest
Sparse forest
Green woodland
Young plantation
Sparse thickets
Bamboo forest
Natural grass

Grassland
Artificial grass

Orchard

GardenMulberry Garden
Flowerbed
Nursery
Other fields

Water

CanalsThird class
Dry land
Paddy field
Broadleaf forest
Coniferous and broad-leaved forest
Coniferous forest
Broadleaf shrubs
Coniferous and broad-leaved shrubs
Coniferous forest
Mixed arbor and shrub forest
Sparse forest
Green woodland
Young plantation
Sparse thickets
Bamboo forest
High coverage grass
Medium coverage grass
Low coverage grass
Slope protection shrub and grass
Green grass
Grassland
Other artificial grass
Herb orchard
Tree bush orchard
Fujimoto Orchard
Mulberry Garden
Flowerbed
Nursery
Other herb orchards
Other tree and shrub orchards
Other vine orchards
River
Canal

Total area (ha)3414236.88
23072.00
5502751.56
2411725.44
2440639.63
9639641.88
822653.06
91994.44884543.63
1706.63398922.19
897144.81
405.94555.94
1579812.31
152548.94
58071.25
44424.88
205849.44
1466.31174909.31
558.813264808.75
63838.13
7378.75
17612.00
546811.13
1171.133119.69
803.38750240.50
12463.63

ES value
142.970.94
1318.75
581.50446.771532.06
130.75
14.62
213.280.31
63.40
164.230.06
0.03
188.56
8.08
3.07
9.13
10.90
0.08
9.26
0.11
671.28
13.13
1.77
3.62
112.43
0.24
0.64
0.17
984.07
16.35

898

Sensors and Materials, Vol. 35, No. 3 (2023)

Fig. 4. (Color online) ES values for land use types in Beijing. The classes with a percentage below 0.1% are notshown.

4.

Discussion

4.1

Implications for urban planningThis study shows that the total ES value varies greatly in different districts of Beijing. The
intensities of ES values tend to increase from urban areas to suburban areas. This is mainly
because the vegetation and water in the suburbs are well preserved, while the urban area is more
greatly affected by anthropogenic activities, with low vegetation coverage and a large area ofimpervious surfaces in the core regions of Beijing. The results of this study are consistent withthose of other studies in terms of ecosystem type.(26) The main ecological assets of Beijing’s
landscape are forests and water bodies, where the service types are dominated by regulation andsupport services. Our results have some research implications in urban planning andconstruction. First, we should pay attention to the potential ES values of forest land. Forest
provides various ES values for urban residents, including climate regulation, water purification,and recreational activities. Such regions should be strictly protected. Second, the cultural
services should be more strongly linked with urban residents: cultural services are currently
mainly located outside the core city, and infrastructure should be improved to link the urban andrural regions. Finally, ecological compensation should be conducted between different districts
during urban planning; Miyun District provides the highest ES value and thus should becompensated by other districts.

Sensors and Materials, Vol. 35, No. 3 (2023)

4.2

899

Limitations and improvements

This study provides the spatial patterns of ES values in Beijing. The results are important for
urban planning to achieve sustainable city development. However, this study has some
limitations, which should be addressed in the future. First, land use data were employed to
calculate the ES values in one year. The update of land use data can provide new insights into ES
values for urban ecological management. Second, the flow of ES among different districts may
have important implications for urban planning. More data on the material transfer and trade
between the core urban area of Beijing and the surrounding areas are required. Finally, thedemand for ES, which has a strong leading effect on the future supply of ES, was not consideredin this study. The demand for ES should be examined using more statistical and survey data to
determine the preferences and requirements of citizens, enabling the government to deploy andintegrate resources effectively.

5.

Conclusions

We quantified the ES values of Beijing based on land use and socioeconomic statistical data.
The results are conductive for clarifying the ecological status and resource potential of each
district. We found that the ES value in Beijing was lower in urban areas and higher in suburbs.
Miyun District has the highest ES value, whereas Dongcheng District has the lowest ES value,with a difference of 4.087 × 104 yuan/ha between them. In Beijing, the regulation service value
is highest, while the support service value is lowest. Specifically, the hydrology regulation andclimate regulation services have the highest values, with total values of 1917.6 and 158.048
billion yuan, respectively. Forest landscape is the main source of ES values, accounting for67.2% of the total value. Cultivated land and grassland account for 3.4 and 2.2% of total ES
value, respectively. The results will be useful for urban planners and policy makers to prioritize
the regions for protecting natural ecosystem.

Acknowledgments
This work was supported by the Innovative Methods Project of the Ministry of Science andTechnology of China (grant No. 2020IM020505).

References
1 D. Gretchen, P. Sandra, S. B. Kamaljit, and K. Les: Nature’s Services: Societal Dependence on Natural
Ecosystems (Washington D.C., Island Press, 1997).
2 H. Druckenmiller: Nat. Clim. Chang. 12 (2022) 596. https://doi.org/10.1038/s41558-022-01362-0
3 R. Costanza, R. d’Arge, R. de Groot, S. Farber, M. Grasso, B. Hannon, K. Limburg, S. Naeem, R. V. O’Neill,
J. Paruelo, R. G. Raskin, P. Sutton, and M. van den Belt: Nature 387 (1997) 253. https://doi.org/10.1038/387253a0
4 T. Elliot, J. A. Torres-Matallana, B. Goldstein, J. Babí Almenar, E. Gómez-Baggethun, V. Proença, andB. Rugani: Renewable Sustainable Energy Rev. 162 (2022) 112418. https://doi.org/10.1016/j.rser.2022.112418
5 E. Gómez-Baggethun and D. N. Bar ton: Ecol. Econ. 86 (2013) 235. https://doi.org/10.1016/j.
ecolecon.2012.08.019

900

Sensors and Materials, Vol. 35, No. 3 (2023)

6 K. T. Lourdes, P. Hamel, C. N. Gibbins, R. Sanusi, B. Azhar, and A. M. Lechner: Landscape Urban Plann. 226(2022)104500. https://doi.org/10.1016/j.landurbplan.2022.104500
7 X. Chen, H. Zhang, W. Chen, and G. Huang: Sci. Total Environ. 762 (2021) 143144. https://doi.org/10.1016/j.
scitotenv.2020.143144
8 M. A. Board: Millennium Ecosystem Assessment 13 (Washington, DC, New Island, 2005) 520.
9 J. L. Rolando, C. Turin, D. A. Ramírez, V. Mares, J. Monerris, and R. Quiroz: Agric. Ecosyst. Environ. 236(2017) 221. https://doi.org/10.1016/j.agee.2016.12.010
10 L. V. Pinto, M. Inácio, C. S. S. Ferreira, A. D. Ferreira, and P. Pereira: Sustainable Cities Soc. 85 (2022)
104072. https://doi.org/10.1016/j.scs.2022.104072
11 Y. Wang, Q. Chang, P. Fan, and X. Shi: Environ. Res. 213 (2022) 113664. https://doi.org/10.1016/j.
envres.2022.113664
12 K. M. A. Chan, T. Satterfield, and J. Goldstein: Ecol. Econ. 74 (2012) 8. https://doi.org/10.1016/j.
ecolecon.2011.11.011
13 G. D. Xie, C. X. Zhang, C. S. Zhang, Y. Xiao, and C. X. Lu: Resour. Sci. 37 (2015) 1740.
14 K. Wilson and D. A. Willette: Urban Ecosyst. 25 (2022) 1787. https://doi.org/10.1007/s11252-022-01267-y
15 J. Li, H. Chen, C. Zhang, and T. Pan: Peer J. 7 (2019) e7665. https://doi.org/10.7717/peerj.7665
16 R. Liu, L. Li, L. Guo, L. Jiao, Y. Wang, L. Cao, and Y. Wang: Environ. Monit. Assess. 194 (2022) 434. https://doi.org/10.1007/s10661-022-10086-9
17 W. Chen, T. Zhou, and J. Liang: Environ. Sci. Pollut. Res 30 (2023) 3416. https://doi.org/10.1007/s11356-02222370-918 J. Han, Z. Hu, P. Wang, Z. Yan, G. Li, Y. Zhang, and T. Zhou: J. Cleaner Prod. 363 (2022) 132602. https://doi.
org/10.1016/j.jclepro.2022.132602
19 M. Lu, Y. Zhang, F. Liang, and Y. Wu: Land 11 (2022) 1168. https://doi.org/10.3390/land11081168
20 P. Kang, W. Chen, Y. Hou, and Y. Li: Sci. Total Environ. 636 (2018) 1442. https://doi.org/10.1016/j.
scitotenv.2018.04.427
21 D. R. Richards, R. N. Belcher, L. R. Carrasco, P. J. Edwards, S. Fatichi, P. Hamel, M. Masoudi, M. J. McDonnell,
N. Peleg, and M. C. Stanley: One Earth 5 (2022) 522. https://doi.org/10.1016/j.oneear.2022.04.006
22 J. Tratalos, R. A. Fuller, P. H. Warren, R. G. Davies, and K. J. Gaston: Landscape Urban Plann. 83 (2007) 308.
https://doi.org/10.1016/j.landurbplan.2007.05.003
23 S. Tian, W. Wu, Z. Shen, J. Wang, X. Liu, L. Li, X. Li, X. Liu, and H. Chen: J. Environ. Manage. 319 (2022)
115774. https://doi.org/10.1016/j.jenvman.2022.115774
24 K. A. García-Pardo, D. Moreno-Rangel, S. Domínguez-Amarillo, and J. R. García-Chávez: Urban For. Urban
Greening 74 (2022) 127636. https://doi.org/10.1016/j.ufug.2022.127636
25 T. McPhearson, E. M. Cook, M. Berbés-Blázquez, C. Cheng, N. B. Grimm, E. Andersson, O. Barbosa,D. G. Chandler, H. Chang, M. V. Chester, D. L. Childers, S. R. Elser, N. Frantzeskaki, Z. Grabowski,
P. Groffman, R. L. Hale, D. M. Iwaniec, N. Kabisch, C. Kennedy, S. A. Markolf, A. M. Matsler, L. E. McPhillips,
T. R. Miller, T. A. Muñoz-Erickson, E. Rosi, and T. G. Troxler: One Earth 5 (2022) 505. https://doi.org/10.1016/j.
oneear.2022.04.007
26 M. Zhao and Y. Shan: Environ. Prot. Sci. 5 (2022) 32. https://doi.org/10.16803/j.cnki.issn.1004-6216.2021120030