Please wait a minute...
Shopping Cart Email List Chinese
Advanced Search Fig/Tab
Frontiers of Environmental Science & Engineering

ISSN 2095-2201

ISSN 2095-221X(Online)

CN 10-1013/X

Postal Subscription Code 80-973

2018 Impact Factor: 3.883

Featured Articles  |  Most Downloaded  |  Most Reading
Online Submission Subscribe to Our RSS Content Feed
Front. Environ. Sci. Eng.    2024, Vol. 18 Issue (8) : 102    https://doi.org/10.1007/s11783-024-1862-x
Analyzing the spatiotemporal evolution and driving forces of gross ecosystem product in the upper reaches of the Chaobai River Basin
Jiacheng Li1,2, Qi Han1,2, Liqiu Zhang1,2, Li Feng1,2(), Guihuan Liu3()
1. Beijing Key Laboratory for Source Control Technology of Water Pollution, College of Environmental Science and Engineering, Beijing Forestry University, Beijing 100083, China
2. Engineering Research Center for Water Pollution Source Control & Eco-remediation, College of Environmental Science and Engineering, Beijing Forestry University, Beijing 100083, China
3. Center of Eco-compensation, Chinese Academy of Environmental Planning, Ministry of Ecology of Environment, Beijing 100041, China
 Download: PDF(5305 KB)   HTML
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

● From 2005 to 2020, GEP in the Chaobai River’s upper reaches increased by 58%.

● GEP changes in the Chaobai River’s upper reaches exhibited spatial differentiation.

● POP, GDP, and LD were the main driving force factors.

● The interactions between different factors had higher impact than single factor.

The Chaobai River Basin, which is a crucial ecological barrier and primary water source area within the Beijing–Tianjin–Hebei region, possesses substantial ecological significance. The gross ecosystem product (GEP) in the Chaobai River Basin is a reflection of ecosystem conditions and quantifies nature’s contributions to humanity, which provides a basis for basin ecosystem service management and decision-making. This study investigated the spatiotemporal evolution of GEP in the upper Chaobai River Basin and explored the driving factors influencing GEP spatial differentiation. Ecosystem patterns from 2005 to 2020 were analyzed, and GEP was calculated for 2005, 2010, 2015, and 2020. The driving factors influencing GEP spatial differentiation were identified using the optimal parameter-based geographical detector (OPGD) model. The key findings are as follows: (1) From 2005 to 2020, the main ecosystem types were forest, grassland, and agriculture. Urban areas experienced significant changes, and conversions mainly occurred among urban, water, grassland and agricultural ecosystems. (2) Temporally, the GEP in the basin increased from 2005 to 2020, with regulation services dominating. At the county (district) scale, GEP exhibited a north-west-high and south-east-low pattern, showing spatial differences between per-unit-area GEP and county (district) GEP, while the spatial variations in per capita GEP and county (district) GEP were similar. (3) Differences in the spatial distribution of GEP were influenced by regional natural geographical and socioeconomic factors. Among these factors, gross domestic product, population density, and land-use degree density contributed significantly. Interactions among different driving forces noticeably impacted GEP spatial differentiation. These findings underscore the necessity of incorporating factors such as population density and the intensity of land-use development into ecosystem management decision-making processes in the upper reaches of the Chaobai River Basin. Future policies should be devised to regulate human activities, thereby ensuring the stability and enhancement of GEP.
Keywords Ecosystem pattern      Gross ecosystem product (GEP)      Spatiotemporal evolution      Optimal parameter-based geographical detector (OPGD)      Chaobai River Basin     
Corresponding Author(s): Li Feng,Guihuan Liu   
Issue Date: 11 July 2024
 Cite this article:   
Jiacheng Li,Qi Han,Liqiu Zhang, et al. Analyzing the spatiotemporal evolution and driving forces of gross ecosystem product in the upper reaches of the Chaobai River Basin[J]. Front. Environ. Sci. Eng., 2024, 18(8): 102.
 URL:  
https://academic.hep.com.cn/fese/EN/10.1007/s11783-024-1862-x
https://academic.hep.com.cn/fese/EN/Y2024/V18/I8/102
Fig.1  Study area.
Fig.2  The research workflow of this study.
First-level indicator Second-level indicator Abbreviation
Material products Agricultural products AG
Forestry products FO
Animal husbandry products AH
Fishery products FI
Regulating services Water conservation WC
Soil conservation SC
Carbon sequestration CS
Oxygen release OR
Water purification WP
Air purification AP
Climate regulation CR
Flood regulation and storage FR
Cultural services Ecological tourism ET
Landscape aesthetics LA
Tab.1  Indicator system for GEP accounting in the basin
Category Indicator Abbreviation
Natural Geographical factors Remote Sensing Ecological Index RSEI
Fractional Vegetation Cover FVC
Precipitation PRE
Digital Elevation Model DEM
Temperature TEM
Slope SL
Net Primary Productivity NPP
Socioeconomic factors Proportion of Urban Land PL
Gross Domestic Product GDP
Population Density POP
Nighttime Light NL
Land-use Degree Density LD
Tab.2  Social–ecological drivers
Ecosystem types 2005 2010 2015 2020 2005–2020Change (km2)
Area (km2) Proportion (%) Area (km2) Proportion (%) Area (km2) Proportion (%) Area (km2) Proportion (%)
Agriculture 7491.2 24.9 7364.9 24.5 7336.7 24.4 7294.2 24.3 –197
Forest 13599.9 45.2 13704.6 45.6 13688.1 45.5 13681.9 45.5 82
Grassland 7485.9 24.9 7321.9 24.4 7299.9 24.3 7274.1 24.2 –211.8
Water 949 3.2 822.7 2.7 827.3 2.8 851.0 2.8 –98
Urban 478.6 1.6 797.5 2.7 859.5 2.9 898.8 3.0 420.2
Bare land 51.7 0.2 44.7 0.1 44.4 0.1 54.2 0.2 2.5
Tab.3  Ecosystem area and proportion in the basin from 2005 to 2020
Fig.3  Ecosystem type transitions from 2005 to 2020.
Ecosystemtypes 2005–2010 2010–2015 2015–2020 2005–2020
Area (km2) Dynamicdegree (%) Area (km2) Dynamicdegree (%) Area (km2) Dynamicdegree (%) Area (km2) Dynamicdegree (%)
Agriculture –126.3 –0.34 –28.2 –0.08 –42.5 –0.12 –197 –0.18
Forest +104.7 0.15 –16.5 –0.02 –6.2 –0.01 +82 0.04
Grassland –164 –0.44 –22 –0.06 –25.8 –0.07 –211.8 –0.19
Water –126.3 –2.66 +4.6 0.11 +23.7 0.57 –98 –0.69
Urban +318.9 13.33 +62 1.55 +39.3 0.91 +420.2 5.85
Bare land –7 –2.71 –0.3 –0.13 +9.8 4.41 +2.5 0.32
Tab.4  Changes in ecosystem area and dynamic degree
Fig.4  GEP accounting results for 2005, 2010, 2015, and 2020.
Fig.5  The values of various services in GEP for 2005, 2010, 2015, and 2020.
Fig.6  GEP of each county (district) in the basin from 2005 to 2020.
Fig.7  Factor detection for social-ecological driving factors from 2005 to 2020. (a) 2005; (b) 2010; (c) 2015; and (d) 2020.
Category Indicator Average explanatory power
Natural geographical RSEI 0.002
FVC 0.045
PRE 0.037
DEM 0.008
TEM 0.043
SL 0.001
NPP 0.032
Socioeconomic PL 0.004
GDP 0.104
POP 0.071
NL 0.002
LD 0.115
Tab.5  Average explanatory power of indicators, 2005–2020
Fig.8  Interaction detection results for social-ecological driving factors from 2005 to 2020.
1 J M Alatalo, A K Jägerbrand, J Dai, M D Mollazehi, A S G Abdel-Salam, R Pandey, U Molau. (2021). Effects of ambient climate and three warming treatments on fruit production in an alpine, subarctic meadow community. American Journal of Botany, 108(3): 411–422
https://doi.org/10.1002/ajb2.1631
2 E Allan, P Manning, F Alt, J Binkenstein, S Blaser, N Blüthgen, S Böhm, F Grassein, N Hölzel, V H Klaus. et al.. (2015). Land use intensification alters ecosystem multifunctionality via loss of biodiversity and changes to functional composition. Ecology Letters, 18(8): 834–843
https://doi.org/10.1111/ele.12469
3 L C Braat, R de Groot. (2012). The ecosystem services agenda: bridging the worlds of natural science and economics, conservation and development, and public and private policy. Ecosystem Services, 1(1): 4–15
https://doi.org/10.1016/j.ecoser.2012.07.011
4 R Buckley. (2011). The economics of ecosystems and biodiversity: ecological and economic foundations. Austral Ecology, 36(6): e34–e35
https://doi.org/10.1111/j.1442-9993.2011.02253.x
5 Q Chen, R E Mcroberts, C Wang, P J Radtke. (2016). Forest aboveground biomass mapping and estimation across multiple spatial scales using model-based inference. Remote Sensing of Environment, 184: 350–360
https://doi.org/10.1016/j.rse.2016.07.023
6 R Costanza, Groot R de, L Braat, I Kubiszewski, L Fioramonti, P Sutton, S Farber, M Grasso (2017). Twenty years of ecosystem services: how far have we come and how far do we still need to go? Ecosystem Services, 28: 1–16 10.1016/j.ecoser.2017.09.008
7 R Costanza, R De Groot, P Sutton, S Van Der Ploeg, S J Anderson, I Kubiszewski, S Farber, R K Turner. (2014). Changes in the global value of ecosystem services. Global Environmental Change, 26: 152–158
https://doi.org/10.1016/j.gloenvcha.2014.04.002
8 U N S Division (2017). System of Environmental-Economic Accounting 2012. Geneva: World Bank Publications
9 L Fang, L Wang, W Chen, J Sun, Q Cao, S Wang, L Wang. (2021). Identifying the impacts of natural and human factors on ecosystem service in the Yangtze and Yellow River Basins. Journal of Cleaner Production, 314: 127995
https://doi.org/10.1016/j.jclepro.2021.127995
10 J Gao, L Zuo. (2021). Revealing ecosystem services relationships and their driving factors for five basins of Beijing. Journal of Geographical Sciences, 31(1): 111–129
https://doi.org/10.1007/s11442-021-1835-y
11 Y Hu, J Gong, X Li, L Song, Z Zhang, S Zhang, W Zhang, J Dong, X Dong. (2023). Ecological security assessment and ecological management zoning based on ecosystem services in the West Liao River Basin. Ecological Engineering, 192: 106973
https://doi.org/10.1016/j.ecoleng.2023.106973
12 C Hao, S Wu, W Zhang, Y Chen, Y Ren, X Chen, H Wang, L Zhang. (2022). A critical review of Gross ecosystem product accounting in China: status quo, problems and future directions. Journal of Environmental Management, 322: 115995
https://doi.org/10.1016/j.jenvman.2022.115995
13 S S Hasan, L Zhen, M G Miah, T Ahamed, A Samie. (2020). Impact of land use change on ecosystem services: a review. Environmental Development, 34: 100527
https://doi.org/10.1016/j.envdev.2020.100527
14 Y Hou, Y Chen, Z Li, Y Li, F Sun, S Zhang, C Wang, M Feng. (2022). Land use dynamic changes in an arid inland river basin based on multi-scenario simulation. Remote Sensing, 14(12): 2797
https://doi.org/10.3390/rs14122797
15 T Hua, W Zhao, F Cherubini, X Hu, P Pereira. (2021). Sensitivity and future exposure of ecosystem services to climate change on the Tibetan Plateau of China. Landscape Ecology, 36(12): 3451–3471
https://doi.org/10.1007/s10980-021-01320-9
16 H Jiang, W Wu, J Wang, W Yang, Y Gao, Y Duan, G Ma, C Wu, J Shao. (2021). Mapping global value of terrestrial ecosystem services by countries. Ecosystem Services, 52: 101361
https://doi.org/10.1016/j.ecoser.2021.101361
17 J Klugman, F Rodríguez, H J Choi. (2011). The HDI 2010: new controversies, old critiques. Journal of Economic Inequality, 9(2): 249–288
https://doi.org/10.1007/s10888-011-9178-z
18 D Li, W Cao, Y Dou, S Wu, J Liu, S Li. (2022a). Non-linear effects of natural and anthropogenic drivers on ecosystem services: integrating thresholds into conservation planning. Journal of Environmental Management, 321: 116047
https://doi.org/10.1016/j.jenvman.2022.116047
19 W Li, L Wang, X Yang, T Liang, Q Zhang, X Liao, J R White, J Rinklebe. (2022b). Interactive influences of meteorological and socioeconomic factors on ecosystem service values in a river basin with different geomorphic features. Science of the Total Environment, 829: 154595
https://doi.org/10.1016/j.scitotenv.2022.154595
20 X Li, X Yu, K Wu, Z Feng, Y Liu, X Li. (2021). Land-use zoning management to protecting the Regional Key Ecosystem Services: a case study in the city belt along the Chaobai River, China. Science of the Total Environment, 762: 143167
https://doi.org/10.1016/j.scitotenv.2020.143167
21 Y Li, W Liu, Q Feng, M Zhu, L Yang, J Zhang, X Yin. (2023). The role of land use change in affecting ecosystem services and the ecological security pattern of the Hexi Regions, Northwest China. Science of the Total Environment, 855: 158940
https://doi.org/10.1016/j.scitotenv.2022.158940
22 Y Liu, X Yuan, J Li, K Qian, W Yan, X Yang, X Ma. (2023a). Trade-offs and synergistic relationships of ecosystem services under land use change in Xinjiang from 1990 to 2020: a Bayesian network analysis. Science of the Total Environment, 858: 160015
https://doi.org/10.1016/j.scitotenv.2022.160015
23 Z Liu, S Wang, C Fang. (2023b). Spatiotemporal evolution and influencing mechanism of ecosystem service value in the Guangdong-Hong Kong-Macao Greater Bay Area. Journal of Geographical Sciences, 33(6): 1226–1244
https://doi.org/10.1007/s11442-023-2127-5
24 S J Ma, R S Wang (1984). The social-economic-natural complex ecosystem. Acta Ecologica Sinica, 4(1): 1–9 (in Chinese)
25 Millennium Ecosystem Assessment (2005). Ecosystems and Human Well-Being: Synthesis. Washington, DC: Island Press
26 H T Odum. (1988). Self-Organization, transformity, and information. Science, 242(4882): 1132–1139
https://doi.org/10.1126/science.242.4882.1132
27 Z Ouyang, C Song, H Zheng, S Polasky, Y Xiao, I J Bateman, J Liu, M Ruckelshaus, F Shi, Y Xiao. et al.. (2020). Using gross ecosystem product (GEP) to value nature in decision making. Proceedings of the National Academy of Sciences of the United States of America, 117(25): 14593–14601
https://doi.org/10.1073/pnas.1911439117
28 Z Ouyang, H Zheng, Y Xiao, S Polasky, J Liu, W Xu, Q Wang, L Zhang, Y Xiao, E Rao. et al.. (2016). Improvements in ecosystem services from investments in natural capital. Science, 352(6292): 1455–1459
https://doi.org/10.1126/science.aaf2295
29 Z OuyangC ZhuG YangW XuH Zheng Y ZhangY Xiao (2013). Gross ecosystem product: concept, accounting framework and case study. Acta Ecologica Sinica, 33(21): 6747-6761 (in Chinese)
https://doi.org/10.5846/stxb201310092428
30 C Qin, Q Xue, J Zhang, L Lu, S Xiong, Y Xiao, X Zhang, J Wang. (2024). A beautiful China initiative towards the harmony between humanity and the nature. Frontiers of Environmental Science & Engineering, 18(6): 71
https://doi.org/10.1007/s11783-024-1831-4
31 P Shi, Z Li, P Li, Y Zhang, B Li. (2021). Trade-offs among ecosystem services after vegetation restoration in China’s loess plateau. Natural Resources Research, 30(3): 2703–2713
https://doi.org/10.1007/s11053-021-09841-5
32 Y Song, J Wang, Y Ge, C Xu. (2020). An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: cases with different types of spatial data. GIScience & Remote Sensing, 57(5): 593–610
https://doi.org/10.1080/15481603.2020.1760434
33 C Song, H S He, K Liu, H Du, J Krohn. (2023). Impact of historical pattern of human activities and natural environment on wetland in Heilongjiang River Basin. Frontiers of Environmental Science & Engineering, 17(12): 151
https://doi.org/10.1007/s11783-023-1751-8
34 J F Wang, C D Xu (2017). Geodetector: principle and prospective. Acta Geographica Sinica, 72(1): 116–134 (in Chinese)
35 J F Wang, T L Zhang, B J Fu. (2016). A measure of spatial stratified heterogeneity. Ecological Indicators, 67: 250–256
https://doi.org/10.1016/j.ecolind.2016.02.052
36 H Xia, S Yuan, A V Prishchepov. (2023). Spatial-temporal heterogeneity of ecosystem service interactions and their social-ecological drivers: implications for spatial planning and management. Resources, Conservation and Recycling, 189: 106767
https://doi.org/10.1016/j.resconrec.2022.106767
37 G Xie, C Zhang, L Zhen, L Zhang. (2017). Dynamic changes in the value of China’s ecosystem services. Ecosystem Services, 26: 146–154
https://doi.org/10.1016/j.ecoser.2017.06.010
38 J Zeng, T Zhou, E Tan, Y Xu, Q Lin, Y Zhang, X Wu, J Zhang, X Liu, Q Zhang (2024). Evaluate the differences in carbon sink contribution of different ecological engineering projects. Carbon Research, 3(10) 10.1007/s44246-024-00105-4
39 J Zhang, C Liu, H Wang, X Liu, Q Qiao. (2023). Temporal–spatial dynamics of typical ecosystem services in the Chaobai River basin in the Beijing–Tianjin–Hebei urban megaregion. Frontiers in Ecology and Evolution, 11: 1201120
https://doi.org/10.3389/fevo.2023.1201120
40 H Zheng, T Wu, Z Ouyang, S Polasky, M Ruckelshaus, L Wang, Y Xiao, X Gao, C Li, G C Daily. (2023). Gross ecosystem product (GEP): quantifying nature for environmental and economic policy innovation. Ambio, 52(12): 1952–1967
https://doi.org/10.1007/s13280-023-01948-8
41 Z Zou, T Wu, Y Xiao, C Song, K Wang, Z Ouyang. (2020). Valuing natural capital amidst rapid urbanization: assessing the gross ecosystem product (GEP) of China’s ‘Chang–Zhu–Tan’ megacity. Environmental Research Letters, 15(12): 124019
https://doi.org/10.1088/1748-9326/abc2f8
[1] FSE-24041-OF-LJC_suppl_1 Download
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed