Analyzing the spatiotemporal evolution and driving forces of gross ecosystem product in the upper reaches of the Chaobai River Basin
Jiacheng Li, Qi Han, Liqiu Zhang, Li Feng, Guihuan Liu
Analyzing the spatiotemporal evolution and driving forces of gross ecosystem product in the upper reaches of the Chaobai River Basin
● 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.
Ecosystem pattern / Gross ecosystem product (GEP) / Spatiotemporal evolution / Optimal parameter-based geographical detector (OPGD) / Chaobai River Basin
[1] |
Alatalo J M, Jägerbrand A K, Dai J, Mollazehi M D, Abdel-Salam A S G, Pandey R, Molau U. (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
CrossRef
Google scholar
|
[2] |
Allan E, Manning P, Alt F, Binkenstein J, Blaser S, Blüthgen N, Böhm S, Grassein F, Hölzel N, Klaus V H.
CrossRef
Google scholar
|
[3] |
Braat L C, de Groot R. (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
CrossRef
Google scholar
|
[4] |
Buckley R. (2011). The economics of ecosystems and biodiversity: ecological and economic foundations. Austral Ecology, 36(6): e34–e35
CrossRef
Google scholar
|
[5] |
Chen Q, Mcroberts R E, Wang C, Radtke P J. (2016). Forest aboveground biomass mapping and estimation across multiple spatial scales using model-based inference. Remote Sensing of Environment, 184: 350–360
CrossRef
Google scholar
|
[6] |
CostanzaR, de Groot R, BraatL, KubiszewskiI, Fioramonti L, SuttonP, FarberS, GrassoM (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] |
Costanza R, De Groot R, Sutton P, Van Der Ploeg S, Anderson S J, Kubiszewski I, Farber S, Turner R K. (2014). Changes in the global value of ecosystem services. Global Environmental Change, 26: 152–158
CrossRef
Google scholar
|
[8] |
DivisionU N S (2017). System of Environmental-Economic Accounting 2012. Geneva: World Bank Publications
|
[9] |
Fang L, Wang L, Chen W, Sun J, Cao Q, Wang S, Wang L. (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
CrossRef
Google scholar
|
[10] |
Gao J, Zuo L. (2021). Revealing ecosystem services relationships and their driving factors for five basins of Beijing. Journal of Geographical Sciences, 31(1): 111–129
CrossRef
Google scholar
|
[11] |
Hu Y, Gong J, Li X, Song L, Zhang Z, Zhang S, Zhang W, Dong J, Dong X. (2023). Ecological security assessment and ecological management zoning based on ecosystem services in the West Liao River Basin. Ecological Engineering, 192: 106973
CrossRef
Google scholar
|
[12] |
Hao C, Wu S, Zhang W, Chen Y, Ren Y, Chen X, Wang H, Zhang L. (2022). A critical review of Gross ecosystem product accounting in China: status quo, problems and future directions. Journal of Environmental Management, 322: 115995
CrossRef
Google scholar
|
[13] |
Hasan S S, Zhen L, Miah M G, Ahamed T, Samie A. (2020). Impact of land use change on ecosystem services: a review. Environmental Development, 34: 100527
CrossRef
Google scholar
|
[14] |
Hou Y, Chen Y, Li Z, Li Y, Sun F, Zhang S, Wang C, Feng M. (2022). Land use dynamic changes in an arid inland river basin based on multi-scenario simulation. Remote Sensing, 14(12): 2797
CrossRef
Google scholar
|
[15] |
Hua T, Zhao W, Cherubini F, Hu X, Pereira P. (2021). Sensitivity and future exposure of ecosystem services to climate change on the Tibetan Plateau of China. Landscape Ecology, 36(12): 3451–3471
CrossRef
Google scholar
|
[16] |
Jiang H, Wu W, Wang J, Yang W, Gao Y, Duan Y, Ma G, Wu C, Shao J. (2021). Mapping global value of terrestrial ecosystem services by countries. Ecosystem Services, 52: 101361
CrossRef
Google scholar
|
[17] |
Klugman J, Rodríguez F, Choi H J. (2011). The HDI 2010: new controversies, old critiques. Journal of Economic Inequality, 9(2): 249–288
CrossRef
Google scholar
|
[18] |
Li D, Cao W, Dou Y, Wu S, Liu J, Li S. (2022a). Non-linear effects of natural and anthropogenic drivers on ecosystem services: integrating thresholds into conservation planning. Journal of Environmental Management, 321: 116047
CrossRef
Google scholar
|
[19] |
Li W, Wang L, Yang X, Liang T, Zhang Q, Liao X, White J R, Rinklebe J. (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
CrossRef
Google scholar
|
[20] |
Li X, Yu X, Wu K, Feng Z, Liu Y, Li X. (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
CrossRef
Google scholar
|
[21] |
Li Y, Liu W, Feng Q, Zhu M, Yang L, Zhang J, Yin X. (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
CrossRef
Google scholar
|
[22] |
Liu Y, Yuan X, Li J, Qian K, Yan W, Yang X, Ma X. (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
CrossRef
Google scholar
|
[23] |
Liu Z, Wang S, Fang C. (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
CrossRef
Google scholar
|
[24] |
MaS J, Wang R S (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] |
Odum H T. (1988). Self-Organization, transformity, and information. Science, 242(4882): 1132–1139
CrossRef
Google scholar
|
[27] |
Ouyang Z, Song C, Zheng H, Polasky S, Xiao Y, Bateman I J, Liu J, Ruckelshaus M, Shi F, Xiao Y.
CrossRef
Google scholar
|
[28] |
Ouyang Z, Zheng H, Xiao Y, Polasky S, Liu J, Xu W, Wang Q, Zhang L, Xiao Y, Rao E.
CrossRef
Google scholar
|
[29] |
OuyangZZhu CYangGXuWZhengH ZhangYXiao Y (2013). Gross ecosystem product: concept, accounting framework and case study. Acta Ecologica Sinica, 33(21): 6747-6761 (in Chinese)
CrossRef
Google scholar
|
[30] |
Qin C, Xue Q, Zhang J, Lu L, Xiong S, Xiao Y, Zhang X, Wang J. (2024). A beautiful China initiative towards the harmony between humanity and the nature. Frontiers of Environmental Science & Engineering, 18(6): 71
CrossRef
Google scholar
|
[31] |
Shi P, Li Z, Li P, Zhang Y, Li B. (2021). Trade-offs among ecosystem services after vegetation restoration in China’s loess plateau. Natural Resources Research, 30(3): 2703–2713
CrossRef
Google scholar
|
[32] |
Song Y, Wang J, Ge Y, Xu C. (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
CrossRef
Google scholar
|
[33] |
Song C, He H S, Liu K, Du H, Krohn J. (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
CrossRef
Google scholar
|
[34] |
WangJ F, Xu C D (2017). Geodetector: principle and prospective. Acta Geographica Sinica, 72(1): 116–134 (in Chinese)
|
[35] |
Wang J F, Zhang T L, Fu B J. (2016). A measure of spatial stratified heterogeneity. Ecological Indicators, 67: 250–256
CrossRef
Google scholar
|
[36] |
Xia H, Yuan S, Prishchepov A V. (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
CrossRef
Google scholar
|
[37] |
Xie G, Zhang C, Zhen L, Zhang L. (2017). Dynamic changes in the value of China’s ecosystem services. Ecosystem Services, 26: 146–154
CrossRef
Google scholar
|
[38] |
ZengJ, Zhou T, TanE, XuY, LinQ, ZhangY, Wu X, ZhangJ, LiuX, ZhangQ (2024). Evaluate the differences in carbon sink contribution of different ecological engineering projects. Carbon Research, 3(10) 10.1007/s44246-024-00105-4
|
[39] |
Zhang J, Liu C, Wang H, Liu X, Qiao Q. (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
CrossRef
Google scholar
|
[40] |
Zheng H, Wu T, Ouyang Z, Polasky S, Ruckelshaus M, Wang L, Xiao Y, Gao X, Li C, Daily G C. (2023). Gross ecosystem product (GEP): quantifying nature for environmental and economic policy innovation. Ambio, 52(12): 1952–1967
CrossRef
Google scholar
|
[41] |
Zou Z, Wu T, Xiao Y, Song C, Wang K, Ouyang Z. (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
CrossRef
Google scholar
|
/
〈 | 〉 |