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[1]Zhao Zulun,Luo Junhua,Li Wei,et al.Evolution of Ecosystem Service Value and Influencing Factors in Karst Mountainous Urban Agglomeration[J].Research of Soil and Water Conservation,2024,31(05):410-420.[doi:10.13869/j.cnki.rswc.2024.05.013]

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Evolution of Ecosystem Service Value and Influencing Factors in Karst Mountainous Urban Agglomeration

Research of Soil and Water Conservation[ISSN 1005-3409/CN 61-1272/P] Volume: 31 Number of periods: 2024 05 Page number: 410-420 Column: Public date: 2024-08-10

Title:: Evolution of Ecosystem Service Value and Influencing Factors in Karst Mountainous Urban Agglomeration

Author(s):: Zhao Zulun, Luo Junhua, Li Wei, Huang Liang, Yin Linjiang, Lyu Sisi, Zhao Weiquan; (Institute of Mountain Resource of Guizhou Province, Guizhou Academy of Science, Guiyang 550001, China)

Keywords:: ecosystem service value; Markov-FLUS model; land use simulation; Geodetector; urban agglomeration of central Guizhou Province

CLC:: X826, P901

DOI:: 10.13869/j.cnki.rswc.2024.05.013

Abstract:: [Objective]The aims of this study are to investigate the evolution of ecosystem service value(ESV)and its influencing factors urbanization in the Karst city cluster and to provide a scientific basis for regional eco-environmental protection, ecological function zoning, and ecological compensation decision-making. [Methods]Based on the land use data of urban agglomeration of central Guizhou Province from 2000 to 2020, 16 drivers were selected from natural and socio-economic perspectives, and a Markov-FLUS model was constructed to simulate the land use pattern in 2030 under the scenarios of natural development and ecological constraints. Then, the modified ESV coefficients were used to assess the spatial and temporal evolution of ESV and analyze the factors influencing the spatial differentiation of ESV using Geodetector. [Results](1)The artificial surface seriously encroached on cropland(53%), forest land(12%), and grassland(10%)from 2000 to 2020. Under the ecological constraint scenario, significant nature reserves were effectively protected, and the artificial surface increased by 55 500 hectares, with a slower growth rate.(2)The ESV as a whole showed a trend of rising and then falling, with regulating services(71.08%)>supporting services(23.59%)>cultural services(4.51%)>supplying services(0.82%). The value of ESV in 2030 under the natural development scenario was 59.211 billion yuan, and that was 58.906 billion yuan under the ecological constraint scenario.(3)The explanatory power of ESV spatial differentiation factors followed the order:area of districts and counties(0.79)>value-added of agriculture, forestry, animal husbandry and fishery(0.51)>value-added of primary industry(0.50)>GDP(0.41)>GDP per capita(0.30)>area of built-up area(0.09)>resident population(0.04). The area of districts and counties, value-added of agriculture, forestry, animal husbandry and fishery, and value-added of primary industry were significantly and positively correlated with ESV, and GDP was significantly and positively correlated with ESV. The area of districts and counties, value-added of agriculture, forestry, animal husbandry and fishery, and value-added of primary industry were significantly positively correlated with ESV, and GDP was significantly negatively correlated with ESV. [Conclusion]Regional ecosystem services are mainly regulating services, and water and woodland ecosystems have a more significant impact on the overall ESV, which is the center of gravity of regional environmental protection and ecological construction.

References:: [1]Costanza R, de Groot R, Sutton P, et al. Changes in the global value of ecosystem services[J]. Global Environmental Change, 2014,26:152-158.
[2]Aziz T, Nimubona A D, Van Cappellen P. Comparative valuation of three ecosystem services in a Canadian watershed using global, regional, and local unit values[J]. Sustainability, 2023,15(14):11024.
[3]Costanza R, d'Arge R, de Groot R, et al. The value of the world's ecosystem services and natural capital[J]. Nature, 1997,387:253-260.
[4]Sharma S, Hussain S, Singh A N. Impact of land use and land cover on urban ecosystem service value in Chandigarh, India:A GIS-based analysis[J]. Journal of Urban Ecology, 2023,9(1):juac030.
[5]欧阳志云,王效科,苗鸿.中国陆地生态系统服务功能及其生态经济价值的初步研究[J].生态学报,1999,19(5):607-613.
Ouyang Z Y, Wang X K, Miao H. A primary study on Chinese terrestrial ecosystem services and their ecological-economic values[J]. Acta Ecologica Sinica, 1999,19(5):607-613.
[6]周鹏,周婷,彭少麟.生态系统服务价值测度模式与方法[J].生态学报,2019,39(15):5379-5388.
Zhou P, Zhou T, Peng S L. Measurement modalities and methodologies of ecosystem services valuation[J]. Acta Ecologica Sinica, 2019,39(15):5379-5388.
[7]韩然,叶长盛.珠江三角洲典型基塘生态系统服务价值演变:以佛山市为例[J].热带地理,2020,40(3):562-574.
Han R, Ye C S. Evolution of ecosystem service value of typical dike-pond in the Pearl River Delta:A case study of Foshan[J]. Tropical Geography, 2020,40(3):562-574.
[8]Schenau S, van Berkel J, Bogaart P, et al. Valuing ecosystem services and ecosystem assets for The Netherlands[J]. One Ecosystem, 2022,7:e84624
[9]谢高地,张彩霞,张雷明,等.基于单位面积价值当量因子的生态系统服务价值化方法改进[J].自然资源学报,2015,30(8):1243-1254.
Xie G D, Zhang C X, Zhang L M, et al. Improvement of the evaluation method for ecosystem service value based on per unit area[J]. Journal of Natural Resources, 2015,30(8):1243-1254.
[10]谢高地,甄霖,鲁春霞,等.一个基于专家知识的生态系统服务价值化方法[J].自然资源学报,2008,23(5):911-919.
Xie G D, Zhen L, Lu C X, et al. Expert knowledge based valuation method of ecosystem services in China[J]. Journal of Natural Resources, 2008,23(5):911-919.
[11]谢高地,张彩霞,张昌顺,等.中国生态系统服务的价值[J].资源科学,2015,37(9):1740-1746.
Xie G D, Zhang C X, Zhang C S, et al. The value of ecosystem services in China[J]. Resources Science, 2015,37(9):1740-1746.
[12]刘志涛,王少剑,方创琳.粤港澳大湾区生态系统服务价值的时空演化及其影响机制[J].地理学报,2021,76(11):2797-2813.
Liu Z T, Wang S J, Fang C L. Spatiotemporal evolution and influencing mechanism of ecosystem service value in the Guangdong-Hong Kong-Macao Greater Bay Area[J]. Acta Geographica Sinica, 2021,76(11):2797-2813.
[13]封清,周忠发,陈全,等.基于易地扶贫搬迁视角的喀斯特生态脆弱区生态系统服务价值的时空演变[J].生态学报,2022,42(7):2708-2717.
Feng Q, Zhou Z F, Chen Q, et al. Spatial-temporal evolution research of ecosystem service value in ecologically vulnerable Karst regions under the perspective of poverty alleviation relocation[J]. Acta Ecologica Sinica, 2022,42(7):2708-2717.
[14]Kulaixi Z, Chen Y N, Wang C, et al. Spatial differentiation of ecosystem service value in an arid region:A case study of the Tarim River Basin, Xinjiang[J]. Ecological Indicators, 2023,151:110249.
[15]韩会庆,苏志华.喀斯特生态系统服务研究进展与展望[J].中国岩溶,2017,36(3):352-358.
Han H Q, Su Z H. Research progress and prospects of Karst ecosystem services[J]. Carsologica Sinica, 2017,36(3):352-358.
[16]黄木易,岳文泽,方斌,等.1970—2015年大别山区生态服务价值尺度响应特征及地理探测机制[J].地理学报,2019,74(9):1904-1920.
Huang M Y, Yue W Z, Fang B, et al. Scale response characteristics and geographic exploration mechanism of spatial differentiation of ecosystem service values in Dabie Mountain Area, central China from 1970 to 2015[J]. Acta Geographica Sinica, 2019,74(9):1904-1920.
[17]Liang X, Liu X P, Li X, et al. Delineating multi-scenario urban growth boundaries with a CA-based FLUS model and morphological method[J]. Landscape and Urban Planning, 2018,177:47-63.
[18]Liu X P, Liang X, Li X, et al. A future land use simulation model(FLUS)for simulating multiple land use scenarios by coupling human and natural effects[J]. Landscape and Urban Planning, 2017,168:94-116.
[19]李立,胡睿柯,李素红.基于改进FLUS模型的北京市低碳土地利用情景模拟[J].自然资源遥感,2023,35(1):81-89.
Li L, Hu R K, Li S H. Simulations of the low-carbon land use scenarios of Beijing based on the improved FLUS model[J]. Remote Sensing for Natural Resources, 2023,35(1):81-89.
[20]张晓瑶,张潇,李冬花,等.城市土地利用变化对生态系统服务价值影响的多情景模拟:以深圳市为例[J].生态学报,2022,42(6):2086-2097.
Zhang X Y, Zhang X, Li D H, et al. Multi-scenario simulation of the impact of urban land use change on ecosystem service value in Shenzhen[J]. Acta Ecologica Sinica, 2022,42(6):2086-2097.
[21]谢凌凌,许进龙,臧俊梅,等.基于Markov-FLUS模型的广西土地利用变化模拟预测[J].水土保持研究,2022,29(2):249-254,264.
Xie L L, Xu J L, Zang J M, et al. Simulation and prediction of land use change in Guangxi based on Markov-FLUS model[J]. Research of Soil and Water Conservation, 2022,29(2):249-254,264.
[22]牟燕,冯飞.基于土地利用的北京市多情景模拟下生态服务价值影响评估[J].北京林业大学学报,2023,45(5):14-24.
Mou Y, Feng F. Impact assessment of ecological service value under multi scenario simulation in Beijing based on land use[J]. Journal of Beijing Forestry University, 2023,45(5):14-24.
[23]袁雪.基于PLUS模型的武汉市土地利用变化情景模拟与景观生态风险研究[D].抚州:东华理工大学,2022.
Yuan X. Scenario Simulation of land use change and landscape ecological risk research in Wuhan based on PLUS model[D]. Fuzhou:East China Institute of Technology, 2022.
[24]周寒冰,王志泰,王志杰,等.喀斯特山地城市生态系统服务价值地形梯度响应:以贵阳市中心城区为例[J].水土保持研究,2021,28(6):337-347.
Zhou H B, Wang Z T, Wang Z J, et al. Response of ecosystem service value of Karst mountainous city to terrain gradient—a case study of downtown of Guiyang city[J]. Research of Soil and Water Conservation, 2021,28(6):337-347.
[25]韩会庆,刘悦,蔡广鹏,等.快速城镇化背景下山地城市生态系统服务价值变化梯度特征:以贵阳市为例[J].水土保持研究,2020,27(5):295-303.
Han H Q, Liu Y, Cai G P, et al. Characteristics of gradient change of the value of mountain ecosystem services with the rapid urbanization in urban area:A case study of Guiyang[J]. Research of Soil and Water Conservation, 2020,27(5):295-303.
[26]谢高地,鲁春霞,冷允法,等.青藏高原生态资产的价值评估[J].自然资源学报,2003,18(2):189-196.
Xie G D, Lu C X, Leng Y F, et al. Ecological assets valuation of the Tibetan Plateau[J]. Journal of Natural Resources, 2003,18(2):189-196.
[27]王劲峰,徐成东.地理探测器:原理与展望[J].地理学报,2017,72(1):116-134.
Wang J F, Xu C D. Geodetector:Principle and prospective[J]. Acta Geographica Sinica, 2017,72(1):116-134.

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Last Update: 2024-08-10