京津冀地区生态系统服务权衡与协同关系及驱动因素分析  

Analysis on ecosystem service trade-offs/synergies and drivers in Beijing-Tianjin-Hebei region

作　　者：闫语 秦耀伟 东嘉琪 曹建生[2] 肖捷颖[1] YAN Yu;QIN Yaowei;DONG Jiaqi;CAO Jiansheng;XIAO Jieying(College of Environmental Sciences and Engineering,Hebei University of Science and Technology,Shijiazhuang 050018;Agricultural Resources Research Center,Institute of Genetic and Developmental Biology,Chinese Academy of Sciences,Shijiazhuang 050022)

机构地区：[1]河北科技大学环境科学与工程学院,石家庄050018 [2]中国科学院遗传与发育生物学研究所农业资源研究中心,石家庄050022

出　　处：《环境科学学报》2025年第3期493-506,共14页Acta Scientiae Circumstantiae

基　　金：科技部科技基础资源调查项目(No.2022FY100104)。

摘　　要：京津冀地区作为中国北方重要的生态安全屏障和社会经济核心区,明确当地生态系统服务之间复杂的权衡与协同关系,并确定其驱动因素,对京津冀地区生态系统保护、区域可持续发展具有重要意义.本研究基于InVEST模型量化评估2010—2022年京津冀地区5种生态系统服务(碳储量、粮食生产、生境质量、土壤保持、产水量),通过Pearson相关性和地理加权回归模型量化生态系统服务间权衡与协同关系,使用地理探测器模型分析生态系统服务及其权衡与协同关系的驱动因素.结果表明:2000—2022年京津冀地区碳储量、生境质量、土壤保持等服务呈山区高、平原低的分布特征,粮食产量和产水量空间分布与其相反,呈山区低、平原高的分布特征;全局生态系统服务间以权衡关系为主,主要发生在粮食产量、产水量分别与碳储量、生境质量、土壤保持3种生态系统服务间;山区生态系统服务间关系与全局相似,平原主要以协同关系为主,其权衡关系主要发生在生境质量与粮食生产、产水量、土壤保持间.地理探测器模型结果表明,归一化植被指数、降水、地形及土地利用类型是全局生态系统服务的重要驱动因素;在山区和平原,生态系统服务主要受归一化植被指数、土地利用类型、坡度、降水和人口密度共同作用的影响.地形因子是全局生态系统服务间关系的重要驱动因素,而山区生态系统服务间关系主要受降水因子的影响,平原生态系统服务间关系则主要受到地形、降水及社会经济的共同作用影响.定量化研究京津冀地区分区角度的生态系统服务及权衡协同关系并探究其影响因素,制定针对性策略,可为区域可持续生态系统管理和生态系统修复机制提供参考依据.Beijing-Tianjin-Hebei(BTH)region is the most important area of China as it’s great position in political economic and ecological aspects.It is important to clarify the complex trade-offs/synergies relationship among ecosystem services(ESs),and to identify their drivers,which are significant for local ecological protection and promotion of sustainable development in this region.In this study,InVEST model was used to quantitatively assess 5 ESs,such as carbon storage(CS),food production(FP),habitat quality(HQ),sediment delivery ratio(SDR),water yield(WY),in the BTH region during 2000 to 2022.The trade-offs/synergies between ESs were analysed by Pearson's correlation and geographicallyweighted regression methods,the drivers were identified by Geodetector model.The results showed that from 2000 to 2022,CS,HQ,and SDR were distributed higher in mountains and lower in plains,while the spatial distribution of FP and WY showed the opposite situation;It was detected that a strong trade-off exists among the overall ESs,mainly between FP,WY respectively and the three ESs of CS,HQ,and SDR;The relationships among ESs in the mountains were similar to overall,and the plains were dominated by synergistic relationships,with trade-offs occurred mainly between HQ and FP,WY and SDR.Normalized difference vegetation index(NDVI),precipitation,topography,and land use type(LUT)were important drivers of overall ESs;In the mountains and plains,ESs were mainly influenced by the combined effects of NDVI,LUT,slope,precipitation,and population density.Topographic factor was a significant driver of overall ESs relationships,while mountains ESs relationships were mainly influenced by precipitation,and plains ESs relationships were dominated by a combination of topography,precipitation,and socioeconomics.To quantify the ESs and trade-offs in the BTH region from a subarea perspective,explore the influence factors,and formulate targeted strategies,which can provide a reference basis for sustainable ecosystem management and ecosystem restoration me

关 键 词：生态系统服务 权衡与协同 驱动因素 地理加权回归模型 InVEST模型 京津冀 

分 类 号：X24[环境科学与工程—环境科学]