Threshold Dynamics in Coastal Vulnerability and Adaptive Governance Pathways: A Case Study of Jiaozhou Bay Social-Ecological System

The purpose of this data is to specifically and comprehensively reflect the historical evolution mechanism of the Jiaozhou Bay coastal zone and analyze the changing trend of social-ecological vulnerability. It collects data from China from 1980 to 2022 over a period of more than 40 years, integrates the DPSIR (Driver-Pressure-State-Impact-Response) model and the ESA (Exposure-Sensitivity-Adaptability) assessment framework, establishes a coastal zone social-ecological vulnerability evaluation system consisting of 39 indicators, and realizes the dynamic fitting of the evolution process of the social-ecological vulnerability of the Jiaozhou Bay coastal zone and the scenario simulation of governance paths. These data come from authoritative sources such as "Shandong Statistical Yearbook", "Qingdao Statistical Yearbook", "China Marine Yearbook", "China Marine Economic Statistical Yearbook", "China Fishery Statistical Yearbook", "China Fishery Economic Statistical Yearbook", "China Bay Handbook", "China Third Industry Statistical Yearbook", "China Urban Statistical Yearbook", "China Port Yearbook", "Chemical Environment Evolution of Jiaozhou Bay", "Volume of Lake Wetland Bay Ecosystem (Jiaozhou Bay)", Qingdao Statistical Bulletin, Qingdao Bureau of Statistics, Qingdao Marine Development Bureau, Qingdao Port Authority, China Academy of Ocean Sciences Data Center for Marine Science, China Academy of Earth Data Science Data Center, and some data published on the statistics bureau websites. Some missing data points are calculated using the linear interpolation method. The evaluation system consists of three levels: the target level, the criterion level, and the indicator level. The target level is for analyzing the vulnerability of the social-ecological system of the Jiaozhou Bay coastal zone, and the five criterion levels are the drivers, pressures, states, impacts, and responses. Each criterion level includes first-level indicators related to natural environmental conditions and human social and economic activities, and specific second-level indicators are selected based on the decomposition of system factors. Indicators X1 to X8 reflect the drivers, X9 to X16 reflect the pressures, X17 to X23 reflect the states, X24 to X33 reflect the impacts, and X34 to X39 reflect the responses. Our key findings reveal a “spoon-shaped” trajectory of vulnerability, characterized by three distinct phases: natural stability (1980s–2000), rapid increase (2000–2015), and gradual improvement (2015–2022). Climate change and regional economic risks were identified as major drivers exacerbating vulnerability, while technological innovation significantly mitigated it. Among the governance pathways, the ecological-oriented approach proved most effective in reducing exposure and sensitivity, though all pathways contributed to vulnerability reduction to varying degrees.本数据集旨在系统性、全面地揭示胶州湾滨海带的历史演化机制，并解析社会-生态脆弱性（social-ecological vulnerability）的变化趋势。数据集采集了中国1980年至2022年（跨度超40年）的相关数据，整合了DPSIR（驱动力-压力-状态-影响-响应，Driver-Pressure-State-Impact-Response）模型与ESA（暴露度-敏感度-适应力，Exposure-Sensitivity-Adaptability）评估框架，构建了包含39项指标的滨海带社会-生态脆弱性评价体系，并实现了胶州湾滨海带社会-生态脆弱性演化过程的动态拟合与治理路径的情景模拟。 本数据集数据来源权威，涵盖《山东统计年鉴》《青岛统计年鉴》《中国海洋年鉴》《中国海洋经济统计年鉴》《中国渔业统计年鉴》《中国渔业经济统计年鉴》《中国海湾手册》《中国第三产业统计年鉴》《中国城市统计年鉴》《中国港口年鉴》《胶州湾化学环境演化》《湖泊湿地海湾生态系统卷（胶州湾）》、青岛统计公报、青岛市统计局、青岛市海洋发展局、青岛港务局、中国海洋科学研究院海洋科学数据中心、中国地球数据科学研究院数据中心，以及部分统计局官方网站发布的公开数据。针对部分缺失数据点，本数据集采用线性插值法完成补全计算。 本评价体系共设三级架构：目标层、准则层与指标层。其中目标层用于解析胶州湾滨海带社会-生态系统的脆弱性水平；准则层共包含5项维度，分别为驱动力、压力、状态、影响与响应。每项准则层均涵盖与自然环境条件、人类社会经济活动相关的一级指标，并通过系统因子分解筛选得到具体的二级指标。其中，指标X1至X8对应驱动力维度，X9至X16对应压力维度，X17至X23对应状态维度，X24至X33对应影响维度，X34至X39对应响应维度。 本研究核心发现显示，胶州湾滨海带社会-生态脆弱性呈现“勺型”演化轨迹，可划分为三个显著阶段：自然稳定期（1980年代—2000年）、快速加剧期（2000年—2015年）与逐步改善期（2015年—2022年）。研究识别出气候变化与区域经济风险是加剧脆弱性的核心驱动因素，而技术创新则可显著缓解脆弱性水平。在各类治理路径中，以生态为导向的治理模式在降低暴露度与敏感度方面成效最为突出，尽管所有路径均在不同程度上实现了脆弱性的削减。