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.