Data for: "Greener, Wilder, Better: Valuing Complexity and Diversity in Urban Riparian Areas"

Description:This dataset contains the survey data and experimental design files from a discrete choice experiment (DCE) assessing public preferences and willingness to pay for biodiversity-enhancing interventions in urban riparian areas in Poland. Data were collected in 2024 through a computer-assisted web interview (CAWI) with a stratified sample of 796 adult residents living in Polish cities (>20,000 inhabitants) with adjacent river infrastructure. The dataset includes: Choice experiment responses for 48 unique hypothetical riparian management scenarios (blocked into 3 sets), covering eight ecologically grounded attributes: vegetation type, landscape complexity, vegetation cover, species diversity, presence of dead wood, management intensity, proportion of impermeable surfaces, and annual tax payment. Socio-demographic variables (age, gender, education, income, municipality size, region). Recreational use patterns for urban rivers (visit frequency, preferred activities). Preference validation data from a direct attribute ranking exercise. Survey design metadata, including attribute levels, coding structure, and choice card allocations. The data are anonymised and provided in .mat format (Matlab), with accompanying codebook and attribute descriptions to enable replication of the analyses presented in the manuscript. Potential uses: Replication of mixed logit and willingness-to-pay space models. Meta-analysis of urban ecosystem service valuation studies. Comparative analysis of biodiversity valuation in Central and Eastern Europe.数据集说明： 本数据集包含一项离散选择实验（Discrete Choice Experiment, DCE）的调查数据与实验设计文件，该实验用于评估波兰城市滨水区域生物多样性提升干预措施的公众偏好与支付意愿。数据采集于2024年，通过计算机辅助网络访谈（Computer-Assisted Web Interview, CAWI）完成，通过分层抽样选取了796名成年城市居民作为调查对象，这些居民居住于人口超过2万且毗邻河道基础设施的城市。 本数据集包含： - 针对48个独特假设滨水管理场景的选择实验应答数据（分为3个组块），涵盖8项基于生态学原理的属性：植被类型、景观复杂度、植被覆盖度、物种多样性、枯木存在情况、管理强度、不透水表面占比与年度纳税额。 - 社会人口统计学变量：年龄、性别、受教育程度、收入、城镇规模、所属区域。 - 城市河流休闲使用模式数据：到访频率、偏好活动类型。 - 来自直接属性排序练习的偏好验证数据。 - 调查设计元数据，包括属性水平、编码结构与选择卡片分配规则。 本数据集已完成匿名化处理，以.mat（Matlab）格式提供，并附带编码手册与属性说明文档，可用于复现论文中呈现的分析结果。 潜在应用场景： - 混合Logit模型与支付意愿空间模型的复现研究 - 城市生态系统服务价值评估研究的元分析 - 中东欧地区生物多样性价值评估的比较分析