用于多分辨率环境数据的部分功能动态结构方程模型
A Partially Functional Dynamic Structural Equation Model for Multi-Resolution Environmental Data
AI总结：
研究大气污染物排放与其多方面决定因素的复杂关系，提出部分功能动态结构方程模型，整合功能协变量和标量预测变量，捕捉非平稳时间依赖和变量间相关性，应用于中国省份数据获因素与污染物关联结果。
摘要：
理解大气污染物排放与其多方面决定因素之间的复杂关系面临双重挑战：驱动因素在根本不同的时间分辨率下运行，且其相互联系随时间动态演变。为应对这些挑战，我们提出了一个部分功能动态结构方程模型（PFDSEM），该模型在统一的动态结构框架内连贯地整合了功能协变量（如高频气象数据）和标量预测变量（如经济和人口指标）。该模型通过条件自回归（CAR）结构与共区域化线性模型（LMC）相结合来捕捉非平稳时间依赖性和变量间相关性，而功能协变量则通过贝叶斯P样条平滑的基扩展来纳入。通过马尔可夫链蒙特卡罗进行贝叶斯推断提供了完整的不确定性量化。全面的模拟研究证实了在不同条件下准确的参数恢复和对先验规范的稳健性。将PFDSEM应用于中国30个省份（2015 - 2020年）的污染物排放数据，我们确定了十类社会环境因素与十种主要空气污染物（包括CO₂）之间随时间动态变化且特定于省份的关联。结果揭示了这些关联在强度和方向上的显著跨省份异质性以及气象效应中明显的季节性模式，为设计时间上自适应和区域上量身定制的环境政策提供了定量证据基础。
英文摘要：
Understanding the complex relationships between atmospheric pollutant emissions and their multifaceted determinants presents a dual challenge: driving factors operate at fundamentally different temporal resolutions, from continuously monitored meteorological variables to annually reported socio-economic indicators, and their interconnections evolve dynamically over time. To address these challenges, we propose a Partially Functional Dynamic Structural Equation Model (PFDSEM) that coherently integrates functional covariates (e.g., high-frequency meteorological data) and scalar predictors (e.g., economic and demographic indicators) within a unified dynamic structural framework. The model captures non-stationary temporal dependencies and inter-variable correlations via a Conditional Autoregressive (CAR) structure combined with a Linear Model of Coregionalization (LMC), while functional covariates are incorporated through basis expansion with Bayesian P-spline smoothing. Bayesian inference via Markov Chain Monte Carlo provides full uncertainty quantification. Comprehensive simulation studies confirm accurate parameter recovery and robustness to prior specifications under diverse conditions. Applying the PFDSEM to pollutant emissions data from 30 Chinese provinces (2015--2020), we identify temporally dynamic and province-specific associations between ten categories of socio-environmental factors and ten major air pollutants, including CO$_2$. The results reveal substantial cross-province heterogeneity in the strength and direction of these associations and pronounced seasonal patterns in meteorological effects, offering a quantitative evidence base for designing temporally adaptive and regionally tailored environmental policies.
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