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Empirical Evaluation of Urban Sustainability from Underlying Causal Structures and Legacy Effects: The Prefecture Cities in China as a Case Study

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Total Socioenvironmental Systems

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Abstract

One innovation separating the total socioenvironmental system (TSES) analytical framework from other coupled human–nature systems is its focus on examining causal relationships between ecological/environmental, socioeconomic, and global climate changes. This chapter advocates the concept of latent variables or hypothetical constructs to investigate causal relationships between ecological, socioeconomic, and climate changes. The adoption of hypothetical constructs enables researchers to examine interactions between a large number of ecological, socioeconomic, and climate change variables and their complex socioenvironmental causal structures. The capability to model multiple sets of variables in multiple dimensions (causal structures) is an innovative attempt to develop big models for examining big data in the context of socioenvironmental sustainability studies. The other TSES innovation is to explore spatial and temporal lagged (legacy) effects of the causal relationships between various socioenvironmental factors. This chapter addresses historical or legacy effects from individual variables to hypothetical system structures. Therefore, this chapter illustrates that the TSES analytical framework can handle complex causal structures through latent variables and their temporal lagged effects (i.e., legacy effects).

This latent variable-based TSES framework is called the cross-lagged panel model (CLPM) for urban sustainability (US). This chapter introduces the literature and basic concepts concerning CLPM-US. It illustrates CLPM-US through a case study of urban sustainability of the 278 prefecture cities in China. The examinations include the formation of causal structures of urban sustainability, the exploration of temporal legacy effects of urban sustainability and their underlying socioenvironmental structures, the computation of urban sustainability scores and rankings, and the relationships between the sustainability rankings and the factor loadings of their underlying causal structures. Furthermore, this chapter provides an alternative approach, the multi-objective optimization problems (MOOPs) algorithm, to assess the relationships between the sustainability rankings and the loadings of their underlying structures.

The total system approach based on the society and nature coevolution theory is vital for examining urban sustainability. The urban sustainability measurements involve all sectors of human society and the environment. Introducing causal constructs through latent variables in CLPM is a workable solution to describe the interactions between these causal structures that determine urban sustainability status. Moreover, urban sustainability is an evolving concept involving history, representing the current, and predicting the future. Lagged and cross-lagged effects of an urban system and its underlying causal structures are critical traits of urban system evolutionary dynamics. Furthermore, the measurement of urban sustainability is not a single composite score but involves a large set of trade-offs between sub-aims of urban sustainability.

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  1. Eastern Michigan University, Ypsilanti, MI, USA

    Yichun **e

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**e, Y. (2023). Empirical Evaluation of Urban Sustainability from Underlying Causal Structures and Legacy Effects: The Prefecture Cities in China as a Case Study. In: Total Socioenvironmental Systems. Springer, Cham. https://doi.org/10.1007/978-3-031-39594-9_5

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