Extending the TPB of residential waste sorting with situational factors using a data-driven approach: The case of Gothenburg, Sweden

Jonathan Cohen, Jorge Gil, Leonardo Rosado
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Abstract

Waste separation at source is perceived as an effective Municipal Waste Management strategy, and the success depends on understanding the drivers of proper waste sorting behaviour. The Theory of Planned Behaviour (TPB) has been extensively applied to determining the importance of different psychological constructs in waste sorting behaviour. Despite evidence of its validity in specific contexts, in urban contexts, one requires an understanding of how the built environment affects waste sorting behaviour. Furthermore, this study introduces the use of Exploratory Factor Analysis as a data-driven approach to define various TPB constructs from a collection of items, including situational factors such as distance to waste bins or the condition of recycling facilities. It shows how this technique outperforms the typical top-down approach of starting from pre-defined items assigned to its constructs. This study surveyed residents of Gothenburg, Sweden, to capture empirical data on factors that affect the planned behaviour of waste separation. Structural Equation Modelling (SEM) is used to evaluate the extended TPB model and extract the drivers of waste sorting behaviour. Results from the study can extend the application of TPB to inform urban planners about the location and maintenance of waste management infrastructure.

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基于数据驱动方法的情境因素扩展生活垃圾分类TPB:瑞典哥德堡案例
废物源头分类被视为有效的都市废物管理策略,而成功与否取决于了解正确废物分类行为的驱动因素。计划行为理论(TPB)已被广泛应用于确定不同心理结构在垃圾分类行为中的重要性。尽管有证据表明它在特定情况下是有效的,但在城市环境中,人们需要了解建筑环境如何影响垃圾分类行为。此外,本研究引入探索性因子分析作为数据驱动的方法,从项目集合中定义各种TPB结构,包括情境因素,如到垃圾箱的距离或回收设施的条件。它展示了该技术如何优于典型的自顶向下方法,即从分配给其构造的预定义项开始。这项研究调查了瑞典哥德堡的居民,以获取影响废物分类计划行为的因素的经验数据。结构方程模型(SEM)用于评估扩展的TPB模型,并提取垃圾分类行为的驱动因素。研究结果可以扩展城市规划的应用,为城市规划者提供有关废物管理基础设施的选址和维护的信息。
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