揭示消费者对区域供暖的看法:Q 方法研究

Uwe Radtke
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引用次数: 0

摘要

本研究采用 Q 方法调查消费者对区域供暖的看法,在五个研究阶段结合了定性和定量方法:基础开发、陈述汇编(Q 集)、参与者选择(P 集)、Q 排序和数据分析。基础的建立采用了不同的资料来源,产生了 39 份陈述。为确保客观性,来自不同学科的专家采用类似德尔菲法的两轮方法参与了语句的选择。随后的步骤包括招募具有不同观点的参与者,进行 Q 排序和数据分析,以揭示思维模式。本研究旨在揭示消费者对区域供热、可持续发展和总体态度的观点,帮助政策制定者推广环保能源实践。本研究采用 Q 方法收集和分析有关区域供热的主观观点,加深我们对消费者观点及其对可持续能源系统影响的理解。研究结果表明,消费者对区域供热持积极态度,并大力支持进一步的研究和投资。此外,报告还强调了消费者对定价机制的不同态度,旨在澄清论文的结果。研究结果表明,受用户行为影响的透明定价机制更容易被接受。通过使用 K 均值聚类分析数据,我们发现了四个群体。这些聚类显示,消费者偏好较长、稳定的低需求运营时间,其次是较短的低需求运营时间,再次是抵制低需求的高运营时间。 未来的研究可以探索特定的消费者特征、私人数据或利用来源的废热。
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Unveiling consumer perspectives on district heating: A Q methodology study
This study uses the Q methodology to investigate consumer perspectives on district heating, combining qualitative and quantitative methods across five research phases: foundation development, statement compilation (the Q-set), participant selection (the P-set), Q sorting   and data analysis. The foundation was built using diverse sources yielding 39 statements. Experts from various disciplines were involved in the selection of statements using a two-round Delphi-like method to ensure objectivity. Subsequent steps included recruiting participants with varied perspectives, conducting Q sorting and analyzing data to reveal thought patterns. This research aims to shed light on consumer viewpoints regarding district heating, sustainability   and general attitudes, assisting policymakers in promoting environmentally friendly energy practices. This research employs the Q methodology to collect and analyze subjective viewpoints concerning district heating, enhancing our understanding of consumer perspectives and their implications for sustainable energy systems. The results indicate a positive attitude towards district heating with strong support for further research and investment. Additionally, it aims to clarify the paper's results by highlighting the diverse consumer attitudes towards pricing mechanisms. The findings suggest transparent pricing mechanisms influenced by user behavior are more likely to gain acceptance. Four groups were found by using K-means clustering to analyse the data. These clusters showed preferences for longer, steady operating hours with low demand followed by shorter hours with low demand and resistance towards low operating hours with high demand.  Future research could explore specific consumer profiles, private data   or leverage waste heat from sources.
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