Authors:

Dongting Cai

Date Created:

Not specified

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Professor:

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About Paper:

With the ongoing decarbonization of the Midwest manufacturing industry, it has become increasingly vital to understand how workers balance wage considerations with non-monetary job attributes. Such understanding directly influences their quality of life and community well-being, indirectly affecting energy production costs. However, a gap in comprehensive data and nuanced analysis of this field impedes this understanding. A key challenge in the shift towards decarbonized manufacturing lies in ensuring worker satisfaction and understanding their preferences in the context of this transition. To address this issue, we are trying to implicate a discrete choice model. With the result of the designed choice-based conjoint survey tailored to the context of decarbonized manufacturing, we could create a Logit-based machine-learning model designed for survey data analysis. The Logit model, selected for its capability to handle categorical data and binary outcomes, aligns well with the choice-based survey data. The model output is expected to aid in fitting a multinomial logit model, further enabling a transformation from the preference space into the willingness-to-pay space. While specific results are yet to be derived as we are at the starting year of this three-year project, we emphasize the importance of designing robust survey instruments and the role of extensive pretesting at the current stage to ensure the derived results are meaningful and reliable. The overarching objective is to contribute to formulating sociotechnical models to promote inclusive industrial practices and facilitate a smoother transition toward renewable resources in the manufacturing sector.

Source:

Purdue University / 2023

Topics:

No topics listed

Co-authors:

Dongting Cai