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Assessing Worker Preferences For Steel Industry Electrification Using Discrete Choice Methods

<p dir="ltr">As nations strive to reduce greenhouse gas emissions, the transformation of energy-intensive industries will significantly impact job quality and worker well-being. This thesis investigates the critical intersection of employment opportunities and just energy transitions in the context of industrial decarbonization, focusing on the U.S. steel sector. We address the challenge of balancing economic, environmental, and social considerations in the shift towards low-carbon manufacturing processes. Semi-structured interviews inform the development of a choice-based conjoint survey of Indiana steelworkers, which helps quantify worker preferences for various job attributes such as shift patterns, overtime hours, and wages. The analysis employs willingness-to-pay models to elucidate the complex relationships between compensation and working conditions in the context of potential changes brought about by renewable energy integration and electrification of steel production. Key findings reveal significant disutility associated with increased overtime hours and an unexpected preference for night shifts over day shifts among respondents. The research also highlights the importance of sociotechnical solutions that account for worker needs in designing decarbonized manufacturing processes. While acknowledging limitations such as potential sample bias, this thesis contributes to the development of integrated modeling approaches that combine worker preferences with operational constraints and energy costs. The results inform strategies for achieving a just energy transition in the steel industry, emphasizing the need for policies that prioritize worker well-being alongside decarbonization goals.</p>

  1. 10.25394/pgs.26337247.v1
Identiferoai:union.ndltd.org:purdue.edu/oai:figshare.com:article/26337247
Date19 July 2024
CreatorsMeenakshi Narayanaswami (19179634)
Source SetsPurdue University
Detected LanguageEnglish
TypeText, Thesis
RightsCC BY 4.0
Relationhttps://figshare.com/articles/thesis/Assessing_Worker_Preferences_For_Steel_Industry_Electrification_Using_Discrete_Choice_Methods/26337247

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