Assessing social acceptance of energy production from food waste in municipal solid waste management

Date

2023-9

Author

Kırer, Tuğba

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A considerable amount of municipal solid waste (MSW) is still disposed of in landfills instead of harnessing its potential for energy production worldwide. The widespread reliance on landfills instead of pursuing more sustainable solutions underscores the concept that the success of managing MSW sustainability is closely linked to social acceptance. Changes in waste management lead to changes in people's lifestyles; thus, it should be investigated whether these changes are socially acceptable. This study applied a quantitative research technique using a sample size of 392 questionnaires to interrogate the dimensions of social acceptance for MSW, encompassing socio-political, community, and market aspects. The structural equation modeling-partial least squares methodology on data gathered via questionnaires was employed, and the multifaceted determinants of social acceptance for deriving sustainable energy from food waste were identified. Factors impacting social acceptance include experience, knowledge, distributive and procedural fairness, trust, perceived benefits and risks, positive affect, personal and social norms, perceived behavioral control, problem perception, and intention to accept. Notably, 'Intention to accept' emerged as the predominant predictor of social acceptance, trailed by social and personal norms and perceptions of risk and benefit. Trust was segmented into three distinct pillars in a novel approach, enhancing analytical depth and revealing nuanced relationships. The study also delves into relationships within the conceptual model across different demographic segments according to the measurement invariance determination and direct and indirect interconnections. The model's constructs, factors, and actors were aligned with dimensions of social acceptance.

Subject Keywords

Social Acceptance, Municipal Solid Waste, Renewable Energy, Structural Equation Modeling-Partial Least Squares

URI

https://hdl.handle.net/11511/105432

Collections

Graduate School of Natural and Applied Sciences, Thesis