Addressing community oppositions in biowaste incineration infrastructure: Key factors and strategic solutions for a sustainable circular bioeconomy

The rapid urbanisation in China has substantially increased municipal waste generation, with biowaste comprising the largest portion. Due to its high organic content, biowaste can be recycled or incinerated to produce bioenergy, making incineration a predominant waste treatment method. However, biowaste incineration facilities have often raised community-level concerns due to worries about environmental pollution and public health risks. These issues are typically classified as “Not In My Backyard” (NIMBY) conflicts in public management. Despite the prominence of these conflicts, research lacks a comprehensive examination of their key driving factors. This study addresses this gap by systematically investigating the primary risk factors underlying NIMBY incidents associated with biowaste incineration infrastructure. A hybrid model combining the Decision-Making and Trial Evaluation Laboratory (DEMATEL), Interpretative Structural Modelling Method (ISM) and Bayesian Network (BN) was applied to conduct causation chain and sensitivity analyses, enabling the identification of critical risk factors. The results reveal that insufficient public participation, inadequate information transparency and misaligned risk perceptions are the primary contributors to NIMBY conflicts. Strategic solutions are proposed for governments and infrastructure enterprises to address these issues, including enhancing public engagement, improving information disclosure and implementing effective risk communication strategies. These measures aim to mitigate NIMBY conflicts, foster social harmony and facilitate the implementation of essential infrastructure projects. This study enhances the understanding of NIMBY conflicts by systematically identifying and analysing the key risk factors specific to biowaste incineration infrastructure, an area that has been underexplored in existing research. Additionally, the use of the DEMATEL-ISM-BN hybrid model introduces a significant methodological innovation, facilitating a comprehensive examination of causality, hierarchical structures, and probabilistic relationships among these risk factors.