Evaluating zero-carbon measures through a probabilistic hesitant fuzzy LOPCOW and WISP approach

Abstract

Climate change is a pressing issue globally, and countries are implementing zero-carbon measures (ZCMs) to combat it. The ranking of such measures is essential, as it facilitates policymakers in preparing action plans for sustainability. As a variant of the hesitant fuzzy set (HFS), the probabilistic hesitant fuzzy set (PHFS) is utilized, which considers both hesitancy during rating and the associated confidence level for each value. Logarithmic percentage change-driven objective weighting (LOPCOW) and simple weighted sum product (WISP) are cuttingedge weighting and ranking methods for multi-criteria decision-making (MCDM), respectively. This study aims to rank ZCMs to achieve SDG13 via a novel integrated LOPCOW-WISP multi-criteria method with PHFSs. So far, there has been no work on developing LOPCOW and WISP methods under PHFSs, and no PHFS-driven research has evaluated ZCMs. Regarding the findings, sustainable agriculture is the foremost measure, followed by research and innovation and decarbonizing industry. Sensitivity and comparison analyses are further conducted to realize the method's robustness. The findings can also help to shape the practical and future end-use vision for energy resources, allowing for significant advantages while incurring zero-carbon emissions costs. Policymakers can readily use this framework in logical decision-making processes. Furthermore, the study proposes an ideal measurement method that prioritizes technological, agricultural, and research and development factors, along with concerns like energy efficiency, to achieve the goal of zero-carbon emissions