Zhang Kecheng, Chen Zhicheng, Wang Yawen
School of Business Administration, Shandong Women's University, Jinan, China.
School of Economics and Management, Shandong Agricultural University, Taian, China.
Sci Rep. 2025 Jan 2;15(1):391. doi: 10.1038/s41598-024-84423-y.
Agriculture is a major contributor to global greenhouse gas emissions, highlighting the urgent need for effective carbon reduction strategies. This study presents an innovative integrated model that employs Fermatean Neutrosophic Set in conjunction with the Weighted Influence Nonlinear Gauge System and the Analytic Hierarchy Process combined with the Entropy Weight Method to assess key factors influencing agricultural carbon reduction. Our study delineates the hierarchical importance of factors influencing carbon emissions, with carbon emission reduction policy (τ4) emerging as the paramount factor, attributed a value of 0.220. The factor prioritization is ordered as τ4 > τ8 > τ3 > τ2 > τ6 > τ9 > τ1 > τ5 > τ7. Concurrently, the causality ranking, derived from the [Formula: see text] values, positions agricultural technology adoption (τ6) as the most influential factor, with a value of 0.7737, and is followed by the sequence τ6 > τ9 > τ8 > τ1 > τ5 > τ2 > 0 > τ3 > τ4 > τ7.The findings emphasize the pivotal role of sustainable agricultural management, carbon emission reduction policy, and agricultural technology adoption in mitigating emissions, and based on this, suggest some policy insights that can be used by policymakers and regulators. The proposed model serves as a robust decision-making tool for policymakers and provides a theoretical framework for developing effective agricultural carbon reduction strategies. This research advances the field by offering a novel theoretical model for complex decision-making under uncertainty, deepening the understanding of agricultural carbon reduction dynamics, and providing actionable insights for sustainable development.
农业是全球温室气体排放的主要贡献者，这凸显了制定有效碳减排策略的迫切需求。本研究提出了一种创新的综合模型，该模型将费马中性模糊集与加权影响非线性规范系统以及结合熵权法的层次分析法相结合，以评估影响农业碳减排的关键因素。我们的研究描绘了影响碳排放因素的层次重要性，其中碳排放减少政策（τ4）成为首要因素，其值为0.220。因素优先级排序为τ4 > τ8 > τ3 > τ2 > τ6 > τ9 > τ1 > τ5 > τ7。同时，从[公式：见正文]值得出的因果关系排名中，农业技术采用（τ6）被列为最具影响力的因素，值为0.7737，随后的顺序为τ6 > τ9 > τ8 > τ1 > τ5 > τ2 > 0 > τ3 > τ4 > τ7。研究结果强调了可持续农业管理、碳排放减少政策和农业技术采用在减排中的关键作用，并据此提出了一些可供政策制定者和监管者使用的政策见解。所提出的模型为政策制定者提供了一个强大的决策工具，并为制定有效的农业碳减排策略提供了理论框架。本研究通过为不确定性下的复杂决策提供一种新颖的理论模型，推进了该领域的发展，加深了对农业碳减排动态的理解，并为可持续发展提供了可操作的见解。
