ArticleScientific reports2025
A novel approach for agricultural carbon emission reduction by integrating fermatean neutrosophic set with WINGS and AHP-EWM.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Artificial Intelligence based on behavioral recognition and optimization for low carbon fertilization in agriculture.Scientific reports · 2026Article
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3 authors.
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Abstract
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.
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