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Data envelopment analysis (DEA) is a non-parametric approach for the estimation of production frontier that is used to calculate the performance of a group of similar decision-making units (DMUs) which employ comparable inputs to produce related outputs. However, observed values might occasionally be confusing, imprecise, ambiguous, inadequate, and inconsistent in real-world applications. Thus, disregarding these factors may result in incorrect decision-making. Thus neutrosophic sets have been created as an extension of intuitionistic fuzzy sets to represent ambiguous, erroneous, missing, and inaccurate information in real-world applications. In this study, we have proposed a technique for solving the neutrosophic form of the Charnes-Cooper-Rhodes (CCR) model based on single-value trapezoidal neutrosophic numbers (SVTrNNs). The possibilistic mean for SVTrNNs is redefined and applied the Mehar approach to transforming the neutrosophic DEA (Neu-DEA) model into its corresponding crisp DEA model. As a result, the efficiency scores of the DMUs are calculated using different risk parameter values lying in [0, 1]. A numerical example is given to analyze the performance of the all India institutes of medical sciences and compared it with Abdelfattah's ranking approach. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) is a non-parametric approach for the estimation of production frontier that is used to calculate the performance of a group of similar decision-making units (DMUs) which employ comparable inputs to produce related outputs. However, observed values might occasionally be confusing, imprecise, ambiguous, inadequate, and inconsistent in real-world applications. Thus, disregarding these factors may result in incorrect decision-making. Thus neutrosophic sets have been created as an extension of intuitionistic fuzzy sets to represent ambiguous, erroneous, missing, and inaccurate information in real-world applications. In this study, we have proposed a technique for solving the neutrosophic form of the Charnes-Cooper-Rhodes (CCR) model based on single-value trapezoidal neutrosophic numbers (SVTrNNs). The possibilistic mean for SVTrNNs is redefined and applied the Mehar approach to transforming the neutrosophic DEA (Neu-DEA) model into its corresponding crisp DEA model. As a result, the efficiency scores of the DMUs are calculated using different risk parameter values lying in [0, 1]. A numerical example is given to analyze the performance of the all India institutes of medical sciences and compared it with Abdelfattah's ranking approach. [ABSTRACT FROM AUTHOR]

Cotton (Gossypium hirsutum L.) is an important fiber crop with high energy and C footprints. This study aimed at tracking C footprints through emission of greenhouse gases and energy flow nexus in cotton cultivation to frame policy to reduce C and energy footprints, while enhancing net ecosystem C budget and C sequestration in soils. We attempted to quantify and optimize C footprints and energy flow in cotton cultivation using integrated Life Cycle Assessment and Data Envelopment Analysis approach. The total input energy of 23,960 MJ ha−1 produced total output energy of 73,964 MJ ha−1. The energy use efficiency and energy productivity were 3.1 ± 0.1 and 0.083 ± 0.003 kg MJ ha−1, respectively. The Charnes–Cooper–Rhodes model based Data Envelopment Analysis approach elucidated 37 (out of total 65), while Banker–Charnes–Cooper model revealed 55 decision-making units as energy efficient. The energy footprints expressed as specific energy were significantly higher under current production situation (13.0 MJ kg−1), compared with optimum production situation (12.0 MJ kg−1). The Data Envelopment Analysis based optimized total energy input was significantly reduced by 1522 MJ ha−1 through chemical fertilizers (2.7–51.5%) and biocides (7.7–25.1%), which led to a significant reduction of ~9.2% of total C equivalent emissions having a technical mitigation potential of 114.3 kg CO2e ha−1. The negative values of net ecosystem C budget for efficient (− 4.04 Mg C ha−1) and inefficient (− 4.94 Mg C ha−1) decision making units revealed that these ecosystems act as net C source. The average technical efficiency of 0.87 ± 0.02 revealed that ~13% of total energy input could be saved without any impact on cotton productivity and environment. These results underpin the overwhelming significance of intensified extension efforts for efficient use of chemical fertilizers and discouraging farmers from unwarranted use of biocides in cotton in the north-western India. [ABSTRACT FROM AUTHOR]