ABSTRACT This study develops a novel two‐stage framework to evaluate financial health, distress and liquidity traps in the banking industry by integrating a Fuzzy Modulatory Data Envelopment Analysis model with a Fuzzy Multivariate Lognormal Regression. The methodology incorporates fuzzy set theory with alpha‐cuts to capture vagueness and uncertainty in inputs, outputs and intermediate variables, addressing inherent data imprecision in financial systems. An empirical analysis of 93 Chinese banks (2014–2023) reveals key drivers of inefficiency, including labour costs, capital adequacy and market concentration, while highlighting the moderating effects of competition and regulatory measures. Results demonstrate improved efficiency trends and convergence in operational practices, influenced by regulatory compliance and macroeconomic policies. The framework further identifies contextual factors—such as the Herfindahl–Hirschman Index and CAMELS ratings—impacting inefficiencies under varying levels of uncertainty, offering a comprehensive risk management and policy evaluation tool. This research contributes to operational research by bridging gaps in banking efficiency analysis and providing actionable insights for enhancing financial stability and resilience under uncertain market conditions.

This research introduces a novel hybrid approach that combines second-order cone programming (SOCP) and multi-criteria decision-making (MCDM) to conduct a cost-benefit analysis (CBA) of global banks. Benefits correspond to the positive criteria in the banking industry , such as loans, securities, and different sources of income. On the other hand, fixed assets, deposits, personnel and interest expenses are the costs to be minimized. The approach makes it possible to measure banking performance precisely by managing massive datasets effectively and accomplishing better decision-making with complex constraints. The empirical evidence revealed that endogenous variables (fluctuations in the cost of capital, labor, loans, and securities) have a significant effect on CBA scores, while exogenous variables such as foreign direct investment abroad (FDI), economic growth (GDP growth), and political stability have minimal effects. This study provides theoretical and practical implications by demonstrating that cost management, particularly regarding operational inputs such as labor and capital expenses, is critical for enhancing bank performance . Regulatory quality, rather than external macroeconomic conditions, emerged as a dominant influence, with China offering a benchmark for effective practices. These insights can guide the refinement of cost-benefit evaluation models and inform the design of policies aimed at strengthening international banking resilience.

Better performance at a country level will provide benefits to the whole population. This issue has been studied from various perspectives using empirical methods. However, little effort has as yet been made to address the issue of endogeneity in the interrelationships between productive performance and its determinants. We address this issue by proposing a Two-Dimensional Fuzzy-Monte Carlo Analysis (2DFMC) approach. The joint use of stochastic and fuzzy approaches – within the ambit of 2DFMCA – offers methodological tools to mitigate epistemic uncertainty while increasing research validity and reproducibility: (i) preliminary performance assessment by fuzzy ideal solutions; and (ii) robust stochastic regression of the performance scores into the epistemic sources of uncertainty related to the levels of physical and human capitals measured in distinct countries at different epochs. By applying the proposed method to a sample of 23 countries for 1890–2018, our results show that the best and worst-performing countries were Norway and Portugal, respectively. We further found that the intensity of human capital and the age of equipment (capital stock) have different impacts on productive performance – it has been established that capital intensity and total factor productivity are influenced by productivity performance, which, in turn, has a negative impact on labor productivity and GDP per capita. Our analysis provides insights to enable government policies to coordinate productive performance and other macroeconomic indicators.

This paper presents an assessment of the Chinese healthcare system in 31 provinces for a 10-year period in light of relevant physical and human resource variables. First, a novel TEA-IS (Trigonometric Envelopment Analysis for Ideal Solutions) model is developed to assess healthcare efficiency at the province level. Machine learning methods are also employed to predict high-low performance and the synergistic Chinese healthcare province in terms of contextual variables. The results indicate that synergy has played a pivotal role in the Chinese healthcare systems, not only by triggering higher performance levels due to the progressive adoption of best practices over the course of time, but also by being closely related to different socioeconomic and demographic variables, such as the illiteracy rate. It is possible to claim that healthcare performance has remained stable in China over the past two decades, performance and synergy at the province level are still heterogeneous.

Abstract Sustainable airline operations have become an increasingly important issue in recent years. With this respect, several initiatives for reducing pollutant emissions—such as carbon dioxide (CO2)—in the airline industry are now under consideration by regulators, policymakers, and companies. The impact of these initiatives upon efficiency levels of airline operations is still being analysed by different authors. This article is focused on the efficiency assessment of 13 major Chinese airlines from 2008 to 2015, applying a modified slack‐based measure model to account for CO2 emissions. The impact of contextual variables related to the airline's age, fleet mix, stock market governance, ownership type, network span, and whether or not it has undergone a previous merger and acquisition process is tested by means of a stochastic non‐linear robust regression approach. Findings suggest that sustainability in Chinese airline operations is dependent upon a number of economic factors such as learning curve, economies of scale, technology type, and network management. Policy implications are derived for Chinese airlines.

Fuzzy models have been increasingly used in decision-making in the energy sector to deal with many uncertainties such as lack of data and climate change. This paper presents a global energetic efficiency analysis based on the time series data of 91 countries from 1960 to 2010, using an integrated two-stage fuzzy approach. More precisely, Fuzzy DEA models for traditional constant and varying returns to scale assumptions are employed in a first stage to assess the relative efficiency of these countries over the course of time. In the second stage, fuzzy regressions based on different rule-based systems are used to predict the impact of a set of demographic and socio-economic variables on energy efficiency. Energetic efficiency appears to be explained by the countervailing forces of urbanization, wealth inequality, and social development. Thus, a transition to a more energetic efficient lower carbon society will depend on how we address certain socio-political factors, such as pursuing a more sustainable urbanization, reducing inequalities and taking into consideration socio-environmental aspects in trade agreements.

Abstract This paper presents a performance assessment of 88 Association of Southeast Asian Nations banks from 2010 to 2013, using an integrated three‐stage approach on financial criteria that emulates the CAMELS rating system. More precisely, fuzzy analytic hierarchy process is used first to assess the relative weights of a number of criteria related to capital adequacy (C), asset quality (A), management quality (M), earnings (E), liquidity (L), and sensitivity to market risk (S) based on the opinion of 88 Association of Southeast Asian Nations experts. Then, these weights are used as technique for order of preference by similarity to ideal solution inputs to assess their relative efficiency. Lastly, neural networks are combined with technique for order of preference by similarity to ideal solution results to produce a model for banking performance with effective predictive ability. The results reveal that contextual variables have a prominent impact on efficiency. Specifically, parsimony in equity leveraging derived from Islamic finance principles may be the underlying cause in explaining higher efficiency levels.