Companies in industries with intensive greenhouse gas emissions are under increasing pressure to balance sustainability with profitability. This study investigates the impact of environmental, social, and governance (ESG) performance on short-term financial outcomes, the return on assets (ROA), the return on equity (ROE), and trading volume in three sectors in the eurozone that have high emissions: manufacturing, transportation, and mining, refining, and power generation. Using panel data for 139 firms for the period 2008-2023 and quantile regression, we find heterogeneous effects. In manufacturing, ESG raises ROA and ROE but reduces trading volume. In transportation, ESG consistently depresses profitability, reflecting compliance costs and regulation. In energy, ESG benefits weaker firms via reputational gains but erodes performance among stronger firms because of high capital intensity and long payback periods. Our results show that ESG's short-run financial implications are sector and firm specific, which demonstrates the need for differentiated policy and investment strategies.

Previous production-based EKC studies in agriculture have largely ignored trade-embodied energy and indirect upstream burdens, risking misinterpretation of domestic decoupling as genuine progress rather than displacement. This study examines energy use in global grain supply chains across 39 countries from 1995 to 2021, using a multi-regional input-output (MRIO) framework to estimate direct on-farm and indirect upstream energy consumption-including energy embodied in imported grain-and method-of-moments quantile regression to capture distributional heterogeneity. Key findings include: (1) no inverted U-shaped EKC for direct energy use across any quantile, with intensity rising monotonically with income; (2) EKC-like patterns for indirect and total energy, but only in higher quantiles (Q6-Q9), suggesting selective decoupling in high-energy-consumption (typically high-income) countries via technological and structural change; and (3) a strong positive effect of calorie intake on energy use across all models, while renewable energy share and agricultural R&D show unexpected positive associations with indirect/total energy in upper quantiles, indicating rebound and supply-chain expansion effects. These results demonstrate that income growth and cleaner energy alone are insufficient for sustainable food systems. Policy priorities include: targeted on-farm regulations to break direct energy lock-in in middle-income countries; lifecycle and trade-aware instruments (e.g., carbon border adjustments) to address indirect energy leakage; demand-side measures to moderate calorie-driven pressure; and rebound-mitigating designs for renewable energy and R&D policies. Integrated reforms balancing production efficiency, consumption patterns, and global trade governance are urgently needed, particularly in low- and middle-income countries where food-system energy demand is accelerating rapidly.

This paper examines the relationship between structural change and aggregate energy productivity in Türkiye from 1978 to 2019. Using a three-sector model the research quantifies sectoral labor and energy productivities relative to the United States, serving as a benchmark. The findings reveal that while Türkiye initially exhibited higher energy productivity than the U.S. across all sectors, this advantage diminished significantly over the study period. Only the manufacturing sector experienced positive energy productivity growth in Türkiye. Declines in agriculture and services were substantial, emerging as key drivers of the aggregate energy productivity gap between the two countries. A decomposition analysis highlights the dominant role of within-sector energy productivity changes, particularly the negative contribution of services in Türkiye, in explaining the overall decline in aggregate energy productivity. The study underscores the need for sector-specific policies targeting energy productivity improvements, particularly in services, to meet its target of a 35% absolute reduction in emission by 2030. The findings contribute to the literature by providing, for the first time, estimates of relative sectoral energy productivities for Türkiye, offering valuable insights for policymakers focused on enhancing national energy productivity.

This study explores the nonlinear tail dependence and tail risk in major cryptocurrencies, stablecoins, and various commodity markets. To achieve this, we apply a novel measure of nonlinear tail dependence across two distinct periods: the COVID-19-induced monetary expansion from January 3, 2020, to December 31, 2021, and the subsequent monetary contraction from January 1, 2022, to September 14, 2022. Unlike previous studies, this research uniquely considers both the right and left tails and the interactions between stablecoins and major cryptocurrencies. Our findings reveal a significant and persistent upper and lower tail dependence between major cryptocurrencies and commodity markets during both the monetary expansion and contraction periods. We also observe consistent upper and lower tail dependence between most stablecoins and commodity markets throughout these periods. Additionally, our analysis underscores the predictive power of commodity markets concerning cryptocurrency performance. Importantly, our results challenge the prevailing view that stablecoins function as safe-haven assets, offering a fresh perspective that diverges from prior research. These insights are precious for investors who diversify their portfolios across different monetary policy regimes.

In recent years, there has been growing interest in the circular economy as a means of ensuring environmental, economic, and social sustainability. This transformation represents a shift from the linear model to an innovative system that prioritizes resource efficiency, minimizes waste, and adapts production processes to closed-loop systems. This study examines the key elements of transitioning to a circular economy, considering technological advancements, policy-making, production systems, innovative business models, and consumer behavior. The findings reveal that the primary obstacles to transitioning to a circular economy are technological infrastructure deficiencies, financial constraints, and regulatory inconsistencies. In particular, circular business models such as product-service systems, the sharing economy, rental-based approaches, and closed-loop production promote the efficient use of resources and sustainability. The integration of digitalization, artificial intelligence, and advanced recycling technologies facilitates the emergence of innovative solutions and new market opportunities. However, corporate resistance, supply chain coordination issues, and cultural habits also create significant limitations in the transformation process. All these factors highlight the need for a systemic, holistic transformation. Green financing, collective learning, stakeholder participation, and integrated reporting systems are critical to the success of the transformation. In conclusion, the circular economy is not merely an operational change; it necessitates a systemic, holistic, and multi-stakeholder transformation. The goal is to overcome environmental challenges while ensuring long-term economic stability.

Background/Objective: Frontline healthcare staff who contend diseases and mitigate their transmission were repeatedly exposed to high-risk conditions during the COVID-19 pandemic. They were at risk of mental health issues, in particular, psychological stress, depression, anxiety, financial stress, and/or burnout. This study aimed to investigate and evaluate the occupational stress of medical doctors, nurses, pharmacists, physiotherapists, and other hospital support crew during the COVID-19 pandemic in Saudi Arabia. Methods: We collected both qualitative and quantitative data from a survey given to public and private hospitals using methods like correspondence analysis, cluster analysis, and structural equation models to investigate the work-related stress (WRS) and anxiety of the staff. Since health-related factors are unclear and uncertain, a fuzzy association rule mining (FARM) method was created to address these problems and find out the levels of work-related stress (WRS) and anxiety. The statistical results and K-means clustering method were used to find the best number of fuzzy rules and the level of fuzziness in clusters to create the FARM approach and to predict the work-related stress and anxiety of healthcare staff. This innovative approach allows for a more nuanced appraisal of the factors contributing to work-related stress and anxiety, ultimately enabling healthcare organizations to implement targeted interventions. By leveraging these insights, management can foster a healthier work environment that supports staff well-being and enhances overall productivity. This study also aimed to identify the relevant health factors that are the root causes of work-related stress and anxiety to facilitate better preparation and motivation of the staff for reorganizing resources and equipment. Results: The results and findings show that when the financial burden (FIN) of healthcare staff increased, WRS and anxiety increased. Similarly, a rise in psychological stress caused an increase in WRS and anxiety. The psychological impact (PCG) ratio and financial impact (FIN) were the most influential factors for the staff’s anxiety. The FARM results and findings revealed that improving the financial situation of healthcare staff alone was not sufficient during the COVID-19 pandemic. Conclusions: This study found that while the impact of PCG was significant, its combined effect with FIN was more influential on staff’s work-related stress and anxiety. This difference was due to the mutual effects of PCG and FIN on the staff’s motivation. The findings will help healthcare managers make decisions to reduce or eliminate the WRS and anxiety experienced by healthcare staff in the future.

Pilot recruitment is critical as they pose a multifaceted challenge for civilian and military organizations due to the complex traits impacting their missions and performance. In this study, a novel set of criteria and sub-criteria were determined to compare twelve candidate pilots. Numerically immeasurable, imprecise, and non-linear continuous fuzzy linguistic traits (variables) were studied which make the work unique and challenging due to individual preferences and disagreements between decision-makers (DMs). The outcomes of three distinct fuzzy multiple criteria decision-making (MCDM) approaches; fuzzy TOPSIS, fuzzy VIKOR, and fuzzy PROMETHEE were evaluated with trapezoidal fuzzy numbers (TFNs) to sort the positions of candidate pilots. Moreover, a unique defuzzification ranking method was employed to adjust the results of fuzzy MCDM methods for the synthesis and evaluation of outcomes of the pilot selection problem. All these efforts make the paper original and outstanding. Our findings and analysis suggested that fuzzy TOPSIS and PROMETHEE methods' outcomes showed maximum close similarity for ranking positions. However, substantial distinctions were noted when comparing these outcomes with the fuzzy VIKOR approach. Yet, the mission of predicting and revealing the best candidates is related to several traits, their weights, and the methods selected. Therefore, since vague information and ambiguous preferences match fuzzy superiority, a comprehensive and unbiased evaluation was achieved, ensuring the integrity of the decision-making process. The results can be employed to enhance the safety and efficiency of airline operations and ensure that the most qualified and competent pilots are selected for the job.

Recent increases in global food demand have made this research and, therefore, the prediction of agricultural commodity prices, almost imperative. The aim of this paper is to build efficient artificial intelligence methods to effectively forecast commodity prices in light of these global events. Using three separate, well-structured models, the commodity prices of eleven major agricultural commodities that have recently caused crises around the world have been predicted. In achieving its objective, this paper proposes a novel forecasting model for agricultural commodity prices using the extreme learning machine technique optimized with the genetic algorithm. In predicting the eleven commodities, the proposed model, the extreme learning machine with the genetic algorithm, outperforms the model formed by the combination of long short-term memory with the genetic algorithm and the autoregressive integrated moving average model. Despite the fluctuations and changes in agricultural commodity prices in 2022, the extreme learning machine with the genetic algorithm model described in this study successfully predicts both qualitative and quantitative behavior in such a large number of commodities and over such a long period of time for the first time. It is expected that these predictions will provide benefits for the effective management, direction and, if necessary, restructuring of agricultural policies by providing food requirements that adapt to the dynamic structure of the countries.

Purpose - The purpose of this paper is to investigate how bank-specific factors affect the riskiness of conventional and Islamic banks in response to shocks in major financial indices as market conditions change.Design/methodology/approach - The authors use a multivariate quantile model using daily equity returns data to analyze financial risk spillovers in the values at risk that may occur between major financial indices and the equity prices of conventional and Islamic banks worldwide. Then, using both quantile and quantile-on-quantile models, the authors examine the effects of bank-specific variables such as leverage ratio, bank size, return on equity and capital adequacy ratio on the initial impact of shocks in major global financial indices on bank equity price returns at different quantiles of shocks and bank-specific variables.Findings - The findings reveal that major financial indices can predict bank stock returns. Moreover, the authors find that the effect of bank-specific factors on the riskiness of banks is heterogeneous in that it depends on the bank type (Islamic vs conventional), the level of banking variable (high vs low) and, more importantly, market conditions.Originality/value - To the best of the authors' knowledge, this is the first study that compares the dual banking system with stock market performance while considering bank-specific variables as market conditions change. The results of this study reveal that the effect of bank-specific variables on bank performance varies according to different quantiles of shocks and bank-specific variables. Islamic banks may echo or differ from conventional banks depending on the specific factor under investigation.

The agricultural system's ability to make decisions on water management and irrigation scheduling depends on knowledge of the soil moisture content. However, when used with large datasets, standard techniques for estimating soil moisture content, like time-domain reflectometry and gravimetric analysis, need a significant amount of time and manual labor. The moisture content of soil is significantly influenced by numerous critical hydrological and soil parameters. As a result, these characteristics can be used to calculate and predict the soil moisture content. This work offers an alternative machine learning (ML) method for modeling and predicting moisture content of soil based on hydrological and soil characteristics. To predict the moisture content of soil from various hydrological and soil properties, such as average water depth (feet), average soil bulk density (g/cm3), average organic matter (%), Cation-Exchange capacity (meq/100g), percentages of clay and sand content (%), and tonnage of residuals (ton/acre), three machine learning techniques were employed: artificial neural network (ANN), and support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) were employed for the prediction of the soil moisture content. The findings demonstrated that all three methods (ANN, SVM, and ANFIS) could accurately predict moisture content, with different prediction error rates. The average prediction error (APE) of ANN, SVM, and ANFIS is 9.057%, 10.834%, and 5.753%, respectively, of which the lowest root mean square error (RMSE) was observed for ANFIS of the testing (0.9979) and training (1.0049) datasets. In nutshell, the created models may be used to forecast the moisture in the soil of any farms with given hydrological and soil characteristics to control the water management system, saving money, effort and scarce water resources in the process of figuring out the soil moisture content.

Calculation of the criticality score and its analysis are important for the reliability-centered maintenance (RCM) of CNC lathe machine components. The tools of a CNC lathe machine include complex critical part and sub-systems. The reliability of parts decline due to the independent system fault. To identify the essential components of CNC lathe machine tools, a system level component criticality analysis method is proposed. In this study, a comprehensive framework of criticality-based RCM implementation through experimentation of a CNC-lathe machine is proposed. The criticality of machine components were identified by investigating their dependency on the criteria and sub-criteria. Five main criteria were found effective for the criticality of the components which are the cost, complexity, sustainability, functional dependency, and safety impacts. Fuzzy analytical network process (FANP) approach was employed to evaluate criticality scores for eleven-component of a CNC lathe machine. This is a preliminary study and aims to contribute to the literature and practitioners in many ways. The primary focus of our method lies in its capability to predict criticality values for a manufacturing system in absence of historical maintenance data relying on the expertise/intuition of the operator(s) for decision making for the predictive maintenance of the system with the help of fuzzy ANP approach. It was found that the turret was the most critical component with a metric of 0.0641 which was also found to be over 3 times more critical than the least critical hydraulic system, which had a criticality metric of 0.0187. This novel FANP framework provides a credible solution to real-world RCM scheduling and can be applied to any manufacturing system-related application.

This paper applies a novel time series-based additive nonparametric quantile regression technique to stress test the credit risk of conventional and participation banks in Turkey. We particularly examine the effects of the exchange rate, unemployment rate, the policy interest rate, and the public debt-to-GDP ratio on the non-performing loan ratio (NPL) of Turkish banks at distinct quantile levels. We find that their effects are heterogeneous and significantly vary across different types of banks and quantiles. Overall, participation banks are more vulnerable to negative scenarios associated with the exchange rate, unemployment rate, and public debt ratio than conventional banks.

This paper examines the tail dependence structure between energy commodities (Brent oil, natural gas and gasoline) and agricultural commodities (wheat, soybean, corn, cotton, sugar, rice, oat, coffee and cocoa) from 01.06.2017 to 09.06.2023, spanning periods before, during and after Covid-19 pandemic. We employ the tail-restricted integrated regression function (IRF), a novel approach for analyzing nonlinear tail dependence, as it offers further insights into tail events by considering a continuum of quantiles, rather than focusing on a single quantile. The results reveal significant and persistent lower and upper tail dependence across all commodity pairs throughout each period, indicating asymmetric risk transmissions from energy commodities to agricultural commodities. Additionally, the findings are corroborated using cross-quantilogram analysis and nonparametric tests for Granger causality in distribution.

A robust test statistic for testing homoskedasticity in spatial panel data models that have entity and time fixed effects is introduced in a quasi maximum likelihood estimation setting. A size-correction approach is introduced to ensure that the score functions have an asymptotic distribution centered around zero in the local presence of certain nuisance parameters. The outer-product-of-martingale-difference (OPMD) method is used to formulate an estimator for the asymptotic variance of the score functions. The OPMD estimator and the adjusted score functions are used to formulate a computationally simple robust test statistic. The suggested test statistic does not require knowing the presence of spatial dependence in the outcome variable and/or the disturbance terms. The asymptotic distribution of the test statistic is established under the null and local alternative hypotheses. Through Monte Carlo simulations, the finite sample size and power properties of the proposed test statistic are investigated. Finally, two empirical applications are provided to illustrate the practical use of the proposed test statistic.

In these unprecedented times, marred by the effects of the Covid-19 pandemic, global warming, and the war in Ukraine that began in February 2022, new approaches such as tail dependence have attracted more interest than conventional market dependence methodologies in analyzing time series in order to evaluate market linkage. In this study, we use the tail-restricted integrated regression function (IRF), introduced as a new methodology for nonlinear tail-mean dependence analysis. The IRF approach has several advantages over the existing tail-dependence measures focused solely on the occurrence of single tail events. To examine market dependence and tail risk, we analyze the nexus between the US and Turkish agricultural commodity markets over the period January 5, 2016–May 31, 2022. In addition to the daily prices of barley, corn, and wheat on agricultural commodity markets, we use Brent oil as a representative for the energy market, interest, and foreign exchange rates for financial markets as variables in our study. The results of upside and downside asymmetric risk spillovers show the direction of impact from the US agricultural market to the Turkish agricultural market. The findings suggest that, following the launch of a spot market in Türkiye, the launch of an agricultural commodity futures market will enhance the completeness and the link between the spot and derivatives markets there.

Achieving the Sustainable Development Goals (SDGs) is one of the crucial matter on the agenda of decision makers in Organization for Economic Cooperation and Development (OECD) countries. The SDGs include many broad environmental goals, such as pollution of air, water, and soil, and the protection of natural resources to combat this pollution. To achieve these goals, carbon emissions and ecological footprint analyzes provide some guidance, but neglect the supply side of nature. The load capacity factor makes it possible to analyze biocapacity (the supply side of nature) and ecological footprint simultaneously to assess whether countries are exceeding the sustainability limit and what factors are influencing that limit. To address this issue and provide a comprehensive environmental assessment of OECD countries, this study examines the influence of human capital, income, natural resources, urbanization, and renewable energy on the load capacity factor for 26 OECD countries over the period 1980-2018. For this purpose, the study uses the newly developed quantile common correlated effects mean group (QMG) estimator and investigates the load capacity curve (LCC) hypothesis. Our results suggest that there is a U-shaped link between income and environmental quality and the LCC hypothesis is valid. The QMG estimator results show that human capital, resource rent, and renewable energy improve the load capacity factor, but urbanization adversely affects environmental quality. The overall results highlight the ecological role of renewable energy, resource rent, and human capital in achieving the SDGs of OECD countries, such as transitioning to a low-carbon economy and reducing water pollution.& COPY; 2023 International Association for Gondwana Research. Published by Elsevier B.V. All rights reserved.

Nations must adapt to the changing and developing world to sustain and develop their competitiveness. Human development and innovation are the two key concepts to increase the competitiveness of a nation. This study aims to examine the relationship between the Human Development Index (HDI), Global Innovation Index (GII), and Global Competitiveness Index (GCI) across different income groups from 2010 to 2019. The main objective is to identify potential variations in these relationships based on the income level of the countries involved. Panel data analyses using Common Correlated Effects Mean Group (CCEMG) and Augmented Mean Group (AMG) estimators are conducted to examine the relationships. Additionally, Pairwise Dumitrescu Hurlin Panel Causality Tests are conducted to examine the causal relationships between variables. The results show that HDI has a significant positive effect on GCI in each income group. Improving human development such as raising living standards and providing equal education opportunities for every member of society can contribute to a country’s competitiveness. Moreover, it is found that the effect of GII on GCI varies by income group. Specifically, the results indicate that the effect of GII on GCI is not supported for upper-middle-income countries. Therefore, while developing strategies to increase competitiveness through innovation, it is important to consider the income group of a nation. The findings of this study may assist policymakers, researchers, academics, and politicians to enhance their perspectives and formulate strategic and effective recommendations for action.

This paper employs a big data source, the Borsa Istanbul's “data analytics” information, to predict 5-min up, down, and steady signs drawn from closing price changes. Seven machine learning algorithms are compared with 2018 data for the entire year. Success levels for each method are reported for 26 liquid stocks in terms of macro-averaged F-measures. For the 5-min lagged data, nine equities are found to be statistically predictable. For lagged data over longer periods, equities remain predictable, decreasing gradually to zero as the markets absorb the data over time. Furthermore, economic gains for the nine equities are analyzed with algorithms where short selling is allowed or not allowed depending on these predictions. Four equities are found to yield more economic gains via machine learning–supported trading strategies than the equities' own price performances. Under the “efficient market hypothesis,” the results imply a lack of “semistrong-form efficiency.”

Since the beginning of COVID-19, human beings have been threatened by various aspects. As of February 14, 2022, this global pandemic has caused about 412 million cases and 5.8 million deaths worldwide. Stock markets are one of the most agile economic indicators. In this context, this study investigates how daily growth in deaths, daily growth in cases, and governmental interventions affect stock market returns in 21 emerging economies from January 22 to December 31, 2020. Our results indicate that government response policies to Covid-19 positively impact stock returns. Besides, the daily growths in deaths and cases negatively affect stock market returns. The results also indicate that government response policies also have an indirect positive effect on stock market returns by weakening the negative impact of the daily growth in COVID-19 confirmed cases and deaths.