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Upper bound on Sharpe ratio helps to evaluate the risk-adjusted performance in real market setting. The purpose of this study is to evaluate the performance of Shariah-compliant indices (SCIs) and also estimate the upper bound on Sharpe ratio in real-market setting. For comparison, the authors also report the same statistics for conventional indices (CIs).

This study considers returns of 12 indices from Asia-Pacific and the USA for the time period May 31, 2013-Aug 25, 2022. These indices are further classified as small-, mid- and large-cap indices. The upper bound is estimated in three settings, an unconstrained setting, correlation constraint and copula constraint.

The authors found that upper bound estimation is sensitive to both the choice of index, geographic location and size of constituents within the index. Interestingly, SCIs outperform CIs both in terms of Sharpe ratio and upper bound estimations. The ability of SCIs to achieve high Sharpe ratio boosts investors’ confidence. The results are robust even after introducing correlation and copula constraints to the model.

Any violation of the upper bound on Sharpe ratio is not necessarily an indication of fraud or dubious Sharpe ratio. It should be interpreted only as a signal for inflated risk-adjusted performance and requires further investigation. Together with risk-adjusted performance, fund managers should report upper bounds to insure investor protection.

Many studies have compared the risk-adjusted performance of SCIs and CI. To the best of the authors’ knowledge, this study is the first effort to evaluate and compare the upper bounds on Sharpe ratio of SCIs in a real-market setting.

Climate change, driven by global warming, poses a significant threat to humanity and disrupts the ecological balance. In Europe, concentrations of air pollutants remain very high, and problems related to air quality and the acceleration of the phenomenon of global warming persist. As a result, carbon taxation has emerged as a key strategy to mitigate climate change. In Romania, environmental taxes are an important instrument of environmental policy as an economic instrument for environmental protection and natural resource management. Using 1990–2021 time series data and an Autoregressive Distributed Lag (ARDL) Bounds cointegration for long-run analysis and the Toda–Yamamoto test for causality analysis, we investigated whether environmental taxes, renewable energy consumption, urbanization and economic growth significantly impact CO₂ emissions in Romania.

This paper differs from the assessment of the Environmental Kuznets Curve (EKC) hypothesis (Grossman and Krueger 1991) and instead aims to determine the impact of environmental taxes, renewable energy consumption, per capita GDP and urbanization on CO₂ emissions in Romania. The study investigates both short- and long-term effects, as well as Toda–Yamamoto causality linkages (Toda and Yamamoto 1995) between these variables. We adopt an ARDL estimation technique with Bound cointegration test and error correction models (Pesaran et al., 2001) to examine the short- and long-term effects.

The findings revealed that environmental taxes positively and significantly reduce CO₂ emissions, while urbanization induces CO₂ emissions, in the long run. Moreover, in the short run, environmental taxes and renewable energy consumption significantly reduce CO₂ emissions while per capita GDP and urbanization significantly increase CO₂ emissions. A unidirectional causality exists between renewable energy consumption and CO₂ emissions. Thus, to realize its 34% target of renewable energy consumption in 2030, Romania should prioritize the implementation of the Casa Verde Plus program and enforce sustainable urban planning to meet near-zero energy standards. Consequently, the government should continue to enforce carbon taxes to promote environmental sustainability.

Empirical evidence supports the cointegration relationship between environmental taxes and CO₂ emissions, with carbon taxes effectively reducing CO₂ emissions and improving environmental quality (Allan et al., 2014; Polat and Polat, 2018; Kiuila et al., 2019, etc.). While existing research (Floros and Vlachou, 2005; Wissema and Dellink, 2007; Aydin and Esen, 2018; Lin and Li, 2011) primarily focuses on country-specific or regional analyses, limited research has been conducted on the impact of carbon taxation on CO₂ emissions in Romania. However, to the best of our knowledge, limited research on this phenomenon in Romania exists in response to recommendations for climate change mitigation. Furthermore, urbanization has significantly contributed to rising atmospheric carbon levels and subsequent global warming and climate change (Woldu, 2021). As economic growth, particularly in countries like Romania, drives urbanization, it leads to increased energy demand, expanding urban areas and mounting environmental concerns. This process involves industrial restructuring, and the development of new infrastructure, all of which exert pressure on energy consumption and CO₂ emissions (Niu and Lekse, 2018). While economic growth is a primary objective, industrialization and urbanization inevitably generate unintended consequences, including CO₂ emissions. However, limited research exists on the impact of urbanization patterns on CO₂ emissions in Romania. This study investigates the dynamic causal relationships among urbanization, per capita GDP, carbon taxes, renewable energy consumption and CO₂ emissions, considering both short-run and long-run effects in Romania.

This paper aims to study boundary and interior layer phenomena in coupled multiscale parabolic convection–diffusion interface problems and to present their efficient numerical resolution and analysis.

This study includes cases in which the diffusion parameters are small, distinct and can differ in order of magnitude. The source term is considered to be discontinuous. The asymptotic behavior of the solution is examined. The layer structure is analyzed, leading to the development of a variant of layer-resolving Shishkin mesh. For efficient numerical resolution, two methods are developed by combining additive schemes on a uniform mesh to discretize in time and an upwind difference scheme away from the line of discontinuity and a specific upwind difference scheme along the line of discontinuity, defined on a variant of layer resolving Shishkin mesh, to discretize in space. The analysis of the numerical resolution is discussed using the barrier function approach. Numerical simulations provide a verification of the theory and efficiency of the approach.

The discontinuity in the source term, along with the inclusion of small and distinct diffusion parameters, results in multiple overlapping and interacting boundary and interior layers. The work demonstrates that the present approach is robust in resolving boundary and interior layers. From a computational cost perspective, the numerical resolution presented in the paper is more efficient than conventional approaches.

Efficient numerical resolution and analysis of boundary and interior layer phenomena in coupled multiscale parabolic convection–diffusion interface problems are provided. The discretization of the coupled system in the approach incorporates a distinctive feature, wherein the components of the approximate solution are decoupled at each time level, resulting in tridiagonal linear systems to be solved, in contrast to large banded linear systems with conventional approaches.

This paper investigates the relationship between foreign direct investment (FDI) and poverty in Uganda.

Using annual time series data from 1983 to 2021, we use the autoregressive distributed lag bounds testing method to cointegration for test the above relationship. Considering the multidimensionality of poverty, we proxy poverty by three proxies: household consumption expenditure, infant mortality rate and life expectancy.

The findings reveal that FDI is important for poverty reduction in both the long- and short-run when using household consumption and infant mortality rate, holding other factors constant. This relationship is robust to alternative specifications and estimation methods. This paper establishes that FDI does matter for poverty reduction in both the short and long run. However, when life expectancy is used to proxy poverty, results show a positive relationship between FDI and poverty.

The long-run relationship between FDI and poverty largely suggests that tackling poverty may require reforming the economic environment by addressing bottlenecks that hinder economic growth, which is a key component in poverty reduction. Thus, it is important to ensure that government expenditure is directed to the productive sectors of the economy, such as education and infrastructure, among others that are paramount in expanding the productive capacity of the economy, which in turn is crucial for poverty reduction.

In this article, we present the numerical solution of fractional Sturm-Liouville problems by using generalized shifted Chebyshev polynomials.

We combine right Caputo and left Riemann–Liouville fractional differential operators for the construction of fractional Sturm–Liouville operators. The proposed algorithm is developed using operational integration matrices of generalized shifted Chebyshev polynomials. We introduce a new bound on the coefficients of the shifted. Chebyshev polynomials subsequently employed to establish an upper bound for error in the approximation of a function by shifted Chebyshev polynomials.

We have solved fractional initial value problems, terminal value problems and Sturm–Liouville problems by plotting graphs and comparing the results. We have presented the comparison of approximated solutions with existing results and exact numerical solutions. The presented numerical problems with satisfactory results show the applicability of the proposed method to produce an approximate solution with accuracy.

The presented method has been applied to a specific class of fractional differential equations, which involve fractional derivatives of a function with respect to some other function. Keeping this in mind, we have modified the classical Chebyshev polynomials so that they involve the same function with respect to which fractional differentiation is performed. This modification is of great help to analyze the newly introduced polynomials from analytical and numerical point of view. We have compared our numerical results with some other numerical methods in the literature and obtained better results.

This study aims to empirically assess how infrastructure development influenced economic growth in Zambia from 2000 to 2020.

The study uses data from the World Development Indicators (WDI), spanning from 2000 to 2020. The selection of this time period was determined by the availability of data related to the research. The Autoregressive Distributed Lag (ARDL) bounds testing approach was used for data analysis.

The findings show that economic growth is cointegrated with capacity to generate electricity, proving the existence of a long-run equilibrium relationship between them. Furthermore, the empirical results established that electricity generation capacity had a positive and significant impact on economic growth. Similarly, in the short run, electricity generation capacity, and mobile cellular services had a positive impact on economic growth.

Policy measures should prioritise increasing capacity for producing electricity and expanding access to energy by relevant economic sectors. Increased access to energy by these sectors can raise productivity, spur economic growth and accelerate industrialisation. Also, in the light of climate change, it is crucial that policymakers explore alternate sources of electricity generation, such as green and renewable sources. Furthermore, policy initiatives should prioritise expanding mobile cellular infrastructure, given that mobile cellular technology has become a vital component of economies and continues to offer unprecedented opportunities for economic growth.

This study presents novel empirical evidence on the unique relationship between infrastructure and economic growth in Zambia, highlighting electricity generation and mobile cellular services as pivotal factors for enhancing productivity and spurring industrial development.

This study aims to investigate the causal relationship between external debt and inflation in Jordan over the period 1970 to 2020.

The external debt–inflation nexus is examined within a multivariate framework by including other determinants of inflation, including money supply and the nominal effective exchange rate. This study uses an ARDL bounds testing approach to cointegration to test the existence of a long-run relationship between the inflation rate and its drivers. An error correction model is estimated to reveal the short-run dynamics of the series. The direction of causality among the variables is examined using a modified version of the Granger non-causality test due to Toda and Yamamoto (1995). The analyses control for the presence of structural breaks in the underlying time series.

The empirical results show that external debt and money supply have a statistically significant positive effect on inflation in the long run. The authors also find that a nominal depreciation of the Jordanian Dinar raises inflation rates in the long run. The Toda–Yamamoto Granger non-causality test findings reveal a statistically significant bi-directional positive causality between inflation and external debt, between the nominal effective exchange rate and inflation and between money supply and inflation.

Proper management of the exchange rate policy, money supply and external debt levels is crucial to control inflation rates in Jordan.

To date, the authors are unaware of any empirical study that examines the impact of external debt on inflation in Jordan, and the current study aims to fill this gap in the literature.

The uncertain supply chain network design (SCND) problem, considering suppliers’ environmental, social and governance (ESG) ratings, has been infrequently addressed in the literature. Looking at the importance of ESG ratings in achieving supply chain sustainability, this study aims to fill the gap by incorporating supplier ESG factors into SCND within an uncertain environment.

This paper presents a multi-period, multi product SCND model that integrates ESG factors and accounts for uncertainties in supply and production capacities. The model seeks to minimize total operational costs by determining the optimal selection of plant and warehouse locations across multiple time periods. Uncertainties in supply and production capacities are managed through a chance-constrained programming approach with right-hand side stochasticity. A Lagrangian relaxation-based heuristic method is applied to address the NP-hard nature of the problem.

The efficacy of the proposed model is illustrated through a numerical example, demonstrating its capability to optimize material flows across the supply chain under uncertain conditions. The model simultaneously considers economic and ESG factors in procurement decisions. A sensitivity analysis is conducted to examine different operational scenarios and their implications on the model’s outcomes.

To the best of the authors’ knowledge, this study is one of the first to integrate ESG factors into SCND under uncertainty. The proposed model provides a robust framework for decision-makers to optimize supply chain operations while considering both economic and ESG objectives in an uncertain environment.

This study aims to optimize the construction site layout planning (CSLP) problem, with a focus on prefabricated projects. It proposes the use of the oMOAHA algorithm, an enhanced version of the multi-objective artificial hummingbird algorithm (MOAHA), to address challenges related to search space exploration and local optimization in CSLP.

The study integrates three techniques – opposition-based learning (OBL), quasi-opposition and quasi-reflection – into the initialization phase of the MOAHA algorithm, creating the oMOAHA variant. This model is applied to all three types of CSLP problems – pre-determined location, grid system and continuous space – to evaluate its effectiveness. Six objective functions (three related to cost, two to safety and one to tower crane efficiency) and four site-related constraints are considered through three case studies taken from previous research and one real project involving prefabricated steel structures.

The oMOAHA algorithm demonstrates superior performance compared to previous models, consistently outperforming traditional approaches in CSLP optimization for prefabricated projects. In the real case study, the proposed model exceeded the actual project plan by 28–43%, indicating its potential to significantly improve both solution quality and project outcomes.

This study is the first to apply an optimization model to all three types of CSLP problems – pre-determined location, grid system and continuous space – within a unified framework. The integration of advanced techniques into the MOAHA algorithm and the model’s successful application in a real prefabricated project underscore its high applicability and effectiveness in modern construction management.

This paper aims to introduce a novel structure for the physical internet (PI)–enabled sustainable supplier selection and inventory management problem under uncertain environments.

To address hybrid uncertainty both in the objective function and constraints, a novel interactive hybrid multi-objective optimization solution approach combining Me-based fuzzy possibilistic programming and interval programming approaches is tailored.

Various numerical experiments are introduced to validate the feasibility of the established model and the proposed solution method.

Due to its interconnectedness, the PI has the opportunity to support firms in addressing sustainability challenges and reducing initial impact. The sustainable supplier selection and inventory management have become critical operational challenges in PI-enabled supply chain problems. This is the first attempt on this issue, which uses the presented novel interactive possibilistic programming method.