Search results

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 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.

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 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.

The purpose of this study is to empirically examine the impact of natural resource rents on income inequality in Ethiopia from 1981 to 2022 and investigate whether investments in manufacturing moderate this relationship.

Dynamic autoregressive distributed lag simulation and Kernel-based regularized least squares (KRLS) models are used to analyses short- and long-run relationships, as well as the potential moderating role of manufacturing.

The bounds test indicates natural resource rents have a long-run positive effect on inequality but a short-run negative impact. The KRLS model finds manufacturing conditions for this linkage in the short run. In the long run, economic growth decreases inequality following an inverted Kuznets pattern, while government expenditures reduce disparities when directed at priority social services.

The findings provide mixed support for theories while highlighting nuances not fully captured without local analyses. Strategic sectoral investments may help optimize outcomes from resource dependence.

The results imply Ethiopia should prudently govern resources, productively invest revenues and prioritize social spending to equitably manage industrialization and uphold stability.

Reducing disparities through inclusive development aligned with empirical evidence could help Ethiopia sustain peace amid transformation and realize its goals of shared prosperity.

This study applies innovative econometrics to provide novel insights into Ethiopia's experience, resolving inconsistencies in the literature on relationships between key determinants and inequality.

This paper aims to improve the selection process of heavy machinery in construction projects by developing an advanced optimization technique using the Improved Particle Swarm Optimization algorithm (IPSOM). The main objectives of such a study are to optimize the key parameters of time and cost while ensuring adherence to a predefined quality benchmarks, thereby facilitating more informed and balanced decision-making in construction management.

A rigorous methodology was applied to identify the relevant optimization parameters, combining a comprehensive literature review with consultations with industry experts. This approach identified the most influential factors affecting machinery selection, ensuring the model’s applicability and relevance across different project scales and complexities. Unique to this study, the model’s novelty lies in its advanced application of the IPSOM tailored to the construction industry’s specific needs, offering a systematic approach to balancing time, cost and quality considerations.

IPSOM was validated through a detailed case study, which provided empirical evidence of the model’s effectiveness in a real-world application. The study introduces a groundbreaking approach to optimizing equipment selection in highway construction, with the dual aims of minimizing costs and project duration while maintaining high-quality standards. The model proposed in the study saved 53% time, reduced costs by 30.8% and increased quality by 26.3%, outperforming traditional equipment selection methods.

The case study analysis demonstrated the model’s adaptability and potential as a crucial tool for decision-making in construction projects.

The main purpose of this study is to analyze the evolution of economic and environmental factors in Finland during the period 1990–2022, exploring the complex interactions between Gross Domestic Product (GDP), nuclear energy production, innovation (measured by patents) and the electric grid load capacity factor (LCF).

To achieve the stated purpose, econometric models such as Autoregressive Distributed Lag and cointegration tests were employed to investigate relationships and trends in the available economic and energy data for Finland. For conducting the proposed analyses, EViews was used for econometric approaches, and the Python language was utilized for constructing the Environmental Kuznets Curve.

Following the conducted analyses, several relevant findings have been observed: 1) a complex relationship between GDP and LCF has been identified, noting a long-term decrease in the electricity grid LCF with GDP growth. This result emphasizes the importance of strategic planning in energy policy to maintain stability and efficiency of the grid amidst economic growth; 2) nuclear energy and innovation have shown a mixed impact on LCF, with both positive and negative effects. This finding highlights the necessity to develop policies that encourage the progressive integration of new technologies to minimize the negative impact on electricity grid efficiency; and 3) to maximize the efficient use of the energy system’s capacity, policymakers should aim to balance economic growth with responsible management of energy resources. The integration of renewable energies and continuous investments in research and development are essential for ensuring a sustainable energy transition in Finland.

The study makes a significant contribution by identifying and analyzing in detail the interdependencies between economic growth, innovation and energy sustainability in Finland, providing new perspectives for the development of public policies and economic strategies in the current context of global climate change and energy transition.

This study attempts to examine the relationship between the banking sector and stock market development in India.

To analyze the relationship between banks and stock market development, the ratio of stock market capitalization to GDP is proxied by stock market development. The determinants of the stock market development are used for analysis namely domestic credit to the private sector as a ratio of GDP is used as a proxy for the development of banks, saving rate, per capita real GDP, and inflation. The autoregressive distributed lag (ARDL)-Bounds testing approach is used for the analysis. The paper also used the unrestricted error correction model and CUSUM and CUSUM square test to check the stability of the model.

The ARDL bounds test found that there is a long-run relationship between stock market development and bank-centered financial development. The results also revealed that the stock market is positively influenced by the development of banks, savings, and per capita real GDP in the short-run as well as long-run.

This paper suggests that improvement of banking sector plays an important role to increase liquidity of the capital market development in India. This paper also suggests that the economic growth and savings rate have positive impact to induce the capital market growth in both short run and long run.

The study has investigated the empirical relationship between the banking sector and the stock market development in a different methodological approach by using an ARDL model which is appropriate for a small sample size. There are few studies related to bank-centered financial development and stock market development in the context of India.

This study aims to explore the influence of Fourier-feature enhanced physics-informed neural networks (PINNs) on effectively solving two-dimensional local time-fractional anomalous diffusion equations with nonlinear thermal diffusivity. By tackling the shortcomings of conventional numerical methods in managing fractional derivatives and nonlinearities, this research addresses a significant gap in the literature regarding efficient solution strategies for complex diffusion processes.

This study uses a quantitative methodology featuring a feed-forward neural network architecture combined with a Fourier feature layer. Automatic differentiation is implemented to ensure precise gradient calculations for fractional derivatives. The effectiveness of the proposed approach is showcased through numerical simulations across various sub-diffusion and super-diffusion scenarios, with fractal space parameters adjusted to examine behavior. In addition, the training process is assessed using the Fisher information matrix to analyze the loss landscape.

The results demonstrate that the Fourier-feature enhanced PINNs effectively capture the dynamics of the anomalous diffusion equation, achieving greater solution accuracy than traditional methods. The analysis using the Fisher information matrix underscores the importance of hyperparameter tuning in optimizing network performance. These findings support the hypothesis that Fourier features improve the model’s capacity to represent complex solution behaviors, providing the relationship between model architecture and diffusion dynamics.

This research presents a novel approach to solving fractional anomalous diffusion equations through Fourier-feature enhanced PINNs. The results contribute to the advancement of computational methods in areas such as thermal engineering, materials science and biological diffusion modeling, while also providing a foundation for future investigations into training dynamics within neural networks.