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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.

The objective of this study is to examine the long-run relationship between workers' remittances and economic growth in Sri Lanka using time series data spanning 1975–2021.

This study employed both exploratory data analysis (EDA) and inferential data analysis (IDA) tools. EDA includes the scatter plots, confidence ellipse with Kernel fit, whereas IDA covers unit root test, the autoregressive distributed lag (ARDL) bounds technique, the Granger's causality test, and impulse response function (IRF) analysis.

EDA confirms that workers' remittances have a positive relationship with per-capita gross domestic product (GDP). All variables used in this study are I(1). This study is exhibited that workers' remittances have a positive long-run relationship with per-capita GDP. The estimated coefficient of the error correction term shows that the dependent variable moves towards the long-run equilibrium path. Workers' remittances have a short-run and long-run causal relationship with per-capita GDP. The IRF analysis indicates that a one standard deviation shock to workers' remittances has initially an immediate significant positive impact on economic growth.

This study provides insights into workers' remittances in economic growth in Sri Lanka. Further, the findings of this study also provide evidence that workers' remittances increase economic growth.

Using ARDL bounds test, Granger's Causality test and IRF analysis for examining the relationship between workers' remittances and economic growth are the originality of this study.

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.

The purpose of this research is to evaluate corruption in the context of India, spanning the period between 1988 and 2021. Additionally, it aims to provide an in-depth comprehension of the factors that drive its prevalence and to propose policy directives for addressing these underlying issues.

The study instead of relying on perception-based measures, takes a distinct approach by formulating a corruption index derived from reported instances, thus ensuring a more objective assessment. Furthermore, we employ stochastic frontier analysis to tackle the issue of under-reporting within the corruption index based on reported cases. Subsequently, an auto regressive distributed lag (ARDL) methodology is applied to ascertain the principal drivers of corruption, encompassing both long and short factors.

This study reveals that corruption in India is notably influenced by economic growth and income inequality. Conversely, government effectiveness and globalization display a tendency to mitigate corruption. However, our rigorous analysis demonstrates that financial development does not wield a substantial influence in our study. Moreover, our inquiry uncovers a nonlinear relationship between economic growth and corruption. Additionally, we ascertain that the long run and short run impacts of corruption remain relatively stable across both models utilized in our study.

This study differs from previous research in the subsequent manners. Primarily, we employed an objective measure to formulate the corruption index, coupled with addressing the underreporting issues via stochastic frontier analysis. Moreover, this study pioneers the identification of a non-linear relationship between corruption and economic growth within the Indian context, a facet unexplored in previous investigations.

The purpose of this study is to develop a novel approach that addresses time-cost tradeoffs in repetitive construction projects while considering the uncertainty in activity durations and the risk preferences of planners.

Our study involves work in three aspects. Firstly, it employs triangular fuzzy numbers to represent activity durations in different units, which facilitates the management of scenarios characterized by limited historical data or the presence of ambiguous information. Secondly, it introduces a fuzzy chance-constrained programming model, which is aimed at minimizing the project budget while ensuring that the risks associated with cost overruns and schedule delays are confined to specified limits. Thirdly, it advances an enhanced genetic algorithm, integrating an electromagnetism-like mechanism and a scheduling repair process, to improve the efficiency and effectiveness of the optimization process.

A real-life street renovation project was analyzed to demonstrate the applicability of the proposed algorithm. The analysis explored three common types of risk preferences: risk-averse, risk-neutral and risk-loving. The results indicate that the proposed algorithm surpasses existing fuzzy repetitive scheduling methods in terms of risk management. It effectively generates schedules that align with the risk preferences of planners and provides worst-case estimates of project performance.

This research makes a significant contribution to the field by developing a fuzzy chance-constrained programming model and an associated optimization algorithm that is specifically designed for time-cost tradeoffs in repetitive construction projects. A key distinction is that this study considers the risk preferences of planners, which sets it apart from previously developed models. As a result, it provides a practical approach for effective risk management.

Climate change negatively affects agriculture and food security, and jeopardizes Myanmar's agriculture, which is vital to ensure food security, rural livelihoods, and the economy. This study explores the asymmetric impacts of climate change on Myanmar's agricultural sector.

We utilize the nonlinear autoregressive distributed lag (NARDL) approach for the years 1991–2020, the Wald test to validate the asymmetric relationship between climate change and agriculture, and the FMOLS and DOLS approaches to confirm the validity of the outcomes.

Our findings reveal that temperature has a positive impact on Myanmar's agriculture, whereas rainfall and CO2 have negative effects over the long and short terms. Evidently, decreasing temperatures more favorably impact agriculture than increasing temperatures, while increasing rainfall more negatively impacts agriculture than decreasing rainfall. Increasing carbon emissions have a more detrimental effect on agriculture than decreasing them.

We gathered data over periods longer than 30 years to provide more robust findings. However, owing to data limitations, such as missing values or unavailability, the study period spans from 1991 to 2020.

This study contributes to the existing literature on the asymmetric effects of climatic and non-climatic factors on agriculture. It is the first study in Myanmar to use the NARDL approach to measuring the effects of climate change on both the agricultural gross production index and value, providing robust findings.

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 examines the effect of foreign direct investment (FDI) on employment and economic growth in Ghana and examines the role of technology in these relationships.

This study applied the autoregressive distributed lag (ARDL) bounds testing approach to cointegration and Granger causality tests to data from 1995 to 2017.

Based on the empirical analysis, the key findings are as follows: FDI does not affect economic growth or employment in Ghana. However, technology moderates the relationship between FDI and economic growth and FDI and employment in the short run. The study also finds that technology exerts a positive effect on economic growth in both short and long run, whereas trade has a significantly negative effect on economic growth in Ghana.

The greatest constraint that faced the authors is the nonavailability of data,.

The transfer of technology agreement enshrined in the GIPC Act should be made more robust and unambiguous, to make it a strict requirement for MNEs to be allowed to operate in Ghana. This increases Ghana's gains from FDI inflow.

The GIPC should tighten its monitoring regime so that MNEs do not exceed their expatriate employment quotas. This will ease the burden of unemployment among the youth in Ghana.

This study adds a new dimension to the literature on the impact of FDI on emerging economies by examining the role of technology in the association between FDI and growth, and FDI and employment.

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.