Vol. 14, No. 2 (2026): Regular Issue

In today's competitive business environment, organizations must collaborate to promote eco-friendly products while ensuring long-term financial sustainability. The rising demand for environmentally conscious goods has driven innovation, particularly in incorporating herbal and natural ingredients. This study introduces a green inventory model for products with non-instantaneous deterioration, designed to help organizations maximize their total annual profit under various trade credit policies. The model provides a mathematical framework to maintain sustainability through investments in environmental awareness and preservation technologies for deteriorating products, alongside trade credit strategies that support sustainable marketing efforts. The primary goal is to achieve sustainability by optimizing pricing, investing in environmental initiatives and preservation technology, and determining the optimal cycle length to maximize total profit. The model assumes that the demand rate is influenced by factors such as selling price, stock levels, and environmental awareness. From the retailer's perspective, the analysis seeks to identify sustainable ordering strategies that maximize annual profit.

Based on a quasi-natural experimental event that China signed the Paris Agreement in 2016, this paper uses a difference-in-difference (DID) model to systematically investigate the impact of rising carbon emission risk on firms' ESG rating and its mechanism. The results show the signing of the Paris Agreement significantly improves the ESG level of firms with high-carbon emission risk. Moreover, this positive effect occurs by increasing the level of corporate green innovation and the attention of analysts. Further heterogeneity analysis suggests that the policy effect is stronger for high-carbon emission firms that are non-state owned, media focused, and low market competition.

This research aims to explore the impact of green accounting on corpo- rate sustainability by incorporating the MFCA approach and assessing resource efficiency. The samples for this study comprise 23 manufacturing companies listed on the Indonesian Stock Exchange (IDX) from 2020 to 2022. Data collection involved reviewing the selected companies' annual and sustainability reports. The data analysis was conducted using path analysis with the assistance of SmartPLS 4 software. The hypothesis testing results indicate that green accounting and MFCA positively affects corporate sustainability. Green accounting positively affects MFCA but negatively affects resource efficiency. MFCA mediates the relationship between green accounting and corporate sustainability. However, resource efficiency has not effectively mediated the relationship between green accounting and corporate sustainability. This pioneering research is the first to combine green accounting with MFCA within the context of manufacturing companies in Indonesia. It provides a fresh perspective on MFCA's role as a mediator between green accounting and corporate sustainability. This research underscores the importance of integrating green accounting and MFCA in the management strategies of manufacturing companies to improve long-term sustainability while seeking solutions to overcome the negative influence of green accounting on resource efficiency. This research emphasizes the importance of adopting green accounting and MFCA practices to reduce manufacturing activities' negative environmental impacts, positively contribute to society through reduced waste and emissions, and improve resource use efficiency.

The subtle patterns within complex datasets can be captured through computational mathematical modeling by resorting to various algorithmic mechanisms toward the solution of complex problems. Deep Neural Networks (DNNs) are poised as feedforward networks owing to the data flow from the input layer direction toward that of the output layer with no change in layer connections. The complexity and heterogeneity of infectious diseases, as a public health concern, lies in the vitality to stop their spread timely. Accordingly, this study introduces a mathematical model incorporating vaccination to analyze the dynamics of lumpy skin disease (LSD) with the fractional Caputo operator. The model comprises five compartments representing susceptible, vaccinated, exposed, infected and recovered classes. First, the problem's qualitative study is addressed, building on existing results and deriving a unique solution by fixed-point theory application. For the semi-analytical solution of the LSD model, the generalized Adams-Bashforth Moulton method is used. The simulation results, considering different initial data, illustrate that the model's solution is stable, converging to a single point. Notably, lower fractional orders demonstrate better stability outcomes. Further, the model is analyzed by the DNN method application for which two hidden layers are taken, the first as the tanh activation function and the other activation function as linear. The dataset concerning fractional order is split into three categories as training, testing and validation. The novel proposed scheme, namely Fractional Epi-DNNs, manifests consistency through the fractional-based epidemic compartmental models accompanied by DNN-based artificial intelligence techniques, which are powerful to analyze the fractional dynamics' intricacies to manage and simulate the spread of LSD by tackling complex data-intensive circumstances.

Large-scale industrialization, population growth, and economic development have led to rising energy consumption and environmental degradation, consistently threatening environmental sustainability. This study examines the effects of natural resource consumption, environmental regulations, energy efficiency and conservation, sustainable economic development, institutional quality, and geopolitical risk on environmental quality. A panel dataset covering China, Pakistan, and India (representing the Global South) from 1990 to 2021 was compiled using data from the World Development Indicators, Global Footprint Network, and World Governance Indicators. To analyze the determinants of environmental quality, the study employs advanced econometric techniques, including Bootstrap OLS, Quantile Regression, and the Dynamic Simulated Autoregressive Distributed Lagged model (DS-ARDL). The results reveal that natural resource consumption significantly deteriorates the environmental quality, reducing it by 0.049%. In contrast, environmental regulations, energy efficiency and conservation, sustainable economic development, and institutional quality improve environmental quality by 0.107%, 0.196%, 0.585%, and 1.450% respectively. Moreover, environmental regulation, energy efficiency and conservation, and strong institutions not only enhance environmental quality but also reduce greenhouse gas emissions, thereby supporting sustainable economic development. The analysis also highlights that interaction effects amplify the impact of some variables on environmental quality. The study underscores the critical role of policy interventions in improving environmental outcomes, and relevant policy implications are discussed.

This study presents two numerical methods for solving tenth-order differential equations: the Vieta-Fibonacci wavelet method (VFWM) and the reproducing kernel Hilbert space method (RKHSM). The VFWM approximates the unknown function using Vieta-Fibonacci wavelets, transforming differential equations into algebraic ones and solving them via the collocation method. A key contribution is the derivation of the operational matrix of derivatives for Vieta-Fibonacci wavelets, enhancing computational efficiency without sacrificing accuracy. The RKHSM generates approximate and analytical solutions in series form, effectively addressing nonlinear problems. Both methods are evaluated for convergence, accuracy, and computational efficiency. Applications to three test problems demonstrate that VFWM excels in handling higher-order derivatives and boundary conditions, while RKHSM offers flexibility for a range of nonlinear issues. These methods are reliable, precise, and efficient, with potential applications in fields such as fluid dynamics and astrophysics. The study concludes with suggestions for future extensions, including fractional-order differential equations and advanced models.

This research develops a deterministic mathematical model for dengue fever transmission using fractal-fractional order differential equations. The proposed model comprises eight compartments, classifying individuals into human and vector populations. By utilizing fixed-point theory, we demonstrate the existence and uniqueness of solutions within the system. We apply fundamental theorems in fractal-fractional calculus alongside the fractional Adams–Bashforth method to obtain approximate solutions. Simulations are performed across various fractional orders and fractal dimensions, offering comparisons with traditional integer-order models. Incorporating fractal-fractional derivatives enhances the model’s ability to capture complex disease dynamics, including memory effects and long-term behavior. This approach provides valuable insights into epidemic control and helps refine intervention strategies. Numerical simulations highlight how arbitrary-order derivatives reveal intricate disease patterns, making them essential for understanding and managing real-world outbreaks.

Vietnam's intensive industrialization has been a driving force behind its economic growth, creating opportunities for potential expansion of the manufacturing industry. However, the proliferation of manufacturing plants and extensive burning of fuels to support production have posed significant environmental challenges. Recognizing the severe deterioration of ecosystems, Vietnamese enterprises have increasingly committed to mitigating the environmental impacts of manufacturing by integrating sustainability goals into their business strategies. This study empirically examines the relationship between carbon dioxide (CO2) emissions and business performance of Vietnamese manufacturing firms from 2016 to 2022. The findings reveal robust evidence of an inverted U-shaped relationship between CO2 emissions growth and firm performance. Furthermore, the growth rate of CO2 emissions was negatively affected by the COVID-19 pandemic and the financial leverage of Vietnamese manufacturing firms. These results are consistent for both domestic and foreign direct investment (FDI) firms. Notably, larger FDI firms emit fewer pollutants per unit of production than micro-, small-, and medium-sized enterprises (MSMEs), although this difference is not statistically significant for domestic firms. This study provides valuable insights for businesses, offering guidance on optimizing resource utilization, enhancing operational efficiency, and reducing environmental impacts to achieve sustainable industrial development.

This study aims at exploring the relationship between digitalized management accounting system (DMAS) and Sustainable Development Goals (SDGs). It also investigates the function of performance management of circular economy practices integrated with inclusive green growth (CEIGG) in the relationship between DMAS and SDGs. Furthermore, it seeks to enhance understanding of how government policy (GP) influences the direct relationships between DMAS and CEIGG, DMAS and SDGs as well as the indirect relationship between DMAS and SDGs via CEIGG, utilizing a moderated mediation model and a hybrid analytical approach with SEM and fuzzy-set qualitative comparative analysis together with a sample of public sector accountants. It revealed that DMAS exerted a considerable and positive impact on SDGs. Additionally, it indicated that CEIGG partially mediated the link between DMAS and SDGs. Furthermore, the results indicated that GP significantly moderated the direct relationships between DMAS and CEIGG, DMAS and SDGs as well as the indirect interaction between DMAS and SDGs via CEIGG. The fsQCA results underscored complex causal relationships among the pertinent factors. Policymakers and practitioners could leverage this study to enhance comprehension of DMAS potential operations and to elucidate strategies, processes, management for the execution as well as optimization of public sector circular economy (CE) practices integrated with inclusive green growth (IGG).

This study analyzes the financial impact of environmental taxation on waste management at the Doura Refinery from 2015 to 2018, focusing on four production units: hydrogenation, wax removal, furfural dealing, and distillation. Results indicate a direct relationship between waste generation and tax liabilities, with the Furfural Dealing Unit contributing the highest waste output (27.4%) and incurring the most significant tax burden. Environmental tax revenue increased from 1,723,688 dinars in 2015 to 4,160,935 dinars in 2018, yet its share in total refinery revenue remained minimal at 0.0376%. The study emphasizes the need for improved cost allocation methods to effectively integrate tax liabilities into financial reporting. Policy recommendations include adopting progressive tax structures, enhancing transparency in tax reporting, and introducing incentives for waste reduction. Future research should explore differentiated tax rates based on waste type and conduct comparative studies across multiple refineries to assess broader applicability. This study provides key insights for policymakers and accountants on leveraging environmental taxation for financial transparency and sustainability in industrial operations.

Considering the importance of the environment in maintaining and continuing the survival of living organisms, the aim of the current research is to provide a model for applying environmental accounting (EA). The current research is of a qualitative type, the data of which was obtained through in-depth semi-structured interviews with 11 financial managers of the reporting units in 2023, and this process continued until theoretical saturation was reached. The method of selecting the respondents is based on the snowball sampling method and the data analysis process is based on the Grounded-Theory. The research findings indicate that the implementation of EA is influenced by two main categories and five subcategories of causal factors, three main categories and seven subcategories of contextual factors, two main categories and seven subcategories of intervening factors, two main categories and five subcategories of strategies, and three main categories and six subcategories of outcomes. Based on the research findings, focusing on the necessary infrastructure and securing governmental and social support can pave the way for the implementation of EA. Additionally, organizational culture and structure, technology and information systems, as well as environmental management information systems, also play a positive role in this process. The results of the present research can not only enrich the academic literature but also provide a foundation for the widespread implementation of EA. Implementing EA can, in addition to fulfilling the professional responsibilities of the accounting field, act as a deterrent to the reckless behavior of humans toward the environment and contribute to the well-being of living organisms.

The accumulation of heavy metals in soil and water adversely affects the environment, human health, and food quality. Contamination primarily arises from industrial effluents, classified into non-metallic and metallic pollutants. This study investigates the effects of arsenic and chromium on plant growth and their uptake by Medicago sativa and Brassica juncea.Developing effective and economical methods to remove these heavy metals is crucial. Medicago sativa and Brassica juncea were chosen for their potential to extract chromium and arsenic from soil and water. The experiment utilized arsenic and chromium concentrations of 2, 4, 6, 8, and 10 ppm, with a maximum of 1000 mg/L. Growth parameters, including plant height and seed germination, were monitored, soil pH analyzed, and arsenic and chromium accumulation assessed using ICP-MS after 45 days. Results show Brassica juncea accumulates more chromium in its roots (68 mg/kg at 10 ppm) than in shoots and leaves, while arsenic uptake was lower (51 mg/kg). Similarly, Medicago sativa exhibited higher chromium accumulation (38 mg/kg in roots at 10 ppm) compared to arsenic (28 mg/kg). Both plants effectively remove heavy metals, particularly chromium, from contaminated sites. This environmentally sustainable and cost-effective phytoremediation approach offers a natural solution for reducing soil contamination. The study concludes that Brassica juncea is more efficient for chromium uptake, while Medicago sativa excels in arsenic absorption across roots and aerial parts.