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pp. 133-145 | DOI: 10.5890/JEAM.2026.06.001
Mamta Keswani, Anshu Kumari, Komal Kumari, Sudhakar Khedlekar, Vinita Dwivedi
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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 noninstantaneous
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.
pp. 147-159 | DOI: 10.5890/JEAM.2026.06.002
Shiyu Lu, Zixin Li, Bo Cheng
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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 highcarbon
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 highcarbon
emission firms that are non-state owned, media focused, and low
market competition.
pp. 161-178 | DOI: 10.5890/JEAM.2026.06.003
Darwis Said1, Eko Ganis Sukoharsono, Darmawati Darmawati, Muhammad Reza Pahlevi Juanda, Alia Rezki Amalia, Afdal Madein
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This research aims to explore the impact of green accounting on corporate
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.
pp. 179-191 | DOI: 10.5890/JEAM.2026.06.004
Yeliz Karaca, Mati ur Rahman, Saira Tabassum, Dumitru Baleanu
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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.
pp. 193-209 | DOI: 10.5890/JEAM.2026.06.005
Zia Ur Rahman, Saeed Ullah, Wajid Khan, Muhammad Kamran, Tahira Awan, Ikram Ullah, Hafsah Batool
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Abstract
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.
pp. 211-259 | DOI: 10.5890/JEAM.2026.06.006
Shivani Aeri, Rakesh Kumar, Ali Akgul, Nourhane Attia
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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.
pp. 261-282 | DOI: 10.5890/JEAM.2026.06.007
Tharmalingam Gunasekar, Shanmugam Manikandan, Murgan Suba, Ali Akgul, Muhammad Sinan
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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 fractalfractional
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.
pp. 283-298 | DOI: 10.5890/JEAM.2026.06.008
Nhung Hong Do, Duy Thai Vu, Minh Binh Tran, Van Mai Trinh, Ngoc Hong Thi Hoang
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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.
pp. 299-329 | DOI: 10.5890/JEAM.2026.06.009
Pham Quang Huy, Vu Kien Phuc
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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).
pp. 331-344 | DOI: 10.5890/JEAM.2026.06.010
A. H. Hanan, S. B. Rajaa, I. W. Amel, O. L. AlSeddiq
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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.
pp. 345-356 | DOI: 10.5890/JEAM.2026.06.011
Mehrdad Sadrara, MohammadJavad Tasaddi Kari, Mojtaba Bahari Dalivand
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Abstract
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.
pp. 357-370 | DOI: 10.5890/JEAM.2026.06.012
S. Monisha, S.P. Sangeetha
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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.
