Vol. 13, No. 2 (2025): Regular Issue

Organizations are increasingly focusing on environmental sustainability as a crucial aspect of their operations in pursuit of sustainable development goals. This study investigates the influence of environmental strategies and green intellectual capital on environmental performance, while considering the mediating effect of environmental management accounting. Data were gathered through self-administered questionnaires from 411 manufacturing organizations in Pakistan. The analysis was conducted using SPSS and AMOS software, employing structural equation modelling to test the study hypotheses. To the best of our knowledge, this study breaks new grounds by offering a comprehensive examination of the relationship among environmental strategies, green intellectual capital, and environmental performance, particularly within manufacturing organizations with ISO 14001 certification, emphasizing the critical role of environmental management accounting as a mediator. Drawing from the natural resource orchestration approach, this study offers valuable insights for managers and shareholders, emphasizing the significance of strategic environmental management and intellectual capital in achieving long-term sustainability objectives beyond financial gains.

This study aims to investigate the solution of fractional order delayed lumpy skin infection model with Caputo operator numerically as well as analytically. This delay factor helps to control and slow down the spread of infection in individuals. In this study, existence and uniqueness of the underlying model is discussed. Equilibria of the lumpy skin model are computed along with reproductive number ($R_0$), if $R_0>1$ refers spread of illness and if $R_0<1$_\ means control of disease. Local and global stability of fraction delayed model is also presented. Moreover, positive and bounded solution of proposed model are investigated. For the numerical solution of this model, we use Grunwald Letnikov non-standard finite difference scheme. The key properties of the numerical scheme are also investigated like positivity and boundedness. Numerical example is given to present the graphical solution of the fractional order delay epidemic model.

Efficient water management is crucial in semi-arid regions like Setif, Algeria, where wheat production faces challenges due to water scarcity. Recent research has highlighted the potential of remote sensing for retrieving crop water requirements through mathematical modeling and analysis of vegetation indices. This study investigates the application of linear regression models to estimate wheat evapotranspiration in the semi-arid Setif region of Algeria, contributing to the understanding of data-driven dynamical systems in agricultural contexts. Utilizing Sentinel 2 data, we derived vegetation indices (NDVI, NDRE, MSAVI, ReCI, NDMI) and analyzed their relationship with evapotranspiration values obtained from a smartphone application through linear regression analysis. Results revealed strong correlations between the indices and crop water requirements, particularly during the January-March period (R2 values exceeding 0.9 for several indices), with corresponding root mean square error (RMSE) values as low as 1.68 mm/decade. These findings demonstrate the efficacy of satellite remote sensing and vegetation indices, coupled with linear regression techniques, for modeling and estimating crop water needs in semi-arid environments.

Retraction Notice

Retraction: “Unveiling Environmental Corrosion Mechanisms in Ancient Egyptian Bronze Spearheads for Conservation”

The Editor has decided to retract the article by Ashraf M. El-Shamy, Mohamed M. Megahed, Noha H. El-Ashery, and Saleh M. Saleh, published in Journal of Environmental Accounting and Management, 13(2), 151–190 (2025), https://doi.org/10.5890/JEAM.2025.06.004.

An editorial examination identified substantial problems with the article’s references. Twenty cited sources could not be independently verified, and twelve additional references contain DOIs that identify publications different from the cited authors and titles. These concerns extend beyond isolated bibliographic errors and undermine confidence in the accuracy and reliability of the article’s supporting literature.

Given the extent of these problems, the Editor considers a correction insufficient and has concluded that the article should be retracted to preserve the integrity of the scholarly record.

Original Abstract

This research examines twelve bronze spearheads from the Salah El-Din Military Museum in Cairo, Egypt, exhibiting significant corrosion. The study's objective is to analyze the corrosion, identify resultant compounds, and determine the metals constituting these artifacts. This analysis aims to understand the corrosive factors and degradation mechanisms and inform scientifically based conservation treatments. Metallographic Microscope (ME), Scanning Electron Microscope & energy dispersive analysis (SEM&EDS), and X-ray diffraction (XRD) were used for examination. The artifacts were found to be primarily composed of bronze (copper and tin) with impurities such as iron and sulfur. Corrosion products identified included Cuprite, Brochantite, Paratacamite, Antlerite, and Quartz. Based on these findings, a chemical cleaning approach was deemed optimal, followed by the application of an advanced acrylic coating with Nanocomposite material to preserve and protect the spearheads from future deterioration.

A data-driven digital twin model is developed for rapid prediction of greenhouse gas emissions such as CO$_2$ during flame combustion, and then the interactions between combustion states and vortices are elucidated using Lagrangian analysis method. First, numerical solutions are obtained from Reynolds Averaged Navier-Stokes (RANS) simulations and used to train a nested U-shaped neural network. By encoding and decoding the characteristics of the mixed flow field, the flame temperature, velocity and emission concentration are predicted. In addition, the accuracy of the prediction is discussed through three quantitative metrics. The analyzed results demonstrate the effectiveness and accuracy of the method on the current dataset. Finally, the transport and mixing processes of CO$_2$ are analyzed based on the predicted data and the coherent structure is identified from Lagrangian viewpoint. Importantly, the interaction of the flame and the flow structures is characterized, and the correlations are evaluated by the coherence ratio and mixing parameters. As a conclusion, the coupling of neural network and Lagrangian analysis allows for predictive modeling of turbulent flames, visualization of internal processes, what-if analysis, and control of greenhouse gas emissions.