Purpose: This study investigates the mediating effect of Environmental Attachment (EA) among consumers in an emerging market, concentrating on the impact of two key factors: Green Environmental Awareness (GEA) and Sense of Responsibility (SOR) on Sustainable Product Consumption (SPC). Design/methodology/approach: A thorough online survey was carried out with Google Docs and distributed to 304 Pakistani consumers who now use or are considering purchasing sustainable or green products. Structural Equation Modeling (SEM) was used to rigorously test the suggested model utilizing a non-probability sampling technique, specifically the stratified purposive sampling approach. Findings: Green environmental awareness (GEA) and a sense of responsibility (SOR) have been shown to have a substantial impact on creating environmental attachment (EA) in both existing and potential customers of sustainable products. The findings of this study also revealed that environmental attachment (EA) plays an important role as a mediator in the links between green environmental awareness (GEA) and the consumption of sustainable goods (SPC), as well as between a sense of responsibility (SOR) and SPC. Despite this, it is crucial to note that the projected direct effect of GEA on SPC was shown to be statistically insignificant. This conclusion implies that additional factors outside the scope of this study may influence the relationship between GEA and SPC. Research limitations/implications: It is vital to highlight that the focus of this study is on an online sample of consumers near Punjab, Pakistan. Future studies should look at other parts of Pakistan to acquire a more complete picture of sustainable consumption trends. Furthermore, our findings suggest that characteristics impacting sustainable consumption, such as Green Environmental Awareness (GEA) and Sense of Responsibility (SOR), may differ among countries. As a result, performing a comparison analysis involving two or more countries could provide valuable insights into projecting sustainable product consumption among current and potential sustainable product customers. Originality/Value: This study contributes to the literature by investigating the factors of sustainable consumption using the lens of the Norm Activation Model theory (NAM), notably Green Environmental Awareness (GEA) and Sense of Responsibility (SOR), to predict sustainable product consumption. The findings are important for promoting long-term goals in Pakistan and provide a framework that can be applied in other emerging markets.

The Organic Rankine Cycle (ORC) is an electricity generation system that uses organic fluid instead of water in the low temperature range. The Organic Rankine cycle using zeotropic working fluids has wide application potential. In this study, data mining (DM) model is used for performance analysis of organic Rankine cycle (ORC) using zeotropik working fluids R417A and R422D. Various DM models, including Linear Regression (LR), Multi-Layer Perceptron (MLP), M5 Rules, M5 Model Tree, Random Committee (RC), and Decision Tree (DT) models are used. The MLP model emerged as the most effective approach for predicting the thermal efficiency of both R417A and R422D. The MLP’s predicted results closely matched the actual results obtained from the thermodynamic model using Genetron software. The Root Mean Square Error (RMSE) for the thermal efficiency was exceptionally low, at 0.0002 for R417A and 0.0003 for R422D. Additionally, the R-squared (R²) values for thermal efficiency were very high, reaching 0.9999 for R417A and R422D. The findings demonstrate the effectiveness of the DM model for complex tasks like estimating ORC thermal efficiency. This approach empowers engineers with the ability to predict thermal efficiency in organic Rankine systems with high accuracy, speed, and ease.

This paper is the third in a series focused on bridging the gap between secondary and higher education. Our primary objective is to develop a robust theoretical framework for an innovative e-business model called the Undergraduate Study Programme Search System (USPSS). This system considers multiple criteria to reduce the likelihood of exam failure or the need for multiple retakes, while maximizing the chances of successful program completion. Testing of the proposed algorithm demonstrated that the Stochastic Gradient Boosted Regression Trees method outperforms the current method used in Lithuania for admitting applicants to 47 educational programs. Specifically, it is more accurate than the Probabilistic Neural Network for 25 programs, the Ensemble of Regression Trees for 24 programs, the Single Regression Tree for 18 programs, the Random Forest Regression for 16 programs, the Bayesian Additive Regression Trees for 13 programs, and the Regression by Discretization for 10 programs.

The world has never been more developed, yet humanity is on the brink of irreversible environmental loss. Despite the urgency of the situation, there is a limited body of studies addressing environmental concerns in higher education institution, particularly in developing countries, i.e., Saudi Arabia. Sustainable development is the only viable solution, albeit it requires the courage to initiate and sustain efforts dedicated to preserving the environment for the well-being of future generation. The article delves into this issue and examines the impact of environmental education program (EEP) on environmental performance (EP) via waste minimization behaviour (WMB). The research involved meticulous data collection from a sample of 597 students, representing diverse genders and academic specialties at the esteemed public university—King Faisal University (KFU) in Saudi Arabia. The study used statistical software (including SPSS and AMOS, v 25) for rigorous analysis and revealed significant findings. Firstly, the study showed a significant and positive relationship between EEP and EP. Secondly, it revealed a significant and positive association between EEP and WMB. Thirdly, the study ascertained a significant and positive association between WMB and EP. Finally, the study found that the relationship between EEP and EP remains significant even after presenting WMB as a mediator, proposing that WMB has a partial mediation role between EEP and EP. The results highlighted the significance role of EEP in stimulating WMB and achieving EP in the Saudi universities, which contributes to national initiative of green Saudia.

Creating products and services that satisfy individual and community needs is impossible without raw materials. This study takes a novel approach by integrating the economic dynamics and raw material consumption indicators of the European Union (EU). The study uses different econometric methods to analyze the relationship between GDP (gross domestic product) and the EU’s raw material consumption (RMC) from 2014–2023. Among the results, the panel data analysis model shows that the resource productivity of the EU improved during the period under review, whereas the material intensity decreased significantly. These trends significantly contributed to the relative decoupling of material consumption from GDP in the last decade. The results of the K-means cluster analysis highlight the regional economic differences within the EU. According to the results of the correlation analysis, EU member countries differ significantly in the efficiency of raw material use. Nevertheless, five member countries are robustly vulnerable to large-scale raw material use. The divergence calculation results show that while some countries use raw materials extremely efficiently to produce GDP, others achieve low efficiency. This unique approach and the resulting findings provide a new perspective on the complex relationship between economic growth and raw material use in the EU.

This study examines the impact of state highway construction contracts on state spending efficiency controlling for production structure, service demands, and situational factors. The theoretical argument is that because highway construction projects are relatively large in scale, complex, and can be monitored through objective performance measurement, state highway construction programs may save government production costs through contracts. Contracting helps highway producers achieve efficiency by optimizing production size based on workload and task complexity. The unit of analysis is 48 state governments’ highway construction contracts from 1998 to 2008. Through a two-stage analysis method including a Total Function Productivity (TFP) index and system dynamic panel data analysis, the results suggest that highway construction contracts enhance state highway spending efficiency, especially for large-scale construction projects.