• Energy Research
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Improving access to sustainable energy services and reducing the carbon footprint and deforestation of developing countries presents a substantial challenge to the development industry. This paper aims to critically assess two prevalent business models emerging to meet this challenge: financial institution–technology provider partnerships and the one-stop-shop, pay-as-you-go (PAYG) model. The authors find that collaborations between financial institutions and energy providers need to address grey areas such as product quality standards, marketing, and post-sales services, and incorporate feedback loops to continually evaluate the performance of the partnership. Pay-as-you-go presents an intriguing business case to leapfrog more traditional energy access programmes. However, more robust results from the field need to be reported before the PAYG movement can claim success. Ultimately, collaborative and integrated actions by all stakeholders are needed to achieve universal energy access.

Purpose This study aims to consider environmental sustainability, a global challenge under the preview of sustainable development goals, highlighting the significance of knowledge economy in attaining sustainable aggregate demand behavior globally. For this purpose, 155 countries that have data available from 1995 to 2021 were selected. The purpose of selecting these countries is to test the global responsibility of the knowledge economy to attain environmental sustainability. Design/methodology/approach Results are estimated with the help of panel quantile regression. The empirical existence of aggregate demand-based environmental Kuznets curve (EKC) was tested using non-linear tests. Moreover, principal component analysis has been incorporated to construct the knowledge economy index. Findings U-shaped aggregate demand-based EKC at global level is validated. However, environmental deterioration increases with an additional escalation after US$497.945m in aggregate demand. As a determinant, the knowledge economy is reducing CO2 emissions. The knowledge economy has played a significant role in global responsibility, shifting the EKC downward and extending the CO2 reduction phase for every selected country. Further, urbanization, energy intensity, financial development and trade openness significantly deteriorate the environmental quality. Originality/value This study contains the empirical existence of aggregate demand-based EKC. The role of the knowledge economy is examined through an index which is calculated by using four pillars of the knowledge economy (technology, innovations, education and institutions). This study is based on a combined panel of all the countries for which the data was available.

This study searches the impact of tourism growth on emission pollutants in Cyprus (north), which is a small island in the Mediterranean and has shown significant development in hotel and casino sectors in the last two decades. Results from time-series analyses reveal that an inverted U-shaped EKC hypothesis is confirmed for Cyprus with and without tourism development. Tourism also exerts positively significant and long-term effects on the levels of carbon emissions, revealing that growth in the tourism sector causes degradation in the environment.

Microplastics are a growing concern in the environments and especially agricultural soil due to numerous negative impacts on the ecosystems and human health. This study aimed to investigate the properties of microplastics (including quantity, shape, size, and polymer types) in five samples (DA1-5) taken from agriculture soil in Dong Anh district, Hanoi using Nicolet iN10 MX Fourier transform infrared (µFTIR) microscope. The study found that the microplastic content in these soils ranging from 494 ± 292 items/kg dry weight to 1031 ± 379 items/kg dry weight. In terms of shape, microplastic fragments accounted for the majority of microplastics found in soil (65% - 86% of the total microplastics). Fifteen types of microplastic polymers were identified in the soil samples, with PET being the most common polymer of 38%, followed by urea-formaldehyde resin of 15%, and nylon of 13%. Microplastics with sizes ranging from 50-150 µm were the dominant group (39% - 57% of the total microplastics). This study provides an initial assessment of the presence of microplastics in the agricultural soil of the suburban area of Hanoi, highlighting the potential risk of contamination that may effect on environment and pose a threat to human health.

Room occupancy prediction based on indoor environmental quality may be the breakthrough to ensure energy efficiency and establish an interior ambience tailored to each user. Identifying whether temperature, humidity, lighting, and CO2 levels may be used as efficient predictors of room occupancy accuracy is needed to help designers better utilize the readings and data collected in order to improve interior design, in an effort to better suit users. It also aims to help in energy efficiency and saving in an ever-increasing energy crisis and dangerous levels of climate change. This paper evaluated the accuracy of room occupancy recognition using a dataset with diverse amounts of light, CO2, and humidity. As classification algorithms, K-nearest neighbors (KNN), hybrid Adam optimizer–artificial neural network–back-propagation network (AO–ANN (BP)), and decision trees (DT) were used. Furthermore, this research is based on machine learning interpretability methodologies. Shapley additive explanations (SHAP) improve interpretability by estimating the significance values for each feature for classifiers applied. The results indicate that the KNN performs better than the DT and AO-ANN (BP) classification models have 99.5%. Though the two classifiers are designed to evaluate variations in interpretations, we must ensure that they have accurate detection. The results show that SHAP provides successful implementation following these metrics, with differences detected amongst classifier models that support the assumption that model complexity plays a significant role when predictability is taken into account.

The Flameless Combustion (FC) regime has been pointed out as a promising combustion technique to lower the emissions of nitrogen oxides (NOx) while maintaining low CO and soot emissions, as well as high efficiencies. However, its accurate modeling remains a challenge. The prediction of pollutant species, especially NOx, is affected by the usually low total values that require higher precision from computational tools, as well as the incorporation of relevant formation pathways within the overall reaction mechanism that are usually neglected. The present work explores a multiple step modeling approach to tackle these issues. Initially, a CFD solution with simplified chemistry is generated [both the Eddy Dissipation Model (EDM) as well as the Flamelet Generated Manifolds (FGM) approach are employed]. Subsequently, its computational cells are clustered to form ideal reactors by user-defined criteria, and the resulting Chemical Reactor Network (CRN) is subsequently solved with a detailed chemical reaction mechanism. The capabilities of the clustering and CRN solving computational tool (AGNES—Automatic Generation of Networks for Emission Simulation) are explored with a test case related to FC. The test case is non-premixed burner based on jet mixing and fueled with CH4 tested for various equivalence ratios. Results show that the prediction of CO emissions was improved significantly with respect to the CFD solution and are in good agreement with the experimental data. As for the NOx emissions, the CRN results were capable of reproducing the non-monotonic behavior with equivalence ratio, which the CFD simulations could not capture. However, the agreement between experimental values and those predicted by CRN for NOx is not fully satisfactory. The clustering criteria employed to generate the CRNs from the CFD solutions were shown to affect the results to a great extent, pointing to future opportunities in improving the multi-step procedure and its application.

Abstract This study presents and evaluates the feasibility of a novel hybridization of modified Kalina cycle, reverse osmosis desalination, and low-temperature water electrolysis utilizing geothermal energy to yield power, distilled water, and hydrogen, respectively. The scientific impact of the current work has been improved considering the features of Sabalan flash-binary geothermal wells in Iran as a real model through a case study. In addition to designing a novel setup, the smart use of multi-heat recovery technique, modifying the base cycle, and utilizing a part of generated distilled water to produce hydrogen by the electrolyzer are the other structural originalities, distinguishing the current work from the previous studies. The suggested system is scrutinized via a parametric study and optimized based on a genetic algorithm. The parametric study demonstrated that the highest sensitivity of varying the performance criteria of the whole system is attributed to the change in flash tank pressure. Moreover, the multi-objective optimization led to achieving the exergy efficiency and trigeneration gain output ratio as 51.3% and 1.7 for the system, respectively. Furthermore, the system was able to produce 4795 kW of power, 5.3 kg/h of hydrogen, and 19.9 kg/s of distilled water.

Closed-loop supply chain networks are gaining research popularity due to environmental, economic and social concerns. Such networks are primarily designed to overcome carbon footprints and to retrieve end of life products from customers. This study considers a multi echelon closed-loop supply chain in the presence of machine disruption. A multi-objective model is presented to optimize the total cost, the total time and emissions in a closed-loop supply chain network. The aim is to analyze the trade-off between the objectives of cost, time, and emissions and how these decisions are impacted by the selection of different available machines. A number of solution approaches are tested on a case study from the tire industry. The results suggest the improved performance of the hybrid heuristic and the importance of controlling disruption in a closed-loop supply chain network. Furthermore, there is a trade-off between the different objective functions which can help the decision maker to choose a particular solution according to the preference of an organization. Finally, conclusion and future research avenues are provided.