• Energy Research
• 13. Climate action close
• 1. No poverty close
• 3. Good health close
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This study aimed to realize Sustainable Development Goals (SDGs), i.e., no poverty, zero hunger, and sustainable cities and communities through the implementation of an intelligent cattle-monitoring system to enhance dairy production. Livestock industries in developing countries lack the technology that can directly impact meat and dairy products, where human resources are a major factor. This study proposed a novel, cost-effective, smart dairy-monitoring system by implementing intelligent wireless sensor nodes, the Internet of Things (IoT), and a Node-Micro controller Unit (Node-MCU). The proposed system comprises three modules, including an intelligent environmental parameter regularization system, a cow collar (equipped with a temperature sensor, a GPS module to locate the animal, and a stethoscope to update the heart rate), and an automatic water-filling unit for drinking water. Furthermore, a novel IoT-based front end has been developed to take data from prescribed modules and maintain a separate database for further analysis. The presented Wireless Sensor Nodes (WSNs) can intelligently determine the case of any instability in environmental parameters. Moreover, the cow collar is designed to obtain precise values of the temperature, heart rate, and accurate location of the animal. Additionally, auto-notification to the concerned party is a valuable addition developed in the cow collar design. It employed a plug-and-play design to provide ease in implementation. Moreover, automation reduces human intervention, hence labor costs are decreased when a farm has hundreds of animals. The proposed system also increases the production of dairy and meat products by improving animal health via the regularization of the environment and automated food and watering. The current study represents a comprehensive comparative analysis of the proposed implementation with the existing systems that validate the novelty of this work. This implementation can be further stretched for other applications, i.e., smart monitoring of zoo animals and poultry.

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.

Abstract This paper investigates the impact of urbanization on energy consumption by applying the Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) in case of Malaysia. The study covers the time period of 1970Q1–2011Q4. The unit root test and the ARDL bounds testing approach have been applied to examine integrating properties and long run relationship in the presence of structural breaks. Our results validated the existence of cointegration and exposed that urbanization is a major contributor in energy consumption. Affluence raises energy demand. Capital stock boosts energy consumption. Trade openness leads to affluence and hence increases energy consumption. The causality analysis finds that urbanization Granger causes energy consumption. The feedback effect is found between energy consumption and affluence and, energy consumption and capital. The bidirectional causality exists between trade openness and energy consumption.

Harnessing the Sun’s energy via the conversion of solar photons to electricity has emerged as a sustainable energy source to fulfill our future demands. In this regard, solution-processable, size-tunable PbS quantum dots (QDs) have been identified as a promising active materials for photovoltaics (PVs). Yet, there are still serious challenges that hinder the full exploitation of QD materials in PVs. This dissertation addresses two main challenges to aid these QDs in fulfilling their tremendous potential in PV applications. First, it is essential to establish a large-scale synthetic technique which maintains control over the reaction parameters to yield QDs with well-defined shape, size, and composition. Rigorous protocols for cost-effective production on a scale are still missing from literature. Particularly, previous reports of record-performance QD-PVs have been based on small-scale, manual, batch syntheses. One way to achieve a controlled large-scale synthesis is by reducing the reaction volume to ensure uniformity. Accordingly, we design a droplet-based continuous-flow synthesis of PbS QDs. Only upon separating the nucleation and growth phases, via a dual-temperature-stage reactor, it was possible to achieve high-quality QDs with high photoluminescence quantum yield (50%) in large-scale. The performance of these QDs in a PV device was comparable to batch-synthesized QDs, thus providing a promise in utilizing automated synthesis of QDs for PV applications. Second, it is crucial to study and control the charge transfer (CT) dynamics at QD interfaces in order to optimize their PV performance. Yet, the CT investigations based on PbS QDs are limited in literature. Here, we investigate the CT and charge separation (CS) at size-tunable PbS QDs and organic acceptor interfaces using a combination of femtosecond broadband transient spectroscopic techniques and steady-state measurements. The results reveal that the energy band alignment, tuned by the quantum confinement, is a key element for efficient CT and CS processes. Additionally, the presence of interfacial electrostatic interaction between the QDs and the acceptors facilitates CT from large PbS QD (bandgap < 1 eV); thus enabling light-harvesting from the broad near-infrared solar spectrum range. The advances in this work – from automated synthesis to charge transfer studies – pave new pathways towards energy harvesting from solution-processed nanomaterials.

AbstractA better understanding of how climate affects growth in tree species is essential for improved predictions of forest dynamics under climate change. Long-term climate averages (mean climate) and short-term deviations from these averages (anomalies) both influence tree growth, but the rarity of long-term data integrating climatic gradients with tree censuses has so far limited our understanding of their respective role, especially in tropical systems. Here, we combined 49 years of growth data for 509 tree species across 23 tropical rainforest plots along a climatic gradient to examine how tree growth responds to both climate means and anomalies, and how species functional traits mediate these tree growth responses to climate. We showed that short-term, anomalous increases in atmospheric evaporative demand and solar radiation consistently reduced tree growth. Drier forests and fast-growing species were more sensitive to water stress anomalies. In addition, species traits related to water use and photosynthesis partly explained differences in growth sensitivity to both long-term and short-term climate variations. Our study demonstrates that both climate means and anomalies shape tree growth in tropical forests, and that species traits can be leveraged to understand these demographic responses to climate change, offering a promising way forward to forecast tropical forest dynamics under different climate trajectories.

The purpose of this study is to investigate the potential of airborne particulate matter (PM10 and PM2.5) and its impact on the performance of the photovoltaic (PV) system installed in the Sargodha region, being affected by the crushing activities in the hills. More than 100 stone crushers are operating in this region. Four stations within this region are selected for taking samples during the summer and winter seasons. Glass–fiber papers are used as a collection medium for particulate matter (PM) in a high-volume sampler. The concentration of PM is found above the permissible limit at all selected sites. The chemical composition, concentration, and the formation of particulate matter (PM10 and PM2.5) layers on the surface of the photovoltaic module varies significantly depending on the site’s location and time. The accumulation of PM layers on the PV module surface is one of the operating environmental factors that cause significant reduction in PV system performance. Consequently, it leads to power loss, reduction of service life, and increase in module temperature. For the PV system’s performance analysis, two PV systems are installed at the site, having higher PM concentration. One system is cleaned regularly, while the other remains dusty. The data of both PV systems are measured and compared for 4 months (2 months for the summer season and 2 months for the winter season). It is found that when the level of suspended particulate matter (PM10 and PM2.5) increases, the energy generation of the dusty PV system (compared to the cleaned one) is reduced by 7.48% in May, 7.342% in June, 10.68% in December, and 8.03% in January. Based on the obtained results, it is recommended that the negative impact of PM on the performance of the PV system should be considered carefully during the decision-making process of setting solar energy generation targets in the regions with a high level of particulate matter.

Abstract Energy poverty has evolved as an important issue of research from academic and policy perspectives during the last two decades. The energy poverty estimates exhibit widespread variations across regions, methodologies and datasets, making the task of energy and development policies challenging. The variations in energy poverty estimates necessitate a systematic and rigorous analysis of these empirical studies to obtain a true measure of energy poverty. The present study conducts a meta-analysis of 30 studies and 103 estimates of energy poverty. On average, 71.1 percent of the population in the overall sample studies has been identified as energy poor. High I-square statistic values suggest heterogeneity bias in the sample studies; therefore, analysis at the subgroup level has been conducted. Meta-regression analysis of overall sample and subgroups has identified type of methodology, citations, impact factor of the journal, regional dummies, average key crude oil price and gross domestic product as significant factors that explain heterogeneity in the energy poverty effect sizes. We recommend inclusion of local and regional factors in assessment of energy poverty for developing a uniform methodology to assess energy poverty across developing economies.

Lightning is a natural hazard that can have severe effects both on electrical power system and in general. In order to plan for lightning protection it is necessary to map out the likely occurrence of lightning at any location across the country. In this paper lightning density map of Pakistan has been developed using Pakistan Metrology Department (PMD) data over the period 1960–1990 on Arc-GIS software® platform. Such map is helpful in planning protection system for utility company's network and in general for lightning risk assessment at country level. Further lightning map also helps in predicting aviation hazards severe storm and in understanding the weather in any country. The developed lightning map shows that Pakistan lightning flash density is highest in region around Murree reaching a maximum of 9.4 flashes per km2 per year and most of the areas of Pakistan have lowest density reaching a minimum value of hardly single flash/km2/yr.