• Energy Research
• engineering and technology close
• 11. Sustainability close
• 1. No poverty close
• 3. Good health close
• PK close

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.

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.

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.

As an effective emission reduction approach, CO2 capture and storage (CCS) combined with enhanced water recovery (EWR) technology can not only reduce CO2 emissions, but can also recover deep saline water resources to relieve pressure on regional water resources, and can ensure the energy supply and both social and economic development. However, the environmental benefits and application costs of CCS-EWR are uncertain, and are determined by the technology level, geological conditions, and other physical factors. In this study, an optimal source-sink matching model and a techno-economic assessment model were developed to evaluate the contributions of CCS-EWR to carbon emission reduction and the increase of the water supply by considering various uncertain factors, as well as the corresponding costs. In addition, the Yellow River Basin (YRB) in China was selected as the research region because, while there are abundant coal-fired power plants (CFPPs) in the YRB, the water resources are scarce. The results revealed the following. (1) The maximum CO2 capture capacity of the 236 CFPPs in the YRB is about 738.77 Mt/a, and nearly 13.14 Gt of fresh water could be provided until the 236 CFPPs in the YRB retire, which can partially relieve the pressure on the supply of water resources. (2) With the consideration of the CCS-EWR benefits, the average cost of the 236 CFPPs in the YRB in their residual lifetime to reduce their CO2 emissions by 90% will be no more than 180 CNY/t. (3) The incentive effect of the increase of the industrial water price on the profits of CCS-EWR projects is not significant. CCS-EWR technology has better application prospects in China under the dual constraints of carbon-neutral targets and water shortages, and more policy support is required for its deployment.

Present climate change consists of global warming that is caused by the emission of greenhouse gases, generally carbon dioxide. The study examines the pollution haven, pollution halo, and environmental Kuznets curve for a number of Asian countries during the period of 1985 to 2020. Outcomes suggest that urbanization, gross domestic product per capita, energy consumption, and foreign direct investment inflow have positive effects, while gross domestic product square, foreign direct investment square, and tourism have negative effects on emissions of carbon dioxide. Furthermore, findings support the validity of the environmental Kuznets curve, pollution haven, and pollution halo hypothesis for the selected Asian countries. We also find robust results of rationality of the environmental Kuznets curve hypothesis for Pakistan, Bangladesh, India, China, Indonesia, Korea, Japan, Malaysia, Vietnam, and Singapore; of pollution haven hypothesis for Bangladesh, China, Indonesia, Japan, Pakistan, and Singapore; and of pollution halo hypothesis for Bangladesh, China, Indonesia, Japan, Pakistan, and Singapore.

A key factor in social development and sustainable urban expansion is the establishment of sustainable and affordable transport systems. This study aims to investigate the impact of transport infrastructure investment on environmental degradation and economic growth and to test the validity of the EKC hypothesis in China, India, Russia, and Japan over the period 1995-2020. The results show that GDP has a significant positive effect, and GDP2 and GDP3 have significant adverse effects on environmental degradation, respectively. These results confirm the validity of the inverted U-shaped EKC hypothesis in selected emerging Asian economies. Rail infrastructure investment has significant adverse effects, while road infrastructure investment and aviation infrastructure investment have significant positive effects on environmental degradation. Likewise, the impact of investment in transport infrastructure system (roads, rail, and aviation) on economic growth is positive and statistically significant. Country-level estimates confirm the validity of the inverted U-shaped EKC hypothesis in China, India, and Russia, while the N-shaped EKC is only valid in Japan. Investments in rail infrastructure based on modern rail systems that run on electricity are believed to be less polluting in the transport mix, help create sustainable and safe transport systems, and reduce emissions at the urban and intercity levels in emerging Asian countries. In addition, the growing impact of free trade on environmental pollution should be strengthened to harmonize the strict enforcement of environmental conditions dominated by trade agreements.

In this work, two meta-heuristic bio-inspired algorithms and our proposed hybrid technique (Bacterial foraging optimization algorithm (BFA) and BAT algorithm (BA) (HBB)) are proposed for optimizing and scheduling the appliances of residential consumers. BFA, BA and our proposed technique HBB are used for scheduling the appliances in order to find the optimal solution. Appliances have different power ratings and power consumption patterns. Three different operational time intervals of 5, 30 and 60 minutes are taken in this work and their comparison is carried out. Eighteen appliances are considered and they are classified into three categories: interruptible, non-interruptible and base load appliances. Single home scenario is considered in this work. Results show that proposed technique has significantly reduced electricity cost and peak-to-average ratio. Consumers have not only supposed to pay less electricity bill, however, utilities also have to bear less stress especially in on-peak hours.