• Energy Research
• 11. Sustainability close
• 1. No poverty close
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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.

The importance of the sustainability concept has gained increasing attention from scholars and practitioners. In this context, the aim of this study was to examine the determinants of intention to continue to use the new Metro in Qatar, which is considered a sustainable mode of travel. Therefore, in this paper, a special attention was paid to certain variables, such as environmental concern, perceived quality, and perceived benefits. A quantitative method approach was employed, in which data were collected based on self-administered questionnaires. A total of 1334 Qatar residents responded to the survey questionnaire, and Structural Equation Modeling (SEM) was used for hypothesis testing. The results confirmed the direct effects of perceived behavioral control, social influence, and attitudes on the intention of residents to continue to use the Metro network in their travel. Moreover, perceived behavioral control was found to play a mediating role, enhancing the indirect relationship between perceived benefits and intention to continue to use the Metro network. More importantly, environmental concerns significantly and positively affected the attitudes and perceived behavioral control of travelers; furthermore, these variables presented a mediated influence—through service quality—on the intention to continue to use the Metro network. This study enriched the sustainability behavior literature by examining the influences of crucial factors. Thus, this study is considered of great value for policy-makers who hope to maximize the awareness level of environmental sustainability among the population, in order to encourage them to embrace changes in their lifestyle towards becoming more environmentally responsible.

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.

One of the major environmental problems is the fast accumulation of brick kiln emissions near the peripheries of almost all urban centers. An enormous quantity of substandard coal, wood, rubber tires, or saw wood is used as fuel in these brick kilns, creating environmental pollution through carbon and heavy metals present in the emissions. The present study was designed to determine the distribution pattern of heavy metals in a soil-water-plant system under the influence of brick kiln emissions. Ash, soil (0-15 cm), water, and plant samples were collected from the adjacent areas of the brick kilns. Geo-accumulation index and contamination factors were used to assess the toxicity of metals to soil and plants. It was found that the concentrations of Cd in water, soil, coal, and plant samples were found in the range of 0.004-0.024 mg L-1, 0.02-0.66, 0.002-0.042, and 0.06 to 0.07 mg kg-1, respectively, while Pb concentration was found within the range of 0-0.08 mg L-1, 0.30-8.60, 0.011-0, and 0.01-0.08 mg kg-1 in water, soil, and plant samples, respectively. The concentration of Cd in brick kilns ash was in the range of 0-0.04 mg kg-1 while Pb was found within the range of 0.01-0.05 mg kg-1 for ash. Geo-accumulation index and contamination factor values for Cd were found higher suggesting proper pollution prevention measures to be implemented. Based on the results, it was concluded that Cd concentrations in soil, plants, and water samples were higher in the subject area due to brick kiln emissions and higher Cd concentrations in the fuel material.