• Energy Research
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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.

Sufficient production, consistent food supply, and environmental protection in urban +settings are major global concerns for future sustainable cities. Currently, sustainable food supply is under intense pressure due to exponential population growth, expanding urban dwellings, climate change, and limited natural resources. The recent novel coronavirus 2019 (COVID-19) pandemic crisis has impacted sustainable fresh food supply, and has disrupted the food supply chain and prices significantly. Under these circumstances, urban horticulture and crop cultivation have emerged as potential ways to expand to new locations through urban green infrastructure. Therefore, the objective of this study is to review the salient features of contemporary urban horticulture, in addition to illustrating traditional and innovative developments occurring in urban environments. Current urban cropping systems, such as home gardening, community gardens, edible landscape, and indoor planting systems, can be enhanced with new techniques, such as vertical gardening, hydroponics, aeroponics, aquaponics, and rooftop gardening. These modern techniques are ecofriendly, energy- saving, and promise food security through steady supplies of fresh fruits and vegetables to urban neighborhoods. There is a need, in this modern era, to integrate information technology tools in urban horticulture, which could help in maintaining consistent food supply during (and after) a pandemic, as well as make agriculture more sustainable.

Pakistan is an energy deficient country with depleting energy reserves and increasing energy demand. Due to excessive population growth, the domestic and commercial energy sectors are experiencing rising demand. To meet the requisite demand, renewables are favored rather than conventional counterparts. In this study, we model hybrid power systems using solar, wind and biomass resources for electrifying remote areas. The four locations are chosen for the study around a developing country, Pakistan, where each site is designed according to an isolated microgrid with maximum indigenous resources potential as per the requisite demands. A survey is conducted for the load demand and biomass availability. Optimization is conducted across objectives of minimum levelized cost of the generated energy, least the net present cost and lesser payback period. The optimal results were achieved in-terms of required objectives across southern sites as compared to northern counterparts. The cost of generated energy is comparable to grid electricity and ensures 24 h power supply without cut off and load shedding to the un-electrified rural area. The hybrid power system has a low carbon footprint across emissions due to the use of renewable resources. All the estimated load of rural communities is met with the available resources and mid-career impact has also been conducted across 10 years of the project life to fulfill the increasing load demand of the communities after installation. The results are validated via comparative analysis and show the effectiveness of the proposed study.

Analyzing the efficiency of the water, energy, and food (WEF) nexus is critical for effective governance strategies. Therefore, three-stage data envelopment analysis (DEA) was used to measure the efficiency level of WEF in the 36 districts of Punjab, Pakistan, for the period from 2015 to 2021. Furthermore, the stochastic frontier was used to analyze the effect of external environmental factors on these efficiency scores of the WEF nexus. The results of the DEA showed that the number of frontier efficiency districts decreased, and most districts experienced rank change over time. Overall, the performance of 50% of the districts declined over time. The relative decline in efficiency was found to be higher in districts Bahwalnaghar and Rahim Yar Khan. The performance of districts Multan and Sheikhupura increased over time, while districts Vehari and Sargodha were the most complete and efficient in actual performance. According to the SFA’s findings, the WEF nexus efficiency of South Punjab districts was negatively impacted by external environmental factors (urbanization rate, manufactured industry output, population), leading to severe stress across WEF sectors. Districts in central and southern Punjab, however, were more likely to have lower rankings because of the positive impact of external environmental factors on the efficiency of the WEF nexus. The substantial rise of external environmental variables focused on scale expansion rather than quality improvement, which created a wide gap in WEF inputs and, hence, reduced the efficiency of the WEF nexus in the districts. The findings of this study provide valuable insights for developing governance strategies based on external environmental factors and WEF resource endowment, and they complement the efficiency calculation of WEF nexus research. Future research should focus on the Baluchistan region, the most deprived area in terms of water, energy, and food.

Expansion of modern power systems due to increasing energy demands face the challenges of grid reinforcement cost, size and complexity, transmission losses, and environmental factors. Placement of renewable energy sources (RES) based generation systems addresses these challenges. However, the size and placement location of RES-based system require optimization of installation and operational cost with better return on investment and reduction of greenhouse gas emissions. This paper presents an optimized solution for RES-based generation system to be installed with the existing power system of Azad Jammu and Kashmir (AJK) region that is facing power shortfall and load shedding. The weather and climate data from NASA and National Renewable Energy Laboratory (NREL) have been used and various models of on-grid hybrid renewable energy system (HRES) are compared to highlight their techno-economic benefits. An optimal hybrid photovoltaic, wind, and hydroelectric energy-based generation system is proposed with a significant reduction in cost of energy, net present cost, initial costs, and GHG emissions. Installation of the proposed hybrid RES-based generation system guarantees reduction in system power losses and line flows with an improved voltage profile of the system.

The over-exploitation of groundwater resources is a significant concern due to the potential risks associated with the depletion of this valuable freshwater source. Future planning must consider changes in groundwater availability and urban expansion which are critical for understanding urban growth patterns. This study aims to investigate the impact of land cover change on groundwater depletion. Further, the Land surface temperature (LST) analysis has been performed to find the spatial spread of urbanization and its impact on surface temperature. The Gravity Recovery and Climate Experiment (GRACE) data for groundwater storage monitoring and Landsat data for land cover and LST mapping have been used. The GRACE-based Groundwater Storage (GWS) anomaly has been correlated with Tropical Rainfall Measuring Mission (TRMM)-based precipitation data. The GWS is further cross validated with the groundwater monitoring stations in the study area and the correlation of 0.7 is found. The time series analysis of GWS and the land cover maps with a decadal interval from 1990 to 2020 has been developed to find the impact of groundwater change due to urbanization. The results demonstrate a rapid increase in groundwater depletion and urbanization rates over the past decade. The LST spatial pattern is increasing similarly with the study area’s urban expansion, indicating the temperature rise due to urbanization. The study highlights the limitation of effective policies to regulate groundwater extraction in urban areas and the importance of proper planning to ensure the long-term sustainability of freshwater resources.

City green infrastructure (CGI) makes cities more resilient and sustainable, as required by the United Nations’ (UN) Sustainable Development Goal 11–Sustainable Cities and Communities. Based on the CGI policies of Beijing, land use/land cover (LULC) changes of two Asian capitals, Beijing, China and Islamabad, Pakistan, are simulated. LULC maps for 2010 and 2015 are developed by applying object-based image analysis (OBIA) to Landsat imagery. Dynamics of land system (DLS) model was used to simulate the LULC changes for 2020 and 2025 under three scenarios: (1) business-as-usual (BAU); (2) urban green space work plan (UGWP); and (3) landscape and greening policies (LGP). Results reveal that DLS is efficient than other simulation models. The BAU scenario predicts an overall expansion in Beijing’s greenery, while Islamabad will encounter a decline by 7.3 km2 per year. Under the UGWP scenario, urban green spaces and other vegetation area of Beijing will expand by 7.6 km2, while, for Islamabad, vegetation degradation rate will slow down to 6.9 km2 per year. The LGP scenario envisage a massive increase of 23.5 km2 per year in green resources of Beijing and Islamabad’s green land loss rate will further slowdown to 6.1 km2 per year. It is inferred from the results that vegetation degradation in Islamabad need to lessen by implementing LGP policy after basic amendments according to the local conditions and available resources.

This work intended to explore some motivations that influence people’s eating habits towards sustainability. This was an observational, cross-sectional study, carried out by questionnaire survey on a non-probabilistic sample of 10,067 participants from 13 countries (Argentina, Brazil, Croatia, Greece, Hungary, Latvia, Lithuania, Poland, Portugal, Serbia, Slovenia, Romania and United States). Results indicated that people prefer fresh local foods from the season, being important because it allows transportation and storage to be reduced, which in many cases implies refrigeration systems and consequent energy expenditure. Although people avoid food waste at home, the awareness for the waste at restaurants still needs to be improved. Consumers seem to prefer foods that have been produced and packed in sustainable ways but still give importance to the package—understandable for food products. The results also indicated significant differences in the food choice motivations between groups for all sociodemographic variables tested (age, sex, marital status, education, professional area, living environment and country), but the association was high only for variable country. Additionally, a tree classification analysis allowed to identify the relative importance of the influential variables on the sustainable food choices, with country being the most important, followed by age and sex. Additionally, discriminant function analysis allowed establishing a model for the relation between country and six variables accounting for preservation of biodiversity, respect for life, save natural resources, save energy, reduce industrial pollution and minimal packaging. Although with some limitations, this study brings valuable insight into some aspects linked with sustainable food choices on a number of countries and how people shape their food choices according to some sustainability issues.

A solution to reduce the emission and generation cost of conventional fossil-fuel-based power generators is to integrate renewable energy sources into the electrical power system. This paper outlines an efficient hybrid particle swarm gray wolf optimizer (HPS-GWO)-based optimal power flow solution for a system combining solar photovoltaic (SPV) and wind energy (WE) sources with conventional fuel-based thermal generators (TGs). The output power of SPV and WE sources was forecasted using lognormal and Weibull probability density functions (PDFs), respectively. The two conventional fossil-fuel-based TGs are replaced with WE and SPV sources in the existing IEEE-30 bus system, and total generation cost, emission and power losses are considered the three main objective functions for optimization of the optimal power flow problem in each scenario. A carbon tax is imposed on the emission from fossil-fuel-based TGs, which results in a reduction in the emission from TGs. The results were verified on the modified test system that consists of SPV and WE sources. The simulation results confirm the validity and effectiveness of the suggested model and proposed hybrid optimizer. The results confirm the exploitation and exploration capability of the HPS-GWO algorithm. The results achieved from the modified system demonstrate that the use of SPV and WE sources in combination with fossil-fuel-based TGs reduces the total system generation cost and greenhouse emissions of the entire power system.