Green Computing Research Articles

Antecedents and outcomes of green information technology Adoption: Insights from an oil industry

Growing environmental concerns have led to increased demand for ‘green’ or environmentally friendly business. This has resulted in growing interest in the research of Green Information Technology (GIT). However, to date, such research has had a disproportionate emphasis on organisational antecedents while often overlooking outcomes. The current study aims at giving a better insight into the state of GIT adoption among oil companies in Sudan. If these companies were to adopt a green business model, it would significantly impact the environment given that they typically contribute significantly to environmental degradation. To this end, this study a) determines the level of awareness of GIT adoption among employees of oil companies in Sudan, b) identifies the key factors affecting the GIT adoption, c) examines the effect of training, top management support, perceived ease of use, perceived usefulness, relative advantages, and GIT behaviour on GIT adoption, and (d) examines the effect of GIT adoption on outcomes, namely business performance, competitive advantage, and process innovation. From a sample of 292 respondents, the result revealed that top management and GIT behaviour were two of the four antecedents not supported by data, thereby rendering them insignificant. Surprisingly, the survey data supports all three hypotheses that recognise a positive relationship between GIT adoption and the outcomes. This study provides important empirical evidence from oil companies that lack a green adoption policy that encourages them to consider joining the green bandwagon. The study concludes that most respondents are aware of GIT.

Green Supply Chain Systems and Technology and Their Impact on the Performance of Economic Units in the Oil Sector

The research aims to find out the impact of the practices of green supply chain systems and technology - reverse logistics, green information systems and green manufacturing - on the environmental performance of the economic units operating in the oil sector. Realizing the impact of the practices of green supply chain systems and technology - reverse logistics, green information systems and green manufacturing - on the operational performance of economic units in the oil sector. Examining the effect of the practices of green supply chain systems and technology - reverse logistics, green information systems and green manufacturing - on the operational performance of economic units in the oil sector. Testing the proposed model for the effect of green supply chain systems and technology practices on the performance of the economic unit in the oil sector.(SPSS 23), where the researchers concluded, that green supply chain systems and technology are contemporary and important topics for economic units, because they seek to reduce negative environmental impacts and production costs, as well as an important way to make economic units sustainable and green. The researchers recommended it, the need for economic units to realize the importance of green supply chain systems and technology and their operations, because they have the full perception and vision and direct influence in aspects of economic units.

How green is Green IT? A multidisciplinary bibliometric study and research agenda

Scholarly interest in Green IT has been rapidly growing over the past 15 years in a wide variety of disciplines including computer science, information systems, sustainability, production management and engineering. This is not surprising given the growing environmental impacts of information and communication technologies. This article presents a bibliometric analysis of academic literature across 1862 sources to improve our understanding of the evolution and current state of this multidisciplinary field. The corpus includes 4067 published journal and conference papers identified using the Scopus database. A descriptive performance analysis of the corpus using Bibliometrix software first allows us to identify the most influential works, authors, sources, institutions, and countries in the field. Examination of its conceptual structure reveals that most work has focused on the design and use phases of ICTs and neglected the manufacturing and end-of-life phases. Further insights are provided and opportunities for future research are identified.

Green Emerging Digital Technologies, Green Supply Chains, and the Performance of Environmentally Friendly Firms: The Underpinning Role of Human Resources

Anchored in the resource-based view, this research analyzes the relationships between green information systems (Green IS), green human resource management (GHRM), and green supply chains (GSCM). It then examines the effects of these relationships on the environmental and financial performance of organizations. To meet the proposed objectives, a survey was carried out with 211 Brazilian companies, with the data analysis stage employing the structural equation modeling technique. The main findings include that green information systems play an important role in supporting green supply chain practices, and indirectly contribute to environmental and financial performance by facilitating organizational processes. Furthermore, our results reveal that human resources partially mediate the relationship between information systems and green supply chains, through employee engagement in environmental management and rewards based on environmental performance. This study also brings a number of theoretical and practical contributions. In terms of theory, it highlights a previously unexplored relationship between the prevailing concepts of Green IS, GSCM, and GHRM, and their impacts on firm performance, which enriches the body of knowledge in the IS, HR, and engineering management fields. For practitioners, this study reveals how they can strategically articulate HR, IS, and supply chain functions to support sustainability strategies. The findings clarify that blending technical (IS) and soft (HR) skills is pivotal to developing valuable GSCM capabilities to support and improve companies’ performance.

IS3CQL : Intelligent sleep scheduling of small cells in high density 5G MIMO networks via incremental Deep Q learning optimizations

Effective management of small cell networks is crucial as the demand for fast, lowlatency communication grows as a result of the development of 5G and Massive Multiple Input Multiple Output (M-MIMO) technology. Small cell sleep cycle scheduling in highdensity networks frequently encounter issues as it tries to strike a compromise between energy efficiency, quality of service (QoS), and maintaining a high throughput. Additionally, these traditional methods ignore the complex spatial and temporal communication characteristics, resulting in subpar network performance. In order to overcome these difficulties, we suggest a brand-new intelligent sleep scheduling approach that makes use of both fundamental green computing ideas and incremental deep learning. In order to determine the best sleep schedules for tiny cells, our model takes into account both temporal and spatial communication factors, such as end-to-end delay, packet delivery ratio, throughput, and energy consumption of the nodes. The model adjusts the small cells’ sleep scheduling based on the ongoing spatial-temporal fluctuations in the network, providing the best possible use of resources. Additionally, its focus on green computing helps to lower energy consumption during extensive connections, making it an environmentally beneficial alternative for different scenarios. Under enormous MIMO situations, the results from our model show a significant improvement over the current approaches. These improvements include a notable improvement in communication consistency, a notable increase in throughput, a noticeable increase in packet delivery ratio, and a noticeable decrease in energy consumption and delay. In summary, our concept is a major step forward in the continuous development of intelligent, resilient, and effective 5G MIMO networks.

Hybrid Technique of Topic Modelling and Text Summarization: A Case Study on Predicting Trends in Green Computing

Hybrid Technique of Topic Modelling and Text Summarization: A Case Study on Predicting Trends in Green Computing

Green computing: the growing concern on information management, e-waste, safety, health and environment

Green computing: the growing concern on information management, e-waste, safety, health and environment

Sequential reinforcement of intra/interlayer interfaces to design flexible, transparent electromagnetic interference shielding film for “Green Electronics”

A sequential reinforcement strategy is used to prepare multilayer composite films by enhancing intra/interlayer interactions via physical and chemical bonds. The films show flexibility, transparency, EMI shielding, gas barrier, and fire retardancy.

Biodegradable and flexible ι-carrageenan based RRAM with ultralow power consumption

Transient memories, which can physically disappear without leaving traceable remains over a period of normal operation, are attracting increasing attention for potential applications in the fields of data security and green electronics. Resistive random access memory (RRAM) is a promising candidate for next-generation memory. In this context, biocompatible ι-carrageenan (ι-car), extracted from natural seaweed, is introduced for the fabrication of RRAM devices (Ag/ι-car/Pt). Taking advantage of the complexation processes between the functional groups (C–O–C, C–O–H, et al.) and Ag metal ions, a lower migration barrier of Ag ions and a high-speed switching (22.2 ns for SET operation/26 ns for RESET operation) were achieved, resulting in an ultralow power consumption of 56 fJ. And the prepared Ag/ι-car/Pt RRAM devices also revealed the capacities of multilevel storage and flexibility. In addition, thanks to the hydrophilic groups of ι-car molecule, the RRAM devices can be rapidly dissolved in deionized (DI) water within 13 minutes, showing excellent transient characteristics. This work demonstrates that ι-car based RRAM devices have great potential for applications in secure storage applications, flexible electronics and transient electronics.

Assessing the Performance of Energy Conservative Minfit Algorithm for Green Fog Computing in the Context of Naturaldisasters

Assessing the Performance of Energy Conservative Minfit Algorithm for Green Fog Computing in the Context of Naturaldisasters

A photo-enzyme coupled system for carbon dioxide conversion to solar fuel: The rate-matching and compatibility between photocatalysis and enzyme catalysis

Using reduced nicotinamide adenine dinucleotide (NADH) as cofactor, formate dehydrogenase (FDH) can reversely catalyze CO2 reduction to formic acid. However, the industrialized application of FDH is fairly limited due to its high cost and stoichiometric cofactor consumption. Enzyme immobilization and cofactor regeneration are vital solutions to these problems. Ascribed to the benefits of utilizing a cheap, environmentally friendly, and renewable energy source, photocatalytic NADH regeneration is naturally sustainable and promising. Nonetheless, the rate-matching and compatibility between photocatalysis and enzyme catalysis have not been well revealed. In this study, polyethyleneimine (PEI) modified hollow fiber membrane (HFM) as the enzyme support was in-situ integrated with a visible-light-driven NADH regeneration system using thiophene-modified macroporous graphitic carbon nitride (ATCN-CN) as the catalyst. First, NADH regeneration conditions were regulated to accommodate enzyme catalysis. Then, the photo-enzyme coupled system (PECS) for the sustainable synthesis of formic acid with triethanolamine (TEOA) or H2O as electron donor was constructed, and the influencing factors were investigated. At last, the PECS was compared with other methods as well as a system that used FDH immobilized on silica nanoparticles. The results show that the reaction pH, electron donor, and catalyst concentration have a significant impact on NADH regeneration. For the PECS with TEOA as electron donor, the optimal pH value and photocatalyst concentration for formic acid synthesis are 7.0 and 1.2 mg mL−1, respectively. With the initial addition of 1 mmol L−1 NAD+, a formic acid yield of 35.3% was obtained after 5 h, standing out among the reported values. H2O as a green and low-cost electron donor meets the desire for low-cost photocatalysis. Although the production of formic acid after 5 h was lower in the PECS with H2O as electron donor than in the TEOA system, it was still 14.6 times that of the immobilized enzyme system without cofactor regeneration, and the yield of formic acid could reach 14.8% and 98.3% with 1 or 0.1 mmol L−1 NAD+, validating that current PECS is feasible to a sustainable and green synthesis of solar fuel from CO2.

Urban gardening education: User reflections on mobile application designs.

Mobile gardening applications offer a wide range of opportunities to shape the environmental behaviour of city dwellers, while stimulating action for greater access and contact with nature. Despite this, their educational potential is not sufficiently recognised and exploited. The aim of this qualitative research is to gain an in-depth understanding of the extent to which existing mobile apps can facilitate digital education for the development of green cities. For this purpose, the user insight approach has been applied. The study analyses 7 980 reviews of fourteen apps applications from Google Play Store. The results reveal the motivations behind users' decision to download urban gardening apps and the features that facilitate or hinder their use. The obtained results are relevant not only for green information systems research but also for app developers, and those involved in the urban education process: city authorities, urban educators, pro-environmental associations, and grassroots activists, among others.

Joint Design of Long-Term Base Station Activation and Short-Term Beamforming for Green Wireless Networks

Base station (BS) activation is a widely-used approach to alleviate the system power cost for device maintenance. However, frequently switching on/off BSs may also introduce extra power and signaling costs, which motivates us to limit the BS switching frequency when performing BS activation. To address this, we study a problem of joint long-term BS activation and short-term beamforming (J-LTBA-STBF) in a network where multiple multi-antenna BSs cooperatively serve multiple single-antenna users. LTBA means that each BS’s active/inactive switching frequency is properly controlled to yield relatively stable BS-user association and limited switching power, while STBF lies in that due to the low cost of refreshing beamformers, the transmit beamformers are allowed to be updated frequently to maximally lower the transmit power. Following the idea, two efficient algorithms are designed, according to the data amount they request to start running, to optimize the active BSs and the BS transmit beamformers in multiple adjacent time slices. Consequently, the transmit, maintenance and switching powers can be well balanced to facilitate power-efficient communications. Numerical results validate the efficacies of the algorithms.

Green-EMulTO: A Next Generation Edge-Assisted Multi-Level Traffic Orchestrator for Green Computing in Consumer Autonomous Vehicles

Green-EMulTO: A Next Generation Edge-Assisted Multi-Level Traffic Orchestrator for Green Computing in Consumer Autonomous Vehicles

Analysis of a New Model of Economic Growth in Renewable Energy for Green Computing

Analysis of a New Model of Economic Growth in Renewable Energy for Green Computing

The Reinforced Factors of Green Computing in Instructive Setting Utilizing a Fake Experiences Approach: Discernment of Omani Students

The Reinforced Factors of Green Computing in Instructive Setting Utilizing a Fake Experiences Approach: Discernment of Omani Students

Towards Sustainable IT Infrastructure: Integrating Green Computing with Data Warehouse and Big Data Technologies to Enhance Efficiency and Environmental Responsibility

Towards Sustainable IT Infrastructure: Integrating Green Computing with Data Warehouse and Big Data Technologies to Enhance Efficiency and Environmental Responsibility

Green Computing with Deep Learning for Data Centers

Due to development in cloud services, lots of data is transferred between users and servers of the cloud. This transmission of data consumes huge amounts of energy. This energy consumption occurs during the operation of network infrastructure, the conversion of electrical to optical signals to travel long distances, and signal amplification. As Green computing is the use of computing devices in an environmentally friendly way, i.e., using electrical energy efficiently as Data centers require a significant amount of electricity to operate and cool the servers, leading to carbon emissions from the burning of fossil fuels. Green computing in cloud services is about optimizing energy consumption and by incorporating deep learning algorithms, we can enhance the energy efficiency of cloud infrastructure. These algorithms can analyze real-time data from sensors, optimize resource allocation, and dynamically adjust power usage. Through intelligent workload scheduling, server consolidation, and power management, deep learning enables the reduction of energy waste and carbon emissions. The integration of deep learning in cloud services not only improves energy efficiency but also enhances performance and cost-effectiveness. Here we are using a deep learning model which can be used for workload prediction and resource provisioning. By analyzing historical workload patterns and user behavior, deep learning algorithmscan predict future resource demands and allocate resources accordingly, leading to more efficient resource utilization and energy savings

Green supply chain management practices and ITS impacts on the sustainable performance / testing the mediating role of green technology

This research aims to use green practices to attain sustained performance. To apply that, an important question is rising, which is, how does GSCMP affect achieve sustained performance via green technology as an intermediate variable? the General Company for Electrical and Electronic Industries in Baghdad is chosen as a field to study the impact of these variables. The research community was represented by the employees of the researched company, and a purposive sample was chosen, which consisted of the administrative leaders, and a total of 221 individuals. To examine the validity of our supposed hypotheses, the related attitudes data of the respondents were collected through the questionnaire form and analyzed based on ready-made software such as SPSS Ver 21, and AMOS 24. Our model reached different results, the most remarkable one is that the substantial direct and indirect outcomes between the research variables. Accordingly, we have come up with some important recommendations, a significant one is that the management of the considered company should pay more attention to GSCMP and the application of green technology because of their prominent role in achieving sustained performance.

Going green with artificial intelligence: The path of technological change towards the renewable energy transition

Research background: The twin pressures of economic downturn and climate change faced by countries around the world have become more pronounced over the past decade. A renewable energy transition is believed to play a central role in mitigating the economic-climate paradox. While the architectural and computational power of artificial intelligence is particularly well suited to address the challenges of massive data processing and demand forecasting during a renewable energy transition, there is very scant empirical assessment that takes a social science perspective and explores the effects of AI development on the energy transition. Purpose of the article: This paper aims to answer two key questions: One is, how does AI software development promote or inhibit the shift of energy consumption towards renewables? The other is, under what policy interventions does AI software development have a more positive effect on promoting renewable energy consumption? Methods: We employ a dataset of 62 economies covering the period 2011–2020 to analyze the impact of AI software development on the energy transition, where possible confounders, including political and economic characteristics and time-invariant elements, are controlled using fixed-effects estimation along with specified covariates. Findings & value added: AI software development can promote the energy transition towards renewables. There is suggestive evidence that the core mechanism linking such a positive relationship tends to lie in improving innovation performance in environmental monitoring rather than in green computing. Government support for R&D in renewable energy technologies is found to be significantly beneficial for harnessing the positive impact of AI software development on the energy transition. Compared to non-market-based environmental policies, market-based environmental policies have a more significant positive moderating effect on the relationship between AI software development and energy transition.