A bibliometric analysis of information system development for environmental sustainability

With the widespread application of the Internet of Things (IoT), computing tasks on the terminal side have surged. Traditional cloud computing models, constrained by high network latency and overloaded central servers, can no longer effectively meet the dual requirements of real-time responsiveness and energy efficiency. The cloud–edge–device collaborative architecture, by enabling distributed resource scheduling, offers a promising solution to reduce both latency and energy consumption. However, optimizing carbon emissions under dynamic operating conditions remains a pressing and unresolved challenge. This paper proposes a carbon-aware dynamic scheduling framework for cloud–edge–device systems, which accounts for the stochastic nature of task arrivals, heterogeneous computing capabilities, and varying carbon intensity across devices and locations. A multi-layer carbon emission model is developed, and the long-term carbon minimization objective is formulated as a stochastic optimization problem. Using the Lyapunov drift-plus-penalty method, the problem is transformed into a tractable deterministic optimization framework, upon which a Carbon-Efficient Computation Offloading (CECO) algorithm is designed. CECO jointly optimizes local computation frequency, data transmission rate, and edge resource allocation to dynamically balance task queue stability and carbon emission intensity. Theoretical analysis and simulation results validate that the proposed algorithm significantly reduces system-level carbon emissions while maintaining quality of service, demonstrating strong potential for enabling green computing in intelligent distributed environments.

Green Cloud Computing: Integrating Sustainable Practices with Energy-Aware Solutions.

The development of information systems and information technology (IS/IT) has significantly transformed higher education; however, inadequate strategic planning often leads to inefficiencies and environmental impacts, such as high energy consumption and electronic waste. Despite the growing importance of sustainability, existing IS/IT strategic planning approaches generally do not explicitly integrate environmental considerations, thus creating a gap between digital transformation and sustainability objectives. This study aims to develop an IS/IT strategic planning framework that integrates green IT principles within the Ward and Peppard methodology. A qualitative case study approach was applied at Akademi Farmasi Yarsi Pontianak using data collected through interviews, observations, and questionnaires. The analysis incorporated multiple frameworks, including the Balanced Scorecard, PEST, Porter’s Five Forces, SWOT, and Critical Success Factors. The results produced integrated IS/IT strategies that align organizational goals with sustainability principles, including the development of information systems, environmentally oriented IT infrastructure, and governance mechanisms that support green IT implementation. The findings indicate that integrating green IT into IS/IT strategic planning can enhance operational effectiveness while contributing to environmental sustainability. This study contributes by extending the Ward and Peppard framework through the explicit incorporation of environmental sustainability as a strategic dimension, providing a practical and replicable model for higher education institutions pursuing sustainable digital transformation.

The rapid proliferation of computing technologies, cloud infrastructure, and Internet of Things (IoT) devices has intensified global energy consumption, accelerated electronic waste generation, and amplified greenhouse gas emissions. These trends pose acute sustainability challenges, particularly for developing economies in Sub-Saharan Africa where power infrastructure remains unreliable, e-waste governance frameworks are nascent, and financial resources for green technology adoption are severely constrained. This paper presents a comprehensive review of green computing techniques and examines their potential to create environmentally sustainable computing environments in developing economies. Drawing on a structured review of peer-reviewed literature published between 2010 and 2024, the study analyses key green computing dimensions including energy-efficient data center design, server virtualization, cloud resource optimization, e-waste lifecycle management, green procurement, AI-driven power management, and renewable energy integration. The findings reveal that, while significant adoption gaps persist in low-income settings, targeted policy interventions, capacity-building programmes, and south-south technology transfer partnerships can substantially advance green computing uptake. The study concludes that green computing is not a luxury exclusive to technologically advanced nations but a strategic imperative for developing economies seeking to leapfrog legacy, energy-intensive ICT infrastructure and achieve sustainable digital development.

The research aims at mapping the evolution of cloud computing studies from 2010 to 2026 through the analysis of academic literature using bibliometrics. This approach will provide information on the structure of the knowledge, major contributions, and the thematic trends within the area. In the present work, the bibliometric analysis is based on Scopus indexation. The research uses several types of analysis: co-authorship, citation, and keyword co-occurrence analyses. VOSviewer was chosen as the main bibliographic software for data mining. The analysis shows that the field is dominated by major researchers representing countries including the USA, China, and India, which indicates a partially fragmented cooperation among experts in the domain. Bibliometric analysis through citation shows that research papers in cloud computing architecture, Internet of things, and edge computing play a crucial role, as they set the basis of the topic and show the trend toward decentralization and integration in computing. In addition, the multidimensionality of research in cloud computing was analyzed through keywords, which revealed not only technical issues but also security, applications, green computing, and energy efficiency. This study contributes to the literature by providing a systematic overview of the development and current landscape of cloud computing research, offering valuable insights for researchers and practitioners in identifying future research directions and opportunities for interdisciplinary collaboration.

Lixotec Project: Environmental Education focused on green information technology (green IT)

The growth of Internet of Things devices has led to energy use and environmental worries. This means we need to make electronic systems more eco-friendly. Green electronics is about reducing energy use and environmental harm. Artificial Intelligence offers ways to optimize. Traditional AI methods have high communication costs, delays and privacy issues. This paper suggests using a Federated Learning-based framework. It helps make green electronics more sustainable, for IoT systems. The model uses ensemble learning and optimization that saves energy. It reduces power use while staying accurate. Tests show that this approach uses energy and reduces communication overhead. It also makes systems more scalable and sustainable. The Internet of Things devices need solutions. Green electronics and Artificial Intelligence can help. The Federated Learning-based framework is a solution. It makes green electronics more eco-friendly. Keywords Federated Learning, Green Electronics, IoT, Energy Efficiency, Sustainability, Distributed Systems, Lightweight Models, Explainable AI

The Rapid rise of AI and cloud services are increasing in the modern data centers, which consume a large amount of energy consumption. There are certain key concerns like Sustainability, operational cost and environmental impact. However, many AI-based approach had been implemented to maintain workload prediction, Resource optimization and renewable energy integration in cloud infrastructure. In existing studies, there is a lack of unified sustainability-oriented framework. This research does a Comprehensive survey which includes AI-based energy prediction model for unified sustainability, that includes techniques like machine learning, Deep learning and reinforcement learning that does predictions such as workload forecasting, intelligent scheduling, predictive maintenance and energy-aware resource management. while doing this study we found some problems in current cloud systems, which includes limited cross-layer coordination, lack of carbon-aware scheduling, data heterogeneity, explainability issues and minimal real-world validation. To overcome these issues we can use a unified multi-layer sustainable cloud framework, this is nothing but building one complete system that connects all layers of the cloud together that is data acquisition, AI-driven prediction, optimization and sustainability monitoring. A Modified and simpler mathematical model is introduced to reduce the maximum energy consumption along with satisfying Quality of Service (QoS), carbon intensity, and renewable usage constraints. This proposed models approach is to provide a better point of view for building intelligent, energy-efficient and environmentally applicable cloud infrastructures that ensures the sustainability as well as supporting the future research in green computing and sustainable digital ecosystems.

The distributed architecture of the SKA Regional Centre Network (SRCNet) aims to provide scientific communities worldwide with efficient computational and storage resources to exploit the massive data volumes produced by the SKA Observatory (SKAO). Given the amount of SKAO data, traditional data management paradigms — where data is transferred to computational resources— are no longer feasible. Instead, computational workflows must increasingly be relocated closer to data storage locations, emphasizing efficient data access strategies and avoiding unnecessary duplication or redundancy. In this context, we present PrepareData, a modular and extensible data delivery service developed within SRCNet prototyping activities. Our proposal for this service addresses the critical challenge of redundant data transfers and duplication at both node and user levels by enabling seamless delivery of requested datasets from local Rucio Storage Elements (RSEs) directly into users’ working environments. PrepareData operates as a local service within each SRCNet node and it is integrated into a broader ecosystem of federated services. Specifically, we designed and evaluated two distinct yet complementary implementations to avoid unnecessary data duplication and to enable a dynamic data bridge between the RSEs and the user storage areas, through: (1) a filesystem-based solution leveraging CephFS, which uses shared filesystem mount points and bind mounts to ensure consistent and immediate data availability of the data across computational nodes, and (2) a Kubernetes model using Persistent Volumes and Persistent Volume Claims, dynamically injecting data into a user’s areas. To tackle this work we detail the architectural design and development, the technical implementation, the integration of both solutions with science enabling tools, such as JupyterHub, CARTA or virtually any application, and finally we provide a performance evaluation. This contribution provides a scalable and sustainable blueprint for data delivery in federated scientific infrastructures, supporting the broader goals of green computing and efficient resource utilisation.

Energy efficiency in computing has emerged as a critical concern due to escalating environmental and financial costs, particularly in the context of cluster computing, where there is an ever-increasing software workload. Achieving meaningful improvements in energy efficiency requires a comprehensive understanding of the interplay between hardware and software. This research investigates how algorithmic optimisations, language choice, and parallelisation strategies influence energy efficiency and how hardware-level strategies such as underclocking, overclocking, cooling, and on-demand computing can further impact energy usage. A set of measures that can be used generally to show the impact trade-off of power and performance are defined, including the Energy Factor (EF) and a new Efficiency–Performance Score (EPS). Validation experiments on a custom-built Raspberry Pi Bramble cluster used workloads like Monte Carlo Pi simulations in Python and C. Energy and performance trade-offs were evaluated using the Energy Factor and Efficiency–Performance Score on a small example cluster to validate the approach. Results show parallelisation greatly improves energy efficiency over serial execution. Cooling slightly boosts speed under heavy loads but increases total energy use. Perhaps counter-intuitively, underclocking actually raises total energy consumption, while overclocking reduces it. Language choice also impacts efficiency, with C offering notable energy savings over Python. The findings support the hypothesis that software optimisation alone can improve energy efficiency, but the most impactful results are achieved when both software and hardware strategies are jointly considered. These insights contribute to the design of future energy-aware computing systems and provide a foundation for sustainable, high-performance computing architectures.

The rapid growth of digital technologies has led to a significant increase in energy consumption and carbon emissions associated with Information Technology (IT) systems. Green Information Technology (Green IT) has emerged as a critical approach to reduce the environmental impact of computing infrastructures. This paper presents a case study of Antarctica.io, a climate-technology platform designed to measure, analyze, and reduce digital carbon footprints. The study examines the platform's working model, features, benefits, and challenges, with a focus on its dashboard-based visualization system. The findings indicate that Antarctica.io enables organizations to improve energy efficiency, enhance transparency, and support sustainable decision-making. The paper concludes that integrating Green IT practices with real-time analytics tools is essential for achieving long-term environmental sustainability in the digital era.

ABSTRACT As major social challenges escalate, enterprises face increasing pressure to adopt responsible leadership and promote responsible innovation. However, empirical research on how responsible leadership (RL) influences responsible innovation remains limited. Drawing on the attention-based view, this study establishes an influence model linking RL to responsible innovation, while examining the mediating role of boundary-spanning search and the moderating effects of green market pressure (GMP) and green information system (GIS). Based on two waves of survey data from 247 Chinese high-tech SMEs, the findings indicate that RL enhances both boundary-spanning search and responsible innovation, with the relationship between boundary-spanning search and responsible innovation following an inverted U-shaped pattern. Boundary-spanning search also mediates the relationship between RL and responsible innovation. Furthermore, GMP positively moderates the effect of RL on boundary-spanning search, and GIS also plays a positive moderating role in this relationship, although their effect is relatively weaker.

Information Technology (IT) plays a central role in the environmental performance of small and medium-sized enterprises (SMEs); however, the meanings that IT managers attribute to Green IT practices remain insufficiently understood. This study examines how IT managers make sense of sustainable IT practices, drawing on sensemaking theory to investigate the cognitive foundations that inform their decisions. A qualitative study was conducted with SMEs from various sectors, utilizing semi-structured interviews and content analysis to identify patterns of interpretation, barriers, and drivers that shape Green IT adoption. The findings reveal that managers predominantly interpreted Green IT through sensemaking dimensions related to identity, experience, organizational environment, contextual dynamism, evidence management, plausibility, and social interaction. Managers associate sustainability primarily with actions such as virtualization, energy efficiency, reduced printing, and recycling; however, financial and technical constraints remain significant challenges. The study contributes to the literature by presenting a framework that explains how individual cognition and organizational context jointly shape the integration of Green IT into business operations. The results provide practical insights for SME leaders seeking to strengthen sustainable IT strategies and underscore the strategic importance of aligning managerial cognitive frameworks with organizational sustainability goals. • Managerial cognition informs the adoption of environmentally responsible IT practices. • Managers’ beliefs, experiences, and identity underpin engagement with Green IT. • Mental models influence how sustainability is understood and enacted in SMEs. • Despite constraints, managers pursue sustainability aligned with perceived feasibility.

Wireless network expansion needs intelligent solutions that combine energy-conscious operations with the maintenance of performance levels. This study presents an eco-friendly wireless ad hoc network framework that combines scheduled node deployment with adjustable packet time period control techniques, thus supporting renewable power sources. The model implementation in MATLAB enabled simulation testing, demonstrating performance evaluation against traditional networking protocols through key performance indicators. The experimental results show that energy consumption decreases substantially as node power consumption decreases from 23.43 Joules to 12.77 Joules in the case of 50 nodes, 93.38 Joules to 44.69 Joules for 200 nodes, and 234.08 Joules to 112 Joules for 500 nodes. The measured network performance improved slightly while the system maintained its initial output. Throughput evaluation revealed values between 4.008 Mbps and 4.084 Mbps, and latency stayed within 0.2467 ms to 0.2499 ms and achieved a packet delivery success rate of up to 90.15%. The proposed green model offers a sustainable and energy-efficient solution for wireless communications, ensuring that operation and service quality are maintained. • A green wireless ad hoc network model integrating scheduled deployment and adjustable packet timing is proposed. • The model employs renewable energy sources and adaptive sleep intervals to reduce power consumption. • Simulation results demonstrate up to 52% energy savings across networks with 50 to 500 nodes. • The proposed system maintains high throughput ( ≈ 4 Mbps) and success rate (>90%) with negligible latency increase. • The scalable green framework provides a practical solution for sustainable 6G and IoT wireless communications.

Research on Heterogeneous Server Upgrade Strategies and Resource Utilization Efficiency Oriented Toward Green Computing Objectives

Abstract The transition toward sustainable electronics demands environmentally benign energy storage systems capable of long-term stability and regeneration. Here, an eco-friendly and self-healing supercapacitor is developed by integrating chitosan-bonded, coconut-shell-derived carbon electrodes with a gelatin–sodium acetate polymer electrolyte. A delayed-assembly approach, involving electrode resting followed by gentle rehydration, improves ion accessibility and interfacial wetting, leading to a ~ 70% reduction in equivalent series resistance (from ~0.83 Ω to ~0.27 Ω) and a ~ 40% increase in gravimetric capacitance (~109 F g⁻¹ at 0.4 A g⁻¹) and a ~ 45% boost in energy density (15 Wh kg⁻¹), while reaching a maximum power density of ~4230 W kg⁻¹. The device retains 95% of its initial capacitance after 550000 cycles and exhibits spontaneous performance recovery through reversible hydrogen-bond reformation. This physical regeneration mechanism provides one of the highest reported endurance levels among eco-friendly supercapacitors. The proposed design strategy offers a scalable and sustainable route towards durable, high-performance energy storage devices for the next generation of sustainable and green electronics.

Lead-free molecular ferroelectrics are expected to promote the development of green electronics technology due to their environmental friendliness and functional diversity. Nevertheless, the limited variety, low Curie temperature (Tc), and uniaxial characteristics pose significant obstacles in their application. Herein, on the basis of (PMA)3MBr6 (PMA+ is benzylammonium, and M is Bi3+ or Sb3+), we constructed a series of lead-free metal halide hybrids (PMA)2(A)MBr6 (A is dimethylammonium (DMA+), formamidinium (FA+), or guanidinium (GA+)) using a cation mixing strategy. The cation mixing strategy introduces cation-π interactions and effectively regulates the hydrogen bonding network in the structure. The different strengths of noncovalent interactions in the structures lead to differences in the phase transition temperature and ferroelectricity of (PMA)2(A)MBr6. In (PMA)2(GA)MBr6 and (PMA)2(FA)MBr6, the excessively strong hydrogen bond network and cation-π interactions result in the system lacking the necessary kinetic degrees of freedom to achieve ferroelectric inversion. It is worth noting that moderate noncovalent interactions can ensure spontaneous polarization below the Curie temperature (Tc) and provide necessary dynamic channels for the flipping of polar units, thus successfully achieving ferroelectricity in (PMA)2(DMA)MBr6 (3̅mFm). The results revealed that in the design of molecular ferroelectrics, a delicate balance needs to be struck between the different strengths of noncovalent interactions. This finding provides a significant and effective pathway to design high-Tc and multiaxial environmentally friendly ferroelectrics.

The escalating global e-waste crisis and demand for sustainable electronics drive the urgent need for high-performance bio-based polymers. Poly(lactic acid) (PLA), a leading bio-based polyester, suffers from inherent brittleness and limited shape adaptability, hindering its application in green electronics. Herein, we report a fully bio-based, tough PLA blend with excellent room-temperature shape adaptability (RTSA) via melt blending with a low-molecular-weight bio-based polyester (l-BPE). Owing to poor compatibility and low molecular weight, the l-BPE disperses as multiscale domains, effectively toughening PLA via a multiple microcracking mechanism while preserving PLA's high glass transition temperature (Tg). The PLA/l-BPE20 achieves a toughness of 71.9 ± 5.6 MJ/m3, a tensile strength of 39.5 ± 1.1 MPa, and excellent RTSA with high shape fixation rates (>99% in tensile mode, >82% in bending mode). The RTSA stems from the synergistic effect of high Tg-restricted chain mobility, oriented chains at microcracks, and low l-BPE resilience. As a wire protective layer, the blend maintains circuit functionality in complex deformed shapes (spiral, W-shape) without cracking or entropy-driven recovery, outperforming rigid PLA and flexible PBAT-based layers. This work provides a facile incompatible-blending strategy to tough PLA and endow RTSA, enabling high-performance bio-based materials for green electronics.

The development of solvent-free processing routes is essential for advancing sustainable manufacturing in soft electronics. Here, we introduce a melt-processable strategy for fabricating stretchable OFETs based on the physical blending of a low-melting point diketopyrrolopyrrole-based polymer and SEBS. Blends prepared across a wide compositional range were characterized to compare solvent-processed and melt-processed films. Materials characterization in the solid state revealed predominantly amorphous morphologies with composition-dependent differences in nanoscale organization. Free-standing tensile testing demonstrated a systematic decrease in Young’s modulus with increasing SEBS content, confirming enhanced flexibility. Rheological measurements further showed pronounced shear-thinning behavior at high SEBS loadings, advantageous for extrusion-based fabrication and compatible with both melt processing and traditional solution-based methods. Notably, organic field-effect transistors fabricated from melt-processed films exhibited charge carrier mobilities comparable to, and in some cases exceeding, those from solvent-processed blends, with stable performance maintained across diverse compositions. Overall, this work presents a sustainable, solvent-free approach to producing stretchable semiconducting films with tunable mechanical properties and robust electronic performance, offering a versatile platform for soft and stretchable electronic devices.