Background of study: The transition to sustainable energy is essential to mitigate climate change. While renewable sources like solar and wind are key to decarbonization, their adoption is hindered by performance intermittency. Current research is often fragmented by technology type, lacking an integrated framework that simultaneously addresses engineering innovations, technical performance, and environmental sustainability.

Aims paper: This study aims to synthesize sustainable engineering innovations by integrating technological development with operational performance and environmental impact assessments. The scope is limited to 20 peer-reviewed articles from the Scopus database published between 2019 and 2025.

Methods: The research follows a Systematic Literature Review (SLR) design and PRISMA 2020 guidelines. Data were analyzed using Biblioshiny for descriptive statistics and VOSviewer for thematic and network visualization

Result: Findings show an annual scientific growth rate of 44.22%. Key innovations focus on hybrid energy systems, smart grids, and energy storage. Recent trends indicate a shift toward digitalization, IoT-based management, and environmentally optimized materials.

Conclusion: Sustainable engineering innovations significantly enhance system efficiency and reliability. Although they reduce greenhouse gas emissions, environmental performance varies by technology. The study concludes that interdisciplinary, system-level approaches are vital for achieving global sustainability goals.

Background of study: Edge computing devices like autonomous robots and IoT sensors need sophisticated AI for real-time decisions, but conventional processors consume 15-300 watts during inference, creating critical limitations for battery-powered deployments. GPU-based accelerators face memory bottlenecks and high energy costs from data movement, making sustained autonomous operation impractical.

Aims of paper: This research compares neuromorphic platforms (Intel Loihi 2, IBM TrueNorth, BrainChip Akida) against conventional accelerators (NVIDIA Jetson, Google Coral) to evaluate if neuromorphic architectures can solve edge AI energy efficiency challenges across five representative workloads.

Methods: Using an experimental design with hardware benchmarking and power analysis, we evaluated five edge AI workloads. ANOVA and regression modeling were then applied to rigorously compare computing paradigms while controlling for variables.

Result: Neuromorphic platforms demonstrated 15-50× improved energy efficiency versus conventional GPU accelerators for event-driven workloads. Intel Loihi 2 achieved 2,400 inferences/joule at 1.8 watts versus 180 inferences/joule at 18.5 watts for NVIDIA Jetson. IBM TrueNorth operated at 70 milliwatts for pattern recognition. BrainChip Akida achieved 94.6% accuracy on keyword spotting at 0.8 watts. Event-driven processing exhibited 0.4ms latency versus 5.1ms for frame-based systems. Neuromorphic chips maintained stable performance without active cooling below 65°C, while conventional accelerators required thermal management above 85°C.

Conclusion: Neuromorphic processors (0.6-5W) excel in power-efficient edge AI for event-driven data. While hybrid architectures optimize performance, adoption is hindered by immature software ecosystems, limited training frameworks, and a 2-4% accuracy gap compared to conventional methods.

Background of study: Wildfires pose a critical threat to global ecosystems, biodiversity, and human safety, with climate change intensifying fire frequency, scale, and unpredictability. Traditional wildfire detection approaches often suffer from delayed response, limited coverage, and insufficient automation, which restrict effective mitigation and early intervention.

Aims and scope of paper: This paper aims to design and evaluate an intelligent Forest Fire Alert System that integrates satellite remote sensing, machine learning models, Internet of Things based environmental sensing, and real time alert communication to enable early wildfire detection and proactive risk assessment.

Methods: The proposed system employs a Convolutional Neural Network to detect active fire regions from multispectral satellite imagery, while a Random Forest classifier estimates wildfire risk levels based on meteorological variables and IoT sensor data. Geospatial positioning through GPS supports precise location mapping, and a web-based dashboard disseminates real time alerts to forestry authorities for rapid response.

Result: Experimental evaluation demonstrates strong performance of the proposed framework. The CNN model achieved an accuracy of 94.7 percent, precision of 92.3 percent, recall of 96.1 percent, and an F1 score of 94.1 percent. The Random Forest model obtained an accuracy of 88.2 percent with a ROC AUC value of 0.91, indicating reliable fire risk prediction capability.

Conclusion: The integrated Forest Fire Alert System outperforms conventional detection methods in terms of accuracy, detection speed, and automation. The proposed approach provides a scalable, IoT enabled, and proactive solution for intelligent wildfire monitoring and management under evolving climatic conditions.

Background of Study: Climate change, driven by human-induced greenhouse gas emissions, presents a global crisis. The construction sector is a primary contributor, responsible for 30–40% of global energy use and over 30% of emissions throughout the building life cycle. Despite innovations like low-carbon materials and Building Information Modeling (BIM), current research remains fragmented. A significant gap exists in integrating technical, economic, and environmental aspects into a single analytical framework.

Aims Paper: This paper aims to identify trends in sustainable materials and green technologies through a systematic review. It evaluates their performance across technical, environmental, and economic dimensions while formulating future research directions to achieve sustainable development goals.

Methods: A Systematic Literature Review (SLR) was conducted using the Scopus database (2016–2025). Five core keywords were used to identify relevant studies, which were then cleaned using OpenRefine and visualized via VOSviewer. Elicit AI assisted in screening and synthesizing the final 12 journal articles.

Result: Analysis of 12 articles shows that experimental studies (50%) and Multi-Criteria Decision-Making (25%) are the dominant methodologies. Research primarily focuses on optimizing specific materials like waste-based concrete and geopolymers. While material innovation is a priority, there is limited integration of technical data with long-term Life Cycle Assessment (LCA) or life-cycle cost analysis.

Cocnlusion: Current sustainable construction research emphasizes technical material optimization through experimental approaches. While decision-support models are evolving, empirical integration of the circular economy and LCA remains limited. Future research must adopt holistic frameworks, expand data sources, and include diverse geographical case studies to support effective sustainability practices.

Background of Study: The global challenge of waste management and resource depletion is driven by the traditional linear "take-make-dispose" model. This study addresses the urgent need to transition toward a circular economy (CE) to mitigate environmental degradation and resource scarcity.

Aims Paper: To systematically synthesize and analyze existing literature to identify strategic pathways for advancing sustainable engineering through circular economy principles, integrating technological innovation, economic impacts, and policy frameworks.

Methods: The research employed a Systematic Literature Review (SLR) following PRISMA 2020 guidelines. Data was sourced from Scopus, Web of Science, and IEEE Xplore (2015–2025). Bibliometric analysis was performed using Biblioshiny and VOSviewer on 15 high-quality selected articles.

Result: Findings show that the field is dominated by techno-economic studies (28.6%), Life Cycle Assessment (21.4%), and resource recovery optimization (21.4%). Quantitative modeling and simulations are the primary methodologies used (42.9%).

Conclusion: The transition to a circular economy requires an integrated approach linking technical advancement with economic viability and policy support. Future research should bridge the gap between technological innovation and social/digital transformation.