This research presents an AI-driven, explainable energy management model that aligns with Net Zero sustainability objectives by optimizing energy consumption, enhancing predictive accuracy, and ensuring transparency. The model integrates machine learning algorithms, like Gradient Boosting Machines (GBM) and Random Forests, and utilizes techniques like SHAP and LIME for interpretability. Data was split 70/30 for training and validation, with 10-times validation to avoid overfitting, achieving a Mean Absolute Error (MAE) of 1.26-1.53 and Root Mean Squared Error (RMSE) of 1.97-2.06. The model's predictive accuracy reached an R² of 0.92, with precision and recall scores of 85-90% and 80-88%, respectively, demonstrating significant improvements over traditional methods. Sensitivity analysis revealed high influence from temperature and historical consumption data, requiring careful monitoring. This model performed robustly across diverse scenarios, reducing CO₂ emissions by 30% and cutting costs by 18%, highlighting its adaptability in real-world applications. Conclusions affirm that the explainable AI model advances sustainable energy management by providing reliable, actionable insights, aligning with Net Zero goals, and supporting informed decision-making through enhanced transparency and accuracy.

This research investigates the application of machine learning models to optimise renewable energy systems and contribute to achieving Net Zero emissions targets. The primary objective is to evaluate how machine learning can improve energy forecasting, grid management, and storage optimisation, thereby enhancing the reliability and efficiency of renewable energy sources. The methodology involved the application of various machine learning models, including Long Short-Term Memory (LSTM), Random Forest, Support Vector Machines (SVMs), and ARIMA, to predict energy generation and demand patterns. These models were evaluated using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Key findings include a 15% improvement in grid efficiency after optimisation and a 10–20% increase in battery storage efficiency. Random Forest achieved the lowest MAE, reducing prediction error by approximately 8.5%. The study quantified CO₂ emission reductions by energy source, with wind power accounting for a 15,000-ton annual reduction, followed by hydropower and solar reducing emissions by 10,000 and 7500 tons, respectively. The research concludes that machine learning can significantly enhance renewable energy system performance, with measurable reductions in errors and emissions. These improvements could help close the “ambition gap” by 20%, supporting global efforts to meet the 1.5 °C Paris Agreement targets.

This study investigates integrating circular economy principles—such as closed-loop systems and economic decoupling—into industrial sectors, including refining, clean energy, and electric vehicles. The primary objective is to quantify the impact of circular practices on resource efficiency and environmental sustainability. A mixed-methods approach combines qualitative case studies with quantitative modelling using the Brazilian Land-Use Model for Energy Scenarios (BLUES) and Autoregressive Integrated Moving Average (ARIMA). These models project long-term trends in emissions reduction and resource optimization. Significant findings include a 20–25% reduction in waste production and an improvement in recycling efficiency from 50% to 83% over a decade. Predictive models demonstrated high accuracy, with less than a 5% deviation from actual performance metrics, supported by error metrics such as Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). Statistical validations confirm the reliability of these forecasts. The study highlights the potential for circular economy practices to reduce reliance on virgin materials and lower carbon emissions while emphasizing the critical role of policy support and technological innovation. This integrated approach offers actionable insights for industries seeking sustainable growth, providing a robust framework for future resource efficiency and environmental management applications.

This research explores the enhancement of electrochemical performance in magnesium batteries by optimising magnesium alloy anodes, explicitly focusing on Mg-Al and Mg-Ag alloys. The study’s objective was to determine the impact of alloy composition on anode voltage stability and overall battery efficiency, particularly under extended cycling conditions. The research assessed the anodes’ voltage behaviour and internal resistance across magnesium bis(trifluoromethanesulfonyl)imide (Mg(TFSI)₂) electrolyte formulations using a systematic setup involving cyclic voltammetry on the anode and electrochemical impedance spectroscopy. The Mg-Al alloy demonstrated superior performance, with minimal voltage drop and lower resistance increase than the Mg-Ag alloy. The results showed that the Mg-Al alloy maintained over 85% energy efficiency after 100 cycles, significantly outperforming the Mg-Ag alloy, which exhibited increased degradation and efficiency reduction to approximately 80%. These findings confirm that incorporating aluminium into magnesium anodes stabilises the anode voltage and enhances the overall battery efficiency by mitigating degradation mechanisms. Consequently, the Mg-Al alloy is identified as an up-and-coming candidate for use in advanced battery technologies, offering energy density and cycle life improvements. This study lays the groundwork for future research to refine magnesium alloy compositions further to boost battery performance.

This research uses advanced statistical methods to examine climate change mitigation policies’ economic and environmental impacts. The primary objective is to assess the effectiveness of carbon pricing, renewable energy subsidies, emission trading schemes, and regulatory standards in reducing CO₂ emissions, fostering economic growth, and promoting employment. A mixed-methods approach was employed, combining regression analysis, cost–benefit analysis (CBA), and computable general equilibrium (CGE) models. Data were collected from national and global databases, and sensitivity analyses were conducted to ensure the robustness of the findings. Key findings revealed a statistically significant reduction in CO₂ emissions by 0.45% for each unit increase in carbon pricing (p < 0.01). Renewable energy subsidies were positively correlated with a 3.5% increase in employment in the green sector (p < 0.05). Emission trading schemes were projected to increase GDP by 1.2% over a decade (p < 0.05). However, chi-square tests indicated that carbon pricing disproportionately affects low-income households (p < 0.05), highlighting the need for compensatory policies. The study concluded that a balanced policy mix, tailored to national contexts, can optimise economic and environmental outcomes while addressing social equity concerns. Error margins in GDP projections remained below ±0.3%, confirming the models’ reliability.

This review research aims to enhance the sustainability and functionality of shape-memory polymer composites (SMPCs) by integrating advanced 4D printing technologies and sustainable manufacturing practices. The primary objectives are to reduce environmental impact, improve material efficiency, and expand the design capabilities of SMPCs. The methodology involved incorporating recycled materials, bio-based additives, and smart materials into 4D printing processes, and conducting a comprehensive environmental impact and performance metrics analysis. Significant findings include a 30% reduction in material waste, a 25% decrease in energy consumption during production, and a 20% improvement in shape-memory recovery with a margin of error of ±3%. Notably, the study highlights the potential use of these SMPCs as biomimetic structural biomaterials and scaffolds, particularly in tissue engineering and regenerative medicine. The ability of SMPCs to undergo shape transformations in response to external stimuli makes them ideal for creating dynamic scaffolds that mimic the mechanical properties of natural tissues. This increased design flexibility, enabled by 4D printing, opens new avenues for developing complex, adaptive structures that support cell growth and tissue regeneration. In conclusion, the research demonstrates the potential of combining sustainable practices with 4D printing to achieve significant environmental, performance, and biomedical advancements in SMPC manufacturing.