The study presents a framework for the sustainable carbon-based nanomaterials, focusing on Carbon Nano Tubes (CNTs). The framework integrates performance, hazard, and economic considerations toward the development of CNT-enabled products. Through Life Cycle Analysis (LCA) and environmental degradation studies, the research highlights the energy-intensive nature of CNT production, the persistence of CNTs in the environment, and the associated ecotoxicity risks. Functionalization of CNTs is emphasized as a crucial strategy to enhance biodegradability and reduce toxicity. The study also addresses the economic trade-offs, noting that while CNTs offer superior functional performance, their high production costs and energy demands must be carefully managed. The proposed framework aims to ensure that CNTs maximize their benefits while minimizing their environmental and health impacts, thereby supporting the sustainable advancement of carbon nanomaterials in various applications. The study found that CNT production is highly energy-intensive, but scaling up can improve efficiency. CNTs persist in the environment, with partial degradation, indicating potential long-term ecological risks. Functionalization enhances biodegradability and reduces toxicity, helping to balance performance with sustainability.

Optimizing business strategies for energy through machine learning involves using predictive analytics for accurate energy demand and price forecasting, enhancing operational efficiency through resource optimization and predictive maintenance, and optimizing renewable energy integration into the energy grid. This approach maximizes production, reduces costs, and ensures stability in energy supply. The novelty of integrating deep reinforcement learning (DRL) in energy management lies in its ability to adapt and optimize operational strategies in real-time, autonomously leveraging advanced machine learning techniques to handle dynamic and complex energy environments. The study’s outcomes demonstrate the effectiveness of DRL in optimizing energy management strategies. Statistical validity tests revealed shallow error values [MAE: 1.056 × 10(− 13) and RMSE: 1.253 × 10(− 13)], indicating strong predictive accuracy and model robustness. Sensitivity analysis showed that heating and cooling energy consumption variations significantly impact total energy consumption, with predicted changes ranging from 734.66 to 835.46 units. Monte Carlo simulations revealed a mean total energy consumption of 850 units with a standard deviation of 50 units, underscoring the model’s robustness under various stochastic scenarios. Another significant result of the economic impact analysis was the comparison of different operational strategies. The analysis indicated that scenario 1 (high operational costs) and scenario 2 (lower operational costs) both resulted in profits of $70,000, despite differences in operational costs and revenues. However, scenario 3 (optimized strategy) demonstrated superior financial performance with a profit of $78,500. This highlights the importance of strategic operational improvements and suggests that efficiency optimization can significantly enhance profitability. In addition, the DRL-enhanced strategies showed a marked improvement in forecasting and managing demand fluctuations, leading to better resource allocation and reduced energy wastage. Integrating DRL improves operational efficiency and supports long-term financial viability, positioning energy systems for a more sustainable future.