The construction sector faces the urgent need to prioritize energy efficiency due to an increasingly stringent regulatory landscape in response to the climate change agenda. Central to this transition is the pivotal role of education in equipping professionals with the necessary knowledge and skills. Educational solutions have emerged as powerful tools for promoting awareness and interventions to mitigate climate change. This article provides a case study that highlights the successful utilisation of computer technology in delivering digital solutions to advance energy education and promote more informed energy practices in the construction industry. The utilisation of digital technologies can enhance collaborative efforts in energy efficiency training, which is of critical significance in ensuring the security, sovereignty, transparency, immutability, and decentralisation of interventions related to energy education. This paper presents a framework that utilises Blockchain technology to facilitate training labelling and authenticity based on smart contracts and mobile passports to provide a secure and efficient solution for the delivery of training and education in the energy domain. Our research examines the challenges and opportunities related to energy efficiency training within the construction industry. By integrating industry-specific insights, exemplifications, and case studies, we provide an in-depth understanding of the interconnection between energy efficiency education and digital solutions with the unique context of the construction industry. We underscore the importance of leveraging digital platforms as educational tools to foster a deeper understanding and adoption of energy-efficient practices. We demonstrate that educational solutions play a pivotal role in driving awareness and interventions for mitigating climate change, greatly empowering individuals and organizations to adopt energy-efficient practices and to address sustainability objectives.

Construction projects involve a complex relationship of interdependence among numerous parties, which often leads to delays and significant coordination costs among contractors. While approaches based on Building Information Modeling (BIM) and Digital Twin (DT) technologies have enhanced monitoring and visualization capabilities, they remain limited in their ability to enable autonomous, decentralized, and tamper-proof resource reallocation. This paper proposes and computationally evaluates a three-layer Tokenized BIM-Twin (TBT) framework under controlled simulation conditions. The framework combines a static IFC-based BIM layer for resource tokenization, a real-time Digital Twin layer to detect anomalies and measure deviations, and a blockchain-based smart contract layer that has the potential to minimize disputes and reallocate resources automatically and transparently under the modelled conditions. The framework is evaluated using an agent-based simulation model defined on an eight-parameter uncertainty space. A total of 150 Monte Carlo iterations per scenario ( n = 450 observations) are performed across three configurations: Baseline (traditional management), Centralized BIM + DT, and the TBT framework. The results are assessed using one-way ANOVA, Welch’s t-tests, and effect size analysis (Cohen’s d), indicating statistically significant differences across all evaluated performance dimensions under the modelled conditions. Schedule overrun is reduced within the simulation environment from a mean of 38.90% (± 6.24) to 3.30% (± 1.38) (F = 2672.5, p < 0.001, d = 7.88). Resource idle rates decline from 35.52% (± 3.22) to 20.04% (± 3.02) (d = 4.96), while disruption response time is reduced from 5.45 days (± 1.23) to 0.44 days (± 0.17) (d = 5.69). In addition to its conceptual contribution, the framework has practical implications, as the framework demonstrates the potential to improve coordination efficiency, reduce disputes, and increase transparency under the modelled conditions. The results suggest that the approach has potential for projects characterized by high uncertainty and complex stakeholder interactions, although empirical validation is still required before real-world performance can be confirmed.

Escalating global production and consumption are driving rapid growth in energy demand, increasing pressure on finite natural resources. In response, this study proposes a data-driven framework that integrates deep learning-based electricity demand forecasting with economy-wide input–output material footprint analysis to support long-term energy planning and policymaking. The innovative aspect of this framework is its ability to jointly assess future electricity generation and related material requirements within a single analytical structure. A comparative analysis is conducted for Türkiye, Germany, and Spain, evaluating the material footprint of electricity generation across renewable and fossil-based energy sources under business-as-usual (BAU) and alternative energy development scenarios. The forecasting models demonstrate strong predictive performance, achieving Mean Absolute Percentage Error (MAPE) values of 1.39% for Türkiye, 4.39% for Germany, and 3.90% for Spain, significantly outperforming conventional statistical methods. Scenario-based results indicate that sustainability-oriented pathways (ST and GCA) can reduce material requirements by approximately 20–30% compared to the BAU scenario, particularly for metal-intensive inputs such as iron and refined oil. The findings underscore the importance of integrating material footprint considerations into energy transition strategies and provide practical insights for policymakers seeking to balance energy security with resource sustainability. The study highlights the value of integrated analytical approaches in supporting more resilient and resource-efficient energy systems.