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The construction industry, a fundamental pillar of global urbanization, faces mounting pressure to minimize its environmental impact, particularly its significant contribution to carbon emissions. Responsible for approximately 39% of global energy-related CO2 emissions, the sector must adopt innovative solutions to mitigate its role in climate change. Artificial Intelligence (AI) and Machine Learning (ML) are emerging as transformative technologies, offering the precision and efficiency required to tackle complex challenges, such as carbon tracking. However, most current research focuses more on operational carbon emissions and often reduces embodied carbon, which is emissions associated with material production, transportation and the construction process.

Addressing this gap, the Smart Build Carbon Predictor introduces a groundbreaking AI-driven model that quantifies carbon emissions across the entire construction lifecycle. By employing advanced methodologies such as Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Artificial Neural Networks (ANN), the system provides an accurate, dynamic means of predicting emissions throughout the construction process. In doing so, it aligns seamlessly with global sustainability objectives and contributes to the growing emphasis on comprehensive lifecycle assessments.

The strong performance of ensemble methods, like Gradient Boosting and AdaBoost, highlights their robustness and reliability in providing precise predictions, essential for advancing sustainable construction practices. To make the system user-friendly and accessible, a prototype was developed using the PySide6 Graphical User Interface (GUI). This interface allows users to input relevant data such as transportation distance, vehicle emission rate, material weight and carbon emission factor. With a simple click of the "Predict" button, an accurate carbon emission prediction for a specific construction stage is obtained.

The Smart Build Carbon Predictor offers significant promise not only in construction but also in broader applications across various sectors. In urban infrastructure planning, municipalities could use the system to assess the carbon footprint of large-scale projects such as bridges, roads, and public transport systems, ensuring that these initiatives align with carbon reduction targets. The model could also be adapted for use in the manufacturing industry, where it could help evaluate emissions across complex production chains, driving cleaner and more sustainable manufacturing practices. Renewable energy projects, such as solar farms and wind turbine installations, could benefit from the system by assessing the lifecycle carbon emissions of these infrastructure investments, ensuring their contributions to sustainable energy goals are accurate and transparent. In agricultural and forestry applications, AI-powered predictions could assist in estimating the carbon footprint of land-use changes, farming operations, and reforestation efforts, supporting climate-smart agricultural practices. These examples demonstrate the flexibility of the Smart Build Carbon Predictor's underlying technology and its potential to address sustainability challenges in multiple high-emission sectors, amplifying its impact on global carbon reduction efforts.

Looking ahead, several research directions could further enhance the Smart Build Carbon Predictor and extend its reach. One key avenue is the integration of real-time data through IoT sensors and satellite monitoring, which would enable dynamic carbon tracking and provide stakeholders with up-to-date emissions data during the construction process. Additionally, global data standardization is essential for ensuring the system's applicability across diverse regions and regulatory environments, enabling it to be adopted universally. Future developments could also expand the system’s capabilities by providing actionable decarbonization recommendations, such as suggesting alternative materials or methods to minimize emissions in the early planning stages. Another promising direction is the inclusion of circular economy principles in the model, allowing for the assessment of emissions reductions from recycling and material reuse, thus providing a more holistic lifecycle analysis. Enhancing the system with interactive visualization tools, such as real-time dashboards or 3D models, would further aid decision-makers by allowing them to visually interpret carbon hotspots and prioritize interventions. These advancements would enhance the system's usability and relevance across various industries.

In conclusion, the Smart Build Carbon Predictor represents a significant leap in the intersection of technology and sustainability. With its innovative AI methods and user-friendly design, the system addresses critical challenges in carbon emission accounting and empowers stakeholders in the construction sector to make informed, data-driven decisions that promote sustainable practices. Moreover, the model’s broader applications in fields such as urban infrastructure, manufacturing, and agriculture, as well as its potential to contribute to real-time emissions monitoring and decarbonization, suggest that its impact could extend far beyond construction. As research progresses and new capabilities are integrated, the Smart Build Carbon Predictor is poised to become a central tool in the global push toward sustainability and climate action, supporting industries in reducing their carbon footprints and advancing toward more sustainable futures.