I aim to submit a full-length research article (approximately 8–10 k words) to a Q1 or Q2 JCR journal that links Construction project management with AI-powered Risk management and prediction while targeting Carbon emissions reduction. Scope • Conduct a comprehensive literature review covering AI in construction project management, current risk-prediction models, and carbon-reduction strategies. • Design a rigorous methodology—quantitative, qualitative, or mixed—capable of generating publishable results. This may involve case studies, simulation modelling, or analysis of open datasets. • Build and validate the AI risk-prediction model using Python, R, or another suitable platform, clearly explaining algorithms and evaluation metrics. • Analyse how predicted risk profiles influence decisions that lower carbon outputs on-site; present findings with tables, charts, and reproducible code. • Draft every manuscript section—abstract through conclusions—formatted to the target journal’s guidelines (structure, citation style, word limits, figure resolution). • Provide a separate cover letter, list of data sources, and any ethics statements needed for peer review. • Remain available for at least one revision cycle after I give feedback. Acceptance criteria 1. Manuscript ready for direct journal submission, free of plagiarism and language errors. 2. All raw data, code, and supplementary materials delivered in editable form. 3. Citations and references fully compliant with chosen journal style. If you have prior publications in high-impact construction or environmental journals and can demonstrate familiarity with JCR submission processes, I’d love to see examples.