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The chemical industry is increasing its efforts to reduce the environmental burdens of chemical production. One focus is to implement energy-efficient processes and green technologies early in the process design to maximize environmental efficiency and to reduce costs. However, as data on many chemical products are scarce, many sustainability studies are hampered by the lack of information on production processes, and chemicals are often neglected or only crudely estimated. Models that estimate production data and environmental burdens can be vital tools to aid sustainability efforts. In addition, they are useful for the environmental assessment of chemicals without access to production data, i.e. in supply-chain management or for the assessment of products using chemicals as materials. Using mass and energy flow data on the petrochemical production of 338 chemicals, we developed models that can estimate key production parameters directly from the molecular structure. The data sources were mostly production data provided by industrial partners, extended by data from the ecoinvent database. The predicted parameters were the Cumulative Energy Demand (CED), the Global Warming Potential (GWP), the Eco-indicator 99 score, a Life Cycle Assessment (LCA) method, and the electricity and heat use over the production cycle. Model outputs include a measure of the prediction uncertainty. The median relative errors of the models were between 10% and 30%, within acceptable ranges for estimations. The modelled parameters offer a thorough insight into the environmental performance of a production process and the model estimates can be of great service in process design, supply-chain management and environmental assessments of chemical products in the early planning and design stages where production data are not available.