Life Cycle Assessment (LCA) has become the main approach for the environmental impact assessment of chemicals. Unfortunately, LCA studies often require large amounts of data, time, and resources. To circumvent this limitation, here we propose a streamlined LCA method that predicts the impact of chemicals from molecular descriptors, thermodynamic properties, and surface charge density distributions of molecules (COSMO-based σ-profiles). Our approach uses mixed-integer nonlinear models to automatically construct predictive equations of the life cycle impact of chemicals from a set of attributes that are more accesible than full LCA inventories. We applied our method to predict the life cycle impact of 90 chemicals from three attribute sets: 15 molecular descriptors, 12 thermodynamic properties, and discretized σ-profiles. Nine impact categories were estimated, including among others the Global Warming Potential and Eco-Indicator99. Results show that models based on molecular and σ-profile attributes show similar performance to those based on molecular and thermodynamic attributes. This facilitates the application of streamlined LCA when developing new chemicals and processes, avoiding the experimental determination of thermodynamic properties. Furthermore, molecular, thermodynamic, and σ-profile attributes used together provide the most accurate predictions. Overall, this work aims to enhance chemical environmental assessment, facilitating their screening and enhancing the development of more sustainable processes and products.