Increasing calls for safer and more sustainable approaches to innovation in the chemical sector necessitate adapted methods for the environmental assessment of emerging chemical technologies. While these technologies are still in the research and development phase, gaining an early understanding of their potential implications is crucial for their eventual introduction into markets worldwide. Life Cycle Assessment (LCA) is a core tool which has been recently adapted for such purpose. Prospective LCA approaches aim to develop plausible future-oriented models which account for the evolution of factors both intrinsic and extrinsic to the technologies assessed. Such future-oriented models introduce many indeterminacies, which could, to some extent, be addressed by Machine Learning techniques. Recent demonstrations of such techniques in the context of prospective LCA, as well as promising avenues for further research, are critically discussed.

MSocial Life Cycle Assessment (S-LCA) uses a life cycle perspective to assess social impacts of products, and the S-LCA guidelines describe developing the system boundaries based on a factory-level perspective. However, such a perspective may exclude stakeholders with a negative social performance which are cooperating with a factory but are not directly involved with the product under study, and it can result in a step back on corporate social responsibility (CSR). Our study aimed to align S-LCA with the CSR concept. Therefore, we designed a case study for the manufacturing sector in which we practiced expanding the system boundaries of S-LCA. Our results showed larger social risks after expanding the system boundaries due to subsidiary and supplier companies located in countries with less strict regulations than the Netherlands, which is where the main organizations and parent company existed. We conclude that system boundaries expansion can result in more complete picture of the involved organizations, and lead practitioners to approach S-LCA with the goal of improving social conditions and identify companies which deserve excellent or poor social scores. Its usefulness is mostly expected when S-LCA practitioners aim to identify social hotspots in supply chains in socially sensitive markets.

Environmental pollution, resource scarcity, and freshwater shortage are critical world challenges facing humanity. Process industry produces large amounts of brine, a waste water with a high salinity level and often critical raw materials. This study applies the social life cycle assessment (S-LCA) to quantify societal benefits and risks in developing brine treatment systems. S-LCA is implemented for hotspot and site-specific levels on four case studies of the Zero Brine project. Hotspot analysis focused on the major commodities. Social Hotspot Database was used as source for data and endpoint indicators. In addition, site-specific analysis regarded the social performance of the case studies companies; interviews and questionnaires were performed with representatives of the four case studies. The collected data were converted to scores with subcategory assessment method and performance reference points. The results show that for all case studies “Labor rights and decent work” and “Health and safety” indicators result in the largest impacts due to imports of commodities from developing countries. Site-specific results show that the overall social sustainability performance of the case study companies is at a good level. The only potential areas for improvement are the “Occupational accidents” and “Contribution to the local community”. The former are minimally higher for silica plant and higher for coal mine in relation to these sectors average accidents rates. Furthermore, the coal mine company can contribute more to the local community and reduce conflicts concerning environmental impacts at the city level. Common identified hotspots among the case studies are: China, India and Congo. Reducing imports from these countries will significantly improve the societal performance of the brine systems.