Life cycle assessment plays a critical role in quantifying environmental impacts, but its credibility remains challenged when data and uncertainty analysis are lacking. In this study, we propose a data compilation framework to address these two issues. The framework first quantifies the correlations of production activities among existing data in temporal, geographical, and taxonomic dimensions. The framework then introduces covariance functions to convert these correlations to a similarity matrix, and the Gaussian process regression model is adopted to predict new data based on these covariance functions. The associated uncertainty is automatically characterized using the posterior distribution of predictions. The framework is demonstrated on the nitrogen fertilizer application rate for food productionan activity recognized for its environmental burdenwith results capable of reflecting temporal and geographical variations. By introducing the concept of phylogenetic distance as a correlation of taxonomy, the framework provides a quantitative basis for predictions in a proxy data usage scenario. The framework can be used in developing temporally and regionally representative life cycle inventories and databases and can facilitate consistent uncertainty quantification in future life cycle assessment methodologies.
Since 2000, China's consumption of energy and mineral resources has grown rapidly, and its consumption of some important mineral resources has even exceeded half of the global consumption. Medium-and long-term resource demand forecast is an important basis for national policy formulation and strategic planning. Based on historical statistics such as China's population, GDP, and mineral resources consumption, this paper adopts the S-shape rule of per capita consumption, the demand analogy and proportional relationship measurement algorithm, and the departmental consumption method, to systematically predict the demand for 43 types of major mineral resources before 2035. Results show that China's demand for mineral resources has changed from high-speed growth to differential growth; its demand for most of the bulk minerals will peak by 2025; the structure of primary energy sources will change dramatically when their demand peak by 2030, with the demand for Coal falling from 60.4% in 2017 to 46.3%, that for natural gas increasing from 6.6% to 13.2%, and that for non-fossil energy increasing from 13.6% to 23.4%; and demand for most strategic emerging minerals will continue to grow before 2035, and the global structure and pattern of supply and demand for energy and mineral resources will change greatly.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.