Resource Footprints are Good Proxies of Environmental Damage.

Environmental footprints are increasingly used to quantify and compare environmental impacts of for example products, technologies, households, or nations. This has resulted in a multitude of footprint indicators, ranging from relatively simple measures of resource use (water, energy, materials) to integrated measures of eventual damage (for example, extinction of species). Yet, the possible redundancies among these different footprints have not yet been quantified. This paper analyzes the relationships between two comprehensive damage footprints and four resource footprints associated with 976 products. The resource footprints accounted for >90% of the variation in the damage footprints. Human health damage was primarily associated with the energy footprint, via emissions resulting from fossil fuel combustion. Biodiversity damage was mainly related to the energy and land footprints, the latter being mainly determined by agriculture and forestry. Our results indicate that relatively simple resource footprints are highly representative of damage to human health and biodiversity.

Critical review of elementary flows in LCA data.

PURPOSE: Elementary flows are essential components of data used for life cycle assessment. A standard list is not used across all sources, as data providers now manage these flows independently. Elementary flows must be consistent across a life cycle inventory for accurate inventory analysis and must correspond with impact methods for impact assessment. With the goal of achieving a global network of LCA databases, a critical review of elementary flow usage and management in LCA data sources was performed. METHODS: Flows were collected in a standard template from various life cycle inventory, impact method, and software sources. A typology of elementary flows was created to identify flows by types such as chemicals, minerals, land flows, etc. to facilitate differential analysis. Twelve criteria were defined to evaluate flows against principles of clarity, consistency, extensibility, translatability, and uniqueness. RESULTS AND DISCUSSION: Over 134,000 elementary flows from five LCI databases, three LCIA methods, and four LCA software tools were collected and evaluated from European, North American, and Asian Pacific LCA sources. The vast majority were typed as "Element or Compound" or "Group of Chemicals" with less than 10% coming from the other seven types Many lack important identifying information including context information (environmental compartments), directionality (LCIA methods generally do not provide this information), additional clarifiers such as CAS numbers and synonyms, unique identifiers (like UUIDs), and supporting metadata. Extensibility of flows is poor because patterns in flow naming are generally complex and inconsistent because user defined nomenclature is used. CONCLUSIONS: The current shortcomings in flow clarity, consistency, and extensibility are likely to make it more challenging for users to properly select and use elementary flows when creating LCA data and make translation/conversion between different reference lists challenging and loss of information will likely occur. RECOMMENDATIONS: We recommend the application of a typology to flow lists, use of unique identifiers and inclusion of clarifiers based on external references, setting an exclusive or inclusive nomenclature for flow context information that includes directionality and environmental compartment information, separating flowable names from context and unit information, linking inclusive taxonomies to create limited patterns for flowable names, and using an encoding schema that will prevent technical translation errors.

Molecular-structure-based models of chemical inventories using neural networks.

Chemical synthesis is a complex and diverse procedure, and production data are often scarce or incomplete. A detailed inventory analysis of all mass and energy flows necessary for the production of chemicals is often costly and time-intensive. Therefore only few chemical inventories exist, even though they are essential for process optimization and the environmental assessment of many products. This paper introduces a newtype of model to provide estimates for inventory data and environmental impacts of chemical production based on the molecular structure of a chemical and without a priori knowledge of the production process. These molecular-structure-based models offer inventory data for users in process design and optimization, screening life cycle assessment (LCA), and supply chain management. They can be applied even if the producer is unknown or the production process is not documented. We assessed the capabilities of linear regression and neural network models for this purpose. All models were generated with a data set of inventory data on 103 chemicals. Different input sets were chosen as ways to transform the chemical structure into a numerical vector of descriptors and the effectiveness of the different input sets was analyzed. The results show that a correctly developed neural network model can perform on an acceptable level for many purposes. The models can assist process developers to improve energy efficiency in all design stages and aid in LCA and supply chain management by filling data gaps.