We at Earthster rely on the most reputed sources for our data. We constantly verify its compatibility without altering the shape or content of that data.
Process data (cycles)
Process data is the most apparent type of data you have when you select a process as an input in your cycle.
You can select models for materials, industrial processes, waste management processes, utilities, etc.
Types of process data are:
input-output cycle data
process cycle data
user cycle data
Input-output cycle data
Input-output databases are top-down: they are built analyzing whole economies and the interaction between their sectors. They are typically broken down into commodities (relatively coarse), and inputs and outputs are calculated dividing the total for the whole economy. Therefore, they represent an average for that given commodity.
These databases can be broken down in LCA models — cycles in Earthster — and can be used as a reference for production.
Earthster takes it one step further, and uses margins data to build a model of the distribution stage, and uses average consumption data to create a model of the use phase. These models are just averages within that commodity, but they are a convenient starting point for anybody modeling a product part of that commodity.
Process-based cycle data
Process databases are bottom-up: they are built as interconnected models of activities in the economy. Because of that, processes can represent materials (all activities to produce that material), industrial processes (the transformation activities that takes a material from one state to another), products (all activities to produce the product, including materials and industrial processes), services, etc.
Each of these processes includes values for their exchanges with nature (emissions and resources, with a direct environmental impact in that process), and inputs/outputs of other processes (upstream). The latter is not always present, and when it is, it creates a network of connected (and coupled) processes that can be used to explore supply chains. That also means that, in order to calculate the impacts of one of the process, we need to already know the impacts of some or most of the other processes.
Earthster implements Ecoinvent (version 3.9.1 with the system model: “Allocation, cut-off by classification”), the leading database of unit processes (the ones including information about what is upstream). And we do so in a way that is computationally efficient, yet allows users to explore supply chains visually and numerically.
User cycle data
One of the critical features of Earthster is that you can use other users' data as quickly as the process databases. They follow the same or enhanced format, where you have enough information to ensure compatibility.
Users may share their full models publicly, with you specifically, or they may give restricted access (e.g. only to the final impacts). And you can do the same! The more we share through Earthster, the better LCA's everybody has.
Exchange data (emissions and resources)
One of the biggest compatibility challenges in LCA is that of "speaking the same language," i.e., having a consistent list of possible emissions and resources to/from the environment. Such emissions and resources are known in LCA literature as Elementary Flows.
Because of that, the US EPA developed the Federal LCA Commons Elementary Flow List (FEDEFL) to act as a common ground among LCA research and practice. They have also developed mappings between other lists of Elementary Flows, for example, Ecoinvent, OpenLCA, eLCI, TRACI, etc.
Earthster implements the complete list in FEDEFL and has mapped other sources based on the official mapping from the US EPA open-source repository.
Earthster uses ReCiPe 2016, published by RIVM, mapped to FEDEFL through the official mappings. Changes to the mappings are committed to the US EPA open-source project to improve the method.
At Earthster, we believe in the power of all the excellent tools and datasets developed by the scientific community. We bring those to you as unadulterated as possible with a substantially better user experience.