Life Cycle Assessment (LCA) evaluates the environmental impact of a product through its entire life cycle, from material extraction to final disposal or recycling. The environmental impacts of an activity depend on both the activity's direct emissions to the environment as well as indirect emissions caused by activities elsewhere in the supply chain. Both the impacts of direct emissions and the provisioning of supply chain inputs to an activity depend on the activity's spatiotemporal scope. When accounting for spatiotemporal dynamics, LCA often faces significant data interoperability challenges. Ontologies and Semantic technologies can foster interoperability between diverse data sets from a variety of domains. Thus, this paper presents an ontology for modeling spatiotemporal scopes, i.e., the contexts in which impact estimates are valid. We discuss selected axioms and illustrate the use of the ontology by providing an example from LCA practice. The ontology enables practitioners to address key competency questions regarding the effect of spatiotemporal scopes on environmental impact estimation.
Life Cycle Assessment (LCA) is a method for analyzing the environmental impact of a
product or a service through all stages of its life cycle [
An important stage of LCA is the creation of a Life Cycle Inventory (LCI), which
consists of representing economic activities as a collection of unit processes linked
together through interdependency relations [
Researchers have long been considering the significance of a spatial perspective in
LCA [
While our work is concerned with LCA as a diverse field that will benefit from the ontological modeling of its data and workflows, spatiotemporal scopes are relevant for a multitude of other domains. Thus, the core part of our ontology can be regarded as an ontology design pattern (ODP). 2
Designing an ontology requires generic use cases to capture the recurring problems in one or multiple domains. Competency questions have been recognized as an effective approach to identify such use cases. Competency questions are frequent queries that subject matter experts would like to submit to a knowledge base to find answers. The following listing shows examples of competency questions that have been identified by international LCA experts during the GeoVoCamp Santa Barbara 2015: – Question 1: "What is the emission of activity a at place p at time t?" – Question 2: "What are the supply chain requirements of activity a when it happens at place p1 and time t1?" – Question 3: "What is the difference of activity a at places p1 and p2 at the same time t?" – Question 4: "What is the difference of activity a at times t1 and t2 at the same place p?"
As can be seen, answering the four competency questions requires four main concepts: Activity, Flow, Place, and Time. An activity in LCA may have Requirement (Question 2) and Outcome (e.g., emission) (Question 1), which collectively make up flows. To effectively link these concepts, proper relations have also been specified. 3
The ontology for spatiotemporal scopes5 is developed based on the existing Activity
ODP, [
SpatiotemporalScope: This class represents the spatial and temporal context under which an LCA activity occurs. We define this class instead of directly using Place and Time, because many requirements and outcomes are associated with the spatiotemporal contexts rather than places and time (intervals) alone. SpatiotemporalScope is associated with the classes Place and Time through the relations occursAtPlace and occursAtTime. Time should be an interval because an LCA activity generally represents the performance of a typical facility or a set of facilities over a time period, rather than at a specific moment. A place is some, typically named, extent in geographic space, e.g., a country or region. We do not specify both classes here and refer to OWL-Time and GeoSPARQL for details (which implies that geometry types such as multi-part polgyons can be used as spatial footprints of places). We assert that each spatiotemporal scope has at least one place and time (see Eq. 1).
Note that we also provide role chains in the ontology (see Eq. 2 as example) but do not discuss them here for lack of space.
hasSpatiotemporalScope occursAtP lace v scopedByP lace
hasSpatiotemporalScope occursAtT ime v scopedByT ime
Activity: The Activity class represents activities in the LCA sense and thereby differs
from other conceptualizations of activities. An activity roughly corresponds to a unit
process as defined in the ISO 14044 standard [
Activity v 9hasRequirement:Requirement u 9produces:Outcome (4) u 9hasSpatiotemporalScope:SpatiotemporalScope : : : (1) (2) (3)
Flow: A Flow is a highly generic concept in LCA mainly defined as a counterpart to an activity. A flow may represent the transfer of matter, such as an emission of combustion gases, or an exchange of services, such as transporting a good. A flow is exchanged between an activity and another activity. Although flows are the products of processes, many flows can exist independently of any process, can be accumulated in reservoirs, and can have properties, such as economic value. A flow can play a role in both the Requirement of one activity and Outcome of another, and thus, we define the relation of hasRole. If the two activities in the exchange are industrial unit processes, the flow is referred to as an intermediate flow. The two activities can be described as “partners” to the exchange. A flow exchanged with the natural environment is called an elementary flow. We formalize the class of Flow as below:
An ontology for flows has recently been developed and formally specifies the
distinctions made above [
Requirement and Outcome: Any activity in LCA has required inputs and resulting outputs. These inputs and outputs are formalized as Requirement and Outcome in our ontology. The provision of Requirements and the disposition of Outcomes depend on the specific place and time that an activity takes place. Thus, we define SpatiotemporalRequirement as a subclass of Requirement, and associate it to the SpatiotemporalScope using the relation isAssociatedWith. Similarly, we define SpatiotemporalOutcome as a subclass of Outcome, and use isInfluencedBy to link it with SpatiotemporalScope.
4
Domain & Range Restrictions and Class Disjointness: In addition to the above axioms, the pattern also defines a set of guarded domain and range restrictions. Specifically, for each object property P pointing from the class A to the class B in Figure 1, we define 9P:B v A as the guarded domain restriction and A v 8P:B as the guarded range restriction, which also acts as a local closure of P . For example, for occursAtP lace property, we have:
Specific for hasRole property, we have:
Finally, we assert class disjointness for every pair of classes in Figure 1, except when the pair of classes are connected via rdfs:subClassOf. (5) (6) (7) (8) (9) (10) (11) (12) (13)
A use case was created by selecting a unit process from the US Life Cycle Inventory
database, in this case “Conditioned log, at plywood plant, US SE,” [
The scope of the activity is a plywood plant in the southeastern United States during the year 2000. The mix of process energy reported is an averaged result of several facilities within the scope. To use the data for LCA, each requirement would need to be linked with an exchange partner that produced it as an outcome. Similarly, this activity’s outcome could be exchanged with another partner activity that requires the logs as input.
The competency questions presented above can be addressed through the selection
of exchange partners. A database can be constructed that accepts an activity
specification and a spatiotemporal scope and returns a list of exchange partners. The logistical
requirements associated with performing the exchange are implicit in the
spatiotemporal scopes of the partners. Differences in emissions resulting from the same activity in
a different scopes can be inferred through the differences in exchange partners.
This paper proposed a compact ontology to capture the spatiotemporal scope of
activities referred to in LCA inventory models. The pattern enables key competency questions
to be addressed by querying a spatiotemporally-explicit data resource. The pattern can
be used as a bridge between sets of activity and flow definitions and spatial modeling
ontologies. Future work will focus on integrating this ontology with other LCA
ontologies and ontology design patterns (such as [