95
ISO 14044 lays out ten general data requirements to support a study’s goal and
scope. Seven are qualitative, two are quantitative, and one has both qualitative and
quantitative features. The qualitative requirements include three that describe
breadth and depth regarding the data coverage parameters of time, geography, and
technology; an overall assessment of how well these and other data represent the
statistical population of interest from their categories; how consistent the methods
for choosing and analyzing data are applied across all components that make up the
study; how reproducible the results of the study are based on the kind of methodology and data value information provided; and what data sources are used. The two
requirements expressed primarily in quantitative terms concern the precision or statistical variance of the data in each category and the completeness measured as a
percentage of flow represented. Inherent uncertainty in the information, the model
structure in which the information is presented, the assumptions bracketing the
study, and any other constituent component require verbal descriptions with numerical detail where appropriate. As such, uncertainty statements can be both qualitative and quantitative.
Adhering to these ten data quality requirements is indispensable for LCA professionals especially when making public statements about the comparative ecological
benefit of a product. Designers who critically evaluate the distinctions created by
stated data quality assumptions in the goal and scope phase of a foundational LCA
can better contextualize and evaluate the appropriateness of a published third-partyverified product declaration based on it.
5.3.11 What Are the Limitations of the Study?
Making design decisions based on an array of qualified facts, methods, and models
laid out in the goal and scope phase may, at first seem, like a recipe for dithering.
Appreciating the limitations on what any study can be used for, however, is essential
to properly integrating them into a design workflow.
Broad statements regarding limitations contextualize the study for all stakeholders. Without them, users can draw inappropriate or even antithetical conclusions to
what the study indicates. For example, a macro-level LCA based on economic input
and output data cannot be used to tailor specific manufacturing processes since the
data don’t come from a process-based gathering methods and are only accurate
when applied to evaluating impacts from a single general economic sector. A statement in the goal and scope description of a study, limiting its use to evaluating
environmental impacts at the larger sectorial scale, safeguards it from being indiscriminately applied to the more fine-grained considerations needed to evaluate,
modify, and improve a single industrial process.
Beyond any individual specific limitation stated in each study, it is critical to
always keep in view LCA’s numerous general methodological and data resource
limitations. These will be discussed further in Chap. 6.
5.3 Goal and Scope Definition
ISO 14044 lays out ten general data requirements to support a study’s goal and
scope. Seven are qualitative, two are quantitative, and one has both qualitative and
quantitative features. The qualitative requirements include three that describe
breadth and depth regarding the data coverage parameters of time, geography, and
technology; an overall assessment of how well these and other data represent the
statistical population of interest from their categories; how consistent the methods
for choosing and analyzing data are applied across all components that make up the
study; how reproducible the results of the study are based on the kind of methodology and data value information provided; and what data sources are used. The two
requirements expressed primarily in quantitative terms concern the precision or statistical variance of the data in each category and the completeness measured as a
percentage of flow represented. Inherent uncertainty in the information, the model
structure in which the information is presented, the assumptions bracketing the
study, and any other constituent component require verbal descriptions with numerical detail where appropriate. As such, uncertainty statements can be both qualitative and quantitative.
Adhering to these ten data quality requirements is indispensable for LCA professionals especially when making public statements about the comparative ecological
benefit of a product. Designers who critically evaluate the distinctions created by
stated data quality assumptions in the goal and scope phase of a foundational LCA
can better contextualize and evaluate the appropriateness of a published third-partyverified product declaration based on it.
5.3.11 What Are the Limitations of the Study?
Making design decisions based on an array of qualified facts, methods, and models
laid out in the goal and scope phase may, at first seem, like a recipe for dithering.
Appreciating the limitations on what any study can be used for, however, is essential
to properly integrating them into a design workflow.
Broad statements regarding limitations contextualize the study for all stakeholders. Without them, users can draw inappropriate or even antithetical conclusions to
what the study indicates. For example, a macro-level LCA based on economic input
and output data cannot be used to tailor specific manufacturing processes since the
data don’t come from a process-based gathering methods and are only accurate
when applied to evaluating impacts from a single general economic sector. A statement in the goal and scope description of a study, limiting its use to evaluating
environmental impacts at the larger sectorial scale, safeguards it from being indiscriminately applied to the more fine-grained considerations needed to evaluate,
modify, and improve a single industrial process.
Beyond any individual specific limitation stated in each study, it is critical to
always keep in view LCA’s numerous general methodological and data resource
limitations. These will be discussed further in Chap. 6.
5.3 Goal and Scope Definition