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design anything, we must first understand the key elements that define and influence
the system we wish to improve; then we must improve it through decisive action
informed by trustworthy data.
6.4.2 Inventory Analysis (LCI)
Data is at the heart of all LCA activities. LCA experts define life cycle inventory
(LCI) as “a methodology for estimating the consumption of resources and the quantities of waste flows and emissions caused by or otherwise attributable to a product’s
life cycle. Consumption of resources and generation of waste/emissions are likely
to occur at multiple sites and regions of the world, as different fractions of the total
emissions at any one site (the fraction required to provide the specified functional
unit; allocation amongst related and nonrelated co-products in a facility such as a
refinery, etc.), at different times (e.g. use phase of a car compared to its disposal),
and over different time periods (multiple generations in some cases, e.g. for landfilling)” (Rebitzer et al. 2004). ISO 14040 and ISO 14044 provide guidance in how to
carry out LCI activities.
LCA practitioners have, since the 1990s, taken data quality into consideration.
Established data pedigree matrices qualify individual bits of information that flow
into the models that describe product systems. Pedigree typically includes indicators such as source reliability, completeness, temporal correlation, geographical
correlation, and technological correlation (Weidema 1998; Frischknecht et al.
2005). Chap. 7 will further discuss the essential LCI data foundations and how they
support design workflows tools.
6.5 Dominance of Geometric Data Models
in Design Education
Even when there is general sympathy with integrating new and better data-driven
approaches, established visual cultural practices may resist change. Just as the single financial bottom line can dominate all other concerns in professional design
practices, the aesthetic bottom line typically reigns in schools of design where
financial constraints can be disregarded.
There are both challenges and opportunities in the wider adoption of LCA in
professional architectural education. In an Architectural Design article on LCA
data and design workflows, I noted that “Life Cycle Assessment is fundamentally
abstract and technical, as opposed to visual and intuitive, which may account for
its slow adoption by most architecture firms” (Cays 2017). This observation continues to be reinforced by the conversations I have with designers and design
students today.
6.5 Dominance of Geometric Data Models in Design Education
design anything, we must first understand the key elements that define and influence
the system we wish to improve; then we must improve it through decisive action
informed by trustworthy data.
6.4.2 Inventory Analysis (LCI)
Data is at the heart of all LCA activities. LCA experts define life cycle inventory
(LCI) as “a methodology for estimating the consumption of resources and the quantities of waste flows and emissions caused by or otherwise attributable to a product’s
life cycle. Consumption of resources and generation of waste/emissions are likely
to occur at multiple sites and regions of the world, as different fractions of the total
emissions at any one site (the fraction required to provide the specified functional
unit; allocation amongst related and nonrelated co-products in a facility such as a
refinery, etc.), at different times (e.g. use phase of a car compared to its disposal),
and over different time periods (multiple generations in some cases, e.g. for landfilling)” (Rebitzer et al. 2004). ISO 14040 and ISO 14044 provide guidance in how to
carry out LCI activities.
LCA practitioners have, since the 1990s, taken data quality into consideration.
Established data pedigree matrices qualify individual bits of information that flow
into the models that describe product systems. Pedigree typically includes indicators such as source reliability, completeness, temporal correlation, geographical
correlation, and technological correlation (Weidema 1998; Frischknecht et al.
2005). Chap. 7 will further discuss the essential LCI data foundations and how they
support design workflows tools.
6.5 Dominance of Geometric Data Models
in Design Education
Even when there is general sympathy with integrating new and better data-driven
approaches, established visual cultural practices may resist change. Just as the single financial bottom line can dominate all other concerns in professional design
practices, the aesthetic bottom line typically reigns in schools of design where
financial constraints can be disregarded.
There are both challenges and opportunities in the wider adoption of LCA in
professional architectural education. In an Architectural Design article on LCA
data and design workflows, I noted that “Life Cycle Assessment is fundamentally
abstract and technical, as opposed to visual and intuitive, which may account for
its slow adoption by most architecture firms” (Cays 2017). This observation continues to be reinforced by the conversations I have with designers and design
students today.
6.5 Dominance of Geometric Data Models in Design Education