Environmental Environmental Assessment of IT ProductsDevelopment of the PAIA Tool

El A Oli tti1 M li L Z l 1

Development of the PAIA Tool

Elsa A. Olivetti1, Melissa L. Zgola1, Christopher Weber2, Sarah Boyd3, Ramzy Kahhat4, Eric Williams4, Randolph E. Kirchain1

1Massachusetts Institute of Technology, 2Carnegie Mellon University, 3University of California at Berkeley and 4Arizona State University

Slide 1

The IT Market LandscapeLabeling efforts proliferating globallyg p g g y

L b li t d i L b li t d i Labeling trends require quantitative

environmental information

Labeling trends require quantitative

environmental informationenvironmental informationenvironmental information

Slide 3Slide 3

Meeting the Need of ICT for QuantificationSetting a Goal, Identifying the Challengesg , y g g

• Goals– Develop near term, quantitative approach for labelingDevelop near term, quantitative approach for labeling

• Resolve product types (13” vs 15” not 4310 vs 6410)• Provide insight into major sustainability levers

– Create breadboard toolCreate breadboard tool

• Challenges– Product Data

Clear need: Clear need: • Number / diversity of products • Complexity and dynamics of product • "Distance" between designer and impact

Clear need: Efficient & effective

Clear need: Efficient & effective

– Process Data• Specialized materials and processes • Depth / dynamics of the supply chain

approach to LCA

approach to LCA

Slide 4Slide 4

Meeting the Need of ICT for QuantificationTranslating the Goal to Objectivesg j

• Goals– Develop near term, quantitative approach for labelingDevelop near term, quantitative approach for labeling

• Resolve product types (13” vs 15” not 4310 vs 6410)• Provide insight into major sustainability levers

– Create breadboard toolCreate breadboard tool

• Objectives for approach / tool– Efficient

Clear need: Clear need: • Minimum user input• Minimum data collection

– Effective

Clear need: Efficient & effective

Clear need: Efficient & effective

• Resolve product type• Provide actionable insight• Transparent & Flexible

approach to LCA

approach to LCA

Slide 5Slide 5

Project Strategy:Realizing Efficient / Effective IT LCAg

Two major strategies to meet goals1. Product Attribute to Impact Algorithm (PAIA)

– An approach that maps product attributes to their environmental impactp

2. Probabilistic Triage and Targeted Refinementg g

Initial focus– Product: Laptop– Impacts: Energy & carbon

Slide 8Slide 8

Product Attribute to Impact Algorithm (PAIA): The Basic PAIA Conceptp

Inputs Results

Product Type Product Type Product Type Attributes

Laptop15 “ Screen250 GB Hard drive

ypImpacts

MJ EnergyKg CO2

Gal H2O

Product Attribute to

I t Al ith6 Layer PWB…

…Impact Algorithm

• Minimum user input, attributes which are– Important

• Significant effect on results• Viewed as critical by stakeholder

– Knowable (Measurable at “low” cost)

Slide 9Slide 9

Knowable (Measurable at low cost)

Realizing the Product Attribute to Impact AlgorithmIncorporating Engineering Models with Existing Tools

Existing LC tools

p g g g g

Inputs Results

Product Type Product Type

Attribute-to-ActivityM d l

ImpactAssessmentM d l

BOA

LCI

InventoryDatabase

ypAttributes

Laptop15 “ Screen250 GB Hard drive6 L

ypImpacts

MJ EnergyKg CO2

Gal H2O

Model ModelA IDatabase6 Layer PWB…

…

Activity AmountAluminum 20 g

Electricity 140 KWh

Activity AmountAnhydrite, in ground 0.1 kg

Carbon dioxide, in air 1.2 kg

Total g Al = a*LCDsize + b*HDD capacity

Total g PC = c*Chassis + d*PWB area

g Lithography = e* layers PWB + y

Lithography 0.5 g

Inj. molding 40 g

Transport 4 tkm

g

Oil crude, in ground 3.6 g

Land Transformation 40 km2

Zinc, in ground 0.2 kg

g g p y yd*Ictype

Etc…

Slide 10Slide 10

Numbers are for illustration only

Developing the Attribute to Activity Model

Training LCAs*

Developing the Attribute to Activity Model

Attributesivity

CorrelativeFunction

Attribute- to-ActivityModel B

Training LCAs*

HDD algorithm (capacity type)LCD algorithm (screen size, format)

Product Type Attributes

Laptop

Attributes

Attribute

Act

ModelOA

HDD algorithm (capacity, type)Battery algorithm (no. of cells)PWB algorithm (area)…

15 “ Screen250 GB Hard drive6 Layer PWB…

*Training data enables development of parametric How do we:

Know an activity / attribute is important?algorithms by component

Training data include: Literature, Existing data within industry Commercially available LCA data

Know an activity / attribute is important?Minimize the time / effort to collect data?

Slide 11Slide 11

within industry, Commercially available LCA data

Project Strategy:Realizing Efficient / Effective IT LCAg

Two major strategies to meet goals1. Product Attribute to Impact Algorithm (PAIA)

– An approach that maps product attributes to their environmental impactp

2. Probabilistic Triage and Targeted Refinementg g

Initial focus– Product: Laptop– Impacts: Energy & carbon

Slide 13Slide 13

Realizing Quantitative Streamlined LCA:Tradeoff between Comprehensiveness and Specificityp p y

Comprehensiveness Idealized

GoalGoal

Results accurate

Screening

Targeted resources

Significant uncertainty

Results precise

Resource intensive

Specificity

Omissions indefensible

Slide 14Slide 14

Specificity

Realizing Quantitative Streamlined LCA:Even with high uncertainty, targeted data & input meets goalg y, g p g

Comprehensive, uncertain assessment

Comprehensive, uncertain assessment

Comprehensive, uncertain assessment ul

t Initial Result

uncertain assessmentuncertain assessment

y in

Res

u

Bulb technology

Printed wiring boards

ncer

tain

ty

Targeted Data Refinement

Targeted User Input

Capacitors Un

Sufficiency

Data Refinement Priorities Specificity

Slide 15Slide 15

p y

Examples of Sources of Uncertaintyp y

• Data availability concerning– Bill of materials (laptop vs. particular model)– Suppliers practices and location

• Representativeness of secondary data– Variation in supplier technology

G hi i ti– Geographic variation• Grid mix, efficiency, transportation

– Temporal variationTemporal variation• Process and process evolution

Uncertainty may be resolvable at an acceptable cost

Slide 16Slide 16

Uncertainty may be resolvable at an acceptable cost

Supplier Derived Variability:Significant Variation Exists in Real-world Suppliersg pp

International Aluminum Institute 2003

Slide 17Slide 17

Overall Triage Approach to Creating PAIAg pp g

1. Leverage existing data to create best available estimateGather existing BOA and LCI data– Gather existing BOA and LCI data

– Assemble uncertainty information• LCI database mining and data reduction• Manufacturing and grid market data• Government or third party usage studies• Extreme conditions(e.g., rail vs. truck)

22. Develop & execute LCI simulation (Monte Carlo) model3. Triage (screen) for high impact activities4 Develop PAIA modules to relate attributes to activities4. Develop PAIA modules to relate attributes to activities

– Assemble training LCAs– Create correlative models

Slide 22Slide 22

Developing the Laptop PAIAp g p p

ManufacturingManufacturing

LogisticsEoL

PackagingUse

Slide 24Slide 24

Comprehensive Probabilistic Screening:Analysis breakdown by LC phasey y p

Overall Overall 450.0

q)  Overall

Coefficient of Variation

30%

Overall Coefficient of Variation

30%250 0

350.0

kg CO

2‐e

q

~30%~30%150.0

250.0

GWP (k

‐50 0

50.0

95% of statistical trials indicate that 90% of the impact attributed to M tl & Mf d U h

‐50.0

Slide 25Slide 25

Matls & Mfg and Use phase * transport phase

Comprehensive Probabilistic Screening:Analysis of Components (Matls & Mfg.)y p ( g )

300.0

) 

200.0

250.0

CO2‐e

q)

100.0

150.0

WP (kg C

0.0

50.0GW

95% of trials indicate that 75% of the impact attributed to

LCD M i b d d Ch i

Slide 26Slide 26

LCD, Mainboard, and Chassis

Supply Chain Characterization & Screening Identifies Major Levers for LCDsj

• Consumer Perceivable Performance Attributes

• Use drivers –addressed at productPerformance Attributes

– Screen size– Resolution*

addressed at product level

– LifetimeUse location• Product Attributes

– Backlight technology*– ICs/PWB

– Use location– Profile (duty cycle,

power in idle, sleep off) ICs/PWB

• Manufacturing context– Location– Efficiency– PFC emission

abatement*Not fully quantified

Slide 27Slide 27

*Not fully quantified

Targeted analysis around LCD:Each resolved driver lowers COV

0.5

0 3

0.4

Varia

tion

0.2

0.3

ficie

nt o

f V

0

0.1

Coe

ff

0Unresolved Screen

sizePFC

abatementLocation Energy

efficiencyIC/PWB

Slide 28Slide 28

Current set of overall model inputs:Main drivers of impactp

• Manufacturing Context– Location and efficiency

• Hard drive– CapacityLocation and efficiency

• LCD– Size – PFC abatement

Capacity– Technology (SSD-future tech)

• Battery – Number of cells– PFC abatement

– Bulb technology– IC/PWBs

• M i b d

– Number of cells

• Transportation –assembly to customer

– Mode, distance• Mainboard– IC impact node, chip area,

yield, PFC abatement,Integration

Mode, distance

• Packaging*– Mass and recycled content

U h– PWB impact area

• Chassis– Materials

• Use phase– Duty cycle, power, grid,

lifetime

*M h t b tt i th h t t

Slide 29Slide 29

*More a hot button issue than a hot spot

Targeted assessment:Results in lower overall variation

350.00450.0

eq) 

150.00

250.00

250.0

350.0

(kg CO

2‐e

50.0050.0

150.0

GWP (

‐50.00‐50.0

Targetedassessment

Overall COV <10%

Targetedassessment

Overall COV <10%

Comprehensive assessment

Overall COV ~ 30%

Comprehensive assessment

Overall COV ~ 30%

Slide 30Slide 30

Overall COV 10%Overall COV 10%Overall COV 30%Overall COV 30%

Project Accomplishmentsj p

• Project accomplishmentsT i d t t d fi t id tifi– Triage and targeted refinement identifies

• important inputs for user• Important focus for data refinement

– Mapping attributes to impacts is possible / promising• Limited levers account for majority of variation

C ti i k• Continuing work– Revise/update proxy and data– Harmonize with existing and emerging effortsg g g– Correlation between uncertainty factors not well

accounted

Slide 31Slide 31

Lessons learned

• Uncertainty is significant• Triage (screening) is still possible

– Limited levers account for much variation

C ll b ti i k• Collaboration is key– Leverage on suppliers– Knowledgebase is not present in any one firmKnowledgebase is not present in any one firm

• Data collection still a challenge and necessary– Characterize the product– Characterizing specialized process– Projecting the future (technology is dynamic)

Slide 32Slide 32

Thanks to our sponsorsp

Randolph KirchainMaterials Systems Laboratory

Massachusetts Institute of Technology

kirchain@mit edu

Slide 33

kirchain@mit.edu