Slide 1

Randolph E. Kirchain1, Elsa A. Olivetti1, Melissa L. Zgola1,

Christopher Weber2, Sarah Boyd3, Ramzy Kahhat4, Eric Williams4

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

Environmental Assessment of IT ProductsDevelopment of the PAIA Tool

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Meeting the Need of ICT for QuantificationIdentifying the Challenges

• Data availability

– Process Data

• Specialized materials and processes

• Depth / dynamics of the supply chain

– Product Data

• Number / diversity of products

• Complexity and dynamics of product

• "Distance" between designer and impact

• Cost / Speed• Data collection and analysis

• Action-ability• Relating design and operations decisions to

product impact

• Credibility• Significant uncertainty exists in estimates; must

be acknowledged and accommodated

Clear need: Efficient &

effective

approach

to LCA

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Meeting the Need of ICT for QuantificationTranslating the Challenges to Project Objectives

• Goals

– Develop near term, quantitative approach for labeling

• Resolve product types (13” vs 15” not 4310 vs 6410)

• Provide insight into major sustainability levers

– Create breadboard tool

• Objectives for approach / tool

– Efficient

• Minimum data collection & Minimum user input

– Effective

• Resolve product type

• Provide actionable insight

– Credible

• Uncertainty is explicitly estimated

• Transparent & Flexible

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Project Strategy:Realizing Efficient / Effective IT LCA

Two major strategies to meet goals

1. Probabilistic Triage and Progressive Refinement

2. Product Attribute to Impact Algorithm (PAIA)

– An approach that maps product attributes to their environmental impact

Initial focus

– Product: Laptop

– Impacts: Energy & carbon

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Realizing Quantitative Streamlined LCA:Tradeoff between Comprehensiveness and Specificity

Comprehensiveness

Specificity

Idealized

Goal

Results precise

Resource intensive

Omissions indefensible

Results accurate

Targeted resources

Significant uncertainty

Screening

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Examples of Sources of Uncertainty

• Data availability concerning

– Bill of materials (laptop vs. particular model)

– Suppliers practices and location

• Representativeness of secondary data

– Variation in supplier technology

– Geographic variation

• Grid mix, efficiency, transportation

– Temporal variation

• Process and process evolution

Uncertainty may be resolvable at an acceptable cost

Slide 9Slide 9

Supplier Derived Variability:Significant Variation Exists in Real-world Suppliers

International Aluminum Institute 2003

Slide 11Slide 11

Project Strategy:Realizing Efficient / Effective IT LCA

Two major strategies to meet goals

1. Probabilistic Triage and Progressive Refinement

2. Product Attribute to Impact Algorithm (PAIA)

– An approach that maps product attributes to their environmental impact

Initial focus

– Product: Laptop

– Impacts: Energy & carbon

PAIA Streamlines Process

Slide 12Slide 12

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

• Minimum user input, attributes which are

– Important

• Significant effect on results

• Viewed as critical by stakeholder

– Knowable (Measurable at “low” cost)

Inputs Results

Product Type Attributes

Laptop

15 “ Screen

250 GB Hard drive

6 Layer

PWB

…

Product Type Impacts

MJ Energy

Kg CO2

Gal H2O

…

Product Attribute to

Impact Algorithm

Slide 13Slide 13

Attribute-

to-Activity

Model

Impact

Assessment

Model

BOA

LCI

Inventory

Database

Existing LC tools

Realizing the Product Attribute to Impact Algorithm

Incorporating Engineering Models with Existing Tools

Inputs Results

Product Type Attributes

Laptop

15 “ Screen

250 GB Hard drive

6 Layer

PWB

…

Product Type Impacts

MJ Energy

Kg CO2

Gal H2O

…

Activity Amount

Aluminum 20 g

Electricity 140 KWh

Lithography 0.5 g

Inj. molding 40 g

Transport 4 tkm

Activity Amount

Anhydrite, in ground 0.1 kg

Carbon dioxide, in air 1.2 kg

Oil crude, in ground 3.6 g

Land Transformation 40 km2

Zinc, in ground 0.2 kg

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

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

g Lithography = e* layers PWB +

d*Ictype

Etc…

Numbers are for illustration only

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Overall Triage Approach to Creating PAIA

1. Leverage existing data to create best available estimate

– 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)

2. Develop & execute LCI simulation (Monte Carlo) model

3. Triage (screen) for high impact activities

4. Develop PAIA modules to relate attributes to activities– Assemble training LCAs

– Create correlative models

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Developing the Laptop PAIA

Manufacturing

Logistics

PackagingUse

EoL

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Comprehensive Probabilistic Triage:

Analysis breakdown by LC phase

95% of statistical trials indicate that

90% of the impact attributed to

Matls & Mfg and Use phase * transport phase

-50

50

150

250

350

450

550

650

GW

P (

kg C

O2

-e

q)/

pro

du

ct c

lass

Overall

Coefficient

of Variation ~35%

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Comprehensive Probabilistic Triage:

Analysis of Components (Matls & Mfg.)

95% of trials indicate that 75%

of the impact attributed to

LCD, Mainboard, and Chassis

0

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200

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300

GW

P (

kg C

O2

-e

q)/

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ct c

lass

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Targeted analysis around LCD:

Each resolved driver lowers COV

0

0.1

0.2

0.3

0.4

0.5

Unresolved Screen size

PFC abatement

Location Energy intensity

IC/PWB area

Co

eff

icie

nt

of

Vari

ati

on

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Current set of overall model inputs:

Main drivers of impact

• Manufacturing Context– Location and efficiency

• LCD– Size

– PFC abatement

– Bulb technology

– IC/PWBs

• Mainboard– IC impact node, chip area,

integration, PFC abatement

– PWB area

• Chassis– Materials (Mg vs. Plastic)

– Inner cages

• Hard drive– Capacity

– Technology (SSD-future tech)

• Battery – Number of cells

• Transportation –assembly to customer

– Mode, distance

• Packaging*– Mass and recycled content

• Use phase– Duty cycle, power, grid,

lifetime

*More a hot button issue than a hot spot

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Current model screenshot

Front page example, includes inputs/levers

Example only, not actual results

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-50

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150

250

350

450

550

650

Targeted assessment:

Results in lower overall variation

Targetedassessment

Overall COV ~ 10%

Comprehensive assessment

Overall COV ~ 35%

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50

150

250

350

450

550

650

GW

P (

kg C

O2

-e

q)/

pro

du

ct c

lass

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Project Accomplishments

• Project accomplishments

– 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

• Continuing work– Testing resolution of current Laptop model

– Extending to Desktops and

– Deeper look into LCD Modules

– Primary data collection

– Harmonize with existing efforts

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Lessons learned

• Uncertainty is significant

• Triage (screening) is still possible

– Limited levers account for much variation

– Data collection can be focused

– Limited analytical data requirement should be possible

• Collaboration is key

– Leverage on suppliers

– Knowledgebase 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 27Slide 27

Applying Triage to Firm-wide (Scope 3) AnalysisLow cost corporate carbon footprint analysis

• Perform quantitative footprint analysis of supply chain

• WRI/WBCSD GHG protocol Scope 3 “road test”

GHG Protocol: Scope 1, 2 & 3

GHG Protocol: Scope 3 categories

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Triage Approach Applied to Scope 3

Road Test Project

SAP Query Result

We have adopted Economic I/O LCA to distill:

•More than 600,000 data entries of firm transactions

•Into to ~60 assumptions (I/O sectors)

•Then we calculate carbon impact

Mapping Value Chain

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Triage Approach Provides Significant Screening:Apply Data Collection Efforts to Critical Areas

Farming31%

Materials29%

Ingredients24%

Outsourced Production

11%

Other Goods & Services

5%

Purchased Goods & Services

71%

Downstream Transport & Distribution

14%

Upstream Transport & Distribution

5%

Capital Equipment

3%

Consumer Waste

3% Other4%

•Understand biggest opportunities for innovation

•Provide feedback to GHG Protocol Scope 3 standard

Graphs show percentage of impact from pieces of the value chain

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Why Worry about Resource Use Now?Some Impacts Permanent (Historic Case)

• Historic example of materials constraint: Cobalt in the Late 1970’s

• Zaire compared to World in 1977– Population 0.04%

– GDP 0.09%

– Cobalt resources 40%

• Small scale rebellion in 1977 led to:

– Short term constraint

– Global speculation

Sources: Adelman, K. L. R. Afr. Soc. 1978, v77.

Blechman and Sloss. National Security and Strategic

Minerals, 1985. Canadian Minerals Yearbook 1886-

2004. and USGS Mineral Yearbook and Mineral

Commodity Summary 1932-2006,

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Why Worry about Resource Use Now?Some Impacts Permanent (Historic Outcomes)

• Supply constraint led to ...

– Geography

• Supply relocation

– Operations

• Recycling

• Stockpiling

– Technology

• Process efficiency

• Materials substitution

• Some industries that switched away from Cobalt, have never switched back.

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20

40

60

80

100

120

140

1950 1960 1970 1980 1990 2000

Rea

l P

ric

e o

f C

o (

'00

0s

98

$/t

)

Primary Outcome: Price Increase

Even though price changes

were temporary, effects to firms were permanent

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Can We Identify Materials at Risk?Simple Metrics Fall Short, Models Provide Novel Insights

• Simple metrics

– provide some insight

– do not capture

interrelated aspects of

materials use

• Modeling materials

system provides

insight into

– sources of risk

– impact of strategies to

address it

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Model-based Conclusion:Value of Recycling as a fast-responding supply

0

20

40

60

80

0.7 0.8 0.9 1

Fraction of Base Case Recycling

Avera

ge P

rice (

$/g

)

High Levels of Recycling…– Reduces use of primary supply - slows down ore degradation

– Stabilizes inventory of metal and hence price

• Experiences less significant price variability

• Recovers more quickly

– Reduces supply chain risk

Slide 35

Thanks to our sponsors

Randolph Kirchain

Materials Systems Laboratory

Massachusetts Institute of Technology

kirchain@mit.edu