Assessment of IT Products

Transcription

1 Environmental Assessment of IT Products Development of the PAIA Tool Elsa A. Olivetti 1, Mli Melissa L. Zgola 1, Christopher Weber 2, Sarah Boyd 3, Ramzy Kahhat 4, Eric Williams 4, Randolph E. Kirchain 1 1 Massachusetts Institute of Technology, 2 Carnegie Mellon University, 3 University of California at Berkeley and 4 Arizona State University Slide 1

2 The IT Market Landscape Labeling efforts proliferating globally Labeling trends require quantitative environmental information Slide 3 Slide 3

3 Meeting the Need of ICT for Quantification Setting a Goal, Identifying the Challenges 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 Challenges Product Data Number / diversity of products Complexity and dynamics of product "Distance" between designer and impact Process Data Specialized materials and processes Depth / dynamics of the supply chain Clear need: Efficient & effective approach to LCA Slide 4 Slide 4

4 Meeting the Need of ICT for Quantification Translating the Goal to 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 user input Minimum data collection Effective Resolve product type Provide actionable insight Transparent & Flexible Clear need: Efficient & effective approach to LCA Slide 5 Slide 5

5 Project Strategy: Realizing Efficient / Effective IT LCA Two major strategies to meet goals 1. Product Attribute to Impact Algorithm (PAIA) An approach that maps product attributes to their environmental impact 2. Probabilistic Triage and Targeted Refinement Initial focus Product: Laptop Impacts: Energy & carbon Slide 8 Slide 8

6 Product Attribute to Impact Algorithm (PAIA): The Basic PAIA Concept p Inputs Product Type Attributes Laptop 15 Screen 250 GB Hard drive 6 Layer PWB Product Attribute to Impact Algorithm Results Product Type Impacts MJ Energy Kg CO 2 Gal H 2 O Minimum user input, attributes which are Important Significant effect on results Viewed as critical by stakeholder Knowable (Measurable at low cost) Slide 9 Slide 9

7 Realizing the Product Attribute to Impact Algorithm Incorporating Engineering g Models with Existing Tools Inputs Product Type Attributes Laptop 15 Screen 250 GB Hard drive 6 Layer PWB Attributeto-Activity Mdl Model Existing LC tools B O A Inventory Database L C I Impact Assessment Mdl Model Results Product Type Impacts MJ Energy Kg CO 2 Gal H 2 O Total g Al = a*lcdsize + b*hdd capacity Activity Amount Activity Amount Total g PC = c*chassis + d*pwb area Aluminum 20 g Anhydrite, in ground 0.1 kg g Lithography = e* layers PWB + d*ictype Etc Electricity Lithography Inj. molding 140 KWh 0.5 g 40 g Carbon dioxide, in air 1.2 kg Oil crude, in ground 3.6 g Land Transformation 40 km 2 Numbers are for illustration only Transport 4 tkm Zinc, in ground 0.2 kg Slide 10 Slide 10

8 Developing the Attribute to Activity Model Product Type Attributes Laptop 15 Screen 250 GB Hard drive 6 Layer PWB Training LCAs* Correlative Function ivity Act Attribute LCD algorithm (screen size, format) HDD algorithm (capacity, type) Battery algorithm (no. of cells) PWB algorithm (area) Attributes Attribute- to-activity Model B O A How do we: *Training data enables development of parametric Know an algorithms activity by component / attribute is important? Minimize the time / effort to collect data? Training data include: Literature, Existing data within industry, Commercially available LCA data Slide 11 Slide 11

9 Project Strategy: Realizing Efficient / Effective IT LCA Two major strategies to meet goals 1. Product Attribute to Impact Algorithm (PAIA) An approach that maps product attributes to their environmental impact 2. Probabilistic Triage and Targeted Refinement Initial focus Product: Laptop Impacts: Energy & carbon Slide 13 Slide 13

10 Realizing Quantitative Streamlined LCA: Tradeoff between Comprehensiveness and Specificity Comprehensiveness Screening Idealized Goal Results accurate Targeted resources Significant uncertainty Results precise Resource intensive Omissions indefensible Specificity Slide 14 Slide 14

11 Realizing Quantitative Streamlined LCA: Even with high uncertainty, targeted data & input meets goal g y, g p g Comprehensive, uncertain uncertain uncertain assessment Bulb technology Printed wiring boards Capacitors Un ncertainty in Resu ult Initial Result Targeted Data Refinement Targeted User Input Sufficiency Data Refinement Priorities Specificity Slide 15 Slide 15

12 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 16 Slide 16

13 Supplier Derived Variability: Significant Variation Exists in Real-world Suppliers International Aluminum Institute 2003 Slide 17 Slide 17

14 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 Slide 22 Slide 22

15 Developing the Laptop p PAIA Manufacturing EoL Logistics Use Packaging Slide 24 Slide 24

16 Comprehensive Probabilistic Screening: Analysis breakdown by LC phase GWP (k kg CO 2 eq q) Overall Coefficient of Variation ~30% * transport phase 95% of statistical trials indicate that 90% of the impact attributed to Matls &Mf Mfg and Use phase Slide 25 Slide 25

17 Comprehensive Probabilistic Screening: Analysis of Components (Matls & Mfg.) g) GW WP (kg CO 2 eq) % of trials indicate that 75% of the impact attributed to LCD, Mainboard, and Chassis Slide 26 Slide 26

18 Supply Chain Characterization & Screening Identifies Major Levers for LCDs Consumer Perceivable Performance Attributes Screen size Resolution* Product Attributes Backlight technology* ICs/PWB Manufacturing context Location Efficiency PFC emission abatement Use drivers addressed at product level Lifetime Use location Profile (duty cycle, power in idle, sleep off) *Not fully quantified Slide 27 Slide 27

19 Targeted analysis around LCD: Each resolved driver lowers COV 0.5 Coeff ficient of Variation Unresolved Screen size PFC abatement Location Energy efficiency IC/PWB Slide 28 Slide 28

20 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, yield, PFC abatement, Integration PWB impact area Chassis Materials 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 tbutton issue than a hot spot Slide 29 Slide 29

21 Targeted assessment: Results in lower overall variation GWP (kg CO 2 eq) Comprehensive assessment Overall COV ~ 30% Targeted assessment Overall COV <10% Slide 30 Slide 30

22 Project Accomplishments Project accomplishments Ti Triage and dtargeted t refinement tidentifies important inputs for user Important focus for data refinement Mapping attributes to impacts is possible / promising Limited levers account for majority of variation Continuing i work Revise/update proxy and data Harmonize with existing and emerging g efforts Correlation between uncertainty factors not well accounted Slide 31 Slide 31

23 Lessons learned Uncertainty is significant Triage (screening) is still possible Limited levers account for much variation 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 32 Slide 32

24 Thanks to our sponsors Randolph Kirchain Materials Systems Laboratory Massachusetts Institute of Technology Slide 33