Carbon Footprinting Streamlined Assessment of IT Products: A Case of LCD Displays

Transcription

1 Electronics Goes Green Conference September 11 th 2012 Berlin, Germany Carbon Footprinting Streamlined Assessment of IT Products: A Case of LCD Displays Natalia Duque Ciceri Elsa Olivetti Randolph Kirchain

2 Project Overview Develop methodology that provides sufficient quantitative evaluation of the footprint at reduced cost Develop resource efficient approach to LCA that captures full range of uncertainty Aim to provide sufficient fidelity for a class of products to resolve (obtain more information) Major drivers of impact Relative performance Slide 2

3 Project Strategy Realizing Efficient / Effective LCA Two major strategies to reduce burden of LCA: 1. Probabilistic Triage and Refinement: Screening 2. Product Attribute to Impact Algorithm (PAIA) An approach that maps product attributes to their environmental impact Products: Notebooks Desktops Monitors/TVs Impacts: Global warming potential and Energy Slide 3

4 Project Strategy Realizing Efficient / Effective LCA Two major strategies to reduce burden of LCA: 1. Probabilistic Triage and Refinement: Screening 2. Product Attribute to Impact Algorithm (PAIA) An approach that maps product attributes to their environmental impact Products: Research Questions Notebooks Is thisdesktops proposed strategy for streamlining method viable? What Monitors/TVs type of information is more critical for a diagnostic result? Impacts: Global warming potential and Energy Slide 4

5 Overall Approach to Footprint Streamlining 1. Gather existing data to create best available estimate Both bill of materials and life cycle inventory data Assemble uncertainty information Iterate 2. Develop & execute statistical simulation 3. Screen for high impact activities contributors to total and contributors to uncertainty Refine data where necessary 4. Develop PAIA modules to relate attributes to activities Slide 5

6 Overall Approach to Footprint Streamlining Iterate 1. Gather existing data to create best available estimate Both bill of materials and life cycle inventory data Existing data includes: Published studies Assemble uncertainty information Individual industry work and input 2. Develop & execute statistical simulation Industry association data 3. Screen Commercially for high impact available activities databases contributors to total and contributors to uncertainty Data are of varying quality, age, source and number of points Refine data where necessary 4. Develop PAIA modules to relate attributes to activities Slide 6

7 Product Attribute to Impact Algorithm (PAIA) The Basic PAIA Concept Inputs Product Type Attributes LCD Monitor - 17 Screen - LED edgelight - PWB areas - Product Attribute to Impact Algorithm Results Product Type Impacts MJ Energy Kg CO 2 L 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 7

8 Evaluating Method Viability Discriminating Alternatives 10% Probability density A Probability density B Probability 10% FS µ A µ B FS = False Signal Rate (kgco2eq/product) Slide 8

9 PAIA Step 1 Leveraging existing data to create the best available estimate through: Collection of bill of materials (BOMs) LCD Panel (Module) Display Housing Backlight unit Electric parts Array & Cell part Cover rear Assy. Bezel plastic Stand/Base Assy. CCFL/LED, plate diffuser, optical film, bezel, frame, cable, table, label, Inverter PCBA, Driver IC Glass, Polarizer, ITO, LC, Power Seal, AU Ball, Liquid, Color Filter, Polyimide Cover rear plastic, Tape Anti-noise, Sponge left Bezel Plastic, Plastic Cover, film protect Plastic cover, PE bag, etc. Other Neck set, Cable Assy., Screws, Power Cord etc Packaging Plastic cover, Box, Foam Product main components/attributes (LCD Monitor example) Slide 9

10 PAIA - Step 1 Collection of bill of materials (BOMs) and life cycle inventory (LCI) data LCD Process TFT array Color Filter Cell At each step, inventory information: Electricity, chemicals, gases (Perfluorocompounds - PFCs), etc. Slide 10

11 PAIA Step 2 Developing and executing LCI statistical simulation through Monte Carlo modeling Quantifying the sources of variability and uncertainty Quantity and type Subcomponent Component Activity name array PFC Array PFC mass Array PFC non-abatement Array PFC Emission Factor Array PFC degradation Array PFC GHG Cell Grid Emission Factor Deposition/sputtering electricity Array & Cell part array energy glass energy impacts deposition mass lithography electricity lithography mass etching electricity etching mass cleaning electricity cleaning mass Array electricity GHG Mineral mass (kg) Mineral Emission Factor Mineral GHG Sources / Assumptions Krishnan 2008, IPCC Grid mix ranges Korea and Taiwan Gutowski et al., 2009; Boyd 2009; Williams 2002; EPA 2001 Ranges of glass mass (disassembly) Ecoinvent Slide 11

12 PAIA Step 2 Developing and executing LCI statistical simulation through Monte Carlo modeling Quantifying the sources of variability and uncertainty Quantity and type Quantity Glass Mass (Range of products) Type Impact Factor (Commercial database values for minerals) Subcomponent Component Activity name array PFC Array PFC mass Array PFC non-abatement Array PFC Emission Factor Array PFC degradation Array PFC GHG Cell Grid Emission Factor Deposition/sputtering electricity Array & Cell part array energy glass energy impacts deposition mass lithography electricity lithography mass etching electricity etching mass cleaning electricity cleaning mass Array electricity GHG Mineral mass (kg) Mineral Emission Factor Mineral GHG Sources / Assumptions Krishnan 2008, IPCC Grid mix ranges Korea and Taiwan Gutowski et al., 2009; Boyd 2009; Williams 2002; EPA 2001 Ranges of glass mass (disassembly) Ecoinvent Slide 12

13 PAIA - Step 3 Contribution analysis - Screening for high impact activities: Contribution to total impact Contribution to variation Phase/ component Subcomponent Model Ranges Source Overall COV: 33% Glass 10 20% *wt. MSL BOMs Do not cite without permission Materials Polymers 20-50% *wt. MSL BOMs Metals 20-45% *wt. MSL BOMs kg CO2 eq/monitor USE Phase Lifetime Use Duty cycle Use Energy demand Average lifespan ON mode OFF mode ON mode OFF mode 4 10 years 4-10 hrs/day hrs/day mean: 46.7 Wh SD: 49.5 Wh Wh/day Extreme estimate EPA EPA Energy Star monitor spec v5.0 Panel Materials and Manufacturing Housing Packaging transport Use *wt.=monitor weight Slide 13

14 PAIA - Step 3 Contribution to total impact Components that contribute to impact in materials and manufacturing only Do not cite without permission 70 85% of the time Array and Cell PFC and Electricity accounts for 50-75% of the total impact Slide 14

15 PAIA Step 4 Mapping product attributes to their associated environmental impact Product attributes Product specifications option Viewable Diagonal Size (in) 15" - 30" Screen Type Technology TN, IPS, MVA Backlight Technology CCLF (Bulbs)/ LED (Chips), WLED edgelight system, LED light bar system Activity Correlative Function Display Mode VGA, VESA, XGA, SVGA, WXGA, WSXGA Attribute LCD Screen size dependent Glass, frame, number of LED chips Screen size quasi-dependent PWB Area (screen width) PWB impact factor (layers and PWB Area) Screen type technology dependent Use phase Slide 15

16 Drivers of performance 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 Duty cycle (power in ON and OFF mode) Slide 16

17 Targeted analysis around LCD Each resolved driver lowers COV % Uncertainty Unresolved LCD Screen size PFC abatement Production location Electricity intensity Area of IC & PWBs Slide 17

18 PFC Abatement Resolution Modeling no information vs. some information PFC abatement Process abated Abatement = Unresolved = High PFC GHG Option 1 CVD and Dry Etch High PFC GHG Option 2 CVD only Medium Probability PFC GHG Option 3 No abatement Low GWP Probability = Unresolved = Medium Probability = Unresolved = Low GWP GWP Slide 18

19 Evaluating Model Performance Viability of Discriminating Product Classes 10% Probability density A Probability density B Probability 10% FS µ A µ B FS = False Signal Rate GWP (kgco2e) Slide 19

20 Resolving Ability to resolve between LCD product classes Example: PFC abatement unresolved (default) vs. resolved (medium or high) 17" Default 23" Default 17" High 23" High False signal 30% False signal 11% Probability Probability KgCO2eq/product KgCO2eq/product Slide 20

21 Approaches to Realizing LCD production impact Approach 1: Processbased Modeling Approach 2: Aggregate Industry/Firm Data Pros Highlights technology and operational differences Avoid some allocation Comprehensive Low cost Updating infrastructure Facility-level data Energy consumption Cons Data intensive both in terms of initial collection and infrastructure for long term updating Low resolution Allocation assumptions Non -predictive Hides technology and operational differences Product a Product b Product c Slide 21

22 LCD production facility-level data Emissions data in one of the following forms: Normalized by input or output at each facility (e.g., kgco 2 -eq/m 2 glass sheet) Emissions by facility (e.g., kgco 2 -eq/facility) and production volumes (e.g., m 2 glass input sheet/month) Kg CO 2 -eq/m 2 glass sheet Reported impact from facilities Emissions by facility (e.g., kgco 2 -eq/facility) and categorization of facilities by production volumes or product types: size of facility (e.g., Small, medium, large) or product type (e.g., Small, medium, large product size) Slide 22

23 Conclusions Uncertainty is large, but can be accommodated Screening is possible (should be described and allowed in standards) Balance between sufficiency and precision to reduce costs of these analyses Move towards industry-level data where feasible Resolution ability impeded by uncertainty/variation and data quality Method is able to inform decisions even with data limitations The probability of distinguishing between alternatives improved from 70% to 90% Role of facility-level data in a product-level assessment Proper allocation depending on type of data and accounting for all impacts Example: Chemicals use, IC modeling of precious metals Slide 23

24 Thank you Questions?