National Energy Statistics:Opportunities and Challenges for
Food-Energy-Water (FEW) Data Integration
Eric Masanet, Ph.D.
Associate Professor
McCormick School of Engineering and Applied Science
Director, Energy and Resource Systems Analysis Laboratory (ERSAL)
Guest Faculty Researcher, Argonne National Laboratory
http://ersal.mccormick.northwestern.edu/
Agricultural inputs (fertilizers,
etc.)
Farm
operationsFreight Processing
Preparation
(cooking,
refrigeration)
Waste
managementFreight
Stages of the food life cycle:
Typical representation in national energy statistics
Data
resolution
Sector
Subsector/
mode/end use
Product
= most often = less often = rarely
At what level of data resolution does FEW decision science often
require?
Example of ERSAL California cap and trade modeling of the
tomato processing industry
Methods/opportunities for bridging the gap
National
energy
data
FEWS
decision
models
• Surveys
• Engineering models
• Case studies
• Statistical analysis
• Dataset integration
Source: IEA World Energy Balances, http://www.iea.org/
Some strengths:
• Availability (annual)
• Consistency (int’l standards)
• Reliability (verified)
• Fuel resolution
• Useful for macro-calibration
• Broad energy trends
Some weaknesses:
• Lack of industry resolution:
• Subsectors
• End uses/modes/products
• Non-specified
• Lack of interdependencies
• Lack of spatial resolution
IEA Energy Efficiency Indicators:
Tracking Sectoral Energy Progress
United States
IEA Energy Efficiency Indicators:
Tracking Sectoral Energy-Related CO2 Progress
U.S. Manufacturing Energy Consumption Survey (MECS) end use and subsector data resolution
Evolution of MECS Food Industry Data Resolution
311 Food
3112 Grain and Oilseed Milling
311221 Wet Corn Milling
31131 Sugar Manufacturing
3114Fruit and Vegetable Preserving and Specialty Food
3115 Dairy Product
3116 Animal Slaughtering and Processing
312 Beverage and Tobacco Products
3121 Beverages
3122 Tobacco
2014311 Food
3112 Grain and Oilseed Milling
311221 Wet Corn Milling
31131 Sugar Manufacturing
3114
Fruit and Vegetable Preserving and Specialty
Foods
3115 Dairy Products
3116 Animal Slaughtering and Processing
312 Beverage and Tobacco Products
3121 Beverages
3122 Tobacco
2006
311 Food
3112 Grain and Oilseed Milling
311221 Wet Corn Milling
31131 Sugar Manufacturing
3114 Fruit and Vegetable Preserving and Specialty Food
3115 Dairy Product
3116 Animal Slaughtering and Processing
312 Beverage and Tobacco Products
3121 Beverages
3122 Tobacco
2010
311 Food
311221 Wet Corn Milling
31131 Sugar
311421 Fruit and Vegetable Canning
312 Beverage and Tobacco Products
3121 Beverages
3122 Tobacco
2002
311 Food
311221 Wet Corn Milling
312 Beverage and Tobacco Products
1998
20 Food and Kindred Products
2011 Meat Packing Plants
2033 Canned Fruits and Vegetables
2037 Frozen Fruits and Vegetables
2046 Wet Corn Milling
2051
Bread, Cake, and Related
Products
2061 Cane Sugar, Except Refining
2062 Cane Sugar Refining
2063 Beet Sugar
2075 Soybean Oil Mills
2082 Malt Beverages
21 Tobacco Products
1994
U.S. Manufacturing Energy Consumption Survey (MECS) end use and subsector data resolution
Some strengths:
• End use resolution
• Large sample set
• Useful for macro-calibration
• Broad energy trends
• Many ancillary data:
• Adoption of efficient
technologies
• Floor area
• Economic indicators
Some weaknesses:
• Fewer data at regional level
• Quadrennial
• Lack of interdependencies
• Lack of subsector resolution
2007 U.S. IO Total
Requirements
Matrix (389)
IO Analysis
Output
(purchase) from
IO sector i ($)
Input required from
IO sector 1 ($)
Input required from
IO sector n ($)
…
Environmental Coefficients
for Supply Chain Sector n
Electricity (kWh/$)
Natural gas (Th/$)
Coal (Btu/$)
CH4 (g/$)
…
and so on
X
Fuel Use and Emissions for Supply Chain Sector n
Electricity (kWh) Natural Gas (Th) Coal (Btu) And so on …
=
Producing Sector
Supply Chain Sectors
Black = Input-output model Purple = US national energy statistics
$0.00
$0.10
$0.20
$0.30
$0.40
$0.50
$0.60
$0.70
$0.80
$0.90
$1.00
0 100 200 300 400 500 600 700 800
Co
st o
f C
on
serv
ed
En
erg
y (
2004 $
/Th
-saved
)
Cumulative Savings (MTh)
Industrial Natural Gas Maximum Achievable Potential -California Cumulative through 2012 (MTh)
Friedmann, R., F. Coito, E. Worrell, L. Price, E. Masanet, and M. Rufo (2005). “California Industrial Energy Efficiency Potential.” Proceedings
of the 2005 ACEEE Summer Study on Energy Efficiency in Industry, West Point, New York, ACEEE.
Maintain
boilers
Steam system
controls
Process heater
upgrade
Eff
icie
ncy m
ea
su
re in
ve
stm
en
t co
st
= discrete efficiency technology/practice
Techno-Economic Potentials Analysis
Industrial Natural Gas Efficiency Example
2007 U.S. IO Total
Requirements
Matrix (389)
IO Analysis
Output
(purchase) from
IO sector i ($)
Input required from
IO sector 1 ($)
Input required from
IO sector n ($)
…
Environmental Coefficients
for Supply Chain Sector n
Electricity (kWh/$)
Natural gas (Th/$)
Coal (Btu/$)
CH4 (g/$)
…
and so on
X
Fuel Use and Emissions for Supply Chain Sector n
Electricity (kWh) Natural Gas (Th) Coal (Btu) And so on …
=
Fuel End Use Breakdown
Lighting (kWh) HVAC (kWh) Pumping systems (kWh) Compressed air (KWh) And so on …
Energy-efficient measure 1 Energy-efficient measure n…
Fuel use and
emissions
reduction potentials
Producing Sector
Supply Chain Sectors
Black = Input-output model Green = Techno-economic potentials modelsPurple = US national energy statistics
Results for the California Wine Sector
Source:
Masanet, E., Matthews, H.S., Carlson, D., and A. Horvath (2012). Retail Climate Change
Mitigation: Life-Cycle Emission and Energy Efficiency Labels and Standards. California
Air Resources Board, Sacramento, California.
0 20 40 60 80 100 120
Wholesale trade
Coal mining
Petroleum refineries
Fertilizer Manufacturing
Oil and gas extraction
Iron and steel mills
Truck transportation
Glass container manufacturing
Fruit farming
Wineries
GHG Emissions (g CO2e/$)
Electricity
Process CO2
CH4
N2O
HFC/PFCs
Coal
Natural Gas
Petroleum
Biomass/Waste
Top 10 contributing sectors to the GHG emissions footprint of product manufacture (MJ/$)
0 5000 10000 15000 20000 25000 30000
CH4 reduce methane leaks
Petroleum process heating
Coal Steam Systems
Reduce PFC use
Electric HVAC
Electric refrigeration
Coal Process Heating
CH4 methane capture
Natural gas HVAC
Electric lighting
Petroleum engines
Electric motors
N2O soil nutrient management
Natural gas process heat
Natural gas steam
GHG emission reduction potential (Mg CO2e/yr)
Va
lue
ch
ain
eff
icie
ncy
/ab
ate
me
nt
op
po
rtu
nit
y
Top 15 estimated supply chain efficiency and
GHG mitigation reductions by improvement
opportunity and emissions source
Characterizing Multiple Benefits of Industrial Energy Efficiency Investments:
Water Use ReductionsSteam systems are the largest onsite
consumer of fuel
Estimating Process Water Use via National Energy Statistics
Process Technology Model (Boiler Fuel Input) Nature of Annual Steam System Water Use by Sector
Equivalent to the daily residential water use of Los Angeles!
Walker, M.E., Lv, Z., and E. Masanet (2013). “Industrial Steam Systems and the Energy-Water
Nexus.” Environmental Science & Technology. 47 (22), pp 13060–13067.
Masanet, E., and M.E. Walker (2013). "Energy-Water Efficiency and U.S. Industrial
Steam." AIChE Journal. Volume 59, Number 7, pages 2268-2274. Cover article
U.S. DOE Audits: Untapped Water Savings
Improving Industrial Sector Energy Modeling via Data Mining/Matching
Data Mining/Matching Scope
• Total of 6,928 facilities are included
Additional data sources for mining/matching:
• EPA MACT boiler/process heater registry
• Regional air permit applications
• Plant registries (product data)
• ERSAL process model library
MECS/GHGRP Data Hierarchy
Data Mining/Matching Suggests Different Energy Patternsand Government Data Improvement Opportunities!
311 MECS: 64% of energy use for steam systems
311 matched: 87% of energy use for steam systems
THANK YOU!