International Symposium on Sustainable Systems and Technology

Buffalo, NY, USA

June 26 – 28, 2018

NREL/PO-6A20-71760

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Assessing alignment between a top-down LCA 

model and a bottom-up supply chain model for 

data gap identification and prioritization

Rebecca Hanes1, Xiaoju (Julie) Chen2,3, Alberta Carpenter1, H. Scott Matthews2,3

1. Strategic Energy Analysis Center, National Renewable Energy Laboratory, Golden, CO, USA

2. Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

3. Department of Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA

EEIO

Total Energy:

2.1 × 10−3GJ/kg

MFI

Total Energy:

0.85 × 10−3 GJ/kg

Sulfuric Acid

 Differences in energy accounting between models creates difficulty in deriving an 

“apples to apples” comparison.

 MFI total energy is generally but not universally lower than EEIO total energy, 

contrary to expectations.

 Agreement in energy consumption by sector is variable even among products 

within the same sector.

 Further study and method development is required for these results to provide 

information around data gap identification and prioritization.

TT = (II + AA)ff

TT: Direct throughput ($) by 

sector for production of ff, 

based on economic 

exchange matrix AA

ggtttttttttt = �

ii

ggii

Elements of DD multiply 

columns of BB to obtain 

direct use of fuel ii for 

production of ff.

Top-Down Calculation Method EPA’s U.S. EEIO model (Yang et al., 2017)

Bottom-Up Calculation Method NREL’s MFI tool (Hanes et al., 2016)

MFI-calculated fossil fuel and renewable energy consumption by commodity were 

aggregated using BEA sector codes to obtain direct energy consumption by sector.

EEIO Hydrochloric Acid

Total Energy:

6.9 × 10−3GJ/kg

MFI

Total Energy:

55 × 10−3 GJ/kg

Top Sectors by Direct Energy

Top Sectors by Direct Energy

ggii = bbiiDD

The sum of the direct 

use fuel ii is the total 

energy consumption for 

production of ff. 

IO-LCA Methodology

IO-LCA Results

 Direct fuel consumption takes place within the process of interest (sulfuric acid 

production, in this case) while indirect fuel consumption takes place upstream of 

that process, such as within electricity generation facilities.

 The IO-LCA results show that biomass is the main direct energy source for 

producing sulfuric acid, while natural gas is the main indirect energy source.

 Separating direct and indirect supply chain energy consumption will provide 

additional information on where data gaps exist within the bottom-up MFI dataset, 

and how impactful each data gap is on energy consumption results. 

 Work is ongoing to compare the IO-LCA results, at the sector level, to MFI results 

at the process level. The level of aggregation in the IO-LCA model is expected to 

create some difficulties in this comparison.

xx is total throughput ($) by 

sector for production of ff

Columns of AA are 

scaled by elements of xx

to obtain direct ($) 

inputs to each sector

xx = (II − AA)−1ff DD�jj = xxjjaa�jj EE�jj = �

ii

ddii bb�ii

Elements of DD multiply 

columns of BB to obtain 

direct use of energy 

resources (GJ) by sector

Ratios of direct fuel consumption to 

produce 1 kg Sulfuric Acid

Ratios of indirect fuel consumption to 

produce 1 kg Sulfuric Acid

Approach 1: Compare direct energy by sector

Background: The MFI Tool

 Materials Flows through Industry is a bottom-up

supply chain modeling tool (Hanes et al., 2017).

 A linear network model is used to quantify energy 

consumption and greenhouse gas emissions in U.S. 

commodity supply chains.

 MFI draws on a database of material and energy 

inputs to manufacturing processes, covering 1,536 

processes for 739 commodities.

 This project aims to both identify gaps within the MFI 

database and provide information on the relative 

impact of each gap on MFI results.

Summary

 Two methods for identifying data gaps in a bottomup model are developed and tested.

 This study uses NREL’s Materials Flows through 

Industry (MFI) bottom-up supply chain model and 

an input-output life cycle assessment (IO-LCA) 

model based on a recent EEIO model of the U.S. 

economy.

 Preliminary results indicate that Approach 2 (below 

right) provides sufficient information for data gap 

identification, while Approach 1 (below left) requires 

additional development to yield valuable insights.

Background: The IO-LCA Model

 The Input-Output Life Cycle Assessment model is a 

top-down LCA model.

 The data of the economic component of the IO 

model are from the U.S. Department of Commerce 

Bureau of Economic Analysis (BEA).

 The energy use in the IO model are from the U.S. 

Environmental Protection Agency’s (EPA) U.S. 

Environmentally Extended Input-Output (USEEIO) 

model (Yang et al., 2017) .

 The most recently available inventory of industrial 

energy (year 2007) was used.

Approach 2: Compare direct and indirect fuel inputs

References

Yang Y., W.W. Ingwersen, T.R. Hawkins, M. Srocka and D.E. Meyer (2017). “U.S. EEIO: A new and transparent United States environmentally-extended input-output model.” Journal of Cleaner Production (158): 308-18.

Hanes, R. J. and A. Carpenter (2017). "Evaluating opportunities to improve material and energy impacts in commodity supply chains." Environment Systems and Decisions 37(1): 6-12.

MM = ggtttttttttt − TTBB Indirect fuel consumption = total – direct

rrii = ggii /ggtttttttttt Ratio = fuel ii consumption/ total consumption