GHG Calculations and Market Dynamics for Low Carbon Fuels · GHG Calculations and Market Dynamics for Low Carbon Fuels (S&T)2 Agenda Ø The State of GHG Calculations ... Ø Note:

Don O’Connor (S&T)2 Consultants Inc.

December 5, 2017

GHG Calculations and Market Dynamics for Low Carbon

Fuels

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Agenda Ø The State of GHG Calculations Ø Market Dynamics Ø  Industry Response Ø What’s next?

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GHG Calculations Ø Models such as GREET and GHGenius were

originally developed as tools to assist with policy analysis and development.

Ø They have been adapted to be used as compliance tools. Other tools and modelling frameworks (BioGrace and the EPA RFS2 framework) have also been developed for use as compliance tools.

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LCA for Policy vs Compliance Ø There is actually a shift in emphasis on what is

important in a LCA model between one used for policy development (historical use) and one used compliance (current use).

Ø Historically there was more emphasis on the fuel production process and less on the background processes. That has now shifted.

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LCA for Policy vs Compliance Ø  ISO definitions

Ø Primary Data Ø quantified value of a unit process or an activity obtained

from a direct measurement or a calculation based on direct measurements at its original source.

Ø Secondary Data Ø data obtained from sources other than a direct

measurement or a calculation based on direct measurements at the original source

Ø Note: such sources can include databases and published literature validated by competent authorities.

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Identifying Primary Data Ø  The regulator needs to decide what is primary data and what is

secondary data for a pathway. Ø  It needs to be consistent.

Ø  You can’t have one parameter specified as primary and a related parameter identified as secondary data and thus not modifiable.

Ø  Unfortunately this happens in BioGrace and CA GREET. Ø Biodiesel co-product volumes are a function of yield, the CA

GREET Tier 1 model requires the yield to be plant specific but does not allow any non-glycerine co-product to be accounted for.

Ø Same thing happens with renewable diesel.

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Challenges for Regulators Ø Ensure that the models used for compliance properly

reflect the processes being modelled. Ø  The models are fair to all producers of the same fuel. Ø  The models don’t have built in biases between fuels.

Ø  That the models contain good quality secondary data. Ø  The data needs to be timely and have good geographic

scope. Ø  The models are flexible enough that new feedstocks and

pathways can be added so that innovation is not suppressed.

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Data Quality Ø  How do we assess data quality? Ø  Generally consider five aspects, usually qualitatively.

Ø Reliability Ø Government Sources, verified?

Ø Completeness Ø Does it consider all emissions?

Ø Temporal Representativeness Ø How recent is the data? Is the full set from the same time period?

Ø Geographic Representativeness Ø Is the data from the same region where the study is being

undertaken? Ø Technological Representativeness

Ø The level of activity coverage? Does it cover all of the major technologies employed.

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Quality of Secondary Data

Parameter GREET GHGenius BioGrace EPA RFS2 Reliability Good Good Poor Poor Completeness Generally Good Good Poor Poor Temporal Representativeness Good Very Good Poor Poor

Geographic Representativeness United States Canada, United States,

Mexico, India Europe World

Technological Representativeness Good Good Low Low

Ø Secondary can be hard to come by, even in this age of Big Data. Ø In general the quality of secondary data is improving. Ø There is always room for improvement in the models and none of them

are perfect.

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Allocation

Model Options GREET Displacement, Energy, Mass, Economic GHGenius Displacement, Energy, Mass BioGrace Energy RFS2 Displacement

Ø BioGrace follows the energy allocation methodology specified in the RED Ø GHGenius provides options of using displacement, energy, and mass and hybrids

where different approaches can be used for different aspects of a pathway. With one exception, displacement is used as the default.

Ø GREET has the most options for allocation. It also includes economic and process energy use. It also allows for hybrid approaches.

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Allocation Ø  The energy allocation used by BioGrace compared to the displacement

approach used in GREET and GHGenius drives the lower ethanol GHG emissions in BioGrace.

Ø  Similarly the energy allocation for oilseed crushing compared to the mass allocation in GREET and GHGenius drives higher GHG emissions for oilseed biodiesel in BioGrace.

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Allocation Ø  Energy allocation does not account for the biogenic nature of co-

products from bio-energy system. Ø  Two plants doing the same thing, in the same region can get very

different CIs by changing the location of the fence! Ø  Under an energy allocation system co-products have the same CI as the

main product. Ø  If a plant can utilize the co-product to displace a fossil product within the

plant boundary, it will effectively get a displacement credit. Ø  This is a major issue for renewable and bio diesel plants for systems

that employ energy allocation.

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Allocation Ø  Allocation underestimates the GHG emissions for gasoline and

diesel by ignoring the GHG emissions from the combustion of heavy fuel oil and petroleum coke.

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Model Flexibility Ø There are significant, commercial feedstocks in

use today that did not exist 10 years ago when the basis for some of the models was established.

Ø Models need to be flexible enough that new pathways can be added quickly, efficiently, and accurately. Ø Some regulators have struggled with this need.

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Model Flexibility Ø Some of the EPA RFS2 models are no longer

supported. Ø Adding new feedstocks to the EPA framework proved

insurmountable. Ø One was added, but the results make no sense and no QA

was undertaken to ensure the model still gave the same answers for the “old” feedstocks.

Ø Even the open models like GREET and GHGenius can be a challenge to add pathways if you don’t fully understand the framework or the pathway.

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Challenges for Biofuel Producers Ø The compliance tools don’t always have the

flexibility to properly model their process, supply chain, or proposed innovations.

Ø Models need to ensure that they reward real change that results in real GHG emission reductions.

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Challenges for Biofuel Producers Ø Markets can be fluid, for multi-feedstock plants

the relative scores between regulatory systems for different feedstocks can be a challenge to align the feedstock with the customer demand.

Ø Low carbon fuel programs reward low CI fuels. The reward can be significant (25 to 50 cpl) and producers are interested in investing in lowering their GHG emissions.

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Market Dynamics Ø Lower carbon intensity is worth a lot of money in

some markets.

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Industry Response Ø Low carbon fuel producers are looking to

maximize their return on investment. Ø Investing in projects to lower their CI. Ø Maximizing their production of fuels that can be

used in low carbon fuel programs. Especially important for producers that are located outside the consuming region.

Ø Advocating for better models and policy choices that are fair.

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California Ethanol CI

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Ethanol CI Avg (gCO2e/MJ)

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Maximizing Production Ø Neste offering Bio LPG from their renewable

diesel plants. Ø Some renewable gasoline blending components

are also produced from renewable diesel plants. Ø An opportunity to get LCFS credits for a co-

product. Ø Corn oil from ethanol plants.

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Advocating for Better Models Ø Should the location of a fence have an impact on

a fuel’s CI? Ø How do we adjust the models or policies so this

doesn’t have an impact? Ø Data collection

Ø Industry surveys to aggregate data for use in models.

Ø Working with the ag sector to collect better data.

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Industry Response Ø All of this activity is happening in a market that is

very long on credit supply. Ø What is going to happen when the market is

short?

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Market Dynamics

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What Nobody is Talking About

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What Nobody is Talking About, Yet

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What Nobody is Talking About, Yet Ø Unfair comparisons

Ø Why are we comparing 115 octane ethanol to 87 octane gasoline?

Ø Why not 10% ethanol plus 90% 84 octane gasoline against 87 octane?

Ø What is the impact of lowering the refining intensity and why isn’t it a credit for ethanol?

Ø Is one MJ of ethanol exactly equal to one MJ of gasoline?

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What Nobody is Talking About

Source: Geringer and Szikora

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What Nobody is Talking About Ø When regulators include ILUC emissions why do

they add the average direct emissions and the marginal consequential emissions? Ø A portion of the feedstock emissions are

essentially being counted twice. Ø It wouldn’t be difficult to do the marginal direct

emissions. Ø For some feedstocks it has a significant impact on

the results.

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Conclusions Ø There is no doubt that programs that are

designed to reduce the GHG emission in the transportation sector are working.

Ø But because of policy and modelling issues we can’t be sure how big the reductions are.

Ø There is still a lot of work to do to make the compliance tools better, fairer, and more effective at driving change.

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Questions?