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 Ethanol TagsBarley
Biodiesel
Canola
Coal
Corn
Crude Oil
DME
Diesel
Economic
Electricity
Energy Balance
Ethanol
Exhaust Emissions
Feedstock
Fertilizer
Fischer Tropsch
GHGenius 2.0
GHGenius 2.1
GHGenius 2.5
GHGenius 2.6
GHGenius 3.13
GHGenius 3.14
GHGenius 3.15
GHGenius 3.16
GHGenius 3.2
GHGenius 3.3
GHGenius 3.6
GHGenius 3.9
GHGenius 4.01
GHGenius 4.02
Gasoline
HRD
Hydrogen
Jatropha
Land Use
Lignocellulosic
Marine Oil
Materials
Methanol
Mixed Alcohols
Natural Gas
PHEV
Palm Oil
Peas
Provincial Defaults
PyrolysisOils
Refining
Sequestration
Soybeans
Sugar Beet
Sugar Cane
SuperCetane
Tallow
Used Cooking Oil
Wheat Straw
Wood
Yellow Grease
 Advanced Biofuel Update
 Prepared October 2012
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The US National Renewable Energy Laboratory has published a number of techno-economic assessments over the past several years on advanced biofuel pathways. These studies include:
· Ethanol via biochemical processes using agricultural residues.
· Ethanol via thermochemical process using woody biomass.
· Gasoline via thermochemical process using woody biomass.
· FT Distillate via thermochemical process using woody biomass.
· Gasoline and Diesel fuel via pyrolysis.


Additional information has been found for the wood to DME process and this information is used to update this pathway in GHGenius. All of these pathways are already in GHGenius, having been added over the years. This work updates the default values in the GHGenius model and uses the updated data to model and report on the energy balances and GHG emissions of the pathways. This includes undertaking sensitivity analysis on the important variables.

In GHGenius there are multiple sub-pathways for some of these fuel systems, for example there are four agricultural feedstocks and a wood feedstock for the biochemical route and the pyrolysis pathway has woody biomass feedstock and agricultural residues. We have developed consistent inputs for all of these sub-pathways based on the NREL data, whereas they only provide data for one feedstock.

There are process developers that have hybrid systems, those that use both biochemical and thermochemical process to convert biomass to transportation fuels. These can also be modelled in GHGenius by adapting one of the existing pathways. Two examples of this are presented, but the inputs required for these systems are not included as default values in the model.

The results are presented for Canada for the year 2012 using the IPCC 2007 GWPs, unless otherwise stated. The version of GHGenius that accompanies this report is version 4.02. This version also has some updates to the electric and fuel cell vehicle pathways, which are described in a separate report. The GHGenius manuals have also been updated with the information used in this report.


Tags: DME - Ethanol - Feedstock - Fischer Tropsch - GHGenius 4.02 - PyrolysisOils - Wheat Straw - Wood
 Sugar Cane Ethanol Update
 Prepared May 2012
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This pathway in GHGenius was added to the model in 2006. Sugarcane production systems in Brazil have undergone significant change since that time with the introduction of mechanical cane harvesting, the continued evolution of the sugar and ethanol mills, and the development of a more significant co-generation capacity in the industry as a result of changing government policies.

There is also much more data available on the industry as a result of a significant effort by the Brazilian industry to document their activities and the inclusion of this pathway in the US RFS2 program and in the California Low Carbon Fuel Standard program. There is information available for both manual and mechanical harvesting. This update includes the option to allow the user to choose which harvesting approach to use for modelling. The user can specify how much of the feedstock is harvested manually and how much is done mechanically and the button installs the appropriate defaults throughout the model.

Where possible, the data in the model has been converted to the time series approach that has been used in recent updates to the model. This provides greater certainty for the input data as it effectively deals with year to year variations due to weather.

Particular attention has been paid to soil emissions and fugitive emissions from the vinasse disposal systems. These two issues were identified as areas of uncertainty and ongoing research in Brazil.

The version of the model that accompanies this report is GHGenius 4.01. Several other changes to the model were carried out at the same time but these have their own reports. The sugarcane ethanol system is shown in the following figure.

Tags: Ethanol - GHGenius 4.01 - Sugar Cane
 2010 Biofuel Analysis
 Prepared December 2010
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In 2006, GHGenius was used to analyze the GHG emissions from the biodiesel and fuel ethanol pathways of interest in Canada. That project also investigated the sensitivity of the emissions to some of the parameters than could vary from project to project. That report has been used by a number of government departments, as they study the development of the industry in Canada. It is also one of the more popular reports on the GHGenius website.

Our knowledge of the performance of the biofuels industry in Canada has improved significantly in the four years since that work was done. In addition, GHGenius has been updated with better data on the biofuel production process and feedstock production systems. An updated report is therefore warranted, given the continued interest in the subject and the updated modelling data. The report will also serve as an updated documentation resource for these pathways in the model.

The biofuel pathways that have been analyzed in this report include five ethanol pathways, corn, wheat, barley and sugar cane based systems and a cellulosic ethanol system based on wheat straw feedstock. Six biodiesel feedstocks have been considered, canola, soybeans, tallow, used cooking oil, palm oil and jatropha. Four feedstocks have been considered for hydrotreated oils, palm, canola, tallow, and soybean oil.

For the sensitivity analysis, the focus has been on the issues that can vary from plant to plant, such as co-product drying, the use of combined heat and power, and the energy source for the thermal energy. In addition, issues that still have some uncertainty, such as changes in soil carbon are evaluated.

Tags: Barley - Biodiesel - Canola - Corn - Ethanol - HRD - Jatropha - Palm Oil - Soybeans - Sugar Cane - Tallow - Used Cooking Oil - Wheat - Wheat Straw
 2009 GHGenius Update
 Prepared September 2009
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This report covers an update of the following data in GHGenius. In some cases the structure of the model has been slightly changed to accommodate the time series but any difference in the results is due to the new input data and not due to any structural changes in the way that the model handles the new data.

1. International crude oil energy and emissions. Several new data sources have recently been identified. These include the International Oil and Gas Association (a time series from 2002 of the energy and GHG emissions of crude oil production for various regions of the world), data from the Alberta Energy Research Institute studies (some specific useful information for countries such as Mexico, Venezuela, Iraq, and Saudi Arabia), and the World Bank flaring study.

2. Canadian electricity. A time series of electric power production on a regional basis in Canada from 2000 has been be developed from Statistics Canada data. Regional generation efficiencies and proportions of power types have been extracted from the data.

3. Rail energy. Statistics Canada has a time series data for freight movement on Class 1 railways. This data has been compared to similar information from the United States and incorporated in the model.

4. Potash mining. Statistics Canada, CIEEDAC, and NRCan Comprehensive Energy Use Database provide a time series for energy consumption, quantity and type. This has been compared to the NRCan CIPEC report that was used as a data source in the model. The new information has been incorporated into the model.

5. Nitrogen fertilizer. Statistics Canada, CIEEDAC, and NRCan Comprehensive Energy Use Database all have a time series for information on this sector. These data sets do not include process energy consumption but that can be calculated. The data sets have been compared to the NRCan CIPEC report that was the base of data in the model.

6. Corn and Soybeans. Fertilizer and yield time series available from the USDA. Some Statistics Canada yield data on these crops and other Canadian crops is available as well. This time series data has been incorporated into the model.

7. Ethanol and Biodiesel energy requirements. New data from the United States is available for both these alternative fuels. An update and development of a time series for ethanol has been incorporated into the model.

8. Some users have identified a number of enhancements for the functioning of the EV macro in GHGenius. These modifications have been incorporated into GHGenius. They provide more functionality and having them in the public model will allow them to be continually updated as model enhancements are undertaken.

9. Natural gas update. The Canadian Gas Association has provided some recent information on distribution emissions. Unfortunately the report did not provide activity data but that that has been developed from other sources. In addition Statistics Canada has data on the natural gas sector and this will be reviewed to see if it can be worked into the model.


Tags: Biodiesel - Corn - Crude Oil - Electricity - Ethanol - Fertilizer - GHGenius 3.16 - Natural Gas - Soybeans
 Provincial Default Values
 Prepared March 2009
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The recent strategic development plan for GHGenius identified a need for “Provincial” versions of GHGenius. Some of the provinces wish to develop their own versions of GHGenius so that they can establish the default values for various fuels.

This has been accomplished by the addition of a series of default buttons on the Input Sheet. These buttons change the model to the appropriate region, the correct provincial power mix, the appropriate crude oil slate, and the correct petroleum product distribution patterns. Like other default values in the model the user can still overwrite the cells so that no functionality is lost. There are default values developed for BC, Alberta, Saskatchewan, Manitoba, Ontario, Quebec, and Atlantic Canada.

The defaults have been set up for the various commercial fuels, gasoline, diesel, natural gas, LPG, ethanol, and biodiesel. In some cases, reasonable estimates will be made of what is likely to happen in the province with respect to the supply of ethanol and biodiesel. The version of the model that accompanies this report is 3.15.


Tags: Biodiesel - Diesel - Ethanol - GHGenius 3.15 - Gasoline - Provincial Defaults
 Barley and Peas Ethanol Report
 Prepared November 2008
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Barley and peas both have some attractive features as ethanol feedstocks in western Canada and there are some groups considering using these feedstocks for commercial ethanol production. Barley is used in some European ethanol plants but to the best of our knowledge peas are not used commercially for ethanol anywhere in the world.

The goal of this work was to add barley and production to GHGenius and to use both of these feedstocks individually to produce ethanol.

Agronomic data on barley and peas production in western Canada has been identified from public sources and added to the model.

The new feedstocks have been added to all of the existing uses of ethanol in the model other than the fuel cell pathways. This is consistent with other ethanol pathways that have been added to the model recently.


Tags: Barley - Ethanol - GHGenius 3.14 - Peas
 2008 GHGenius Update
 Prepared August 2008
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There have been a significant number of changes made to the model. The new version is 3.13. These upgrades have included incorporating more recent forecasts of future changes in the Canadian energy infrastructure, the capability of having a choice of GWPs to make comparisons with other studies and models easier, the ability to report emissions per unit of energy on either a higher or lower heating value basis, much expanded capacity to model oil sands operations, many changes to the land use calculations to make the results more regional specific, and a number of smaller changes. The changes have impacted all of the pathways in the model.

The two largest pieces of work included:
1. GHGenius has had default values for the production of synthetic crude oil by an integrated mining process. More and more synthetic crude oil is being produced by in situ mining (Steam Assisted Gravity Drainage or Cyclic Steam Stimulation), so pathways and default values for these alternate production systems have been added. There is now full flexibility in the model for combining bitumen extraction methods and integrated or stand alone upgraders.
2. A major upgrade of the methodology for calculating land use emissions (direct and indirect).
a. The IPCC 2006 guidance document has some small changes in the sources of N2O that are to be calculated as part of a national inventory. This includes N2O emissions resulting from a loss of soil carbon. This source has been added to the model along with an update of the IPCC default values.
b. An update on the issue of N2O emissions from crops that fix their own nitrogen has been included. There has been an update of the approach included in the model.
c. Environment Canada and Agriculture and Agri-Food Canada have made considerable progress in defining the appropriate regional emission factors for agricultural activities such as fertilizer application, cultivation practices and other land use activities rather than relying on the IPCC Tier 1 values. These emission factors, which are found mostly on sheet W, have now been regionalized.
d. The soil carbon changes calculations in the model have been changed to a more straightforward approach.
e. Within the model we have an above ground carbon offset due to nitrogen fertilization of biomass from fertilizer that is lost offsite. This is not included in the IPCC guidelines. We have modified the model so that this source can be included or excluded from the calculations by the user.
f. A discussion of above and below ground carbon changes has been included. The model has been modified so that the land use assumption for ethanol co-product credits are consistent with the energy and GHG emission credit calculations. A discussion of how to model both the direct and indirect land use changes for the biomass feedstocks in included.


Tags: Biodiesel - Canola - Corn - Crude Oil - Electricity - Ethanol - GHGenius 3.13 - Land Use - Soybeans - Wheat
 Bioethanol LCA Models
 Prepared March 2008
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Environmental life cycle analysis (LCA) models are complex tools that can be applied to help assess the relative attractiveness of transportation fuels and other products. LCA can be used to inform government policy makers, industry, community stakeholders and other groups in making more informed decisions for the Canadian and regional environment. However, there are a variety of models available and results from these vary, mostly for valid reasons. This can reduce confidence in LCA results for ethanol. In addition, the quality of conclusions drawn from LCA models are influenced by the assumptions and data used in generating model results. As a result, Environment Canada needed to develop a better understanding of LCA models available as well as the underlying data, assumptions and associated calculations these tools use to generate results.

The main overall purpose of this report is to provide an assessment of existing LCA models that can be applied in determining the environmental footprint of bioethanol and competing fuels (e.g., gasoline) in Canada. The assessment includes analysis to identify the key factors that contribute to differences in the results from different models. This report also provides: an analysis regarding the role of LCA in policy formulation; an overview of other modelling activities oriented to environmental policy development; an overview of what LCA is and how it works; a brief description, assessment and availability (for the purposes and scope of this study) of 37 LCA models, more detailed analysis of 9 models and selection of 2 models for detailed analysis and comparison; sensitivity analysis using one model to identify the factors in the life cycle that most strongly influence LCA results; and recommendations regarding the development and enhancement of LCA modelling for Canada.

Tags: Corn - Ethanol - Wheat
 Land Use Report
 Prepared September 2007
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The purpose of this work was to identify key factors that influence the life-cycle greenhouse gas (GHG) emissions relating to land use choices of biofuel feedstocks. Emissions from land use changes are becoming a larger issue as the quantities of biofuels produced around the world grows. Concern is being raised about the soil carbon changes that might arise from bringing new land into production. Many GHG emissions models (such as GREET) ignore this aspect of the biofuels issue but some, including Dr. Mark Delucchi’s LEM and GHGenius, have the capability of including these emissions in the biofuel production pathways. The problem is that the scenarios that need to be developed are futuristic and therefore up to the modeller to select the appropriate data for modelling. This subjective approach is problematic.

This work investigates some of the issues that impact these emissions and to arrive at some potential recommendations of how the issue could be best modelled in the future.

The work only considered four feedstocks; corn, wheat, canola, and soybeans. These are the primary feedstocks for the first generation biofuels and the ones that are currently facing the greatest growth pressures. The question is where will the feedstocks to produce these biofuels come from? In GHGenius, the default values for most feedstocks assume some combination of increase in yield and substitution for some generic agricultural feedstock and while a case can be made that this has been the historical route it may not apply in the future. This work sought to address a number of questions that impact on this issue.

Avoided transportation emissions resulting from the use of biofuel feedstocks locally rather than exporting these feedstocks have generally been ignored in most discussions regarding land use and bringing more land into production in remote regions. These emissions will vary from country to country depending on transportation modes employed and the destination of customers. The preliminary analysis undertaken here indicates that in the case of Canada these transportation emissions are very large and avoiding these emissions can offset soil carbon losses resulting from brining new land into production elsewhere in the world.

The most significant issue that arose from this work was the impact of the conversion of forests and forestland to biofuel feedstock production. It is this factor that has the potential to eliminate the GHG emissions benefits of most biofuels as they are currently produced. A thorough investigation of this issue is beyond the scope of this work but an overview of the basic facts is presented.

Tags: Biodiesel - Canola - Corn - Ethanol - Land Use - Soybeans - Wheat
 Sugar Beet Ethanol Report
 Prepared June 2007
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There have been proposals recently for the construction of fuel ethanol plants that would process sugar beets as the feedstock. These proposals have been for plants in Prince Edward Island, Ontario and Quebec. Sugar beets are grown for sugar production in Alberta. In the past there were also sugar beets produced in Manitoba but this plant has been closed as they were unable to compete with very low cost imported sugar, and Canada is one of the few developed countries that does not provide protection for their domestic sugar industry. World sugar prices and grain prices have increased significantly in recent years and sugar beets may be a viable feedstock for fuel ethanol in Canada.

A few lifecycle analyses have been done on sugar beet ethanol in a European context but nothing has been done in North America. This work adds the sugar beet to ethanol pathway to GHGenius. GHGenius is now one of the few models that can compare ethanol produced from corn, wheat, sugar cane, and sugar beets based on a consistent data set. Where possible North American data on sugar beet production has been incorporated into the model.

Tags: Energy Balance - Ethanol - GHGenius 3.9 - Sugar Beet
 Wood Ethanol Report
 Prepared November 2006
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- For wood to ethanol the process data is sparse but there is one complete set of mass and energy balance data that was developed by the US DOE several years ago that will be used as the base. This input data was also discussed with researchers at UBC who have been active in this field for many years although they have focussed on individual steps in the process rather than on the overall process. We have also reviewed the process emissions on sheet N in the model for all of the biological ethanol pathways and separated the inputs for wood, ag residues, corn and wheat ethanol.

- For wood to natural gas there is some work underway in Europe investigating and demonstrating at the pilot level the gasification of wood and the upgrading of the gas to produce pipeline quality natural gas. Some preliminary data is available. This data has been reviewed and the model has been updated to reflect recent advances.

- There is some interest in producing pipeline quality gas from coal in Canada. There is one commercial pant in the United States (Dakota Gasification) and the literature surrounding that plant has been reviewed, along with new process developments in the field, to develop the basis for a new pathway in the model. This pathway is similar to the wood to natural gas pathway and the use of the gas will include light and heavy-duty vehicles.

Tags: Coal - Ethanol - GHGenius 3.6 - Lignocellulosic - Natural Gas - Wood
 Biofuel Sensitivity Analysis
 Prepared August 2006
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The purpose of this work was to identify key factors that influence the life-cycle greenhouse gas (GHG) emissions of current ethanol and biodiesel production pathways. This information can then be used by policy makers, fuel producers, distributors, retailers and consumers to assist them in making decisions that positively impact the lifecycle GHG performance of the renewable fuels sector.

For the transportation sector there are generally three ways that GHG emissions can be reduced; improve energy efficiency at all stages of the life cycle, use lower carbon intensity fuel sources, or change transportation modes. Combinations of the three approaches are of course also possible.

Renewable fuel producers have some control over the first two categories but they will be looking to maximize the return on investment when they design and build biofuel facilities and not necessarily minimizing GHG emissions. This may lead to the facilities being energy efficient but the types of energy that are used in the facilities may not be optimized.

For this work we are interested in, among other possibilities, the emissions impact that could arise from different farming practices. These practices could result in soil carbon changes and perhaps in changes in above ground biomass. The default values for modelling have therefore been set so that in the base case there is no change in soil carbon, no change in above ground biomass arising from increased crop yields, and no biomass growth resulting from nitrogen run-off lost offsite.

Tags: Biodiesel - Canola - Corn - Ethanol - Lignocellulosic - Palm - Soybeans - Sugar Cane - SuperCetane - Tallow - Wheat - Yellow Grease
 GHGenius Sequestration Report
 Prepared April 2006
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The report covers work on the expansion of pathways and expanding the results from existing pathways. This work involved the following tasks and deliverables.

1. The potential to include carbon dioxide sequestration to a number of feedstock and fuel production pathways has been added to the model. There was previously a switch in GHGenius to account for carbon sequestration in thermal power generation but this was a very simply approach to the issue and it underestimated the emissions in the upstream portion of power generation. There are a number of other places where sequestration might be employed. These include gasification plants, oil sands upgraders, oil refineries, methanol, and ethanol plants. The capability of adding a sequestration step to all of these facilities has been added to GHGenius and the current switch for electric power plants has been removed to calculate the impact of carbon storage more robustly for power plants.

2. The capability of using biodiesel in the light duty diesel and light duty hybrid diesel vehicles has been added to the model. These pathways have also been added to the LDV Summary sheet and the Light Duty Cost effectiveness output sheets. This involved only the combination of existing fuel and vehicle pathways in the model.

3. The tables 51c and 51e on sheet I have been expanded to include all of the pathways in the model. This included the pathways that are primarily electric in nature. It should be noted that in GHGenius, electric power is treated as a primary energy source where a kWh of power is converted to 3,600 kJ of energy. Some other models consider electric power a secondary source of energy and account for the energy of one kWh based on the energy that went in to the power plant so there may be some differences in the results shown in GHGenius compared to some other models. We may want to consider changing this in the future.

4. For some types of oil production there are surface disturbances that will result in a loss of biomass and soil carbon. The emissions from these disturbances are included in the coal mining pathway but not in the oil sands pathways. The emissions from this source for oil production pathways have been added to the model where appropriate.

GHGenius has been modified to allow the incorporation of carbon capture and storage (CCS) into many of the fuel and energy pathways in the model. This has been accomplished in a manner that provides a significant amount of flexibility for the user. There is still a considerable amount of uncertainty with respect to the actual performance of CCS systems in real world applications. With some large projects now being proposed some real data may become available in a few years that can be used to further refine the values used in GHGenius.

Tags: Crude Oil - Electricity - Ethanol - Fischer Tropsch - GHGenius 3.3 - Hydrogen - Methanol - Mixed Alcohols - Refining - Sequestration
 Construction Emissions
 Prepared March 2006
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Most analyses of energy production pathways do not include any emissions attributable to the construction of the energy production facilities themselves. This simplification of the production pathway is allowed under ISO 14000 guidelines if the emissions are not material. Many researchers make this claim for the construction and decommissioning stage but there are other analysts who often challenge this perspective. These analysts may use the omission of construction emissions as a reason not to trust a comparison between fuel pathways.

This report documents a literature search of previous work on the emissions associated with the construction of electric power facilities (nuclear, hydro, thermal, and wind), oil refineries, ethanol production plants and other production facilities. The identified literature has been assessed on a common basis and conclusions reached about the GHG emissions from the construction phase of a project. The literature search has identified anther approach to quantifying the emissions from the construction phase of projects, the use of economic input-output data which can be used when the quantification of materials and energy inputs are not available to achieve a reasonable estimate of emissions.

Tags: Biodiesel - Electricity - Ethanol - Hydrogen - Materials - Refining
 Sugar Cane Ethanol and Other Updates
 Prepared January 2006
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The report covers work on new pathways and updating data. This work involved the following tasks and deliverables.

1. A new table has been added to sheet I that shows the fossil energy consumption per kilometre driven. This augments the existing Table 51c that shows the total energy consumed per kilometre driven.

2. New information on the energy requirements to remove sulphur from gasoline and diesel fuel in Canada has recently become available. This information has been reviewed, and the formulas on sheet G have been changed to be consistent with this new data.

3. There is increased interest in the concept of plug in hybrid vehicles. These vehicles have a larger battery pack than existing hybrid vehicles and must be plugged in to maximize the battery charge. The vehicles operate with some fraction of the energy consumed provided by the power from the grid rather than having all of the power provided by the gasoline engine. These vehicles can operate as a combination of a battery powered electric vehicle and a gasoline vehicle. This pathway has been added to the results sheets using a user specified fraction of distance provided by the grid power and battery size.

4. There has also been interest in modelling gasoline powered heavy-duty vehicles and gasoline hybrid heavy-duty vehicles. These vehicles may also be medium duty vehicles so the classes of vehicles modelled have been expanded by including vehicle weight as part of the user inputs. This will allow the different weight classes to exhibit different efficiencies. The US EPA guidance on this issue has been followed.

5. The production of ethanol from sugar cane has been added to the model. While the production of ethanol from sugar cane is not likely possible in Canada, there has been some sugar cane ethanol imported into Canada and interest in this pathway remains high in Canada. Some information on the production process is available from Brazilian sources and this information has been reviewed to extract the data required for adding this pathway to GHGenius.

6. The energy consumed in the manufacture of materials that are used in the manufacture of vehicles is found on sheet L in the model. This information was briefly reviewed in the 2004 model update but there were still significant gaps in terms of good quality Canadian data availability. This issue has been reviewed again to search for new information that has become available in the past several years on the subject. Updated data has been incorporated into the model.

The version of the model that accompanies this report is version 3.2.

Tags: Ethanol - GHGenius 3.2 - Materials - PHEV - Sugar Cane
 Economic, Financial, Policy Analysis Biofuels, Pha
 Prepared April 2005
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The transportation sector represents the single largest source of Canada's greenhouse gas (GHG) emissions, accounting for about 27% of the total. Emissions from transportation are growing faster than the national average and are forecast to exceed the 1990 levels by over 25% in 2010 and 40% by 2020. Two transportation fuels that are manufactured from biomass feedstocks have been gaining momentum as suitable fuels for use in gasoline and diesel engines, either as neat fuels or in various blends. These fuels are ethanol, manufactured from grains and lignocellulosic feedstocks, and bio-diesel (methyl esters) manufactured from virgin vegetable oils, re-cycled oils, and animal fat.

The 2003 Climate Change Plan for Canada included $154 million to be invested in measures to support Canada's efforts to reduce GHG emissions from transportation. The funds will support the industry to increase the supply of renewable alternative fuels, such as ethanol and bio-diesel, and the commercial transportation sector to make greater use of these fuels.

The Federal Government included a production goal of 35% of Canadian gasoline to be blended with 10% ethanol by 2010 in its Climate Change Action plan. In 2010, this will likely require 1.5 billion litres of ethanol. They have also established a $100 million Ethanol Expansion Program to assist with the construction of new ethanol plants in Canada. The funding under the Ethanol Expansion Program is part of a larger bio-fuels strategy that also includes the extension of the National Biomass Ethanol Program, research and development under the biotechnology component of the Technology and Innovation Strategy and an investment in bio-diesel.

The Federal Government has included a production goal of 500 million litres of biodiesel by 2010 in its Climate Change Action plan. They have also established an $11.9 million fund that will support research and provide incentives for industrial-scale biodiesel pilot plants, and support demonstrations of its effectiveness to encourage broader use of biodiesel.
In addition to encouraging increased production, the Government of Canada is also promoting greater use of ethanol and biodiesel. In partnership with several gasoline retailers, the Government of Canada is launching a consumer awareness campaign that will promote the benefits of ethanol-blended gasoline to Canadian drivers. There are currently more than 1,000 retail locations selling ethanol-blended gasoline in Canada.
There has been little economic and financial analysis of these fuels within a Canadian context. The few published and unpublished studies carried-out so far for the public sectors have dealt mostly with potential socio-economic impacts and have attracted little interest from the investment community due to their lack of focus on profitability, both short and long term. More detailed feasibility studies have been performed for individual private sector clients but these have not been widely disseminated. Policy and decision makers, financial institutions, and other economic players need the more detailed, formal analysis framework in order to make investment decisions regarding the development of these fuels.

Some essential topics that will be addressed in this work are:
·The appropriate policy and regulatory environment under which investments will flow into ethanol plants;
·The likely source of these investments;
·And the industry structure that will lead to a viable and competitive industry in the longer term.

Much work remains to be done in this area to establish a purely Canadian perspective, if Canada is to entertain the notion of building a bio-based economy as part of its future.

The development of a biofuel industry will require a great deal of investment on behalf of fuel suppliers, fuel marketers, and many levels of governments. The federal government's role will be to encourage the development of the biofuel industry through the implementation of sensible regulatory and policy tools based on sound analytical work. This work will form a foundation for the development of those tools.

Objective and Approach

The primary objective of this study is to assess the current and future economics of ethanol plants in Canada and to develop estimates of demand, supply, and prices (costs and selling) of this fuel. The results are then used to develop a template-like analytical tool for various models of ownership structure, to help assess the financial performance of various types of fuel ethanol (regional and feedstock specific) plants across Canada.

The work was carried out in Phases and stages. This report covers Phase 2, for fuel ethanol and biodiesel.
Phase 2 of the work focuses on quantifying the effects of ethanol and biodiesel production and use from a full cost accounting perspective. The work includes:
·A literature review of existing full cost accounting studies on biofuels.
·Descriptions of the relative benefits and costs of biofuel production within the context of greenhouse gas emissions, air quality, safety risk, employment and tax benefits and resource use.
·Identification of case studies that would optimize the benefits from a full cost accounting perspective.
·Identification of the gaps in the existing understanding of full cost accounting and how they might be addressed in the future.

Tags: Biodiesel - Economic - Ethanol
 Ethanol GHG Emissions Update
 Prepared March 2005
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The primary focus of this work is to update the data, on the materials and energy inputs into the ethanol production processes and co-products produced, used for modelling the lifecycle emissions of the ethanol pathways. Lifecycle emission modelling results are only as good as the quality of the input data so it is important to review this data on a regular basis.

The primary work on the ethanol pathways in GHGenius was undertaken in 1999 and while there have been some minor updates since then, the primary data in the model has not been reviewed in the past five years. There are several important issues that have been investigated and added to the model to better reflect the emissions of modern plants. These include:
1. Reviewed and updated energy requirements for grain ethanol plants. There have been significant reductions in the energy requirements of new grain ethanol plants in the past five years. This progress has been reviewed and incorporated into the model.
2. The addition of the capture and liquefaction of carbon dioxide. Ethanol plants produce a very concentrated carbon dioxide stream, which can be captured and liquefied for use in a variety of industrial applications. Alternative sources of carbon dioxide are less concentrated and require more energy to concentrate and purify. Depending on the degree to which carbon dioxide from ethanol plants displace carbon dioxide from other sources there can be an energy and emissions credit applied to the ethanol plant. This alternative processing scheme has been added to the model with the flexibility to activate or not, either fully or partially.
3. In the development of commercial ethanol from lignocellulose plants there are new co-products being developed. Some of these include fertilizers and soil conditioners. The model has been expanded to include fertilizer co-products.
4. There is some information in the literature that suggests that some animals that consume distillers dried grains have lower levels of flatulence. The literature has been surveyed for further information on this issue and this emission credit has been incorporated into the model.

Updated Data

A major part of any life cycle analysis is the collection of the data on the inputs and outputs of the production cycle being analyzed. The quality of the data has a large impact on the quality of the results being calculated. Data quality must balance the available time and resources against the quality of the data required to make a decision regarding overall environmental or human health impact.
In GHGenius the data on many of the production pathways is continually being updated as new information sources emerge or as processes evolve. Recently new information on the emissions of the fertilizer sector in Canada has become available, and new information on nitrous oxide emissions in the agriculture sector are also available. These changes to the model were described in a recent report on the emissions from biodiesel production ((S&T)2, 2005). These changes also impact the ethanol fuel pathways either directly in the case of fertilizer or indirectly in the case of soybean emissions since the distillers dried grains (DDG) displaces soybean meal and the emissions credit for the DDG is a function of the emissions from the soybean lifecycle.
The input variables are another class of data input. These variables can be expected to differ in different regions of the country or in different countries in response to different practices or environmental conditions. These inputs are generally found on the input sheet in GHGenius as one expects them to be changed in different circumstances.
These input variables for the different ethanol pathways have been reviewed and where better data now exists for some variables, this information has been added to GHGenius. It is important to keep in mind that the general approach to the data in GHGenius is to model the most likely scenarios, not the best-case scenario where practices or existing equipment would have to change to produce the modelled results.

Co-Product Additions

Several new co-products have been added to GHGenius that are applicable to the ethanol pathways. The ability to model the capture of carbon dioxide and have that product displace gas from alternative production sources in now available. Cellulosic ethanol production processes produce large amounts of lignin, which can be processed by different means. It is possible to burn the material and produce electricity or it may be possible to sell the material for other applications. One application may be as a source of fertilizer and this capability has been added to the model. The handling of other co-products such as acetic acid has been improved in the model.
The co-product credits for DDG have been reviewed. The displacement factors for DDG have been reduced as more feed trials suggest lower displacement ratios that are used in other models and have been used in previous versions of GHGenius. The value of DDG as animal feed is a very complicated subject as there are many components in a typical ration and it is almost impossible to design a reasonably sized experiment to isolate the impact of one of the components. There is a significant amount of information on the impact of diets on methane production from cattle. It is clear that introducing dietary supplements, particularly ones with by-pass protein will reduce the methane production rate in dairy and beef cattle. Estimates of the impact of DDG on methane emission rates have been made and incorporated into the model.

Tags: Corn - Ethanol - GHGenius 2.6 - Lignocellulosic - Wheat
 Economic, Financial, Policy Analysis Ethanol
 Prepared November 2004
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The Federal Government included a production goal of 35% of Canadian gasoline to be blended with 10% ethanol by 2010 in its Climate Change Action plan. In 2010, this will likely require 1.5 billion litres of ethanol.

They have also established a $100 million Ethanol Expansion Program to assist with the construction of new ethanol plants in Canada. The funding under the Ethanol Expansion Program is part of a larger bio-fuels strategy that also includes the extension of the National Biomass Ethanol Program, research and development under the biotechnology component of the Technology and Innovation Strategy and an investment in bio-diesel.

There has been little economic and financial analysis of ethanol within a Canadian context. The few published and unpublished studies carried-out so far for the public sectors have dealt mostly with potential socio-economic impacts and have attracted little interest from the investment community due to their lack of focus on profitability, both short and long term. Policy and decision makers, financial institutions, and other economic players need the more detailed, formal analysis framework in order to make investment decisions regarding the development of these fuels.

The primary objective of this study is to assess the current and future economics of ethanol plants in Canada and to develop estimates of demand, supply, and prices (costs and selling) of this fuel. The results are then used to develop a template-like analytical tool for various models of ownership structure, to help assess the financial performance of various types of fuel ethanol (regional and feedstock specific) plants across Canada.

The work was carried out in Phases and stages. This report covers Phase 1, for fuel ethanol. A similar report has been prepared for biodiesel.

The specific objectives of Phase 1, Stage 1 were to:
· Review literature on economic and financial performance of ethanol plants.
· Identify successful plants and reasons for success.
· Quantify feedstock resources and production costs.
· Develop a comprehensive financial model.
· Develop a supply curve.

The objectives of Phase 1, Stage 2 were to:
· Identification of market barriers.
· Evaluate policy tools including.
o Government capital investment
o Favourable tax treatment
o Infrastructure investment
o R&D funding
o Renewable content mandates
o Emission taxes
· Examine the potential for regionalization of tools.
· Quantification of levels of support required.
· Investigate other approaches to market development.

Phase 1, Stage 3 of the work focuses on the international aspects of a developing ethanol industry and considers the threats and opportunities that international trade in biofuels presents. The specific tasks of this stage include:
· Identification of the level of international trade.
· Production cost comparison with the potential exporters of fuel ethanol.
· Analysis of the import alternatives that ethanol users in Canada would face.
· Evaluate the impacts that ethanol imports might face and identify measures that might mitigate the impacts.
· Evaluate the impact of trade agreements on enabling or disabling Canadian industry competitiveness.

The next phase of the work will include some GHG analyses.

Tags: Corn - Economic - Ethanol - Lignocellulosic - Wheat
 Marine Based Biodiesel
 Prepared November 2004
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GHGenius has had pathways for the production of biodiesel from canola, soybeans, animal tallow and yellow grease. This work adds the generic production of biodiesel from marine oils to GHGenius and compares those results to a specific Canadian operation, Ocean Nutrition, producing marine oil biodiesel.
Ocean Nutrition produces an ethyl ester from marine oils in Nova Scotia. The process is commercially unique from several perspectives including, the use of ethanol rather than the more common methanol as the alcohol, the use of marine oils as the feedstock, and the co-processing that is carried out to produce Omega 3 oils for nutritional purposes as well as producing biodiesel.
The goal of this work is to:
· Add the commercial harvesting of fish and its reduction to proteins and oils to GHGenius.
· Add a biodiesel pathway that utilizes marine oils as the feedstock to complement the existing animal fats and vegetable oil pathways.
· Modify and expand GHGenius to allow the use of ethanol rather than methanol in the biodiesel production system. Review the literature to determine how others have addressed this issue since the carbon in the ethanol is renewable whereas in the methanol it is not. It may be that the impact is on the glycerine production and how that is ultimately used.
· Address the allocation issues raised by the co-production of biodiesel and the high value Omega-3 oils produced in the Ocean Nutrition process.

Tags: Biodiesel - Ethanol - GHGenius 2.5 - Marine Oil - Methanol
 Alternative and Future Fuels for Road Vehicles
 Prepared for Transportation Issues Table, National Climate Change Process in July 1999
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The first work that used the Canadian version of LEM and formed the basis for the development of GHgenius.
Considered Light and Heavy Duty Vehicles
14 fuels considered.
Greenhouse gas emission reductions calculated.
Cost effectiveness of GHG reductions calculated.

Tags: Ethanol - Hydrogen - Methanol - Natural Gas
 Update of GHGenius
 Prepared for Natural Resources Canada in March 2004
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As part of this work, there have been several changes to the model. The version of the model that accompanies this report is GHGenius 2.5. A number of revisions and updates to the model were undertaken. The revisions generally have either improved the quality of Canadian data in the model or added functionality that makes the model more powerful for the user. In addition, two new cycles, based on processes developed by NRCan have been added to the model. These new cycles are the subject of a separate report. The objectives of the model updates were to:
  • Allow selection of ethanol cellulosic feedstock from the input sheet. Ethanol from lignocellulosic feedstocks could be produced from a variety of feedstocks including wheat straw, corn stover, switchgrass, and hay.
  • Improve the quality and functionality of the emissions from crude oil production and refining in Canada. The model has been modified to use four different crude oil types, conventional, heavy, bitumen and synthetic.
  • Harmonize the methodology for the production of hydrogen from electrolysis with other methods.
  • Review and update the Canadian electricity mix.
  • Update the emissions of criteria air contaminants from heavy-duty diesel engines on Sheet H in light of the new emission standards being phased in this decade.
  • The latest LEM model by Dr. Mark Delucchi made many changes to sheet L that calculates emissions associated with materials. The documentation that describes the changes to determine the best data to use for GHGenius with particular attention to the data for Canada has been reviewed and the appropriate changes have been made to GHGenius.


Tags: Crude Oil - Electricity - Ethanol - Exhaust Emissions - GHGenius 2.5 - Lignocellulosic - Refining
 Ethanol Production from Wheat
 Prepared for Natural Resources Canada in January 2003
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The production of ethanol from wheat is practiced commercially on a small scale in Western Canada. In addition to the Federal goal of increasing ethanol production, there are also proposals and actions to significantly expand ethanol production in Saskatchewan and Manitoba. The objectives of this work were to:
  • Add the wheat to ethanol upstream fuel cycle to the GHGenius model. The ethanol that is produced could be used wherever ethanol from corn is used so all of those full fuel cycles were added to the model.
  • While the GHGenius model calculates the greenhouse gas emission credits for co-products, the calculation of energy credits for the co-products is not currently included in the model. This improved functionality has been added to the model.


Tags: Ethanol - GHGenius 2.1 - Wheat
 Biodiesel and Ethanol Diesel Blends
 Prepared for Natural Resources Canada in September 2002
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This report accomplished the following objectives:
  • Determined the lifecycle energy balance and greenhouse gas emissions for biodiesel manufactured from waste animal fats, soyoil and Canola oil.
  • Determined the impact on greenhouse gas emissions of using biodiesel blends of 2%, 20% and 100% compared to conventional diesel fuel
  • Determined the impact on greenhouse gas emissions of using ethanol diesel blends of 7% and 15% compared to conventional diesel fuel.
  • Confirmed the appropriate treatment of N2O emissions from agricultural residues in the latest version of GHGenius.
  • Estimated the production costs of biodiesel from the various feedstocks studied.
  • Provided an overview of the policy issues raised by the blending of biodiesel or ethanol diesel blends. These include taxation issues, impact on exhaust emissions, regulatory issues with respect to safety and standards, engine warranty issues and distribution and marketing issues.


Tags: Biodiesel - Canola - Ethanol - Fertilizer - GHGenius 2.0 - Soybeans - Tallow - Yellow Grease
 Ethanol from Lignocellulosic Feedstocks
 Prepared for Agriculture and Agri-Food Canada in December 1999
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This study was undertaken to provide an analysis of the life-cycle emissions and life-cycle energy balance of the production of ethanol from several agricultural lignocellulosic feedstocks and its subsequent use as a motor fuel in blends with gasoline. The study focuses specifically on Southern Ontario, an area with a large agricultural land base, as well as one with a large demand for motor gasoline. Energy and emission analysis was conducted in this study for a base case ethanol production volume of 225 ML per year in 2000 and 2010. Further analysis was done to investigate the effects of annual ethanol production volumes of 500 ML, 750 ML and 1,000ML. The analyses were performed for four feedstocks, switchgrass, hay, corn stover and wheat straw.

Tags: Ethanol - Lignocellulosic
 Ethanol from Corn in Southern Ontario
 Prepared for Agriculture and Agri-Food Canada in August 1999
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This study was undertaken to provide an analysis of the life-cycle emissions and life-cycle energy balance of the production of ethanol from corn and its subsequent use as a motor fuel in blends with gasoline. The study focuses specifically on Southern Ontario, which is the largest corn growing area in Canada, as well as one with a large demand for motor gasoline. Energy and emission analysis was conducted in this study for a base case ethanol production volume of 225 ML per year in 2000 and 2010. Further analysis was done to investigate the effects of annual ethanol production volumes of 500 ML, 750 ML and 1,000ML.

Tags: Corn - Ethanol
(S&T)2 Consultants Inc. 2004 Important Notices