THE STANDARDS FOR BIOMASS SUPPLY CHAIN RISK

3.1.1 BIOMASS AVAILABILITY MULTIPLE (BAM)

Rationale

Biomass Availability Multiple (BAM) indicates the degree of redundancy in a Proponent’s supply chain. BAM is the ratio of biomass feedstock available to a project, at costs, timing, and in quality feasible for the Proponent, divided by the project’s feedstock requirements. BAM is a strong indicator of supply chain resilience when stressed by supply shortage and/or supplier breach.

Reporting

Reporting Requirements

  1. The Proponent shall understand the Biomass Availability Multiple (BAM) for its
  2. If more than one feedstock type is used, a separate BAM shall be understood for each feedstock type.
  3. BAM shall be calculated based on feedstock which is realistically accessible for use by the Project (“Available Biomass”), rather than on theoretically available/modelled feedstock (“Produced Biomass”).
  4. BAM shall be calculated within a band of economically feasible pricing as defined by the Proponent. Biomass feedstock that is available to a project shall be calculated using maximum price required to maintain ongoing operations and service financial
  5. BAM shall be calculated using feedstock that is available to the Proponent in a timely manner so as to prevent operational
  6. Quality of redundant feedstock used in the calculation of BAM shall be useable by the
  7. BAM shall be calculated at a sufficiently detailed geographic

Reporting Recommendations

  1. Demonstrated BAM should be at minimum 1.5, and preferably > 2.0.
Guidance

Guidance for Reporting Requirement 1

Geographic area for redundant feedstock used in the calculation of BAM should be the maximum drive-time distance outside of which sourcing drives feedstock cost beyond economic feasibility for the Proponent. This is the feedstock procurement “red-line” price at which a Proponent is no longer able to maintain operations, or will breach a financial covenant or warranty.

BAM cannot be verified or evidenced by contract as, by definition, BAM is the amount of feedstock available to the Proponent above and beyond that which is needed to meet quantitative requirements. In rare cases however, a Proponent may execute written option agreements giving it the right to procure feedstock if and when required. Such options can be with a contracted supplier (to purchase additional quantities as required) or with new suppliers, and can be powerful supporters of a Proponent’s BAM.

Utility of BAM is less pronounced for operating projects than for greenfield projects.

Guidance for Reporting Requirement 3

Hypothetical or derivative estimates of produced feedstock can provide theoretical indication of BAM. But to understand quantities of feedstock that are actually available, supportive granular data derived from real sources of supply regarding willingness or commitment level to provide feedstock to the Proponent are vastly superior indicators in terms of predictive accuracy.

Just because feedstock is produced proximate to a Proponent does not mean it is necessarily available to that Proponent. A sawmill may generate 20,000 tons of chips per year, but may only have 2000 tons available for purchase—or none at all.

BAM should be established using past and current market availability and trend information from established and defensible data sources. Contributing to this data can be the growth to drain (GTD) or growth removal ratio (GRR) which can serve as an indicator for assessing the long-term availability of sustainable woody feedstock from forests.

Generally, forest inventory data (including harvest licenses, biomass removals, remaining residues etc.) are collected and disseminated by provincial forestry departments (Gunn 2019). Also, Canada’s National Forest Inventory is one source which can be used to acquire forest inventory data necessary to estimate GTD and GRR.

Feedstock extraction rates can vary in different locations. Regional policy can dictate the quantity of forest residues to be left in the forest after harvesting. Suppliers may choose to leave large quantities of forest or agricultural residues on land, either for environmental or economic reasons; agricultural residues provide nutrients for agricultural fields, and farmers leave portions of residue as fertilizer. Primary and secondary research should be conducted indicating regional feedstock extraction rates.

Thiffault et. al. (2014) analyzed the recovery rate of forest residues based on data from temperate and boreal forests from around the world. They determined that the average recovery rate was about 52.2% depending on the local policies and the type of harvesting (e.g., stem-only, bundling, whole-tree harvesting, etc.). In non-Nordic countries, the recovery rate of forest residues ranged from 35.6-60.7% depending on the recovery method.

Agricultural Residues and Energy Crops. Availability of agricultural residues can be assessed through grain yields. The following are accepted conversion ratios (list provided by Gunn 2019):

  • Corn: 1 (Sokhansanj et al. 2006)
  • Beans: 1 (Nelson et al. 2004)
  • Wheat: 1.5 (Nelson et al. 2004)
  • Grass: 1 (Sokhansanj et al. 2006)
  • Barley: 1.5 (Penn State Agronomy Guide 2019-2020)
  • Soybean: 1 (Penn State Agronomy Guide 2019-2020)
  • Oats: 1 (Penn State Agronomy Guide 2019-2020)
  • Sunflower: 3 (Helwig et al. 2002)

Guidance for Reporting Requirement 4

The numerator of the BAM shall be constrained relevant to the maximum price that the Proponent can pay for feedstock. For example, if a Proponent can consistently remain operational and service financial covenants at up to $39 per ton FOB for pulpwood, then BAM should be calculated based on the amount of feedstock that is available at that price but no more.

Guidance for Reporting Requirement 5

It is important to understand the timeline at which additional biomass may be available to a project. If additional feedstock is eventually available to a project but not within a timeframe that prevents operational disruption to the Proponent, then it should not be included in the BAM calculation.

Guidance for Reporting Requirement 6

Quality of feedstock used in calculation of the BAM shall be fungible with primary feedstock. The BAM should not be artificially inflated by the addition of sub-standard feedstock simply because it is “available”. The numerator of the BAM should reflect availability of feedstock where the usability is demonstrated by the Proponent.

Guidance for Reporting Requirements 7

Where possible, the Proponent should use data at higher than jurisdictional boundary (i.e., county, province, forest management unit) resolution. An example of such dataset is the Manitoba Bioeconomy Atlas. The Atlas uses fusion of AAFC Crop Inventory (30 m), Crop Insurance Yield ratios and Statistics Canada sector data to assess high-resolution crop production (Gunn 2019).

Guidance for Reporting Recommendation 1

If BAM is based on derivative or secondary data, and the numerator is derived from a high-level estimate of feedstock “produced” in the supply basin as opposed to what is likely to be actually “available” to the Proponent, then it is recommended that BAM should be higher than 2 in order to accurately indicate supply chain redundancy. It should be noted however, that there are successful existing projects with BAMs as low as 1.5. While bigger tends to be better, there is no optimal BAM; an acceptable BAM depends on creditors’ tolerance of risk.

Agricultural Residues. In general, agricultural residue producers have few markets for their residue. The lack of competition for agricultural residue means that a high BAM is not necessary for low-risk supply (Hladik 2017).

Note that Regenerative Agricultural Practices encourage minimal tillage to improve soil health which leads some producers to minimize residue removal. The Proponent shall take existing agricultural practices into account when making a case for residue removal. Current research can be used for such argument, particularly that long-term research shows most residue can be removed with no adverse impacts (Gunn 2019). An example of such research can be found in Lafond et al. (2012).

Guidance Source

Bloomfield (2017, interview); Carollo (2017, interview); Golecha & Gan (2016); Hladik (2017, interview); James et al. (2012); Lewandowski (2018, interview); Solomon (2019 interview); Spikes (2017, interview); Texas Forest Service (2006); Thiffault et al. (2014); Gunn (2019, feedback); Sokhansanj et al. (2006); Nelson et al. (2004); Penn State Agronomy Guide (2019-2020); Helwig et al. 2002; Lafond et al. (2012)

3.1.2 IMPACT OF INCREASED UTILIZATION OF FEEDSTOCK

Rationale

Feedstock utilization in a supply basin can change over time. Existing consumers of feedstock can expand operations or new facilities can enter the market. Increased utilization puts additional pressure on feedstock and can lead to higher prices, feedstock disruptions, shortages or supplier breach.

Reporting

Reporting Requirements

  1. Increased feedstock utilization scenarios shall be modeled to determine impact of additional demand on Proponent feedstock supply and/or cost. Sensitivity analysis showing impact of increased feedstock utilization on Proponent shall be
  2. Modeled demand scenarios should be realistic, conservative and
  3. If feedstock is a secondary transformation (e.g., forest residue) then the feedstock utilization scenarios shall be run based on the changes in demand for the primary

Reporting Recommendations

  1. Scenarios reflecting potential increase in feedstock production in the long-term due to increased demand for feedstock should be considered.
Guidance

Guidance for Reporting Requirement 1

Increases in feedstock utilization by existing or future competitors can reduce overall availability of feedstock and drive price. Scenario-based modelling should incorporate known information about competitors’ historical and planned expansions, as well as assumptions based on anecdotal knowledge of the market and potential expansion.

Guidance for Reporting Requirement 2

Error bounds on models used to make forecasts shall be available for specific locations of interest and different times of the year so that predictions are accompanied by confidence intervals (Calvert 2011).

Guidance for Reporting Recommendation 1

Modelling should be conducted using geomatics (i.e., Geographic Information Systems) to account for spatial availability of feedstock.

Not all scenarios of increased feedstock utilization by competitors will have exclusively net negative results over the long-term. Increased feedstock utilization by competition, while detrimental in the short-run, can actually function to increase supply, particularly in the long term, as supply can increase to meet demand if infrastructure is the constraint. Economic models are available (e.g., Lamers et al. 2018) to estimate long-term impacts/benefits from increased utilization by competition and the market response and stabilization by subsequent increases in production.

Guidance Source

Abt (2018, interview); Carollo (2017, interview); Calvert (2011); Lamers et al. (2018); SRTS (2018)

3.1.3 FEEDSTOCK SUPPLY CURVE/MARGINAL COST CURVE

Rationale

The greater the feasible transport distance, the more feedstock is accessible to the Proponent, but at a higher delivered cost. The feedstock supply curve, sometimes referred to as the marginal cost curve, is a function of feedstock availability over its cost which is primarily, but not exclusively, a function of distance. The feedstock supply curve is used to determine the availability of redundant feedstock at various price points, and the cost of replacing feedstock with substitutes located at different distances.

Feedstock cost curves are useful in determining supply chain resilience; they provide information about the cost of feedstock availability in times of supply disturbance. Biomass supply chains are prone to supply disturbances over time; suppliers can become insolvent or weather events can temporarily disrupt feedstock availability. When a disturbance occurs, the Proponent may need to source replacement feedstock from different suppliers at different locations and costs. A biomass supply curve indicates quantities of feedstock available at various price levels from suppliers generally located further away than core suppliers.

Reporting

Reporting Requirements

  1. A supply curve representing feedstock availability over price and distance shall be developed and any significant inflection points shall be adequately
  2. If more than one feedstock type is used, then a separate cost curve shall be developed for each feedstock
  3. Feedstock cost curve shall incorporate realistic quantities of feedstock actually available to the Proponent. Feedstock cost curve shall be calculated based on feedstock that is realistically accessible for use by the Proponent (“Available Biomass”) rather than on theoretically available/modelled feedstock (“Produced Biomass”).
  4. Feedstock cost curve shall be calculated within a band of economically feasible pricing as defined by the Proponent. Feedstock that is available to a project shall be calculated using maximum price required to maintain ongoing operations and service financial
  5. Feedstock cost curve shall be calculated using feedstock available to the Proponent that is available in a timely manner so as to prevent operational
  6. Quality of redundant feedstock used in feedstock cost curve shall be useable by the

Reporting Recommendations

  1. Proponent should assess economic burden on the project procuring redundant feedstock (from larger distances) under low, medium and high supply chain stress scenarios.
Guidance

Guidance for Reporting Requirement 1-6

Same as for 3.1.1

Guidance Source

Solomon (2019, interview)

3.1.4 SEASONAL FEEDSTOCK SUPPLY VARIATION

Rationale

Biomass supply can present significant seasonal supply variations. Seasonal supply variations combined with limitations associated with longer-distance transportation and storage can lead to regional biomass supply imbalances (Golecha & Gan 2016) and can manifest in shortages and higher costs for Proponents.

Reporting

Reporting Requirements

  1. Understanding of seasonal feedstock supply variation shall be demonstrated for each feedstock type.

Reporting Recommendations

  1. A mitigation plan to lower the risk of seasonal feedstock supply variation should be demonstrated through one or more of the following methods:
    • Inventory
    • Feedstock substitution
    • Feedstock supply optimization models.
Guidance

Guidance for Reporting Requirement 1

Seasonal feedstock availability is more prevalent in agricultural supply chains (i.e., agricultural residues and energy crops) than in woody biomass supply chains. Optimization models can help quantify the risks of seasonal supply variation. An example of an optimization model for biomass availability can be found in Golecha & Gan (2016).

Proponent should assess the ability of feedstock inventory to meet demands during non- productive seasons. Strategic inventory and storage planning can sustain a steady supply of feedstock to the Proponent.

Primary feedstock may be substituted with secondary feedstocks in times of seasonal feedstock shortage. If secondary feedstocks are being utilized then fungibility with primary feedstock should be demonstrated.

Guidance Source

Ba et al. (2016); Carollo (2017, interview); Gebresslasse et al. (2012), Golecha & Gan (2016); Rob (2017, interview); D. Smith (2019, feedback)

3.1.5 YEAR-TO-YEAR VARIATION IN FEEDSTOCK AVAILABILITY

Rationale

Biomass can have significant year-to-year supply variations due to variability in yield from biomass harvesting operations, particularly with agricultural biomass.

Reporting

Reporting Requirements

  1. Projected year-to-year feedstock supply variation shall be modeled for each feedstock type.
  2. Strategies to counteract estimates of year-to-year supply variation shall be developed.

Reporting Recommendations

  1. Agricultural Biomass. Proponent should obtain historical data on yields of feedstock in the supply basin to make a determination of variability. If historical data are not available for predicting future yields, mathematical models may be used.
  2. Woody Biomass. Models used to demonstrate yield variability should be based on species, region and stand density of timber harvest operations.
Guidance

Guidance for Reporting Requirement 1

Variability manifests in both space and time. Spatial variability can also be judged based on physical and topographic features. For example, a region consisting of large variations in ground surface elevations will have high potential for soil erosion. When this region also has soils that can be easily eroded during precipitation events, the site would be at an increased risk for insufficient production.

Variability in yields of crop residue can be attributed to several factors including climate, soil, management practices and pests. The best evaluations of these factors are made at more macro- scales.

Yield variability could also be high in regions consisting of flat terrain if soil properties, climate conditions or management practices are not suitable for the crops being studied.

Agricultural Biomass. Models based on machine learning, mechanistic plant growth approaches and Bayesian techniques are available to capture variabilities on multiple scales (e.g., at county or regional scales) and make spatially-explicit yield predictions (Newlands and Townley-Smith 2010; Phys-Org 2017; Jeong et al. 2016; Jiang et al. 2017; FAO 2017; Chawla et al. 2016; PNNL 2017; Busby et al. 2017; Jager et al. 2010). While many more approaches exist to predict yields, there are no directly usable models that are available in the public domain to estimate energy crop and crop-residue yields at the farm or field scale. Depending on the crop of interest and location, a more advanced literature analysis is a prudent way of obtaining best estimates of energy crop or crop-residue yields. It should also be noted that new methods based on artificial intelligence and machine learning techniques are being developed and should be explored through a review of the relevant literature.

Because of the dependency on multiple variables, it is recommended that a project in need of such data approach academic or research institutions to obtain defensible predictions of energy crop and crop residue yields based on applicable locations and time periods. It will take considerable time and resources for a new entrant to apply these models to a specific location and time; hence the recommendation to approach experts in the field.

Woody Biomass. An empirical forest biomass yield model has been developed by Nishizono et al. (2005). This table can be used to determine the unutilized woody biomass volume produced by a timber harvest.

Treitz et al. (2012) demonstrates a tool for the estimation of forest inventory variables at the plot and stand levels for specific forest types using a LiDAR point density of 0.5 pulses·m-2. Such a tool could be applied to accurately estimate forest inventory variables and subsequently, biomass yield.

Distinct tools can also be used for modelling the availability of, and suggested extraction rates for deadwood (Venier et al. 2015). Huggard & Kremstater (2007) present a synthesis of published technical literature that can be used to parameterize and model deadwood harvesting projects.

Guidance for Reporting Recommendation 1

Mitigation steps for poor yield variability could include identifying high-yielding regions nearby, improving management practices and crop rotations to improve soil fertility and prairie strips to intersperse energy crops with row crops.

Guidance Source

Abt (2018, interview); Busby et al. (2017); Chawla et al. (2016); FAO (2017); Golecha & Gan (2016); Hladik (2017, interview); Huggard & Kremsater (2007); Jager et al. (2010); Jenkins (2017, interview); Jeong et al. (2016); Jiang et al. (2017); Newlands & Townley-Smith (2010); Nishizono et al. (2005); Passmore (2017, interview); Phys-Org (2017); PNNL (2017); SOFAC (2018); Treitz et al. (2012); Venier et
al. (2015)

3.1.6 DOUBLE-COUNTING FEEDSTOCK

Rationale

Aggregators, intermediaries or brokers organize and distribute feedstock produced by suppliers. If such sources of supply are used in assessing feedstock availability for BAMs or supply curves, Proponent should be sure not to double count feedstock produced by one supplier and traded/supplied by an intermediary.

Reporting

Reporting Recommendation 1

  1. Sources of feedstock for brokers, intermediaries or aggregators should be disclosed.
Guidance

Guidance for Reporting Recommendations 1

In case of brokers and intermediaries, sources of feedstock should be disclosed, otherwise the quantities claimed by brokers/intermediaries could misinform total feedstock availability (Marsollek 2018, interview).

 Non-circumvention agreements may be required prior to disclosure of sources.

Guidance Source

Marsollek (2018, interview)

3.1.7 FRONT-END VALIDATION OF DATA USED IN FEEDSTOCK AVAILABILITY MODELS

Rationale

Feedstock supply models can be complex. Lack of clarity about model assumptions and baseline data can result in confusion on the part of the capital markets and drive financing costs for biomass projects. The adequacy and credibility of assumptions and baseline data is paramount to credible model outputs.

Reporting

Reporting Requirements

  1. Data behind feedstock supply models shall be transparent and credible.
  2. Samples of data underlying feedstock supply models shall be verified.

Reporting Recommendations

  1. If possible, alternative data sources should be considered to validate primary data used on model.
Guidance

Guidance for Reporting Requirement 1

Credibility of any model depends on the credibility of data going into the model. Trust in the model can depend on the understanding of the sources of data and their credibility. Any data used in the model therefore shall be referenced, and if possible, the methodology behind data collection shall be clearly articulated. In case of primary data collected specifically for the Proponent, the methodology behind data collection shall be explained.

Woody Biomass. The most credible source of secondary data pertaining to forest biomass available from Canada, including pulpwood, is Canada’s National Forest Inventory.

Guidance for Reporting Requirement 2

No dataset is a perfect representation of reality. Data collection methods can result in datasets with potentially large errors. This is especially the case of point-source data; meaning, data collected from Proponents. Supplier information should be verified through direct conversations with suppliers or site visits. More general data, such as county-level information, can be verified by cross-referencing with alternative datasets, if available. Whenever possible, data shall be verified by an independent, credible third party.

Guidance for Reporting Recommendation 1

If data are available from more than one source, alternative sources should be considered. Preferably, alternative data sources should be run through the model and results should be compared. A wide discrepancy of results can indicate an unreliability of available datasets, in which case a decision can be made to apply more conservative outputs.

Guidance Source

Dujmovic (2019, feedback); USDA (2014)

3.3.1 DIESEL, OIL AND PRODUCER PRICE INDEX (PPI)

Rationale

Diesel, oil and PPI can impact feedstock cost of harvest and collection over time. Sensitivities to worst case scenarios should be run.

Reporting

Reporting Requirements

  1. Feedstock price risk relating to diesel/oil cost and PPI fluctuations and its impact on the Proponent shall be evaluated for a 10-year period.

NOTE: Impacts of diesel on transport cost should not be calculated here (see 3.5.3).

Guidance

Guidance for Reporting Requirement 1

Historical price fluctuations of feedstock in the supply basin should be modeled with/without diesel/oil prices and PPI to understand the degree to which fluctuations in fuel or labour present long-term supply chain risk.

Techno-economic models can be used where feedstock markets have not yet been established, and lack of historical data does not allow for establishment of correlations. Techno-economic models include feedstock logistics and optimization models designed to estimate transportation routes and costs Lamers et al. (2015a, b).

Guidance Source

Curran (2017, interview); Lamers et al. (2015a, b); Passmore (2017, interview)

3.2.1 HISTORICAL FEEDSTOCK PRICE VARIATIONS AND “RED-LINE” FEEDSTOCK COST

Rationale

If volatility is shown in the historical feedstock price, then the risk of future price fluctuation is elevated. If feedstock prices have historically exceeded the price at which the Proponent would have to cease operations or breach a financial covenant (i.e., the “red-line” feedstock cost), then mitigation measures should be put in place.

Reporting

Reporting Requirements

Proponent shall:

  1. Identify drivers of historical feedstock price variations
  2. Assess probability and impact of price drivers occurring in the future and impact on feedstock availability and price
  3. Compare their “red-line” feedstock cost with historical maximums and describe mitigation measures, if required.
Guidance

Guidance for Reporting Requirement 1

The longer the historical horizon of data provided for predictive analysis, the more accurate the results. At minimum, 5 years of historical feedstock price shall be analyzed. Risk is greater where no historical feedstock price data are available.

Guidance for Reporting Requirement 2

It is also possible to develop quantitative estimates of this probability as shown by researchers at INL using the Stochastic Techno-Economic Model (Hansen et al. 2018).

Feedstock price variations can fluctuate due to multiple factors. For example, catastrophic weather events can cause spikes in feedstock price, surges in the housing market can depress cost of sawmill chips. Understanding timber supply and demand trends can aid in the understanding of variations in historical feedstock prices (Abt et al. 2000; 2009).

Capital-at-Risk (CaR) and Value-at-Risk (VaR) are concepts used in financial risk assessments to evaluate the financial viability of a project (Jorion 2007). Recently, they have begun to be used to assess the financial risks of biomass supply chains. Hansen et al. (Hansen 2018) are investigating the stochastic, financial risk of biomass supply chains using the STEM model. They will be including the VaR and CaR metrics based on the work of Jorion (2007) in the model. These metrics provide quantitative estimates of financial risk at a specified confidence level.

Guidance for Reporting Requirement 3

If the Project cannot withstand historical price highs, they should provide rationale as to why feedstock price is unlikely to reach such highs in the future.

Guidance Source

Abt et al. (2000; 2009); Carollo (2017, interview); Jenkins (2017, interview); Jorion (2007); Hansen et al. (2018); Mills (2017, interview); O’Leary (2017, interview); Santibanez-Aguilar et al. (2016)

3.2.2 LOW HISTORICAL DEMAND FOR FEEDSTOCK IN THE SUPPLY BASIN

Rationale

If Proponent supply basin does not have history of developed, large-scale feedstock procurement, suppliers may not have sufficient expertise in feedstock production to ensure reliable supply, especially in early years. This can be particularity true for forest residues where typically the infrastructure for collection, processing and delivery is immature.

Where supply chains are not well-established, risk can be mitigated when Proponent controls a higher degree of feedstock processing. For example, if a Proponent requires clean wood chips and the historical demand in the woodshed is exclusively for pulpwood, then supply chain risk will be decreased by Proponent’s intake of pulpwood and internal log debarking and chipping, rather than requiring inexperienced suppliers to deliver debarked wood chips.

Reporting

Reporting Requirements

  1. Understanding of historical feedstock production in the supply basin shall be demonstrated, including an assessment of the degree of risk presented by feedstock production history and supply expertise. If necessary, mitigation strategies shall be demonstrated.
Guidance

Guidance for Reporting Requirement 1

Education-based training and transition programs with the local labour force should be considered in areas where lack of previous supply experience is evident.

 As the proportion of feedstock removed from the land increases, so does the cost-per-unit to remove the feedstock. In other words, there is a point at which feedstock removal from the landscape becomes cost prohibitive. Understanding this price point is useful in determining the ideal ratio of residue removal. For example, Ralevic et al. (2010) estimate that due to marginal cost of forest residue extraction, 25-59% of potentially available biomass would remain in the forest.

Guidance Source

Huhnke (2017, Comment 1); Mills (2017, interview); Ralevic et al. (2010); Webster (2017, interview)

3.2.3 HISTORY OF PRODUCTION/FEEDSTOCK IS A NEW/SECONDARY CROP OR A BY-PRODUCT

Rationale

If feedstock is a new/secondary crop or a by-product, suppliers may either lack sufficient experience to mitigate risk, or be unable to react to such risk. Secondary crop or by-product producers may be less likely to prioritize production.

For new crop types, inexperience in planting, harvest, collection and yield data may pose higher levels of risk.

If feedstock is a secondary crop, then production can be subject to variables beyond suppliers’ control

(e.g., changing primary crop prices).

Reporting

Reporting Requirements

  1. Proponent shall determine primary product/crop production levels that are necessary to sustain feedstock production.
  2. Proponent shall demonstrate understanding of the market drivers for primary product on which by-products or secondary feedstock depends.
Guidance

Guidance for Reporting Requirement 1

Employ primary crop yield models to determine secondary crop production. The most accurate models involve satellite data analysis (Muth 2017).

Guidance Source

Muth (2017, interview)

3.3.2 CURRENCY RISK

Rationale

Where feedstock is purchased in a currency different than that which the plant and markets use to operate, currency exchange rates and volatility can constitute risk exposure. Plants near the US-Canada border which intake feedstock from both countries are exposed to such currency risk.

Reporting

Reporting Requirements

  1. Proportion of Proponent feedstock which is sourced cross-border shall be identified by quantity and cost.
  2. Cross-border suppliers of particular importance shall be identified.
  3. The impact of reasonable exchange fluctuations on feedstock cost shall be assessed for at least a 10-year period.
Guidance
Guidance Source

Solomon (2019, interview)

3.3.3 BORDER RISK

Rationale

Where feedstock is transported cross-border to another country, risk exposure to border closures and crossing delays becomes present. The availability of trucks willing to do cross-border runs is limited which can decrease supply chain flexibility and resilience. Plants near the US-Canada border which intake feedstock from both countries are exposed to these risks.

Reporting

Reporting Requirements

  1. Proportion of Proponent feedstock which is sourced cross-border shall be identified by quantity and cost.
  2. Cross-border suppliers of particular importance shall be identified.
  3. The impact of border delays/shutdown on feedstock cost shall be assessed for at least a 10-year period.
Guidance
Guidance Source

Solomon (2019, interview)

3.3.4 TEMPORARY EXTERNALITY-DRIVEN MARKETS FOR FEEDSTOCK

Rationale

Alternative, non-traditional, externality-driven competitors for feedstock can drive feedstock demand (and cost) in unusual circumstances. For example, a Proponent using corn stover as a feedstock would not typically compete with the higher-end animal feed market. However, in times of significant hay shortage (e.g., during drought), farmers may use corn stover as hay replacement, driving the price of stover feedstock and decreasing its availability for bio-projects (Bergtold 2018).

Reporting

Reporting Requirements

  1. Any alternative, non-traditional and externality-driven markets for feedstock shall be identified, and the likelihood and impact of these markets upon the Proponent shall be assessed.
Guidance

Guidance for Reporting Requirements 1

Agricultural Residues. In general, demand for feedstock from high-value markets is small. For example, research in Manitoba shows that estimated maximum needs for cattle feed/bedding and livestock bedding are ~1% of available crop residues (Gunn 2019). Despite this (in most circumstances) negligent impact of high-value markets on agricultural residue availability, the estimated demand should be demonstrated to ease any elevated perception of risk posed by these markets.

Guidance Source

Bergtold (2018, interview); Gunn (2019, feedback)

3.4.1 HARVEST AND COLLECTION PRACTICES AND SCHEDULES

Rationale

Differences in harvest timing and practices used can create risk to both the quantity and quality of feedstock. For example, feedstock harvested by different suppliers in different windows can undergo varying levels of exposure to sun, wind and moisture, leading to variations in delivered feedstock quality.

For example, agricultural feedstocks and energy crops have optimal harvesting windows to ensure minimal moisture content. In certain regions these harvesting windows may coincide with heightened weather risk such as frost or rain.

For forestry biomass, unsightly clear cuts and slash piles (even on plantation forests and especially when located near communities) can provoke unwanted public backlash even when suitable and sustainable replanting regimes are followed.

Reporting

Reporting Requirements

  1. Understanding of feedstock harvest practices and schedules shall be demonstrated.
  2. Understanding of weather risk and its impact on feedstock quantity and quality in relation to harvesting schedules shall be demonstrated.
  3. Understanding of any potential public backlash to harvest practices shall be demonstrated.

Reporting Recommendations

  1. Typical harvesting schedule and Proponent’s planned delivery schedule for feedstock should be prepared.
Guidance

Guidance for reporting requirement 1

Feedstock harvest schedules can be acquired directly from suppliers. Misalignment of suppliers’ harvest schedules with Proponent’s required feedstock quantities can create supply shortages.

Guidance for reporting requirement 2

Preferably, harvesting should be scheduled during periods of low weather risk.

Harvesting, collection and delivery schedules should be based on feedstock’s propensity to degrade (Huhnke 2017).

Guidance Source

Ebadian et al. (2011); Huhnke (2017, interview); Nguyen (2018, comment); Spikes (2017, interview)

3.4.2 HARVESTING AND COLLECTION EQUIPMENT

Rationale

Different types of harvesting and collection equipment used by suppliers can have a significant impact on the quality and availability of feedstock. Use of different types and combinations of harvesting, collection and processing equipment among suppliers can lead to non-homogeneous feedstock. Equipment that is not designed specifically for biomass cultivation, harvesting and collection, can increase feedstock quality risks.

Relevant equipment should be specified for the sake of product consistency and risk reduction.

Reporting

Reporting Requirements

  1. Understanding of equipment requirements to produce high-quality feedstock shall be demonstrated, including all equipment typically used for harvesting, collection and processing.
  2. Understanding of equipment variety in the supply chain, and its impact on feedstock quality consistency shall be demonstrated.
Guidance

Guidance for Reporting Requirement 1

Proponent shall develop a harvesting, collection and processing equipment specification sheet for use by suppliers. Data can be acquired from equipment manufacturers.

Researchers at INL, ORNL, and Antares (FOA Project) maintain a list of recommended harvest equipment, their yields and collection efficiencies, applicable to corn stover and energy crop harvests (Nair et al. 2018a, b).

Guidance for Reporting Requirement 2

Site visits to audit equipment are preferable over verbal information provided by suppliers.

A wide variety of equipment in the supply chain can make it difficult for suppliers to quickly acquire spare parts. For example, Webster (2017) suggests that variations in equipment leading to lower access to spare parts can affect the supply chain’s uptime by 30%. This is because distributors of spare parts keep inventory in locations with higher demand. Regions where a large number of feedstock suppliers own the same equipment will be served better by spare-part distributors.

Guidance Source

Nair et al. (2018a, b); Smith (2017, interview); Spikes (2017, interview); Webster (2017, interview)

3.4.3 VARIATION IN DENSIFICATION METHODS AMONG DIFFERENT SUPPLIERS

Rationale

The shape and density of the unit in which feedstock is supplied can impact feedstock cost and quality. Standard feedstock densification modes for biomass consist of round or square bales, pellets, cubes, chips, or grindings. The size of wood fibre processed in a grinder is less homogenous than if a chipper is used.

Bales of different densities can absorb moisture at different rates. In certain cases, round bales have been viewed as problematic due to their uneven moisture content distribution (Huhnke 2018).

Reporting

Reporting Requirements

  1. Understanding of the impacts of different modes of feedstock densification shall be demonstrated
Guidance

Guidance for Reporting Requirement 1

A third-party company specializing in feedstock harvesting and use of standard densification equipment can function to ensure consistency and decrease risk if a variety of feedstock modes are present in the supply basin.

NOTE: Even if a specific mode of feedstock densification is identified as preferable, suppliers may not have an ability to densify feedstock in such a way. For example, farmers may not have an ability to produce square bales if current equipment is designed for round.

Guidance Source

Huhnke (2018, Comment 1); Nguyen (2018, comment); Webster (2017, interview)

3.4.4 AVAILABILITY OF LABOUR FOR FEEDSTOCK PRODUCTION

Rationale

Skilled labour shortages can be difficult to remedy in the short-term. Availability of suitable labour in an area can impact the ability to procure sufficient feedstock quantities on required schedules. Labour risks are higher for greenfield facilities where supply chains are not yet active; or for Proponent’s for whom large feedstock requirements, or development of new (or expanded) supply chains, demand significant additions to the local labour force.

Reporting

Reporting Requirements

  1. Understanding of labour requirements necessary to produce and deliver feedstock shall be demonstrated, including:
    • Number of trained operators of field equipment
    • Number of harvesters/loggers
    • Experience of existing labour force
    • Necessary labourer certifications.
Guidance

Guidance for Reporting Requirement 1

The Proponent should have an understanding of the number of contractors within a 120km radius, as well as contractors’ total harvesting capacity.

Guidance Source

Ebadian et al. (2011)

3.5.1 FEEDSTOCK TRANSPORTATION COSTS

Rationale

Transportation can be one of the most significant cost components of biomass supply chains. The average transport cost and percentage of total feedstock cost attributable to transport should be known.

Reporting

Reporting Requirements

Proponent shall demonstrate understanding of:

  1. Average transportation cost per-unit of delivered material
  2. The percentage of total (current and projected) feedstock cost attributable to transport
  3. Feedstock cost in relation to transportation cost drivers via a sensitivity analysis
  4. Forecasted transportation

 Reporting Recommendations

  1. Mitigation plan for escalating transportation costs should be demonstrated.
Guidance

Guidance for Reporting Requirement 1

Understanding of feedstock suppliers’ locations, quantity of feedstock sourced at each location, and local infrastructure is necessary for evaluating feedstock transportation costs.

Guidance Source

Curran (2017, interview); Gan & Smith (2011); Roni et al. (2014a, b)

3.5.2 TRANSPORTATION DISTANCES

Rationale

Transport distances of 80-120 km for biomass feedstocks are typical but larger distances can be common. Where average transport distance from suppliers to Proponent is high, the supply chain is subject to greater sensitivities to risks, such as increases in diesel cost, weather impacts, mechanical breakdown, and by the demand for scarce feedstock from competitors closer to the source.

 Understanding average transport distance can help flag higher-risk suppliers where transport distance materially exceeds the average.

Reporting

Reporting Requirements

Proponent shall demonstrate understanding of:

  1. Average transport distance per delivered unit of material
  2. The transport distance from Proponent to all

 Reporting Recommendations

  1. Optimal transport distance should be modelled.
Guidance

Guidance for Reporting Requirement 1

Traditionally, the biomass industry has defined the supply basin as an area within an 80-km drive. This definition differs between various feedstock types. Generally, agricultural residue and energy crop supply basins are smaller than woody biomass woodsheds.

Guidance for Reporting Recommendation 1

Optimal transportation distance is dependent on locations of competitors with respect to suppliers. Since suppliers prefer to deliver to buyers located closer, the optimal transportation distance decreases with higher density of biomass consumers. Transportation optimization models can be used to determine the most optimal mix of suppliers from a transportation cost perspective. An example of feedstock transportation optimization model for a biomass project can be found in Roni et al. (2014a, b).

If average distance is high Proponent may explore possibility of diversifying feedstock type to enable sourcing from closer proximity.

Guidance Source

Roni et al. (2014b); Dujmovic (2019, feedback)

3.5.3 DIESEL

Rationale

Changes in diesel cost impact transport cost over time. Sensitivities to worst case scenarios should be run.

If transport cost is not indexed for diesel or risk of diesel price increase shifts to suppliers, supplier margins can be stressed, and supply chain reliability can suffer as a consequence.

Reporting

Reporting Requirements

  1. The impact of realistic variations in diesel cost shall be evaluated for a 10-year period.
  2. Long-term supplier contracts should contain an index accurately reflecting the local price of diesel.
Guidance

Guidance for Reporting Requirement 2

Diesel price adjustments should be made quarterly or bi-annually.

Guidance Source

Solomon (2018, interview); US Diesel Index Sample; Canadian Diesel Index Sample; Dujmovic (2019, feedback); Gunn (2019, feedback)

3.5.4 TRANSPORT OF FEEDSTOCK REQUIRES SPECIALIZED EQUIPMENT

Rationale

Requirements for specialized transport equipment (e.g., walking-floor trailers) can increase supply chain risk. Where there is low availability in required transportation equipment, equipment owners have increased leverage over transportation prices and supply chain resiliency can be lower.

Reporting

Reporting Requirements

  1. Understanding of the Project’s dependence on specialized transportation equipment and its availability in the supply chain shall be demonstrated.
  2. Where the redundancy of required transportation equipment is low, a transportation equipment shortage mitigation plan shall be demonstrated that includes:
    • Purchase of own transportation equipment
    • Adjustment of receiving infrastructure to lower transportation equipment requirements.
Guidance
Guidance Source

3.5.5 DELIVERY ROUTES THROUGH LOCAL COMMUNITIES

Rationale

Transportation of biomass can become a nuisance to local communities, especially if a large number of trucks pass through residential and school areas. Local communities often have power to force regulations regarding truck transport, impeding a Proponent’s ability to transport feedstock. This risk is greater in greenfield projects than operational ones.

Reporting

Reporting Requirements

  1. Understanding of feedstock transportation routes in relation to current and planned residential developments shall be demonstrated, particularly in relation to downtown, residential or school areas.
  2. Assessment of feedstock transportation nuisance to local communities and an associated risk of community backlash shall be made.
  3. Where the risk of community backlash is significant, a mitigation plan shall be demonstrated, including alternative delivery routes. Overweight permits and increased feedstock load density can function to reduce number of deliveries.
Guidance
Guidance Source

Daly & Halbleib (2017, interview); Tan (2018, interview); Dujmovic (2019, feedback)

3.5.6 TRANSPORTATION REGULATIONS AND LOAD WEIGHT LIMITS

Rationale

In many regions, transportation is regulated based on seasonal road conditions. These regulations (e.g., “frost laws”) often take the form of weight restrictions or limits on the number of trucks allowed on roads. Such regulations can impede the project’s ability to source sufficient feedstock or increase the cost of doing so at certain times of the year.

Reporting

Reporting Requirements

  1. Understanding of relevant feedstock transportation regulations, especially load weight limits and number of trucks allowed on roads, shall be demonstrated, including those involving:
    • Seasonality (e.g., “frost laws”)
    • Jurisdictions
    • Proposed changes to regulations.
  2. Where feedstock transportation restrictions pose a significant risk to the Proponent, a mitigation plan shall be demonstrated, including:
    • The potential to acquire regulatory exemption
    • Alternative delivery methods (e.g., use of smaller trucks)
    • Increasing biomass inventory piles in advance of expected seasonal restrictions.
Guidance

Guidance for Reporting Requirement 1

All Canadian provinces and territories have increased trucking load restrictions during spring thaws, with variations based on region, type of road, specific dates and duration. For example, Ontario roads are divided into four schedules by the Highway Traffic Act (1990). More restrictive weight limits can increase the number of deliveries required to acquire feedstock, resulting in feedstock cost variation. 

Regulations can change jurisdictionally on municipal and provincial levels. Route planning can be conducted according to these jurisdictional differences.

Guidance for Reporting Requirement 2

Certain municipalities, such as small towns seeking economic development, can sometimes be incented to provide regulatory exemptions to feedstock transport.

Guidance Source

Tudman & Hvisdas (2018, interview)

3.5.7 ROAD INFRASTRUCTURE

Rationale

Feedstock cost and availability can be a function of road infrastructure, in particular the accessibility the infrastructure provides to feedstock. Issues with road networks will translate directly to risks to feedstock supply.

Reporting

Reporting Requirements

  1. Understanding of transport supply routes and their effect on feedstock cost and availability shall be demonstrated, including understanding of weather patterns and their effect on road conditions.
Guidance

Guidance for Reporting Requirement 1

Paved roads are preferable to unpaved and resource roads, especially in wet seasons. Some roads can become impassable during heavy rainfall and snowfall conditions (Huhnke 2017).

Guidance Source

Gan & Smith (2011); Huhnke (2017, interview); O’Leary (2017, interview)

3.5.8 BACKHAULS

Rationale

In some cases, Proponent’s transportation economics can be dependent on other non-related industries such as using backhauls to make feedstock economically viable. If viability of transport depends on backhauls, supply chain risk is elevated (Davis 2018). If the backhaul is lost, transport costs can spike or supplies may disappear altogether.

Reporting

Reporting Requirements

  1. Understanding of the degree to which feedstock transportation economics depend on backhauling shall be demonstrated.
Guidance

Guidance for Reporting Requirement 1

“Backhauling” refers to an arrangement where the hauler maximizes transportation efficiencies by filling the truck at or near the point of discharge, preventing an empty return. Since the hauler earns margin both ways, the transport cost on the “head-haul” is reduced.

Guidance Source

Davis (2018, interview)

3.5.9 TRANSPORTATION REDUNDANCY

Rationale

Transport equipment redundancy is important for dealing with seasonally variable feedstock supplies as well as the risk of equipment breakdowns.

Reporting

Reporting Requirements

  1. Understanding of transportation redundancy in the supply basin shall be demonstrated in relation to Proponent’s feedstock requirements and to feedstock production.

 Reporting Recommendations

  1. Where transportation equipment redundancy is low, a mitigation plan should be demonstrated.
Guidance

Guidance for Reporting Requirement 1

Delivery of feedstock after harvesting/processing is often time sensitive, especially when dealing with seasonal feedstocks. Transportation systems should have the capacity to transport increased volumes of seasonally variable feedstock. Benchmarking should involve an analysis of worst-case scenarios.

If risks are high, Proponent may specify use of rental equipment from businesses that have a demonstrably adequate supply of similar equipment to assure redundancy. Particularly high risks can be mitigated by Proponent purchasing transport equipment, or by supporting the purchase thereof by third-party transport companies.

Guidance Source

Cook (2018, interview); (Marsollek 2018, interview)

3.6.1 NUMBER, SIZE MIX AND LOCATIONS OF SUPPLIERS

Rationale

In general, a supply portfolio involving multiple suppliers of various sizes (and from multiple regions) is important for ensuring steady and uninterrupted feedstock supply with minimal price fluctuations. If a small number of large suppliers provides a high proportion of total feedstock, a disruption or supplier breach will have greater impact on the supply chain. In such cases the risk of disruption is lower but the impact of those disruptions is higher. Conversely, a large number of small suppliers are less likely to have the capacity to withstand internal disruptions and thus may be more likely to breach. Here, risk of disruption is higher but their likely impact is lower. The number of suppliers as well as the ratio of small to large suppliers should be optimized.

There is no pre-determined number or optimal ratio of suppliers, although having too many or too few can both pose higher degrees of risk.

Reporting

Reporting Recommendations

Proponent shall demonstrate understanding of:

  1. Minimum number of feedstock suppliers required to minimize feedstock supply risk
  2. Maximum proportion of Proponent’s total feedstock requirement that one supplier controls
  3. Optimal number of small and large suppliers in the supply chain
  4. Impact of geographic locations of suppliers (i.e., supplier density) on supply chain resilience.
Guidance

Guidance for Reporting Recommendation 1

Current best approaches to modelling biomass supply chains are based on agent-based models (Hartley 2017). Examples of such models can be found in De Meyer et al. (2014); Cambero & Sowlati (2014); An & Searcy (2012); Dunnett et al. (2007); and Gharder et al. (2016).

Scenario-based models can be used to test various supply chain configurations and determine the lowest risk configuration. Supply basins should consist of at least 15-20 suppliers to adequately de-risk the supply chain (Rainey 2017).

Guidance for Reporting Recommendation 2

In general, risks can be elevated if more than 1/3 of the total supply is provided by a single supplier.

Guidance for Reporting Recommendation 3

Woody Biomass. Opinions vary with regards to the optimal mix of small to large suppliers. For example, O’Leary (2017) suggests that 1/5 of suppliers should be relatively large, while others say the ratio of small to large suppliers should be closer to 1:1

Guidance for Reporting Recommendation 4

Sustainable and efficient operation of a biomass supply chain is highly dependent on the underlying spatial and temporal components, even for reasonably small supply basins.

Guidance Source

An & Searcy (2012); Cambero & Sowlati (2014); De Meyer et al. (2014); Dunnett et al. (2007); Freppaz et al. (2004); Gan & Smith (2011); Gebreslassie et al. (2012); Gharderi et al. (2016); Golecha & Gan (2016); Hartley (2017, interview); Howes (2018, interview); Jenkins (2017, interview); Nguyen (2017, interview);

O’Leary (2017, interview); Passmore (2017, interview); Rainey (2017, interview); Smith (2017,

interview); Webster (2017, interview); Jenkins (2017, interview)

3.6.2 SUPPLIERS SUBJECT TO SAME EXTERNAL RISK FACTORS

Rationale

When a single risk event can impact the feedstock production ability of all (or most) suppliers, then feedstock risk is higher and supply chain resiliency is lower. Resilience is maximized when biomass supply chains exhibit diversity in spatial location (i.e., geography), production practices and other elements of supply chain structure such that the impact of single high-risk events have varying impacts on suppliers.

Reporting

Reporting Requirements

Proponent shall demonstrate understanding of:

  1. The set of common risks that impact over 75% of the supply chain by feedstock
  2. Factors that mitigate the set of common risks, including:
    • Spatial location (i.e., degree to which suppliers are clustered together)
    • Soil composition (i.e., drainage profile)
    • Land ownership
    • Supply chain structure
    • Regulatory zones
  3. Proportion of suppliers and supply that are subject to the same external risk factors
  4. Maintenance of suitably sized inventory piles and pre-arrangements for redundant feedstock with non-local suppliers where a high proportion of supply is subject to the same external risk factors.
Guidance

Guidance for Reporting Requirement 2

The more geographically compressed suppliers are in the supply chain, the more likely they are to be subject to a single risk. For example, suppliers who are clustered together in a particular area could all be subject to soil condition risks (e.g., sandy versus clay-based soils) that may affect soil drainage and thus limit the ability for forestry operations to access sites after severe rains due to flooding. Having suppliers operating in areas with both types of soils can mitigate the risk of supply disruptions due to excessive rainfall.

It is acknowledged that mitigation can be difficult for common occurrences that can lower the resiliency of the entire supply chain. Examples of these include:

  • Sawmills dependent on the housing market
  • Stover producers dependent on the markets for corn
  • Energy crop producers dependent on rainfall.

Guidance for Reporting Requirement 4

Feedstock inventory, whether at the facility yard or in satellite storage depots, acts as a buffer to supply chain disruptions.

Guidance Source

3.6.3 LAND OWNERSHIP STRUCTURES

Rationale

The ownership (or control) of the land base on which feedstock is produced can have significant impact on Proponent’s feedstock risks. Risk of long-term variation in stumpage cost for wood fibre (i.e., the cost that one pays to a land-owner for the right to cut and purchase their wood fibre) for example are much higher in the US where >90% of the land is private, and thus stumpage cost is determined on a competitive auction basis. Conversely, in Canada >90% of the land is owned by the Crown and stumpage is allocated by the government.

Reporting

Reporting Requirements
Proponent shall demonstrate understanding of:

  1. Land ownership structures for the procured feedstock, including ownership/lease terms with growers and any changes in ownership/lease terms
  2. Impact of land ownership/control on overall long-term feedstock risk.
Guidance

Guidance for reporting requirement 1

Direct ownership of the feedstock production land by the Proponent or suppliers lowers the risk of feedstock supply.

In cases where supply chain depends on suppliers that do not directly own the land on which feedstock is produced, an understanding of relationship between supplier and landowners should be understood and history of previous feedstock supply disruptions be scrutinized.

Lands that are subject to government regulations or programs should be identified. Understanding land tenure is especially important on public lands, where access to feedstock is regulated by the government. Note that some feedstock supply risks may be lowered where feedstock is sourced from public lands. Government backed long-term contracts for fixed quantities of biomass can often be acquired. These regulations can be complicated, and limit or restrict access to feedstock.

It is beneficial to determine how recently an owner came to acquire the land. New owners tend to be more sensitive to price and less resilient to economic downturns. This can lead to inconsistent feedstock supply.

Guidance Source

Carollo (2017, interview); Curran (2017, interview); Hladik (2017, interview); Krigstin (2017, interview)