Bold goals

The Meat Institute has set a target for 100% of its members to deliver greenhouse gas (GHG) reduction targets approved by the Science-Based Targets (SBT) Initiative by 2030, among the Meat Institute’s comprehensive goals for continuous improvement across five focus areas aligned with global goals.

As of June 2023, 12 Meat Institute members have set or publicly committed to set SBTs. As these companies have conducted GHG inventories, many have noted a dearth of technical resources to help provide baseline instructions and answer common questions.

Resource and information gaps

To help address this gap and facilitate adoption of science-based GHG measurement and reduction practices throughout the meat sector, the Meat Institute and the United Nations Global Compact Network USA (UNGC USA) partnered to develop this detailed case study on conducting GHG emission surveys, authored by experts with direct experience in companies of varying sizes.

Practical tools

The first step in setting or committing to set an SBT is to conduct a thorough and accurate inventory of Scope 1, 2, and 3 emissions.

The GHG emission survey guide provides tangible support to help companies that have the desire but not the time or resources to apply SBT methodologies and develop GHG inventories without additional guidance.

The tool defines terms, provides direct citations of GHG Protocol and other relevant international standards, details data collection and calculation methodologies, provides alternative calculation methods, makes note of explanations and exceptions, and shares other resources that might be of assistance to companies conducting GHG inventories. The tool uses real-life data, methodologies, and structural details, with data randomized and company details anonymized to protect proprietary information.

Beyond the elements discussed above, the tool provides a concrete, real-life example that takes readers from defining a company’s operational and organizational boundaries to understanding and measuring its direct, indirect, and value chain emissions. This tool is meant to be an industry-specific, practical complement to the foundational training provided by the UNGC USA’s Climate Ambition Accelerator program.       
 

The Climate Ambition Accelerator is a six-month program designed to equip companies with the knowledge and skills to accelerate progress towards setting science-based emissions reduction targets aligned with the 1.5℃ pathway, putting them on a path towards net-zero emissions by 2050. In this program, participants will gain access to peer-to-peer learning opportunities, capacity-building sessions with industry experts, and on-demand training. At the end of the program participants will have a comprehensive understanding of the Science Based Targets Initiative (SBTi) and the net-zero concept. Additionally, they will have developed communication skills to motivate investors, corporate leadership, employees, and shareholders to set and meet Science Based Targets.        

Please contact Mallory Cannon, Program Associate, Environment and Climate at the UN Global Compact Network USA for more information.

Contributors to this effort

The Meat Institute is a founding partner of the Protein PACT for the People, Animals & Climate of Tomorrow, which unites organizations across the animal agriculture supply chain committed to a common vision for sustaining nutrient-dense animal-source foods for generations to come. Through the Protein PACT, the Meat Institute has set ambitious targets, including for 100% of its members to deliver science-based GHG reduction targets, and has pioneered sector-wide data collection and reporting.

We are the US Chapter of the United Nations Global Compact, the largest corporate sustainability initiative in the world. We are a powerful network of companies and stakeholders dedicated to advancing the Sustainable Development Goals. By connecting our partners with the resources of the greater UN, we support companies that are committed to fully integrating our principles of human rights, labor, environment, and anti-corruption into their business strategies and operations        

 

Nectar's mission is to make climate data accessible to every business on the planet. The company spun out of MIT's Sandbox program—which provides seed funding, mentorship, and tailored entrepreneurship education for MIT students—and builds tools for sustainability teams to avoid manual data tasks and focus instead on higher impact initiatives. They’ve worked with small businesses to Fortune 500 companies. After piloting with Meat Institute members to inform development, Nectar’s Scope 1 and 2 tool (nectarclimate.com) allows unlimited integrations for a single site and $1000 per site beyond the first (some members can record all data under their HQ site). Nectar’s Scope 3 tool (estimator.nectarclimate.com) allows up to 1000 financial transactions for free and up to 10,000 transactions for $2500. Visit nectarclimate.com to learn more, or contact nami@nectarclimate.com or emittenthal@meatinstitute.org with questions.

A note about printing

The html version of this document will not print well; it was produced specifically to be viewed in a browser. We understand that some people work best when reading from an actual sheet of paper, however, so pdf versions are available on request if you send an email to Eric or Ben.

Mobile combustion

Longmont Sausage’s purchases of fuel for the purposes of mobile combustion consist entirely of diesel fuel and gasoline. Emissions from mobile combustion are calculated in a two-step process:

1. CO\(_2\) emissions are a function of the amount of fuel burned, while
2. CH\(_4\) and N\(_2\)O emissions are a function of miles driven, with emission factors varying by the year the vehicles were manufactured.

Activity data for both of these measures—gallons and miles—were available. In each case, we multiplied the number of gallons of fuel by the appropriate CO\(_2\) emission factors, and then multiplied the number of miles driven by the appropriate CH\(_4\) and N\(_2\)O emission factors, to get the total CO\(_2\)e emissions in this category.

Stationary Combustion

Longmont Sausage burns four types of fuels in activities that fall under Stationary combustion: diesel fuel for backup power generation, natural gas and (in one plant) coal for heating or cooking, and wood chips to make smoked sausage. Invoices for purchases of each of these fuels contain quantity information in the appropriate units—and vendors are happy to provide summaries of quantities on request—so calculating emissions from stationary combustion is straightforward.

One non-obvious wrinkle bears mention. According to the EPA:

In the table below, accordingly, the CO\(_2\) factor is shown as zero.

Fugitive Emissions

The term fugitive emissions can be somewhat misleading, because in most other emissions contexts, fugitive means something like accidental or un-measured, and is used to refer to gases escaping from valves or pipe fittings. The GHG Protocol definition is broader:

Never mind the use of the term fugitive emissions within the definition of fugitive emissions. This category covers all gases released within the operational boundary that are not the result of actually burning some fuel.

Scope 1 Totals

An aside about the uncertain future of market-based accounting methods

The essential fungibility of electric power—once power enters the grid, we can’t tell where it came from—can produce some uncertainty when accounting for purchased clean power. How can anyone be sure whether the amount of clean power produced is equal to the amount of clean power claimed by companies in their emissions inventories? It gets even more complicated with instruments designed as carbon offsets or insets, so much so that the latest Land Sector and Removals Guidance document from the GHG protocol says this (in Annex B):

It isn’t clear whether the GHG protocol will continue to require market- and location-based reporting for Scope 2 emissions or allow market-based instruments in other Scopes. For now, we proceed as required.

Simplified example contrasting market- and location-based methods

A simplified example may help. Suppose a region produces 10 units of electricity and in doing so produces 15 units of emissions. Under the location-based approach, each unit of electricity purchased would account for \(\frac{15}{10}=1.5\) units of emissions.

Suppose further that 2 of these 10 total units of electricity generation require zero emissions to produce, and are sold through contractual instruments. Each unit of electricity purchased through these contracts would account for \(\frac{0}{2}=0\) units of emissions, naturally, in the market-based approach. What’s left over—called the “residual”—is 8 units of electricity and all 15 units of emissions; in the market-based approach, each unit of electricity not purchased according to a specific agreement is allocated \(\frac{15}{8}=1.875\) units of emissions.

More generally, in order to properly allocate all emissions from energy generation within a given region, we need three different emission factors:

1. The average emission factor: The average emissions per unit of energy purchased over the entire grid or sub-grid (renewable and non-renewable sources). (\(\frac{15}{10}=1.5\) in the example above)
2. The resource-specific emission factor: The emissions per unit of energy purchased from a specific resource and then purchased via a contractual arrangement. (\(\frac{0}{2}=0\) in the example above)
3. The residual mix factor: The average emissions per unit of energy not purchased from a specific resource. (\(\frac{15}{8}=1.875\) in the example above)

Average and residual mix factors exist for each grid and subgrid, while resource-specific emission factors are particular to a given energy production source.

Location-based calculations

Market-based calculations

Scope 2 Totals

Scope 1 and 2 Totals

Scope 3: Upstream value chain (Scopes 3.1-3.9)

The reference materials produced by the GHG Protocol are, in our opinion, excellent. In almost every case, the advice contained within is unlikely to lead practitioners down the wrong path.

The advice around getting started with Scope 3 calculations is—again, in our opinion only—an exception. Figure 1.2 in the GHG Protocol’s “Scope 3 Calculation Guidance” document (see margin) suggests companies start with a screening exercise and then employ spend-based methods only as a last resort: that is, only in the case where emissions are likely to be small and only total expense data is available. We propose there is a more efficient way to get the work done.

It is highly unlikely that Longmont Sausage—or any company, really—would be able to gather high-quality activity data from all primary and secondary suppliers. Therefore, at least part of Scope 3.1 and 3.2 calculations will have to employ lower-quality spend-based emissions assumptions. Given the way spend-based calculations are done, doing those calculations for all expenditures in the base year requires scarcely more work than doing them for only some expenditures. So rather than conduct a separate screening exercise and then a spend-based model, we took the following course:

1. For all expenses in base year, assign a North American Industrical Classification System (NAICS) industry code and a Scope 3 category, including differentiating Forest, Land, and Agriculture (FLAG) from non-FLAG activities
2. Using the EPA’s supply chain emission factors for US Industries database (and estimates of inflation since this database was calculated), estimate emissions for all base-year expenditures
3. Using these results as the screening exercise,
   • Remove those expenditures (either by Scope 3 category or by NAICS code, as appropriate) that warrant more accurate (that is, activity-based) emissions estimates
   • What remains can be split into Scopes 3.1 and 3.2 without any changes.

This is still a lot of work. Even for moderately-sized organizations without industry codes and hierarchies built into their vendor records system, it would be surprising if any one person (or even small group of people) knows what each vendor actually provides. But it provides the best screening tool available at a very small (marginal) cost, so we recommend it.

Scope 3: Downstream value chain (Scopes 3.10-3.15)

For downstream Scope 3 categories, the screening process was much simpler:

• 3.10 (Processing of sold products) and 3.12 (End of life treatment of sold products) were deemed material due simply to volume—rendering, hides, and packaging are all significant—but no simple spend-based estimates were possible, so activity-based calculations were required.
• 3.11 (Use of sold products) The emissions required to cook our products are material, but no simple spend-based estimates were available, so activity-based calculations were required.
• 3.13-15 (Downstream leased assets, Franchises, and Investments) were all out of scope because Longmont Sausage doesn’t have any activities that fall into these categories.

Scope 3.2: Capital goods

Like any other manufacturing company, Longmont Sausage keeps track of capital spend for many reasons, the most obvious of which is for depreciation and tax calculations. Therefore, 3.2 (Capital goods) was fairly straightforward to calculate.

Using the results obtained directly from the spend-based screening exercise detailed above—we simply subtract capital spend from Scope 3.1 calculations and move them to Scope 3.2—we get the following (here aggregated by NAICS group):

The GHG Protocol Scope 3 guidance provides several other methods to calculate emissions related to capital goods, though the spend based method is likely the most straightforward.

Scope 3.1: Purchased goods and services

Here are the results of the spend-based model for non-meat and -animal expenses:

We could have directly taken the results of the screening exercise detailed above—taking care to remove those items that are accounted for elsewhere—as our answer for all Purchased Goods and Services. Meat and animal purchases, however, warrant a detailed, activity-based treatment. For several reasons, a spend-based approach is sub-optimal:

• The price of meat varies according to a relatively volatile market, which has little to do with carbon emissions. If the price of pork (or beef or poultry) doubles, but nothing else changes, our emissions estimates shouldn’t change either. Using a spend-based approach built from data from any year other than the base year would therefore introduce significant error.
• Longmont Sausage buys and slaughters culled sows for use in our fresh sausage product. Because culled sows are a byproduct of a larger supply chain—pork production—lifetime emissions for these animals starts when the sow is culled (that is, taken out of production) and ends when the animal arrives at our plants. Not accounting for sow-related emissions this way would result in double-counting.
• Due to having to report FLAG emissions separately, we decided that an activity-based approach for these materials would be prudent anyway, irrespective of accuracy concerns noted above.

Scope 3.3: Fuel- and energy-related activities (not included in Scope 1 or 2)

The calculations for Scope 3.3: Fuel- and energy-related activities are simple, provided one can find the proper emission factors. The activity information—gallons of diesel fuel burned, etc—can be taken directly from Scope 1 and 2 calculations. Finding the correct emission factor isn’t always easy, but …

1. Transmission and Distribution losses—to be applied to electricity purchases—are available on a national-average basis from the World Bank.
2. Well-To-Tank (WTT) emission factors—applied to all purchased fuels—are available on an extensive worksheet produced by the United Nations Framework Convention on Climate Change (UNFCCC). This source contains mostly information from the UK, but it was the best source we could find.
3. Emissions from production of electricity—applied to all electricity purchases, on a grid-specific basis—are available from the EPA’s eGRID dataset, under the heading “eGRID subregion annual CO2 equivalent input emission rate (lb/MMBtu)”
4. Because emissions from our live animal purchases are almost entirely made up of fuel use during transportation, we chose to account for the WTT emissions from those fuel purchases here. The US Energy Information Administration publishes average miles per gallon for livestock trucks, which we converted to total gallons and used the same emission factors from #2, above.

Scope 3.4: Upstream transportation and distribution

Longmont Sausage has three primary activities that fall within Upstream Transportation and Distribution: freight on purchased raw materials, intracompany transportation between Longmont Sausage-owned facilities (in vehicles not owned by Longmont Sausage), and inbound freight on purchased live animals and trim meat materials.

Activity data for the first two categories, freight on purchased raw materials and inter-company freight, was not available so we deferred to the spend based approach to estimate our emissions. Many vendors include ‘freight’ or ‘delivery’ as a separate line item on their invoices and our Accounts Payable department isolates this specific spend in a general ledger account designated for inbound freight. Calculating emissions from these two upstream transportation categories was, as a result, straightforward: we multiply dollars spent by the corresponding emission factors from the Environmentally-Extended Input-Output (EEIO) database.

For inbound freight on purchased live animals, we calculated the distance from our live animal suppliers to our facilities and multiplied this value by the number of truckloads of meat received during the base year. We then multiplied this total truck miles value by the emission factors for Medium and Heavy Duty Trucks from the EPA Emission Factors Hub.

Emissions from outbound transport of render and hides (see 3.10 for more information related to emissions estimation from these activities) were calculated in the same way. We first find distance from our facilities to the destination for these materials, calculated the total number of truckloads based on base-year volumes, and applied these total miles to corresponding emission factors from the EPA Emission Factor Hub.

Scope 3.5 and Scope 3.12: Operational and consumer waste

According to the GHG Protocol standards, emissions resulting from the use of the sorts of packaging common in meat companies—anything that includes wasted packaging at the manufacturing plant, basically—should be accounted for in two separate places within Scope 3. Any backing films or trimmed packaging, which would be accumulated within a plant and then disposed of some way, belongs in 3.5: Waste generated in operations. Packaging that makes it to the consumer, however, belongs under 3.12: End of life treatment of sold products.

The distinction between who disposes of packaging materials is probably more salient in other industries, but it made sense for us to consider it all together: for example, all PVC film that Longmont Sausage purchases for its fresh sausage product ends up in the landfill—the EPA says 0% of PVC film actually gets recycled in the United States—whether Longmont Sausage or a consumer sends it there. Accounting for these streams separately would have required knowing the waste percentage for this material (and all other packaging materials), which isn’t information we collect. More importantly, doing so would miss the point. The same argument, making allowances for recycling rates that vary according to material, holds for all packaging materials.

For these reasons, below we break out three separate waste streams: Waste generated in operations except packaging (3.5), End of life treatment of sold products except packaging (3.12), and all packaging materials regardless of proximate source (3.5 + 3.12).

Scope 3.6: Business travel

Longmont Sausage’s business travel spend falls within two main categories: air travel and car rental. Our expense management software tracks this spend and other travel-related details: we used activity data (miles of air travel) and spend data (dollars of car rental spend) where appropriate to apply against the corresponding emission factors.

The air travel emission factors from the EPA Emission Factor Hub are categorized by the length of the flight: a short haul being less than 300 miles, a medium haul being between 300 and 2,300 miles, and a long haul being greater than 2,300 miles. Because our expense management system retains the departure and arrival cities for each flight purchased, including the route distance, we were able to easily categorize each flight as short, medium, or long haul and total the miles of each. The last step is to simply multiply the corresponding emission factors by the total number of miles.

Longmont Sausage does not require staff members to record mileage when using a rental vehicle; therefore, the only data we had available for this activity was the total dollars spent. To estimate the emissions output, we multiplied the total inflation-adjusted dollars spent by the emission factors related to ‘transit and ground passenger transportation’ from the Environmentally-Extended Input-Output (EEIO) database.

Scope 3.7: Employee commuting

Starting with a list of employee addresses and work location, we used Google Maps to calculate the exact commute distance for each employee. We then applied assumptions related to annual working days and salaried members who might work from home for part of the week. These total miles were then applied to the corresponding emission factors found in the EPA Emission Factor Hub.

Scope 3.8: Upstream leased assets

Longmont Sausage has no leased assets that fall in this category in the base year.

Scope 3.9: Downstream transportation and distribution

Longmont Sausage primarily uses over-the-road trucking and rail transport as a means of distributing finished goods to consumers. Our Distribution & Supply Chain team was able to provide the total number of distribution miles by transportation mode. Two complications bear mention:

• Intermodal transport involves moving freight by using two or more modes of transportation. At Longmont Sausage, this was largely a combination of road and rail transport but could include ocean, air, etc. It is important to know which modes are used as this will determine the correct emission factors to use.
• Approximately 30% of the road miles used to distribute Longmont Sausage product are considered ‘less-than truckload’ miles (i.e. the truck contained less than 100% of Longmont Sausage product). In these instances, we apportioned the total miles driven by the share of product on the truck. For example, if a truck that contained 40% of Longmont Sausage product drove 100 miles, we would only include 40 miles in our emissions calculation.

Either miles or spend data can be used to calculate emissions from downstream transportation. At Longmont Sausage, we were able to use the miles obtained from our Supply Chain team and apply the corresponding emission factors from the EPA Emission Factor Hub. As stated above, it was important to differentiate between road and rail miles so the correct emission factors could be used.

For emissions associated with distribution—that is, warehousing and storage activities—we used the corresponding item from our Scope 3 Spend-Based model.

Scope 3.10: Processing of sold products

The GHG Protocol defines intermediate products as ‘products that require further processing, transformation, or inclusion in another product before use’. Longmont Sausage has two intermediate products that fall within this categories: materials sent for rendering, and hides sent for processing into leather goods.

These are the sorts of activities that are common for the meat industry but uncommon everywhere else, so finding reliable emission factors took some work.

1. For hide processing, we found a study in the Journal of Industrial Ecology that provides an estimate of emissions from that process.       
    
2. For rendering, we were able to use a life-cycle analysis performed by our primary rendering customer to estimate emissions for all rendered materials.
3. We also repeated some earlier analysis to estimate the emissions resulting from transport of these materials from our plants to the ‘gate’ of the next process.

Longmont Sausage sells two sizes of sow hides, and tracks the total amount of sales for each in pounds. The emission factor for hide processing is denominated in square meters. So we need to convert from pounds to square meters as such…

… before applying the emission factor referenced above.

Scope 3.11: Use of sold products

It can be tempting to dismiss emissions resulting from the end use of products; cooking meat at home may seem like an insignificant activity when considered in the context of all the other activities detailed above. But given Longmont’s roughly 50-50 split between fresh and fully-cooked sausage, and the fact that natural gas purchases contribute ~25k MT of CO\(_2\)e Scope 1 emissions through their use in relatively efficient industrial ovens, one should be wary of this temptation.

There is no authoritative resource that gives us an emission factor for cooking sausage (or any kind of meat) at home. We know that cooking at home must be less efficient, so the value reported above (equivalent to about 0.5 kg CO\(_2\)e / kg pork) will serve as a lower bound for our estimate.

1. Frankowska et al suggest that the cooking phase makes up 25% of all emissions associated with eating pork, or 3.45 kg CO\(_2\)e / kg pork. Their study did not consider emissions from cooking food on a grill, rather, provides a weighted-average emission factor for pork cooked indoors.

2. Johnson did a study that concentrated specifically on cooking meat on a grill. He found that charcoal grills emit 2.16 kg CO\(_2\)e / kg meat, while LP grills emit 1.03 kg CO\(_2\)e / kg meat

3. In a follow-up study with different methods, Johnson switches the order of efficiency for charcoal (1.4 kg CO\(_2\)e / kg meat) and LP (1.95 kg CO\(_2\)e / kg meat) grills.       
    

All together, the best estimate currently available to us for the emissions associated with the Use of Sold Products is as follows:

This implies that our industrial ovens are ~4.6 times more efficient, from a strict emissions perspective, than the weighted-average home cooking method. This seems within the range of what we might expect, so we’ll proceed with these estimates.

Scope 3.13-15: Downstream leased assets, Franchises, and Investments

Longmont Sausage has no activities that fall within these categories in the base year.

Scope 3 Totals

Non-FLAG target setting

The target-setting tools provided by SBTi are quite simple and intuitive. Only the Scope 1 and Scope 2 totals, and Scope 3 non-FLAG totals, are required for the near-term target setting tool. These values are highlighted in green, below:

The trajectory of emissions from these sources that make up the actual SBTi commitment will depend on choices about base year, commitment length, and contraction approach. For Longmont Sausage, with a base year of 2021, target year of 2030, and using the Absolute Contraction approach, Scope 1 and 2 emissions will need to match this trajectory and will achieve a 42% reduction in absolute emissions:

With the timing and calculation approach assumptions, the non-FLAG Scope 3 calculation output looks like this (no charts are produced for the Absolute Contraction approach):

FLAG target setting

The FLAG tool is somewhat more complicated than the legacy ‘SBTi Target Setting Tool’ but still intuitive and quite helpful. For those activities that are in one of the nine named commodities for which we can use the tool’s default emission factors, all we need is total weight of purchase. For meat, total weight must be expressed in carcass weight (that is, bone-in) equivalents.

These values are highlighted in orange:

The FLAG tool also has non-commodity FLAG emissions as a required input.

The output of the FLAG target tool looks something like this (again, subject to certain decisions and parameters beyond the scope of this discussion):

Target submission form(s)

Once these tools have been used and targets have been calculated, we can move on to actual submission of our targets. As noted above, a detailed discussion of this process is outside the scope of this document. We can, however, outline the kinds of information required to complete the submission forms.

The SBTi Near-Term Target Submission Form and Guidance requires information in four general categories:

1. General Information: Identify the company, describe the business, declare any potential conflicts of interest (such as payments to SBTi for technical assistance)
2. GHG Inventory:
   • General questions: Verify that GHG Protocol guidance was followed, and describe emissions-producing activities, calculation approach, and exclusions (if any)
   • GHG inventory: Scope 1, 2 (market- and location-based), and 3 emissions, with Scope 3 emissions broken out by sub-category, for Base Year and Most recent year.
3. Target Information: Required emissions reduction targets taken from the target-setting tools, any other types of targets the applicant wants to include, as well as descriptions about how these targets are expected to be achieved.
4. Progress Reporting: Confirmation that the company will annually report progress towards these commitments.

The Forest, Land, and Agriculture annex requires the same sort of information, but restricted to FLAG-only activities:

1. General Information: Policy and calculation approach questions, and confirmation of No Deforestation commitment where applicable
2. FLAG GHG Additional Inventory Data: Output from target-setting tool, plus volumes (for intensity calculations) and references.

Authors and Acknowledgements

Ben Peyer

Ben is a corporate sustainability and animal welfare executive with extensive experience helping meat companies devise and implement ambitious sustainability strategies. He can be reached at bpeyer@gmail.com.

Jenine Rinn

Jenine's mission is to elevate meat industry standards in the race to minimize our shared environmental impacts. She and her husband founded Sonoma County Meat Co. in 2014 in Santa Rosa, California. She is working create a more sustainable local food system that provides food security and environmental benefits to uplift the overall health of the region. She can be reached at jenine@socomeatco.com.