The science behind spend-based emission factors

Spend-based emission factors have a bad reputation. But with the right calibrations, they make for an easy and powerful starting point to measure Scope 3 emissions.

Spend-based emission factors are often considered a second-best option, especially compared to activity-based measurements. Critics point out that measuring emissions by spend can come with shortcomings: 

• Firstly, they can be too generic, ignoring differences between products, services, suppliers, industries, countries, and geographies. 
• Secondly, spend is a bad proxy: When your vendor offers you a discount, your emissions decrease, while, in reality, nothing has changed.  

However, spend-based factors are better than their reputation, as long as they are applied with the necessary calibrations, which we will explain in detail below. In this case, they provide companies with an easy starting point to assess their emissions, particularly Scope 3, which otherwise can be a laborious task to assess. 

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The power of spend-based factors

Spend-based assessment’s biggest advantage lies in its pragmatic nature. While the alternative to spend-based emission factors—activity-based emission estimates—are more accurate, the collection and cleaning of all data points is difficult and cumbersome. Due to this complexity, most companies are unable to follow the activity-based approach. As a result, a thorough spend-based approach makes for a powerful alternative.

Here is what we at Climatiq specifically like about the spend-based approach: 

• Companies are generally organised around cost and value. Accounting or procurement departments generally have a solid understanding of which goods or services are purchased across their supply chains and how much money is spent.
• Such data, combined with spend-based emission factors, provides a great first approximation of a company’s Scope 3 carbon footprint. 
• Spend-based emission factors are easy to use. They provide an average level of emissions per unit of currency through high-level modelling. In such a scenario, the modelling assesses the total money spent in a given sector of the economy, the total emissions generated by this sector, and the impact of the trade of goods into and out of the economic region in question..

With all this in mind, the spend-based approach can be a powerful way to measure emissions, as long as the “science” behind it is carefully considered and applied. 

Let’s take a closer look at how spend-based emission factors work. As well, we can determine how companies can mitigate the limitations that come with this approach.

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How are spend-based emission factors modelled? 

Spend-based emission factors are estimated using a) Input-Output Models that are b) Applied internationally and then c) Environmentally Extended (“EE MRIO”) What exactly does this mean?

• Input-Output Analysis (IOA) is an analytical framework that helps to describe, understand and analyse inter-industry dependencies in an economy; originally developed in 1930 by Leontief who was awarded a Nobel Prize for economics in 1976 for his work on econometrics. This analytical method is fundamental for many economic models and frameworks and is based on national accounting matrices known as input-output tables (IOTs) (see Miller and Blair, 2009).

• Multi-Regional (MR) refers to models that contain IOTs corresponding to many countries, often covering a global scale. These global models can describe entire global supply chains. 

• Environmentally Extended (EE) are economic models complemented with environmental variables, which quantify associated impacts of economic activities, such as the impact on water consumption, energy use, and air pollution, including GHG emissions, which are used to derive the spend-based EFs. 

In a broad sense, EE MRIO models represent a snapshot of the global economy in a given year—for more details on how an IOT is constructed, please refer to Miller and Blair, 2009. 

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Let’s make this more concrete

Figure 1 provides a graphical representation of an IOT, which illustrates the inter-industry monetary flows between the 30 economic sectors in China, developed by Mi et al., (2018).

• The rows correspond to the output of a specific sector.
• The columns are associated inputs to generate the sector’s output.
• The orange colouring corresponds to the largest monetary flow and the blue colour to the lowest.

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How to read this table: the sector in column nine, for example, refers to the “Wood and Furnishings” sector. It has the strongest economic ties to sector one, Agriculture and Furnishings, as well as itself, meaning a lot of its products are used by other industries within the same sector. 

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How are spend-based emission factors calculated?

In the environmental extension of IOTs emission data is added to the economic output data for each sector. Combining monetary flows with emissions data then allows us to create spend-based emission factors. 

We now know how much monetary flow was generated in sector one and sector nine, how much sector one has contributed to sector nine, and how many emissions were generated in sector one and sector nine. As a result, we can derive spend-based emission factors by allocating the GHGs according to monetary flows. These emission factors are generally called “multipliers.” 

In scientific terms, these multipliers are represented by the square matrix ĝ in the following equation:

• The matrix ĝ contains emissions factors that are multiplied to final demand, represented by matrix “Y” in order to obtain a matrix of total GHG emissions by sector represented by “G.” 
• Matrix ĝ is obtained by multiplying direct emissions intensities (i.e., emissions per unit of output like Euro) to the so-called “Leontief” matrix. 
• For other basic MRIO calculations look at PyMrio documentation and for more details, refer to Miller and Blair (2009).

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How are spend-based emission factors represented?

Obtained spend-based emission factors are expressed in basic prices (price at the factory gate without taxes) and incorporate upstream emissions, therefore, accounting for the effects of global trade. The use and end-of-life phases are excluded.

IOA is useful to compare consumption and production-based emissions, for example, in the work of Wood et al. (2019), who examined the structure of the carbon footprint and carbon exports of Europe. Peters (2008) is another great example of this scenario.

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With all this in mind, EE MRIOs allow us to create emission factors at the industry and product levels. 

• Industry-level emission factors are based on survey data. 
• Researchers then disaggregate different industries into their products to account for intra-industry flows. 

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The different types of EE MRIOTs 

To account for differences across regions and economic sectors, there are several different IOTs available, such as WIOD (Timmer et al. 2015), EXIOBASE3 (Stadler et al. 2018), GTAP (Andrew and Peters, 2013), EORA (Lenzen at al. 2013).

Climatiq API offers a selection of spend-based EFs derived from the following IOTs:

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Let’s talk about limitations and uncertainty

EE MRIOTs are powerful tools to understand the structure of global trade, as well as the environmental impact across industries and countries. Spend-based carbon footprinting approaches and IOTs are increasingly gaining industry interest due to their simplicity, freely available datasets, and often fine geographical and industrial resolutions. 

However, as a conceptual representation of reality, IOTs come with several limitations that need to be considered.

Here are some of the major sources of errors:

• Homogenisation: IOTs are synthetically constructed databases from several underlying data sources and different methods of homogenisation can introduce uncertainty into final outputs (see Timmer et al., 2015)
• Input data uncertainty: Emissions data for the Environmental Extensions of the IOTs are usually obtained from UNFCCC National Inventory Reports or verified open databases like EDGAR (Crippa et al. 2022). Macroeconomic data is usually obtained from national statistics reports for each country. The reliability of such data can vary from region to region, which directly impacts the calculation of EFs. 
• Heterogeneity of data quality: Some countries, because of the limited data availability, are aggregated into “Rest of the World regions,” sometimes covering entire continents. For example, in EXIOBASE, Africa (other than Egypt and South Africa) is considered a single region. This affects the granularity of derived emission factors. 
• Industry/product averages: Spend-based emission factors are based on the average emission intensity of all industry/products in a given category. In reality, of course, emissions vary by product and supplier. 

In the case of EXIOBASE, which contains more than 8000 entries for each year, assessing the quality of each data point can be complex. At Climatiq, we compare data points within and across categories and geographies in order to detect entries that should be used with caution. We add quality flags to such data. 

You can read more about data quality flags and methods behind the outliers detection here. 

Due to their imperfection, several studies have looked at the uncertainty of IOTs: 

• Moran and Wood (2014) found that carbon footprint results for most major economies disagreed by less than 10% between different MRIOs, including EORA, EXIOBASE and WIOD. 
• Steubing et al. (2022) compared results of LCA-based assessment for different products and industries, based on ecoinvent data, to footprints obtained from EXIOBASE. They have found positive agreement between the two approaches for some sectors, such as agriculture, energy (except for renewable energy), and manufacturing, but poor agreement for the waste and mining sectors: Compared with EXIOBASE, carbon footprint results obtained with ecoinvent: 

• -- were lower for the agricultural sector (-9%) and the manufacturing sectors (-12%), 
• -- were higher for the energy sector (+16%) and even higher for the mining (+43%) and water supply/waste (+90%) sectors.

• In general, they conclude that assessing a carbon footprint using EXIOBASE compared to ecoinvent yields large differences. Despite being a positive match for some sectors, more than half of the product footprints calculated differed by a factor of 2 with carbon footprints calculated from ecoinvent being higher in more than 50% of cases. For more details, refer to the original work of Steubing et al. (2022). 

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How to apply spend-based emission factors 

Now, let’s clarify the ultimate question of how to apply spend-based factors. 

Input-Output analysis is a powerful tool to understand supply-chain emissions and can be used by companies to conduct the first approximation of their Scope 3 emissions. Frameworks such as the GHG Protocol, PCAF, TCFD, alongside others, encourage the usage of spend-based methods in a scenario where activity data is absent or difficult to obtain, bearing in mind their limitations. They are ideal as a first assessment to understand hotspots of emissions, which, where possible, should be assessed with activity-based emission factors. 

Spend-based emission factors are always drawn from a global representation of emissions. They provide a solid, high-level assessment but cannot differentiate or depict nuances between products or suppliers at the local level. 

At Climatiq, we ensure that all data provided has passed our quality controls to ensure that, where activity data is not available, spend-based emission factors can be applied with confidence. 

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Acknowledgement

We thank Dr. Marco Sakai at the University of York for reviewing and contributing to this article. 

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References

Miller, R.E. and Blair, P.D. (2009) Input-Output Analysis: Foundations and Extensions. 2nd Edition, Cambridge University Press, Cambridge.

Timmer, M. P., Dietzenbacher, E., Los, B., Stehrer, R. and de Vries, G. J. (2015), "An Illustrated User Guide to the World Input–Output Database: the Case of Global Automotive Production" , Review of International Economics., 23: 575–605

Daniel Moran & Richard Wood (2014) CONVERGENCE BETWEEN THE EORA, WIOD, EXIOBASE, AND OPENEU'S CONSUMPTION-BASED CARBON ACCOUNTS, Economic Systems Research, 26:3, 245-261, DOI: 10.1080/09535314.2014.935298

Mi, Z., Meng, J., Zheng, H. et al. A multi-regional input-output table mapping China's economic outputs and interdependencies in 2012. Sci Data 5, 180155 (2018). https://doi.org/10.1038/sdata.2018.155 or https://www.nature.com/articles/sdata2018155.pdf 

Richard Wood, Karsten Neuhoff, Dan Moran, Moana Simas, Michael Grubb & Konstantin Stadler (2019): The structure, drivers and policy implications of the European carbon footprint, Climate Policy, DOI: 10.1080/14693062.2019.1639489

Peters, G.P. From production-based to consumption-based national emission inventories. Ecol. Econ. 2008, 65, 13–23.

Lenzen, M., Moran, D., Kanemoto, K. and Geschke, A., 2013. Building Eora: a global multi-region input–output database at high country and sector resolution. Economic Systems Research, 25(1), pp.20-49.

Robbie M. Andrew & Glen P. Peters (2013) A MULTI-REGION INPUT–OUTPUT TABLE BASED ON THE GLOBAL TRADE ANALYSIS PROJECT DATABASE (GTAP-MRIO), Economic Systems Research, 25:1, 99-121, DOI: 10.1080/09535314.2012.761953

Ingwersen, W. AND M. Li. Supply Chain Greenhouse Gas Emission Factors for US Industries and Commodities. U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-20/001, 2020.

Crippa, Monica; Guizzardi, Diego; Muntean, Marilena; Schaaf, Edwin; Monforti-Ferrario, Fabio; Banja, Manjola; Pagani, Federico; Solazzo, Efisio (2022): EDGAR v6.1 Global Air Pollutant Emissions. European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/df521e05-6a3b-461c-965a-b703fb62313e

Steubing, B., de Koning, A., Merciai, S. and Tukker, A., 2022. How do carbon footprints from LCA and EEIOA databases compare?: A comparison of ecoinvent and EXIOBASE. Journal of Industrial Ecology.

Stadler, K., Wood, R., Bulavskaya, T., Södersten, C., Simas, M., Schmidt, S., Usubiaga, A., Acosta-Fernández, J., Kuenen, J., Bruckner, M., Giljum, S., Lutter, S., Merciai, S., Schmidt, J. H., Theurl, M. C., Plutzar, C., Kastner, T., Eisenmenger, N., Erb, K., Koning, A., Tukker, A., 2018, EXIOBASE 3: Developing a Time Series of Detailed Environmentally Extended Multi-Regional Input-Output Tables. Journal of Industrial Ecology.