Transaction to Carbon
Methodology to estimating Carbon Intensity Factors
Estimating the carbon intensity factors
In the Open standard framework for consumer carbon calculations based on payment transactions, emission intensity is measured in grams of CO2e emitted per monetary unit spent in a specific activity and country, which is the emission rate of a given pollutant relative to the intensity of a specific activity or an industrial production process (Du et al., 2018). The concept has been mostly used for energy analysis, for example, grams of carbon dioxide equivalent released per megajoule of energy produced (Ali et al., 2022; Cheng et al., 2018; Hocaoglu & Karanfil, 2011; Zhu et al., 2014), or the ratio of greenhouse gas emissions produced to gross domestic product (Davis & Caldeira, 2010; Garrone & Grilli, 2010; Hocaoglu & Karanfil, 2011).
Here, we provide a summary of Step 2, estimating carbon intensity factors from the Open Standard; thus, the term “user” refers to the company, entity, or organization implementing the Standard; “end-user” refers to the individual using the application or bank interface, using the information generated when implementing this Standard. Further information regarding the rationale of the estimation is provided in the Open Standard, and the methodology document.
The following figure outlines the five-step approach for calculating a person’s transaction-to-carbon footprint.
Figure 1. A five-step process for estimating carbon intensity factors
Selection of the GMRIO table
Global Multi-Regional Input-Output (GMRIO) provides a detailed understanding of the global economic system. As mentioned in Section 3.9, global GMRIO models are widely used to analyze the economic interdependencies between regions in the context of global trade and environmental research. GMRIO has proved useful for describing and understanding supply chains and relationships between the consuming and producing sectors (Huo et al., 2022). Thus, it is the preferred approach for estimating transactions to carbon. MRIO tables connect the sectors in different regions along the supply chain and track both direct and indirect impacts of global production (Huo et al., 2022; Tukker et al., 2020).
Several GMRIO tables are available with different scopes and reach; each GMRIO varies in the country coverage, sectors, and products under analysis (Figure 2). The selection of the GMRIO from which the emission factors will be derived depends on the country of analysis, industry and sector coverage, assumptions, and the latest year available.
Figure 2. Characteristics of existing GMRIOs | Source: (Huo et al., 2022; Mangır & Şahin, 2022; Tukker et al., 2020). For further information on OECD ICIO, see OECD.Stat (2022), GTAP see (Purdue University (2019), EXIOBASE see Stadler et al. (2018), WIOD see Dietzenbacher et al. (2013) and EORA see KGM & Associates Pty. Ltd. (2023).
Several authors have performed an in-depth analysis of the limitations and advantages when applying the different GMRIOs (Dawkins et al., 2019; European Commission. Statistical Office of the European Union., 2021; Huo et al., 2022; Steubing et al., 2022; Tukker et al., 2020), the methodology document presents a summary for the GMRIOs in Figure 2.
Carbon intensities for household consumption categories
To provide a carbon intensity factor for different consumption categories, we use a combination of process-based and input-output approaches as applied by Berners-Lee (2020). Referencing the data available for the European Union, where the EXIOBASE is available, we provide 51 categories (Figure 3) to cover the whole household consumption spectrum. The total number of consumption categories is not fixed as it depends on the quality and data availability specific to the country under study.
Figure 3. Household consumption categories indicate the source of information for the carbon intensity factors.
The calculation rationale and steps to calculate each category are explained in the methodology document.
Capturing individual preferences through end-user engagement
Refinements aim to increase the precision of carbon footprint and increase customer satisfaction. Refinements capture individual preferences; thus, successful implementation requires end-user engagement. The user can refine the estimation by capturing individual preferences regarding diet, energy at home at type of car owned. An adjustment factor is calculated based on individual preferences, which is then understood as a numerical factor to obtain the carbon intensity values of the refinement options from the base value. It adjusts the baseline emission intensity to reflect better users’ consumption preferences.
The carbon footprint associated with groceries is refined by considering individual diet preferences; the refinement is done by applying adjustment factors derived from the research led by Kim et al. (2020), which modeled the greenhouse gas and water footprints of nine diets aligned with criteria for a healthy diet specific to 140 countries. This scientific publication estimates footprint reduction when shifting between diets and carbon and water footprints of different food categories per serving, kilocalories, protein content, and edible kilograms. The model considers trade flows when addressing the environmental impact of national consumption patterns. Furthermore, the GHG and water footprints of international food items are attributed to countries where the food is consumed, focusing accountability on the population responsible for changing demand. The diets identified by the study are then provided as options with adjustment factors developed for each country’s scenario. The complete scope of refinements can be found in the shared upon request here.
Update of the estimations
Estimations should be reviewed and updated yearly. Some estimations will require revision in six months, depending on the data source. The developing team decides this, and a date for the following revision is agreed upon.
- Due to data collection difficulties and data compilation constraints, many existing MRIO databases (Tukker & Dietzenbacher, 2013) do not release annual MRIO tables. This impedes the ability to analyze historical supply chain data and international trade patterns to forecast future trends.(Huo et al., 2022; Huysman et al., 2016).
- The resolution of MRIO assessments is limited to a number of sectors. Anything more detailed requires additional data and deeper analysis. The National Footprint and Biocapacity Accounts have continuous time series from 1961 onwards, while GTAP data are limited to four years (2004, 2007, 2011, 2014).
- MRIOs have different scopes in industries and products; thus, values are not always comparable.
- Constant prices for categories are estimated using the functional units when monetized. Average prices for the last 12 months are used; thus, price differences over time are not considered.
- Data inconsistencies across time and countries, ranging from changes in classifications to modifications of the underlying accounting concepts over time. Understandably, the statistical agencies improve their approaches over time. Still, if the statistical agencies do not revise the older time series according to the new concepts, the time series are not directly usable for analysing structural changes over time (Stadler et al., 2021a).
- Language barriers when dealing with non-European national statistical agencies ‘data. Data available in the original language and English differ, so local knowledge becomes essential for appropriate data usage.
- Lack of transparency regarding the source of information used for the calculation when applying the Transaction to Carbon Approach; the user shall indicate which GMRIO is using as a reference to enhance trust from the end-user.
Selected references; the complete list of references is in the Open Standard and methodology document.
Huo, J., Chen, P., Hubacek, K., Zheng, H., Meng, J., & Guan, D. (2022). Full-scale, near real-time multi-regional input–output table for the global emerging economies (EMERGING). Journal of Industrial Ecology, 26(4), 1218–1232. https://doi.org/10.1111/jiec.13264
Huysman, S., Schaubroeck, T., Goralczyk, M., Schmidt, J., & Dewulf, J. (2016). Quantifying the environmental impacts of a European citizen through a macro-economic approach, a focus on climate change and resource consumption. Journal of Cleaner Production, 124, 217–225. https://doi.org/10.1016/j.jclepro.2016.02.098
Ivanova, D., Vita, G., Steen-Olsen, K., Stadler, K., Melo, P. C., Wood, R., & Hertwich, E. G. (2017). Mapping the carbon footprint of EU regions. Environmental Research Letters, 12(5), 054013. https://doi.org/10.1088/1748-9326/aa6da9
Kim, B. F., Santo, R. E., Scatterday, A. P., Fry, J. P., Synk, C. M., Cebron, S. R., Mekonnen, M. M., Hoekstra, A. Y., de Pee, S., Bloem, M. W., Neff, R. A., & Nachman, K. E. (2020). Country-specific dietary shifts to mitigate climate and water crises. Global Environmental Change, 62, 101926. https://doi.org/10.1016/j.gloenvcha.2019.05.010
Stadler, K., Wood, R., Bulavskaya, T., Södersten, C.-J., 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.-H., … Tukker, A. (2018). EXIOBASE 3: Developing a Time Series of Detailed Environmentally Extended Multi-Regional Input-Output Tables. Journal of Industrial Ecology, 22(3), 502–515. https://doi.org/10.1111/jiec.12715
Stadler, K., Wood, R., Bulavskaya, T., Södersten, C.-J., 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.-H., … Tukker, A. (2021a). EXIOBASE 3 [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5589597
Stadler, K., Wood, R., Bulavskaya, T., Södersten, C.-J., 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.-H., … Tukker, A. (2021b). EXIOBASE 3 (3.8.2) (3.8.2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5589597