Scope 3 Emissions Accounting for Kenyan Companies

environmental-analytics
ESG
A reproducible R workflow to quantify, categorize, and report Scope 3 value-chain emissions using spend-based and activity-based methods.
Author

Kwiz Computing Technologies

Published

April 23, 2026

Keywords

ESG reporting Kenya, Scope 3 emissions accounting, GHG accounting R, climate disclosure Kenya, sustainability analytics

The Part of Your Carbon Footprint You Are Probably Ignoring

For most Kenyan companies, Scope 3 emissions make up more than 70% of their total greenhouse gas footprint, yet virtually none of their climate disclosures touch it. That gap is not a secret: it is a measurement problem, and one that R can help close.

Scope 1 covers direct combustion (your generator, your fleet). Scope 2 covers purchased electricity. Scope 3 covers everything else: the emissions embedded in the goods you buy, the business travel your staff take, the waste your offices produce, and the use of products you sell. The GHG Protocol Corporate Value Chain (Scope 3) Standard, published in 2011, divides this into 15 categories across upstream and downstream activities.

What the GHGP Scope 3 Standard Actually Requires

The GHG Protocol Corporate Value Chain Standard is not a regulation. It is a widely adopted methodology framework that organises the 15 Scope 3 categories into two groups.

Upstream categories (1-8) cover activities related to purchased inputs:

  1. Purchased goods and services
  2. Capital goods
  3. Fuel- and energy-related activities
  4. Upstream transportation and distribution
  5. Waste generated in operations
  6. Business travel
  7. Employee commuting
  8. Upstream leased assets

Downstream categories (9-15) cover what happens after you sell:

  1. Downstream transportation and distribution
  2. Processing of sold products
  3. Use of sold products
  4. End-of-life treatment of sold products
  5. Downstream leased assets
  6. Franchises
  7. Investments

For most Kenyan manufacturers, importers, and service firms, categories 1 (purchased goods and services) and 6 (business travel) tend to dominate. A Nairobi-based FMCG company sourcing raw materials from across East Africa will find that category 1 alone can account for 60-80% of total Scope 3.

The standard distinguishes between required and optional categories depending on your industry, but the expectation from voluntary frameworks like CDP and the emerging ISSB standards is that material categories must be disclosed with a credible methodology.

Two Methods: Spend-Based and Activity-Based

There are two practical approaches for quantifying Scope 3 emissions, each with different data requirements and accuracy levels.

Activity-based methods multiply physical activity data (tonnes of material purchased, kilometres travelled, cubic metres of waste) by a corresponding emission factor. They are more accurate but require granular procurement data that most Kenyan companies do not yet collect systematically.

Spend-based methods multiply financial spend data (cost in USD or KES) by an economic emission intensity factor, expressed in kg CO2e per unit of spend in a given industry sector. This is the practical starting point for organisations beginning their Scope 3 journey.

The US EPA’s Environmentally Extended Input-Output (USEEIO) model provides spend-based emission factors for 411 industry sectors of the US economy. While built on US data, these factors are used globally as a first-approximation tool. Exiobase, a multi-regional input-output database covering 44 countries and 5 rest-of-world regions, offers geographically closer factors for some East African supply chains, though it requires more pre-processing.

For a Kenyan company in 2026, a reasonable starting protocol is: use USEEIO factors for categories where supply chains are internationally sourced (electronics, vehicles, chemicals), and supplement with locally derived activity-based factors where Kenyan context is available.

Building the R Workflow

The workflow below demonstrates Scope 3 category 1 (purchased goods and services) using spend data and USEEIO emission factors. The same pattern extends to any spend-based category.

Step 1: Load USEEIO Emission Factors

The EPA provides the USEEIO model as a public dataset. We use the v2.0 supply chain emission factors, which are available as a CSV.

library(tidyverse)

# Download USEEIO v2.0 supply chain GHG factors
# Source: https://www.epa.gov/system/files/documents/2024-01/useeio_2.0_supply_chain_ghg_emission_factors_v1.1.csv
useeio_url <- paste0(
  "https://www.epa.gov/system/files/documents/2024-01/",
  "useeio_2.0_supply_chain_ghg_emission_factors_v1.1.csv"
)

# Read and clean
useeio_factors <- read_csv(useeio_url, show_col_types = FALSE) |>
  select(
    sector_code   = `2017 NAICS Code`,
    sector_name   = `2017 NAICS Title`,
    ghg_factor    = `Supply Chain Emission Factors without Margins (kg CO2e/2021 USD)`
  ) |>
  mutate(
    sector_code = as.character(sector_code),
    ghg_factor  = as.numeric(ghg_factor)
  ) |>
  filter(!is.na(ghg_factor))

head(useeio_factors, 5)

Step 2: Load Your Procurement Data

Your procurement data needs at minimum: a spend amount, a currency, and a sector or product classification. NAICS codes work directly with USEEIO; for Kenyan companies using local classifications, you will need a mapping table.

# Example procurement data for a Kenyan manufacturer
# In practice, this comes from your ERP or accounts payable system
procurement <- tibble(
  category      = c("Steel & fabricated metals", "Plastic packaging",
                    "Chemical inputs", "Road freight", "IT equipment"),
  naics_code    = c("332", "3261", "325", "4841", "334"),
  spend_ksh     = c(12_500_000, 4_800_000, 7_200_000, 3_100_000, 1_900_000),
  spend_usd     = spend_ksh / 130  # approximate KES/USD exchange rate
)

Step 3: Join Factors and Calculate Emissions

# Aggregate USEEIO factors to 3-digit NAICS for the join
# (USEEIO provides 6-digit detail; our procurement may only have 3-digit codes)
useeio_3digit <- useeio_factors |>
  mutate(naics_3 = str_sub(sector_code, 1, 3)) |>
  summarise(
    ghg_factor = mean(ghg_factor, na.rm = TRUE),
    .by = naics_3
  )

# Join and compute
scope3_cat1 <- procurement |>
  left_join(useeio_3digit, by = c("naics_code" = "naics_3")) |>
  mutate(
    emissions_kg_co2e   = spend_usd * ghg_factor,
    emissions_tco2e     = emissions_kg_co2e / 1000
  )

# Summary
scope3_cat1 |>
  summarise(
    total_spend_usd    = sum(spend_usd),
    total_tco2e        = sum(emissions_tco2e, na.rm = TRUE)
  )

Step 4: Visualise and Report

library(ggplot2)

scope3_cat1 |>
  arrange(desc(emissions_tco2e)) |>
  mutate(category = fct_inorder(category)) |>
  ggplot(aes(x = emissions_tco2e, y = category, fill = emissions_tco2e)) +
  geom_col(show.legend = FALSE) +
  scale_fill_gradient(low = "#aed6b1", high = "#2d6a4f") +
  scale_x_continuous(labels = scales::comma) +
  labs(
    title    = "Scope 3 Category 1: Purchased Goods and Services",
    subtitle = "Spend-based estimates using USEEIO v2.0 emission factors",
    x        = "Estimated emissions (tCO2e)",
    y        = NULL,
    caption  = "Emission factors: EPA USEEIO v2.0 | Spend converted at 130 KES/USD"
  ) +
  theme_minimal(base_size = 13)

This gives you a hotspot chart showing which procurement categories carry the most embedded carbon. The steel and chemicals categories almost always dominate for manufacturers, pointing to where supplier engagement will have the most impact.

Kenya-Specific Context: NSE, NEMA, and What Is Coming

No Kenyan statute currently mandates Scope 3 disclosure. But three forces are changing the calculation for NSE-listed companies and large private firms.

First, the Capital Markets Authority’s draft sustainability reporting guidelines, aligned with ISSB’s IFRS S2, include climate-related disclosures. IFRS S2 explicitly references the GHG Protocol and expects material Scope 3 categories to be disclosed once the standard is adopted, which is anticipated for listed companies.

Second, multinational buyers are increasingly passing climate requirements down their supply chains. A Kenyan exporter selling to European retailers operating under the EU Corporate Sustainability Reporting Directive (CSRD) will find that their Scope 3 category 1 emissions become their customer’s Scope 3 category 1 concern. Supply chain pressure is already the fastest path to mandatory Scope 3 disclosure for Kenyan exporters.

Third, NEMA’s environmental management framework continues to expand. While NEMA’s current focus is on direct environmental impacts assessed through EIAs (see our post on ESIA reproducibility), GHG accounting is increasingly part of environmental management plans for large projects.

The East African context also shapes which Scope 3 categories matter most. Companies sourcing from smallholder agricultural supply chains in the region (coffee, tea, cut flowers, macadamia) carry significant upstream land-use change emissions in category 1 that USEEIO factors, built on US industry data, will systematically underestimate. For these sectors, activity-based accounting with Africa-specific emission factors from sources like the IPCC national inventory guidelines or the Cool Farm Tool is more appropriate.

Limitations and the Path to Better Data

The spend-based method is a starting point, not a final answer. USEEIO factors represent average US industrial emissions intensity, which diverges from Kenyan supply chain reality in significant ways. The electricity grid in Kenya is far cleaner than the US average (roughly 90% renewable generation), so any factor applied to energy-intensive local processes will overestimate actual emissions. Conversely, for imported goods produced in coal-heavy economies, USEEIO may underestimate.

The improvement path is sequential. Start with spend-based estimates to identify material hotspots. Then focus data collection effort on the top two or three categories by estimated emissions. Transition those categories to activity-based accounting using actual supplier data or industry-specific factors. The remaining categories can stay on spend-based estimates indefinitely if they are not material.

For organisations building this capability from scratch, our Kenya open data post covers how to access public datasets programmatically. The same principles apply to structuring your internal ESG data for repeatable analysis.

Building a Defensible, Auditable GHG Inventory

A Scope 3 inventory only has value if it can withstand scrutiny. Three practices make the difference between a credible disclosure and a spreadsheet exercise.

Document your methodology explicitly: which categories you calculated, which method you used per category, which emission factor database and vintage you applied, and what assumptions you made on currency conversion or NAICS mapping. The GHG Protocol provides an assurance readiness checklist as part of its Scope 3 guidance.

Version-control your calculation code. An R script under git history is a better audit trail than an Excel file with “final_v3_FINAL.xlsx” in the name. Quarto reports render the methodology narrative and the computed results together, so there is no gap between what you documented and what you calculated. This mirrors the reproducibility principles we apply to environmental impact assessments.

Apply uncertainty bounds. Spend-based emission factors carry large uncertainty ranges, typically plus or minus 30-50%. Reporting a single point estimate without acknowledging this overstates precision. A simple Monte Carlo simulation over the factor distributions communicates the range of possible outcomes and builds credibility with sophisticated reviewers.

Scope 3 accounting separates organisations that are genuinely working through their climate exposure from those producing disclosure documents for appearances. The methodology is not opaque, and the tools are not expensive. What it requires is structured procurement data, a clear methodology, and a willingness to show your work.

If your organisation is working through this for the first time, what is the biggest obstacle you are running into: data quality, methodology uncertainty, or internal buy-in for the process?

Kwiz Computing Technologies provides ESG analytics services for Kenyan and East African organisations, including GHG inventory design, reproducible Scope 3 workflows in R, and sustainability reporting support. Reach out if you want to build this capability in-house or need a partner for your next climate disclosure.