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The Union’s risk is shared across borders and carried by services that do not yet reach everyone

Part I describes systems and their couplings. It assesses no authority and ranks no member state.

6 min read2 figures2 tables14 notesNR-COUNTRY-AU-2026-BASELINE

Contents
  1. ·Edition home
  2. ·Executive summary
  3. 1The Union’s risk is shared across borders and carried by services that do not yet reach everyone
    1. 1.1 The questions this part answers
    2. 1.2 The argument
    3. 1.3 Twenty couplings between five systems
    4. 1.4 Reinforcing loops
  4. 2Key findings
  5. 3Systems and sectors at baseline
  6. 4Risks and capability
  7. 5Compound drought, price and outbreak year
  8. 6Readings from the other desks
  9. 7The 2027 agenda
  10. 8Systems in depth
  11. AMethodology
  12. BData appendix
  13. CReferences and record
  14. ·Complete edition, for print

1.1The questions this part answers

Key questions

  • Where is the African Union’s risk concentrated, and in which systems?
  • Which couplings carry shocks from one system to the next?
  • Which capabilities hold under compound stress, and which are only documented?
  • What must the 2027 programme measure first?

In this part

  • Narrative
  • Headline finding
  • Lead figure
  • Five system chapters
  • Twelve-domain reading
  • Sector screens
  • Dependency matrix
  • Risk objects
  • Capability ladder
  • Scenario
  • Three lenses
  • Portfolio

1.2The argument

The African Union has 55 member states that share river basins, rangelands, coasts, grids and disease pathways1. This baseline reads its exposure as three forces acting together: a changing climate, a young and growing population, and basic services that do not yet reach every household.

The demographic layer is large. Sub-Saharan Africa had 1.32 billion people in 2025, growing 2.4% a year, and 40.4% were under 15456. Only 3.2% were 65 or over, so the exposure of older people to heat is not the leading demographic pattern here7.

The service layer is thin. In 2024, 55.1% of people had electricity, 22.9% had clean cooking, 32.4% had safely managed drinking water and 25.9% had safely managed sanitation17182223. Each gap raises the cost of a shock for health, food and income.

The food layer is tied to farming. Agriculture employed 49.8% of workers in 2024, and 22.3% of people were undernourished1516. A season of drought or a spike in prices therefore reaches half the labour force and the households least able to absorb it.

The climate layer is rising. WMO reports that 2024 was one of the warmest years on record for Africa and that the continent had its warmest decade2. The IPCC finds that climate hazards already harm food, water and health systems on the continent38.

This edition therefore reads the Union’s risk as a property of the couplings between these forces. The matrix, risk objects, capability ladder and scenario that follow ask the same two questions: where does a shock travel next, and who holds the capability to stop it?

Figure 1.1How far do basic services reach? Share of people with each service, Sub-Saharan Africa, 2024 (%)

No service reached more than 56% of people in 2024. Clean cooking was lowest at 22.9%17182223.

ItemBar, scaled to the largest valueValue
Electricity55.1
Safely managed drinking water32.4
Safely managed sanitation25.9
Clean cooking22.9

Source: as cited in the note to this figure. Year 2024.

Figure ID FIG-COUNTRY-AU-2026-BASELINE-01-003

55.1%

electricity, 202417

32.4%

safely managed drinking water, 202422

25.9%

safely managed sanitation, 202423

22.9%

clean cooking, 202418

1.3Twenty couplings between five systems

The African Union matrix types the twenty couplings between the five core systems. Strengths are Nexus estimates from the cited evidence and analogue events; they set the starting graph that the Q1 2027 Foundry cut will test.

Figure 1.2Couplings between the five systems

Each line is one coupling from one system to another, drawn by the strength the edition assigns. Strengths are Nexus estimates from the cited evidence and analogue events.

  • Strong
  • Moderate
  • Weak

Source: this edition’s coupling matrix (Table 1.1); status Estimated.

Table 1.1Coupling matrix: from the row system to the column system
From, toWATENEFOOHLTBIO
WAT Water·STRONG
nuclear cooling · hydro		STRONG
irrigation · rain-fed yield		MODERATE
drinking water		STRONG
flows · wetlands
ENE EnergyMODERATE
pumping · treatment		·STRONG
fuel · fertiliser · cold chain		STRONG
facility power · cooling		MODERATE
reservoirs · land use
FOO FoodMODERATE
withdrawals · nutrients		WEAK
biofuel feedstock		·STRONG
nutrition · food safety		STRONG
land conversion
HLT HealthWEAK
workforce		WEAK
workforce		MODERATE
labour · disease		·WEAK
residues
BIO BiodiversitySTRONG
regulation · filtration		MODERATE
fire on lines · biomass		STRONG
pollination · soils		MODERATE
zoonoses · smoke		·
Strength: strong, moderate or weak; a Nexus estimate (status Estimated) from cited evidence and analogue events. The scale is defined in Annex A.

Transmission paths

  1. 01

    CLI → FOO

    Drought and heat cut crop and livestock output.

    Inferred IPCC assessment38

  2. 02

    CLI → WAT → HLT

    Low flows and heat reduce safe water and raise disease risk.

    Inferred water access2238

  3. 03

    ENE → HLT

    No power or clean fuel raises household health and clinic risk.

    Inferred access1718

  4. 04

    FOO → HLT

    Price and income shocks raise undernourishment.

    Reported undernourishment trend16

  5. 05

    BIO → WAT, FOO

    Forest loss changes water regulation and land use.

    Inferred forest trend27

1.4Reinforcing loops

A shock entering either system of a loop can return amplified through the other.

Table 1.2Pairs of systems where both directions are rated strong
LoopFirst directionSecond direction
WAT and BIOWater to biodiversity: flows · wetlandsBiodiversity to water: regulation · filtration
FOO and BIOFood to biodiversity: land conversionBiodiversity to food: pollination · soils
A causal-loop reading of the matrix (Sterman, Business Dynamics, 2000): structural, from estimated strengths, not a measured feedback.

Notes

Numbers are the edition’s reference numbers, so a note reads the same in every chapter. The full list, with every source and its licence, is in Annex C. Annex C, references

  1. 1African Union Commission (ed.), Member States of the African Union. African Union, Addis Ababa, page states 55 member states, retrieved 2026-11-03.back
  2. 4World Bank (ed.), “Population, total, Sub-Saharan Africa,” World Development Indicators, SP.POP.TOTL. World Bank, Washington DC, observation 2025, retrieved 2026-11-03.back
  3. 5World Bank (ed.), “Population growth (annual %), Sub-Saharan Africa,” World Development Indicators, SP.POP.GROW. World Bank, Washington DC, observation 2025, retrieved 2026-11-03.back
  4. 6World Bank (ed.), “Population ages 0-14 (% of total population), Sub-Saharan Africa,” World Development Indicators, SP.POP.0014.TO.ZS. World Bank, Washington DC, observation 2025, retrieved 2026-11-03.back
  5. 7World Bank (ed.), “Population ages 65 and above (% of total population), Sub-Saharan Africa,” World Development Indicators, SP.POP.65UP.TO.ZS. World Bank, Washington DC, observation 2025, retrieved 2026-11-03.back
  6. 17World Bank (ed.), “Access to electricity (% of population), Sub-Saharan Africa,” World Development Indicators, EG.ELC.ACCS.ZS. World Bank, Washington DC, observation 2024, data updated 8 October 2026, retrieved 2026-11-03.back
  7. 18World Bank (ed.), “Access to clean fuels and technologies for cooking (% of population), Sub-Saharan Africa,” World Development Indicators, EG.CFT.ACCS.ZS. World Bank, Washington DC, observation 2024, retrieved 2026-11-03.back
  8. 22World Bank (ed.), “People using safely managed drinking water services (% of population), Sub-Saharan Africa,” World Development Indicators, SH.H2O.SMDW.ZS, series compiled by the WHO and UNICEF Joint Monitoring Programme. World Bank, Washington DC, observation 2024, retrieved 2026-11-03.back
  9. 23World Bank (ed.), “People using safely managed sanitation services (% of population), Sub-Saharan Africa,” World Development Indicators, SH.STA.SMSS.ZS, series compiled by the WHO and UNICEF Joint Monitoring Programme. World Bank, Washington DC, observation 2024, retrieved 2026-11-03.back
  10. 15World Bank (ed.), “Employment in agriculture (% of total employment) (modelled ILO estimate), Sub-Saharan Africa,” World Development Indicators, SL.AGR.EMPL.ZS. World Bank, Washington DC, observation 2024, retrieved 2026-11-03.back
  11. 16World Bank (ed.), “Prevalence of undernourishment (% of population), Sub-Saharan Africa,” World Development Indicators, SN.ITK.DEFC.ZS, series compiled by the Food and Agriculture Organization. World Bank, Washington DC, observation 2024, retrieved 2026-11-03.back
  12. 2World Meteorological Organization (ed.), State of the Climate in Africa 2024. WMO, Geneva, 12 May 2025, summary page retrieved 2026-11-03.back
  13. 38C. H. Trisos, I. O. Adelekan, E. Totin et al., “Africa,” in H.-O. Pörtner et al. (eds.), Climate Change 2022: Impacts, Adaptation and Vulnerability, Contribution of Working Group II to the Sixth Assessment Report of the IPCC, chapter 9. Cambridge University Press, 2022.back
  14. 27World Bank (ed.), “Forest area (% of land area), Sub-Saharan Africa,” World Development Indicators, AG.LND.FRST.ZS. World Bank, Washington DC, latest observation 2023, retrieved 2026-11-03.back

Cite this chapter

Valadbaygi, S. (Ed.). (2026). Chapter 1: The Union’s risk is shared across borders and carried by services that do not yet reach everyone. In African Union Systems Risk and Resilience Report 2026: The State of the African Union’s Interdependent Systems (Baseline Edition 2026, Report No. NR-COUNTRY-AU-2026-BASELINE) [Draft under review]. The Global Centre for Risk and Innovation (GCRI).

Every citation form of the whole report, BibTeX and RIS included, is on the edition home. Cite this report

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