The document discusses the challenges and opportunities for measuring, reporting, and verifying (MRV) greenhouse gas emissions from livestock in East Africa, focusing on national commitments to reduce emissions compared to a business-as-usual (BAU) scenario. It emphasizes the need for countries to adopt tier 2 approaches to capture emissions from productivity improvements, as most current systems use a tier 1 approach that only considers animal population changes. The document highlights the importance of aligning national MRV systems with donor requirements and improving capacity for effective climate finance utilization in the livestock sector.

1. © UNIQUE forestry and land use GmbH© UNIQUE forestry and land use GmbH
LIVESTOCK MRV AT NATIONAL LEVEL
East Africa awareness raising workshop
Timm Tennigkeit

2. Context: country ambitions
Livestock included in
NDC
Type of mitigation
objective
Livestock NAMA in
development
Ethiopia

Reduction compared to
BAU
Kenya

Reduction compared to
BAU

Tanzania

Reduction compared to
BAU
Uganda

Reduction compared to
BAU

With aims for poverty reduction, meeting growing consumption demand for
livestock products and ambition to limit GHG impacts, all 4 countries’ NDCs
propose emission reductions compared to BAU scenario: increased
productivity with lower emission intensity

3. Current sources of investment in livestock sector
3
Investment by farmers the main source:
• How can climate finance support further investment by farmers?
• How can mitigation ambition leverage additional, well-targeted international
investment?
0
200
400
600
800
1000
1200
1400
1600
1800
2000
Ethiopia Tanzania Uganda Kenya
FDI
ODA
public
on-farm investment
Source: FAOSTAT; UNCTAD; IFPRI SPEED; OECD-CRS

4. Trends in ODA in the livestock sector
4
0%
10%
20%
30%
40%
50%
60%
70%
0
100
200
300
400
500
600
700
2010 2011 2012 2013 2014 2015
East Africa total
mitigation
adaptation
Most ODA funds for livestock sector in the region are marked with
adaptation benefits; the proportion marked with mitigation benefits is also
increasing
Source: OECD-CRS

5. Potential of climate finance?
5
Source: CPI (2017) Global Landscape of Climate Finance
382
0
50
100
150
200
250
300
350
400
450
2016
mitigation
REDD+
dual benefits
adaptation
Globally, most climate
finance is for
mitigation
Question is: how to tap
into climate finance by
aligning
• national ambitions
for mitigation
• mitigation
opportunities
• sources of finance
Ability to measure
and report mitigation
effects is a key
requirement to access
mitigation finance

6. Key MRV elements at national level
6
NDC
BAU
scenario
GHG
inventory
NAMA
MRV
Donor
reporting
National MRV systems have several
components
Few countries have all the elements in
place, especially for the livestock sector
What are the challenges and options?

7. Challenge number 1
7
NDC
BAU
scenario
GHG
inventory
NAMA
MRV
Donor
reporting
NDCs target increased production output,
with lower GHG emission intensity. How to
measure change?

8. Tier 2 approach needed…
8
Source: GRA & CCAFS (2017)
…to measure GHG effects of livestock productivity improvements
Tier 1 approach Tier 2 approach
All East African countries’ inventories use a Tier 1 approach, where
emissions only change with a change in animal population.
A Tier 2 approach is needed to capture effects of productivity improvement.

9. Challenge number 2
9
NDC
BAU
scenario
GHG
inventory
NAMA
MRV
Donor
reporting
Emission reductions are compared to a
BAU scenario: How to develop the BAU
scenario for the sector and for specific
mitigation interventions?

10. Developing BAU scenarios using a Tier 2 approach
10
Source: Kenya National Climate Change Action Plan
Kenya INDC BAU for
livestock is based on a
linear projection of
historical trends, using a
Tier 1 approach where
only animal numbers
change
This is aligned with the
Tier 1 inventory, but not
able to reflect change in
productivity, e.g. due to
dairy sector investments

11. Kenya’s NAMA BAU is based on requirements to
meet policy target of per capita milk consumption
of 200 liters per year
11
Source: Kenya Dairy NAMA feasibility study
Kenya dairy NAMA’s BAU
scenario is based on policy
projections:
• How to align with NDC BAU
scenario?
• How to revise BAU over time?
Elements of Kenya’s MRV system
will need to be aligned over time

12. Challenge number 3
12
NDC
BAU
scenario
GHG
inventory
NAMA
MRV
Donor
reporting
Aligning NAMA MRV system with GHG
inventory and donor MRV systems

13. Challenge number 3
13
Source: Kenya Dairy NAMA feasibility study
MRV approaches in Kenya’s dairy NAMA
Mitigation
measure
MRV method Links with GHG inventory Links with donor reporting
Energy
efficiency in
milk
processing
Energy audits No links; dairy sector energy
use is small part of national
energy emissions
Ex ante energy audits
commonly used by donors
Biogas CDM
methodologies
Different baselines
depending on fuel sources
displaced; biogas not in
national inventory
CDM methodologies most
likely widely accepted
Dairy
productivity
Gold Standard
methodology
GS methodology uses Tier 2
approach; now trying to align
with national inventory
May need to increase
donors’ understanding of
new methodologies
GHG sources and sinks affected by livestock mitigation actions are not limited
to livestock-specific GHG sources in the GHG inventory. MRV of mitigation
actions must be designed to follow the logic of intervention, while also aligning
with GHG inventory and meeting donors’ requirements.

14. Conclusions
14
1. Building capacities for
Tier 2 approaches in
national MRV systems
will be essential
2. Close coordination and
cooperation between
MRV institutions is
needed
3. MRV systems can be
improved and converge
over time
4. Mitigation interventions
provide an opportunity
to begin the journey…
NDC
BAU
scenario
GHG
inventory
NAMA
MRV
Donor
reporting

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© UNIQUE forestry and land use GmbH
Contact: Timm Tennigkeit
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Wilkes A, Reisinger A, Wollenberg E, van Dijk S. 2017. Measurement, reporting and
verication of livestock GHG emissions by developing countries in the UNFCCC:
current practices and opportunities for improvement. CCAFS Report No. 17.
Wageningen, the Netherlands: CGIAR Research Program on Climate Change,
Agriculture and Food Security (CCAFS) and Global Research Alliance for Agricultural
Greenhouse Gases (GRA). http://hdl.handle.net/10568/89335