Presented virtually by Laura Cramer at the Chort IX of the AGNES Climate Leadership Program, 22 August 2022
Opening presentation to the decision support tool unit of the Climate Governance, Diplomacy and Negotiations Leadership Program administered by the African Group of Negotiators Expert Support (AGNES).
Decision Support Tools: Application in policy, planning and implementation
1. Climate Governance, Diplomacy and Negotiations Leadership Program
Accelerating Impacts of CGIAR Climate
Research for Africa (AICCRA)
Module 6, Unit 2:
Decision support tools
Application in policy, planning and
implementation
Laura Cramer, AICCRA Theme Leader on Policies and Priorities for CSA
Virtual presentation to Cohort IX of the AGNES Climate Leadership Program
22 August 2022
2. Climate Governance, Diplomacy and Negotiations Leadership Program
Accelerating Impacts of CGIAR Climate
Research for Africa (AICCRA)
SESSION
TEAM
WITH MATERIAL FROM
PHILIP THORNTON
TODD ROSENSTOCK
CONSTANCE NEELY
SABRINA CHESTERMAN
ROMY CHEVALLIER
ANDREEA NOWAK
CINIRO COSTA JUNIOR
LAURA CRAMER, ILRI/AICCRA
Part 1, Introduction
Part 2, Examples
ARUN KHATRI-CHHETRI, CONSULTANT
EVAN GIRVETZ, ALLIANCE OF
BIOVERSITY & CIAT
3. Objectives
Understand the range of tools available for helping evaluate adaptation and
mitigation interventions within different agriculture sub-sectors
Recognize different strategies for developing baselines and undertaking
planning under conditions of low data availability
Be familiar with some available tools to generate long-term projections
Be aware of resources for additional information and help from national or
regional partners
Appreciate the need to evaluate trade-offs across different sectors affected
by and contributing to climate change
4. Today’s plan
Session 1
1:30 pm Opening and introductions (AGNES)
1:35 pm Overview of the topic of decision support tools and their uses (Laura Cramer)
2:00 pm Q&A
2:10 pm Bringing results of DSTs into planning processes and addressing socio-economic issues
not covered in data-driven models
2:20 pm Instructions for self-led exercise
2:30pm Session closes
Session 2
4:00 pm Opening, recap of what was covered earlier (Laura)
4:05 pm Feedback on self-led exercise on use of DSTs for decisions making
4:15 pm Example of a DST: gender hotspot mapping in Rwanda (Arun) + Q&A
5:00 pm DST examples for adaptation planning (Evan)
5:45 pm Final Q&A
6:00pm Session closes
5. Key lessons on previous
topics on long-term policy
planning?
10. Decision support tools
What are they?
Ways of storing,
visualizing and
interpreting data and
information to help
people make decisions
Who builds them?
Anyone who wants to
codify information &
knowledge to
strengthen the scientific
basis of decisions
11. Many tools
available to
supply
different
information
needs
Address uncertainty | Address multiple (competing) objectives | Evaluate the
consequences of certain actions/ pathways | Provide a legitimate process and
Basic analyses
(graphs, maps,
spreadsheets,
reports, GIS);
Impact models
(e.g., ecosystem
models, crop
models, water
resource models,
disease models)
Earth systems
models (e.g., general
circulation models,
climate forecast
models)
Emission calculators (e.g.,
Life Cycle Analysis, GHG
accounting, Carbon sink
accounting tools)
Economic models
(e.g., cost-
effectiveness and
cost-benefit analysis)
Policy simulations (role
play workshops,
computer-based
simulations)
Integrated
assessment models
(climate, energy,
economic, etc.)
Participatory
processes
(stakeholder/
expert elicitation)
12.
13. What kind of decisions might you make based on these maps?
What other info would you need to pair with this?
https://www.icpac.net/seasonal-forecast/
15. An example of a theory of change for decision
support tools
StakeHolder Approach to Risk informed and Evidence-based Decision-making - www.worldagroforestry.org/shared
16. Many types of
climate
information
for many user
types and
needs
Emissions reductions scenarios (Rwanda NDC)
Source: https://unfccc.int/sites/default/files/NDC/2022-06/Rwanda_Updated_NDC_May_2020.pdf (pg. 3)
19. How to
visualize
information
depends on
various
factors • Information type
• Availability of data
• Audience
• Skills, qualifications, resources for data analysis
• Others
The way data /
information is
packaged influences
its accessibility
21. Choose one of the following decision support tools
to explore during the break
Click around, looking at inputs needed, time
needed, and possible outputs
Come prepared to discuss in the next session
Interactive exercise
22. Example decision support tools:
1. ICPAC East Africa Hazards Watch:
https://eahazardswatch.icpac.net/
2. Cool Farm Tool:
https://coolfarmtool.org/
3. En-ROADS simulation: https://en-
roads.climateinteractive.org/
4. Geoportail Senegal:
https://retd1.teledetection.fr/climap/pr
oj/
Notas do Editor
Impacts of climate change on the livestock food supply chain; a review of the evidence
https://doi.org/10.1016/j.gfs.2020.100488
It’s a lot to think about. How do we make sense of it all?
This example is from the livestock sector. Is there literature documenting effects on the sector you represent or are interested in?
If we want evidence-informed decision-making, we need DSTs
We see DSTs in our everyday lives
How do we achieve all the things listed at the bottom? With Decision Support Tools…
Rwanda NDC
Univ of Texas: https://www.sciencedirect.com/science/article/pii/S096014811730527X
Peter 2017: https://www.sciencedirect.com/science/article/pii/S0013935117315712
Rwanda NDC
Univ of Texas: https://www.sciencedirect.com/science/article/pii/S096014811730527X
Peter 2017: https://www.sciencedirect.com/science/article/pii/S0013935117315712
Rwanda NDC
Univ of Texas: https://www.sciencedirect.com/science/article/pii/S096014811730527X
Peter 2017: https://www.sciencedirect.com/science/article/pii/S0013935117315712
Information type
Some visualizations are more useful for certain information types than others; qualitative and quantitative data is reported in different formats
Availability of data
E.g., if you want to use a trendline, you need to have data for multiple timeframes (years, months). Otherwise, you’d show it in a table
Audience
Technical audiences may require more complex visualizations compared to a non-academic public
Skills, qualifications, resources for data analysis
Design skills of data analysists of the data
En-ROADS: Energy Rapid Overview and Decision-Support
Senegal help video in French: https://retd1.teledetection.fr/climap/