From Numbers to Insights: Interpreting climate-economy modelling results for policymakers
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From Numbers to Insights:
Interpreting climate-economy
modelling results for policymakers
The PARIS REINFORCE project has received funding from the
European Union’s Horizon 2020 Research and Innovation
Programme under grant agreement No 820846.
The basic challenge:
From here to there
No Policy Baseline
Current Policy Scenario
Unconditional NDC Scenario Here
Conditional NDC Scenario
Median 2oC Scenario
There
Median 2oC Scenario
Source: UN Emissions Gap Report 2018
The PARIS REINFORCE project has received funding from the
European Union’s Horizon 2020 Research and Innovation
Programme under grant agreement No 820846.
What do we want to know?
- Impact of climate change
- Impact of mitigation policies
- Both questions are highly interdisciplinary and very complex -> require multiple
methodological approaches
Role of economic models:
- Economics seeks to translate impacts and constraints into numbers to establish the impact
of decarbonisation pathways [What, where, when to decarbonise]
- This is an impossible task, but
- Models are a useful tool to organise knowledge and build consensus
Note: Academia and policy-consulting use different models
- Developing a deeper understanding of the drivers - stylized models built for analytic
insight
- Answering concrete policy question - complex numeric models used in policy advising
The PARIS REINFORCE project has received funding from the
European Union’s Horizon 2020 Research and Innovation
Programme under grant agreement No 820846.
Models are a key tool to inform policy-making
Word count in the 2030 Impact
Assessment
- Models can provide arguments for
action, highlighting the requirements,
obstacles and trade-offs of meeting a
certain goal
- Efficient pathways (which sectors,
which countries, how fast)
- GDP effects
- Comparison of policy tools
The PARIS REINFORCE project has received funding from the
European Union’s Horizon 2020 Research and Innovation
Programme under grant agreement No 820846.
We cannot use only a
single model
- Impact can be measured very differently: GDP, consumption, utility, SDGs, … and
aggregation over actors and over time matters [No universally optimal solution]
- Choice of key-mechanics represented in models depends on question: e.g. inclusion of
financial sector
- Choice of geographic scope: national, regional, global
- Time horizon
- Choice of granularity: one energy sector production function vs. individual wind turbines
techno/economic characteristics
• Model quality is linked to its usefulness for a specific purpose rather than its universal
truth
The PARIS REINFORCE project has received funding from the
European Union’s Horizon 2020 Research and Innovation
Programme under grant agreement No 820846.
And models need MANY inputs
5 Policies (taxes, subsidies, R&D, targets, regulations, etc)
1 2 3 4
What technologies How much do
How much energy How do emissions
and fuels can those fuels and
and other goods / relate to
provide that technologies cost,
services does the temperature
energy and those now and in the
world need? changes?
goods / services? future?
Population Fossil fuel resource Innovation Climate
sensitivity
Wealth Renewable / Scale
nuclear resource
Behaviour
Technology availability /
cost / performance
The PARIS REINFORCE project has received funding from the
European Union’s Horizon 2020 Research and Innovation
Programme under grant agreement No 820846.
The problem for policy-makers
• Understanding a model requires massive time investment:
• Jargon/Acronym-rich world of modellers: SSPs, RCPs, IAMs,
GCMs, CGEs, ABMs, DSGEs, …
• Complex concepts (e.g., social cost of carbon)
• Individual result metrics [GDP impact of net-zero by 2050] are
virtually meaningless - Numbers need to be put in context
• Even experts disagree on many assumptions / model choices
The PARIS REINFORCE project has received funding from the
European Union’s Horizon 2020 Research and Innovation
Programme under grant agreement No 820846.
Today’s presentation: Interpreting model results
How should policymakers
look at modelling studies?
What questions should
they ask modelers?
The PARIS REINFORCE project has received funding from the
European Union’s Horizon 2020 Research and Innovation
Programme under grant agreement No 820846.
1. Who designed the questions?* • Questions already imply judgement (“…”) and set agendas • So it is important to understand who asked the question and whether he/she was representative of a relevant group of stakeholders? * And who paid for the study. The PARIS REINFORCE project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 820846.
2. Which model is used to answer the question?
• What are potentially relevant interactions
that need to be considered when answering If the model is too big for the question,
the question? there is a risk of generating noise
• Which model (type) is needed to address
this?
• Often several models needed for different
aspects
Question
Model
The PARIS REINFORCE project has received funding from the
European Union’s Horizon 2020 Research and Innovation
Programme under grant agreement No 820846.3. What is the baseline?
To enable fair comparison, Paris Reinforce
• Fixed reduction compared to a very harmonised the baselines of its modelling ensemble
dirty baseline, might be cheaper than 4,5
compared to a very clean baseline Trajectory of energy CO2 emissions
4,0
• Comparing to an impossible baseline
3,5
(no investments needed until 2050)
3,0
can make climate policy results look ICES
EU-TIMES
excessively expensive 2,5
GtCO2/y
GEMINI-E3
NEMESIS
• We cannot directly compare results 2,0
42 E3ME
from models with different baselines 1,5 TIAM
MUSE
(If one model assumes population 1,0 GCAM
growth and the other declining 0,5
population, keeping emissions at a 0,0
certain level will be more expensive in
the first)
The PARIS REINFORCE project has received funding from the
European Union’s Horizon 2020 Research and Innovation
Programme under grant agreement No 820846.4. How strongly should we believe our results?
a. Sensitivity
Lowest cost, 55% emission reduction results in
• The future is between 0 and up to 500 Mt CO2 abated by CCS,
uncertain and even depending on the model
history/present is not
1000
fully known 900
800
• What happens when
CO2 Captured [MtonCO2/y]
700
key assumptions are 600
500
incorrect? 400
300
-> sensitivity analysis 200
100
0
-100
0% 10% 20% 30% 40% 50% 60% 70% 80% 90%
CO2 emissions reduction [% rel. to 1990]
TIAM EU-TIMES E3ME GCAM GEMINI-E3 MUSE NEMESIS
The PARIS REINFORCE project has received funding from the
European Union’s Horizon 2020 Research and Innovation
Programme under grant agreement No 820846.4. How strongly should we believe our results?
b. Differences across models
• Addressing the same question with Investments in energy supply and power generation
different models is beneficial:
• Getting the same result from
different models provides 450
Investment in Energy Supply - 2020
some comfort 400
393 Investment in Energy Supply - 2030
• Getting different results from Investment in Energy Supply - 2040
350
different models does not Investment in Energy Supply - 2050
Investment in Power Generation -
300
mean any of them is “wrong”, 273 2020
bn€2010/y
Investment in Power Generation -
2030
250
but modelers should be able 203
to explain key drivers of 200
152
differences 150 127 124
136 130129128133
108 109
98100 10097 97
100 87 83
89 93
65 88
• EU IA 55% 50
53
76
92
76
0
E3ME EU-TIMES GCAM GEMINI-E3 NEMESIS
The PARIS REINFORCE project has received funding from the
European Union’s Horizon 2020 Research and Innovation
Programme under grant agreement No 820846.4. How strongly should we believe our results?
c. Intuitive explanation of sign and size
• Are modellers able to provide a convincing explanation of the sign and size of the
results they find?
Where are emissions heading? Paris Reinforce example
• Even with harmonised assumptions, models
show significant variation in emissions, why?
In this case, they use different representations of:
• How changing population and wealth affect
demand for energy – real world behavioural
change.
• a) which technologies are available, and b) how
quickly low-carbon technology options can
substitute for high-carbon options – real world
uncertainty about feasible transition speeds.
The PARIS REINFORCE project has received funding from the
European Union’s Horizon 2020 Research and Innovation
Programme under grant agreement No 820846.5. Are there accessible method for interacting with model scenarios • Use for appendix/online presentation of additional comparison on results The PARIS REINFORCE project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 820846.
Conclusion • Numbers need context to be useful • Structuring and improving the quality of the very complex and interdisciplinary climate policy discussion are key • Can this be efficiently achieved by self-organization of individual policy-makers and researchers? The PARIS REINFORCE project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 820846.
Thank you!
#parisreinforce
ParisReinforce
paris-reinforce
parisreinforce
The PARIS REINFORCE project has received funding from the
European Union’s Horizon 2020 Research and Innovation
Programme under grant agreement No 820846.From Numbers to Insights:
Interpreting climate-economy
modelling results for policymakers
Outlook and next steps for PARIS REINFORCE
The PARIS REINFORCE project has received funding from the
European Union’s Horizon 2020 Research and Innovation
Programme under grant agreement No 820846.Overall planetary integrity (biodiversity/environment, etc.)
• Tighter consideration of environment in
energy system transformations
• Policy prescriptions must do more than
‘carbon budgets’
• For example, what does RES deployment
entail (in Greece, a quarter of installed/
new wind falls within Natura 2000)?
• Risk perception: possible environmental
consequences or social implementation
barriers?
• Proposal: social licensing protocol guiding
dialogue among local nature conservation
teams, associations and communities at an Source: The Green Versus Green Trap and a Way
early stage of renewable energy planning Forward. Energies, 13(20), 5473.
The PARIS REINFORCE project has received funding from the
European Union’s Horizon 2020 Research and Innovation
Programme under grant agreement No 820846.Game changers (tech innovations, lifestyle change, etc.)
• From what and where, to when and for whom
• What about innovation/infrastructure and
investments plus enabling policies?
• Better representation of emerging/early-stage
technologies (carbon removal, hydrogen/electricity
in transport, nuclear fusion, hydrogen in industry)
• Sectors not yet contributing to climate action – e.g.
shipping, and their potential role.
• Think outside the box, e.g., how a different
container type – Container 2.0, can impact shipping
emissions
Source: Low-cost emissions cuts in container shipping: Thinking inside the box.
Transportation Research Part D: Transport and Environment, 94, 102815
Source: The desirability of transitions in demand: Incorporating
behavioural and societal transformations into energy modelling.
Energy Research & Social Science, 70, 101780.
The PARIS REINFORCE project has received funding from the
European Union’s Horizon 2020 Research and Innovation
Programme under grant agreement No 820846.COVID-19 and extremes, recovery, equity, employment
• Impact of pandemic on emissions, sectoral • How to structure a robust, equitable, green
activity, value chains is becoming clearer. recovery?
Upcoming: Input to EC’s COP26 policy
publication on RRF (and green recovery funds
announced in big emitters) to further cut
emissions while creating jobs
• How to draw ambitious mitigation policy that
considers employment and market
imperfections?
Source: Temporary reduction in daily global CO 2 emissions
during the COVID-19 forced confinement. Nature Climate
Upcoming: Paris-compliant scenarios in
Change, 10(7), 647-653. consideration of employment gains
• But how to prepare for similar extremes?
• See also PR policy brief (2021).
Source: Energy modellers should explore extremes more
The Delignitisation Roller Coaster in Greece:
systematically in scenarios. Nature Energy, 5(2), 104-107. An Old Car and a Steep Slope Ahead. The
Future of Work.
The PARIS REINFORCE project has received funding from the
European Union’s Horizon 2020 Research and Innovation
Programme under grant agreement No 820846.Reducing model response ‘noise’
• Harmonised input, robust output, digestible policy prescriptions
Source: Challenges in the harmonisation of global integrated assessment models: A comprehensive
methodology to reduce model response heterogeneity. Science of The Total Environment, 783, 146861.
• Outlook: explaining in detail why models differ
The PARIS REINFORCE project has received funding from the
European Union’s Horizon 2020 Research and Innovation
Programme under grant agreement No 820846.Stakeholders in the scientific process
• Building ownership and ensuring policy relevance, through co-creation (see also Involve citizens in
climate-policy modelling. Nature, 590(7846), 389-389.)
Upcoming: Where is the EU headed given its current climate policy? A stakeholder-driven model
inter-comparison
1st Series of National 2nd Series of
Workshops National Workshops
•Greece, Japan •From numbers to •Greece; UK; Germany;
•Brussels (Nov 2019) COVID19 disruption Norway; Italy; Spain
insights
•Kenya, India, Central Asia
Caspian, Russia, •Canada; Brazil; Kenya;
Switzerland, USA, France, Ukraine; Mexico
Netherlands, China
Regional Workshop EU Webinar
The PARIS REINFORCE project has received funding from the
European Union’s Horizon 2020 Research and Innovation
Programme under grant agreement No 820846.You can also read
