From Numbers to Insights: Interpreting climate-economy ...

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




The basic challenge: From here to there

Source: UN Emissions Gap Report 2018

No Policy Baseline

Unconditional NDC Scenario

Conditional NDC Scenario

Median 2oC Scenario

Median 2oC Scenario

Here

There

Current Policy Scenario




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




Models are a key tool to inform policy-making

Word count in the 2030 Impact Assessment

- Models can provide arguments foraction, 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




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




And models need MANY inputs

How much energy

and other goods /

services does the

world need?

1

What technologies

and fuels can

provide that

energy and those

goods / services?

2

How much do

those fuels and

technologies cost,

now and in the

future?

3

Population

Wealth

Behaviour

Fossil fuel resource

Renewable /

nuclear resource

Technology availability /

cost / performance

Innovation

Scale

How do emissions

relate to

temperature

changes?

4

Climate

sensitivity

Policies (taxes, subsidies, R&D, targets, regulations, etc)5




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




Today’s presentation: Interpreting model results

How should policymakers look at modelling studies?

What questions should they ask modelers?




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.




2. Which model is used to answer the question?

• What are potentially relevant interactions that need to be considered when answering the question?

• Which model (type) is needed to address this?

• Often several models needed for different aspects

Question

If the model is too big for the question, there is a risk of generating noise

Model




3. What is the baseline?

• Fixed reduction compared to a very dirty baseline, might be cheaper than compared to a very clean baseline

• Comparing to an impossible baseline (no investments needed until 2050) can make climate policy results look excessively expensive

• We cannot directly compare results from models with different baselines (If one model assumes population growth and the other declining population, keeping emissions at a certain level will be more expensive in the first)

E3ME

EU-TIMES

GCAM

GEMINI-E3

ICES

MUSE

NEMESIS

TIAM

42

0,0

0,5

1,0

1,5

2,0

2,5

3,0

3,5

4,0

4,5

GtC

O2/y

To enable fair comparison, Paris Reinforceharmonised the baselines of its modelling ensemble

Trajectory of energy CO2 emissions




4. How strongly should we believe our results?a. Sensitivity

• The future is uncertain and even history/present is not fully known

• What happens when key assumptions are incorrect?

-> sensitivity analysis

-100

0

100

200

300

400

500

600

700

800

900

1000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

CO

2C

aptu

red

[M

ton

CO

2/y

]

CO2 emissions reduction [% rel. to 1990]

TIAM EU-TIMES E3ME GCAM GEMINI-E3 MUSE NEMESIS

Lowest cost, 55% emission reduction results in between 0 and up to 500 Mt CO2 abated by CCS, depending on the model




4. How strongly should we believe our results?b. Differences across models

• Addressing the same question with different models is beneficial:• Getting the same result from

different models provides some comfort

• Getting different results from different models does not mean any of them is “wrong”, but modelers should be able to explain key drivers of differences

• EU IA 55%

Investments in energy supply and power generation

83

130

100

129

97

128109

133127

53

152

6589

9887

203

76

92

100108

273

76

93

124136

393

88

97

0

50

100

150

200

250

300

350

400

450

E3ME EU-TIMES GCAM GEMINI-E3 NEMESIS

bn

€2

01

0/y

Investment in Energy Supply - 2020




Investment in Power Generation -2020Investment in Power Generation -2030




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.




5. Are there accessible method for interacting with model scenarios

• Use for appendix/online presentation of additional comparison on results




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?




Thank you!

#parisreinforce

ParisReinforce

paris-reinforce

parisreinforce




From Numbers to Insights:Interpreting climate-economy modelling results for policymakers

Outlook and next steps for PARIS REINFORCE




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 early stage of renewable energy planning

Source: The Green Versus Green Trap and a Way Forward. Energies, 13(20), 5473.




Game changers (tech innovations, lifestyle change, etc.)

• 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.

• From what and where, to when and for whom




COVID-19 and extremes, recovery, equity, employment

• Impact of pandemic on emissions, sectoral activity, value chains is becoming clearer.

• But how to prepare for similar extremes?

Source: Energy modellers should explore extremes more systematically in scenarios. Nature Energy, 5(2), 104-107.

Source: Temporary reduction in daily global CO 2 emissions during the COVID-19 forced confinement. Nature Climate

Change, 10(7), 647-653.

• How to structure a robust, equitable, green 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?

Upcoming: Paris-compliant scenarios in consideration of employment gains

• See also PR policy brief (2021). The Delignitisation Roller Coaster in Greece: An Old Car and a Steep Slope Ahead. The Future of Work.




Reducing model response ‘noise’

• Harmonised input, robust output, digestible policy prescriptions

• Outlook: explaining in detail why models differ

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.




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

•Brussels (Nov 2019)

Regional Workshop

•Greece, Japan

COVID19 disruption

•Kenya, India, Central Asia

Caspian, Russia,

Switzerland, USA, France,

Netherlands, China

1st Series of National

Workshops

•From numbers to

insights

EU Webinar

•Greece; UK; Germany;

Norway; Italy; Spain

•Canada; Brazil; Kenya;

Ukraine; Mexico

2nd Series of

National Workshops

From Numbers to Insights: Interpreting climate-economy ...

Documents

Transcript of From Numbers to Insights: Interpreting climate-economy ...