transrisk synthesis of key findingstransrisk-project.eu/sites/default/files/transrisk... ·...
TRANSCRIPT
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Transitions Pathways and Risk Analysis for Climate Change Mitigation and Adaptation Strategies
Presented by: Dr. Jenny Lieu, TRANSrisk Co-Pricipal Investigator
SPRU, Science Policy Research Unit
ETH Zurich, TDLab
TRANSrisk synthesis of key findings
Presentation in BrusselsNovember 6th, 2018
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TRANSRISK PARTNERSHIP: WHO WE ARE
12 partners
70+ researchers
Interdisciplinary
team
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CASE STUDY COUNTRIES: AREAS STUDIED
Americas1. Canada
(SPRU)
2. Chile (CLAPESUC)
Europe3. Sweden (SEI)4. Netherlands (JIN)5. UK (SPRU)6. Poland (IBS)7. Austria (Uni Graz)8. Switzerland (ETHZ)9. Spain (BC3)10. Greece (NTUA/ UPRC)
Africa11. Kenya (SEI)
Asia12. China (SPRU)
13. India (SPRU)
14. Indonesia (SEI)
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RESEARCH QUESTIONS
Overarching Questions:
What are the costs, benefits and risks & uncertainties associated with
feasible transitions pathways for climate change mitigation policies?
1. How should the future look like and how might we get there?
2. What changes are required for us to get to our desired future(s)?
3. What are the policy options based on the (national) context?
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Uncertainty (e.g. quantifiable probability, possibility, likelihood)
Out
com
epo
sitiv
ene
gativ
e
RisksImplementation risks: barriers
Consequential risks: negative outcome
Benefits; positive outcomes
Enablers, synergies
Sustainable transition pathway
BenefitsContributors: Susanne Hanger ETH Zurich, Oscar van Vliet ETH Zurich, Alexandros
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Problem 1:
There are often conflicting priorities between macro-level climate goals and local development priorities
(where do we need to go and how do to get there?)
Action(s):
• Starting point of low carbon pathways needs to come from stakeholders and NOT researchers (consultation AND inclusion)
• Implementation of actions: need to build an inclusive approach to capture stakeholder preferences (TalanoaDialogue)
KEY MESSAGES (1) :
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Problem 2:
We are overwhelmed by the number of risks and challenges in the implementation of low carbon pathways
Action(s):
• Stakeholder knowledge (SH)and priorities along with models provides a powerful integrate approach in unveiling keybarriers to or negative impacts of low carbon pathways
• Let's learn TOGETHER: Many similar lessons to learn across all countries regardless of socio-economic development patterns
• Canadian oil sands development and Kenya geothermal development
• Chile: methods of integrating SH knowledge and models
KEY MESSAGES (2) :
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Problem 3:
Climate change is not a driver for pushing forward urgent mitigation action
Action(s):
• Short term: Urgent action must be co-ordinated with current socio-economic and environmental challenges (Poland, Austria, Greece: employment; China and Chile: air pollution)
• Medium term: Mainstream climate change into development goals: multi-sector multi-governance and multi-stakeholder approach (Indonesia, China, Switzerland)
• Longer term: Support a value change in society at the end-user level and country level through education programmes and raising awareness through media (nearly all case studies)
KEY MESSAGES (3) :
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Methods: Book with Springer
“Understanding risks and uncertainties in energy and climate policy: Multidisciplinary methods and tools towards a low carbon society”
Editors: Doukas, H., Flamos A, Lieu, J,
To be published by end of 2018
Narratives: Book with Routlege
“Transitions narratives towards a Low-Carbon Future: Assessing Risks & Uncertainties”
Editors: Hanger-Kopp S, Lieu, J, Nikas A.
To be published beginning of 2019
Integration of stakeholder and models: Special issue in Environmental Innovation and Societal Transitions. Elsevier.
“Assessing risks and uncertainties of low-carbon transition pathways”.
Guest editors: Lieu J., Hanger-Kopp S, Sorman A., van Vliet O.
To be published by end of 2018-beginning of 2019
F INDINGS: OPEN SOURCE PUBL ICATIONS
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J e n n y L i e uT R A N S r i s k C o - P r i n c i p a l I n v e s t i g a t o r
j . l i e u @ s u s s e x . a c . u k
j e n n y. l i e u @ u s y s . e t h z . c h
Thank-you!
RISK & UNCERTAINTIES: POTENTIAL FUTURES
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Risks and chal lenges: the case of low carbon options in the energy and steel sectors
Andreas Tuerk1 and Karl W. Steininger1,2
1) Wegener Center for Climate and Global Change, University of Graz2) Department of Economics, University of Graz
GREEN-WIN / TRANSrisk / CD-LINKS Policy Day7.11.2018, Brussels
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▪ Synergies or trade-offs? Uncertainty framework and methods
▪ Types of uncertainty
▪ Operationalization of uncertainty and risks
▪ Two interrelated examples
▪ EU-wide transition of iron and steel sector to hydrogen based technology
▪ Electricity transition: The effects of pricing in the “carbon bubble” and of “de-
risking” renewables
▪ Quantitative exploration of uncertainties in economic modeling
▪ Types of uncertainties
▪ Quantitative ranges of uncertainties
▪ Policy conclusions
▪ Risks
▪ Synergies between climate policy and SDG target achievement
OUTLINE
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Steel: in depth case study on transition of iron and steel sector to
hydrogen based technology
▪ Interviews with a range of steel companies in the EU
▪ Several technological options investigated, e.g.
▪ CCS and Carbon Capture and use (CCUS) e.g. for chemicals
▪ Hydrogen
▪ Alternative carbon sources (e.g. biomass)
▪ Incremental improvements/recycling
RISKS AND UNCERTAINTIES IN STEEL SECTOR
TRANS IT ION
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Input supply risk ▪ Will there be sufficient renewable electricity?
Price risks ▪ H2 steel making needs electricity prices of 0,3-0,5 Euro/MWh
Market power risks/new dependencies ▪ No global H2 market expected▪ New dependendies e.g. on utilites or storage
operators▪ New value chains may cause new risks
Technology risk ▪ When will technology costs be competitive to conventional steel production?
Risk of overcapacities ▪ Reducing incentives to invest
Environmental risks ▪ Large amount of renewables, leakage risk ofstored carbon in case of CCS
Policy Risks ▪ Lifetime of plants far longer than policy cycles
TYPES OF RISKS IN THE STEEL SECTOR
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▪ Company profile
▪ Positioning on the market
▪ Types of products produced
▪ Windows for new investments
▪ Low-carbon transition in other sectors
▪ What would the competition with other consumers mean for price formation and merit order? Will steel be at the lower end of the merit order curve?
▪ Synergies and cooperation between sectors
▪ Hydrogen production could offer flexibility to the energy system
➢ Options to manage the transition may be gradual deployment and use of new technologies e.g. a varying ratio between natural gas and hydrogen could be used in a transition phase reducing risks
FACTORS IMPACTING R I SKS
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▪ The dearbonisation in the steel sector can have synergies with
economic and SDG goals but there is also the risk of trade-offs
▪ Risk potentials may turn into synergies or trade-offs depending
on
▪ Socioecononic context (eg technology costs…)
▪ Policy context (global climate policies, economic policies…)
▪ Scale of the mitigating sector
▪ Scale and timing of technolgy deployment by others
➢ Some of these contextual factors can be considered in models in
form of scenarios capturing uncertainties
R I SKS : S YNERGIES AND TRADE - OFFS
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Uncertainty appraisal: To create different “worlds” in which the switch takes
place Variations on four different “uncertainty layers”:
1. Technology layer
2. Socio-economic layer
3. Climate policy layer
4. Macroeconomic layer
[Bachner et al., 2018]
UNCERTAINTY FRAMEWORK AND METHODS
“epistemic” uncertainty
“paradigmatic” (and “translational”) uncertainty
[Kunreuther et al., 2014]
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Socio-economic development (SSP)
Macroeconomic state
IRON AND STEEL:
UNCERTAINTY FRAMEWORK
Climate policy
Technological uncertaintySocioeconomic uncertainty (main scenario: SSP2)
Climate policy uncertainty (main scenario: globally reluctant)
Macroeconomic uncertainty (main scenario: full capacity)
Process emission-free technologies
Low-costHigh-cost
Baseline technology
Δ
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S O C IO - EC O NO M IC UNC ERTA INTY
C HANGES IN EU - WID E I RO N AN D S TE E L O UTPUT
Change of EU-wide iron and steel output, relative to the baseline scenario with SSP variations on the socio-economic uncertaintylayer (assuming reluctant climate policy and full capacity utilization).
-25%
-20%
-15%
-10%
-5%
0%
+5%
+10%
20
11
20
16
20
21
20
26
20
31
20
36
20
41
20
46
20
50
Change i
n iro
n a
nd s
teel outp
ut
SSP2 (high cost)
SSP3 (high cost)
SSP5 (high cost)
SSP2 (low cost)
SSP3 (low cost)
SSP5 (low cost)
Socio-econ.uncertainty
techn.uncertainty
[Bachner et al., 2018]
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-2.0%
-1.5%
-1.0%
-0.5%
0.0%
+0.5%
20
11
20
16
20
21
20
26
20
31
20
36
20
41
20
46
20
50
Change i
n G
DP
SSP2 (high cost)
SSP3 (high cost)
SSP5 (high cost)
SSP2 (low cost)
SSP3 (low cost)
SSP5 (low cost)
S O C IO - EC O NO M IC UNC ERTA INTY
C HANGES IN EU - WID E GDP
Socio-econ.uncertainty
techn.uncertainty
Change of EU-wide GDP, relative to the baseline scenario with SSP variations on the socio-economic uncertainty layer (assumingreluctant climate policy and full capacity utilization).
[Bachner et al., 2018]
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-2.0%
-1.5%
-1.0%
-0.5%
0.0%
+0.5%
20
11
20
16
20
21
20
26
20
31
20
36
20
41
20
46
20
50
Change i
n G
DP
full capacity (high cost)
full capacity (low cost)
output gap (high cost)
output gap (low cost)
M ACRO - E CO NO MIC UN CE RTA IN TY
C HANGES IN EU - WID E GDP
Change of EU-wide GDP, relative to the baseline scenario assuming SSP2, globally reluctant climate policy and variations on themacroeconomic uncertainty layer (“full capacity utilization” or “output gap” assumption).
Macro-econ.uncertainty
[Bachner et al., 2018]
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▪ Capital costs of particular relevance with renewables
▪ Analysis usually:
▪ weighted average costs of capital (WACC) parameters chosen without
differentiation across technologies and regions
▪ explicit focus on the WACC of renewables only (fossil: “standard” from literature)
▪ WACC reflect risks of investment
▪ Here: weighted average costs of capital (WACC) to reflect different risk
across technologies and regions
E LECTRIC ITY TRANSIT ION: DE-RISK ING OF
RENEWABLES
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E LECTRIC ITY TRANSIT ION: DE-RISK ING OF
RENEWABLES
RENEWABLES SCENARIOS
Nuclear
Gas
Petroleum
Solid Fuels
Hydro
Biomass
PV
Wind0% 20% 40% 60% 80% 100%
Nuclear Gas Petroleum Solid Fuels Hydro Biomass PV Wind
0% 20% 40% 60% 80%100%
AUT
0% 20% 40% 60% 80%100%
EEU
0% 20% 40% 60% 80%100%
GRC
0% 20% 40% 60% 80%100%
NEU
0% 20% 40% 60% 80%100%
SEU
0% 20% 40% 60% 80%100%
WEU
RES-e target
EU-ref target
Benchmark
RES-e target
EU-ref target
Benchmark
Capros et al. 2016
Pleßmann/B., 2018
[Bachner et al., 2018b]
Pleßmann/B., 2018
Capros et al. 2016
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E LECTRIC ITY TRANSIT ION: GDP AND
WELFARE EFFECTS
uniform main scenario carbon bubble RES-e backed-up WACC combination
-4%
-2%
0%
2%
4%
6%
8%
10%
12%
-4%
-2%
0%
2%
4%
6%
8%
10%
12%
201
1
201
6
202
1
202
6
203
1
203
6
204
1
2046
205
0
WEU
-4%
-2%
0%
2%
4%
6%
8%
10%
12%
-4%
-2%
0%
2%
4%
6%
8%
10%
12%
201
1
201
6
202
1
202
6
203
1
203
6
204
1
2046
205
0
SEU
-4%
-2%
0%
2%
4%
6%
8%
10%
12%
-4%
-2%
0%
2%
4%
6%
8%
10%
12%
201
1
201
6
202
1
202
6
2031
203
6
204
1
204
6
205
0
NEU
-4%
-2%
0%
2%
4%
6%
8%
10%
12%
-4%
-2%
0%
2%
4%
6%
8%
10%
12%
201
1
201
6
202
1
2026
203
1
203
6
204
1
204
6
205
0
EEU
-4%
-2%
0%
2%
4%
6%
8%
10%
12%
-4%
-2%
0%
2%
4%
6%
8%
10%
12%
201
1
201
6
202
1
202
6
203
1
203
6
204
1
204
6
205
0
GRC
-4%
-2%
0%
2%
4%
6%
8%
10%
12%
Ch
an
ge
in
we
lfa
re
-4%
-2%
0%
2%
4%
6%
8%
10%
12%
201
1
201
6
202
1
202
6
203
1
203
6
204
1
204
6
205
0
Ch
an
ge
in
GD
P
AUT
[Bachner et al., 2018b]
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▪ (Quantitative) models important for policy design
▪ Reveiling link between decarbonisation and socio-economic development
▪ Modeling in TRANSRISK shows the sensitivity range for main socio-economic indicators
(GDP, employment, welfare) to variation in socio-economic & technological assumptions
▪ Risks related to decarbonising one sector have implications for other sector(s) and
other policy goals
▪ Solid understanding of links essential, including on related uncertainties
▪ Transition to be aligned across sectors (timelines, mutual synergies, resource needs)
▪ Synergies between climate policy and SDG target achievement fostered by
▪ Stable & reliable long-term policy framework that considers cross-sectoral links
▪ Financial market conditions reflecting climate policy ambition
POLICY CONCLUSIONS
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Thank you !
Andreas TürkKarl W. Steininger
Wegener Center for Climate and Global Cange
University of Graz
www.wegcenter.at
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Back up slides
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▪ Technologies and electricity price assumptions
“High-” and “low-cost” technology specification
UNCERTAINTY FRAMEWORK AND METHODS
TECHNOLOGY LAYER
Techno-economic specification ConventionalHigh cost
specific.
Low cost
specific.
Electricity price assumption [€cents/kWh] - 5 3
Integrated iron and steel technology BF-BOF DRI-H-EAF PDSP
Technolo
gy-s
pecif
ic
cost
com
ponent
[€/t
ste
el]
Refinery products (coke) 84 0 0
Electricity* 0 219 131
Iron pellets 0 84**
Iron ore 189 189 189
Services 45 40 40
Unskilled labour 5 4 4
Skilled labour 44 40 40
Capital (wear and tear) 48 48 48
Net total unit costs [€/t steel] 415 624 452
Difference to BF-BOF [€/ t steel] - +209 +37
Process emission factor [tCO2/t steel] 1.5 - -
*electricity costs for hydrogen production (and EAF in the case of DRI-H-EAF)
**additional costs due to the intermediate stage of producing iron pellets out of iron ore
(Sources: CEPS, 2013; UBA, 2017; Stakeholder dialogue)
140€/tCO225€/tCO2
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Using two different global macro-economic models
▪ General Equilibrium Model (WEGDYN)
▪ representing full capacity utilization
▪ Recursive-dynamic
▪ Calibrated to GTAPv9 (base year 2011)
▪ 17 regional aggregates: focus on AUT & 4 EU regions
▪ 17 economic sectors: special emphasis on iron and
steel (I_S) sector
▪ Econometric model (E3ME)
▪ representing output gap (idle capacities)
UNCERTAINTY FRAMEWORK AND METHODS
MACROECONOMIC STATE
Iron and Steel
Crude steel
BF-BOFcrude steel
Crude steelalternative technology
Further processing
CO2 processemissions
s:0
s:0
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socio-economic
climate policy
macro-economic
regional*
GDP
R ESULTS – UNC ERTA INTY RANGES
EU - WID E GDP E F F EC TS
socio-economic
climate policy
macro-economic
regional*
GDP
socio-economic
climate policy
macro-economic
regional*
GDP
-2.5
%
-2.0
%
-1.5
%
-1.0
%
-0.5
%
0.0
%
+0
.5%
+1
.0%
+1
.5%
socio-economic
climate policy
macro-economic
regional*
GDP
socio-economic
climate policy
macro-economic
regional*
GDP
- - - EU+3 2050
| | long-run range
x short-run range extension
* excl. GRC
hig
h c
ost
low
cost
-2.5
%
-2.0
%
-1.5
%
-1.0
%
-0.5
%
0.0
%
+0
.5%
+1
.0%
+1
.5%
socio-economic
climate policy
macro-economic
regional*
GDP
socio-economic
climate policy
macro-economic
regional*
Welfare
Main scenario (2050)
Range in 2050
Hig
h c
ost
sp
ecif
icat
ion
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E LECTRIC ITY TRANSIT ION: WACC SCENARIOS
Uniform Carbon bubbleAUT GRC EEU NEU SEU WEU AUT GRC EEU NEU SEU WEU
Solid fuels 6.0% 6.0% 6.0% 6.0% 6.0% 6.0% 9.7% 10.2% 9.1% 9.7% 9.6% 10.6%
Petroleum 6.0% 6.0% 6.0% 6.0% 6.0% 6.0% 9.7% 10.2% 9.1% 9.7% 9.6% 10.6%
Gas 6.0% 6.0% 6.0% 6.0% 6.0% 6.0% 9.7% 10.2% 9.1% 9.7% 9.6% 10.6%
Nuclear 7.0% 7.0% 7.0% 7.0% 7.0% 7.0% 7.2% 7.7% 6.6% 7.2% 7.1% 8.1%
Hydro 6.0% 6.0% 6.0% 6.0% 6.0% 6.0% 6.7% 7.2% 6.1% 6.7% 6.6% 7.6%
Biomass 7.0% 7.0% 7.0% 7.0% 7.0% 7.0% 7.2% 7.7% 6.6% 7.2% 7.1% 8.1%
Wind* 7.0% 7.0% 7.0% 7.0% 7.0% 7.0% 6.5% 12.0% 9.0% 6.8% 9.0% 5.2%
PV* 7.0% 7.0% 7.0% 7.0% 7.0% 7.0% 5.2% 9.6% 7.2% 5.5% 7.2% 4.2%
Main Scenario RES-e backed-upAUT GRC EEU NEU SEU WEU AUT GRC EEU NEU SEU WEU
Solid fuels 6.7% 7.2% 6.1% 6.7% 6.6% 7.6% 6.7% 7.2% 6.1% 6.7% 6.6% 7.6%
Petroleum 6.7% 7.2% 6.1% 6.7% 6.6% 7.6% 6.7% 7.2% 6.1% 6.7% 6.6% 7.6%
Gas 6.7% 7.2% 6.1% 6.7% 6.6% 7.6% 6.7% 7.2% 6.1% 6.7% 6.6% 7.6%
Nuclear 7.2% 7.7% 6.6% 7.2% 7.1% 8.1% 7.2% 7.7% 6.6% 7.2% 7.1% 8.1%
Hydro 6.7% 7.2% 6.1% 6.7% 6.6% 7.6% 6.7% 7.2% 6.1% 6.7% 6.6% 7.6%
Biomass 7.2% 7.7% 6.6% 7.2% 7.1% 8.1% 7.2% 7.7% 6.6% 7.2% 7.1% 8.1%
Wind* 6.5% 12.0% 9.0% 6.8% 9.0% 5.2% 5.2% 5.2% 5.2% 5.2% 5.2% 5.2%
PV* 5.2% 9.6% 7.2% 5.5% 7.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2% 4.2%
• Uniform standard values from the literature
• no differentiation by region • RES have higher WACC than
fossils
• Standard values from the literature …
• … differentiated by region
• Increased risk for fossils due to “carbon bubble”
• Differentiation by region
• Increased risk (WACC) for fossils due to “carbon bubble”
• Decreased risk (WACC) for renewables
• Differentiation by region
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Scenario setting Simulation
run
Integrated WACC assumptions
UniformEU-ref Uniform
RES-e Uniform
Main scenarioEU-ref Main scenario
RES-e Main scenario
Carbon bubbleEU-ref Carbon bubble: Doubling of WACC for Solid fuels, Petroleum and Gas
RES-e Main scenario
RES-e backed-upEU-ref Main scenario
RES-e RES-e backed-up: Minimum regional WACC applied to Wind & PV
WACC
combination
EU-ref Carbon bubble: Doubling of WACCt for Solid fuels, Petroleum and Gas
RES-e RES-e backed-up: Minimum regional WACC applied to Wind & PV
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Transitions Pathways and Risk Analysis for Climate Change Mitigation and Adaptation Strategies
Sectorial and regional/national/local trade-offs and synergiesbetween climate, economic and sustainable development goals
Asst. Prof. Haris Doukas
Management & Decision Support Systems Lab
National Technical University of Athens (NTUA)
TRANSrisk: Trade-offs in balancing socio-economic and environmental priorities
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TABLE OF CONTENTS
1. What are the co-impacts of climate action with other SDGs?
2. What are the socioeconomic impacts of energy transitions?
3. Is it feasible to achieve urgent climate and energy goals in light of risks?
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TRADE-OFFS AT THE REGIONAL LEVEL
Case study: Eastern AfricaBurundi, Djibouti, Ethiopia, Eretria, Kenya, Madagascar, Rwanda, Somalia, Sudan, SouthSudan, Uganda
Research question1. What are the co-impacts of climate action with other SDGs?
Methods, tools, approaches• GCAM (+TM5-FASST, HAP-Model)
• Portfolio analysis
Carried out by
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STRESS-TEST ING RESULTS
SDG3: Good health & well-being
SDG7: Affordable & clean energy
SDG13: Climate action
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EX – POST ANALYS IS MATTERS
Dominance of traditional biomass and free fuelwood—consequences for energy transitions.
Budget Insights
LPG high share at high budgets, negligible otherwise.Biogas is a clear winner—but not for a USD10 billion budget.
Context Insights
Improving cook stoves very important for limited budgets—except if land policy in place.
Modelling and real-world gap
Combinations of land policies and technology subsidization — tailored to (sub)national context.Subsidising costs of a shift to modern energy over a long period: not necessarily cost-effective.
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TRADE-OFFS AT THE NATIONAL LEVEL (PL)
Case study: PolandLong-term perspective
Research question2. What are the socioeconomic impacts of energy transitions?
Methods, tools, approaches• MOEM
• MEMO
• Fuzzy cognitive maps
• Stakeholder engagement
Carried out by
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1. National contexts are fundamentalWe should not be afraid to reverse theresearch/policy question.
2. The socioeconomic cost of decarbonisation is not amajor burdenThe transition has similar impacts on costs, growth,employment, investments in the long-term
4. Hidden risks – beyond the modeller’s eyeNon-adaptability of miners; energy security; barriers of entry for domestic energysuppliers; uncertain absorptive capacity levels; etc.
STAKEHOLDERS BRIDGE KNOWLEDGE GAPS
3. ‘Talanoa’ is not trivialBringing stakeholders closer to modelling:identification of hidden risk channels.
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TRADE-OFFS AT THE NATIONAL LEVEL (GR)
Case study: GreeceShort-term perspective
Research question3. Is it feasible to achieve urgent climate and energy goals in light of risks?
Methods, tools, approaches• TIMES (+WASP IV)
• Multi-criteria group decision making
• Portfolio analysis
• Fuzzy cognitive maps
• Stakeholder engagement
Carried out by
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• 2020 target is unachievableRiskiest policy mix is 0.6MTOE short.
• Anything >1MTOE is uncertain
• Very poor trade-off >1.2MTOERisk highly diversified from very low (1.2MTOE) to very high (1.26MTOE)
NEAR OPTIMAL POLICY M IXES
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CONCLUS IONS
Quantitative models are dependentAggregations, mathematical structure, background, assumptions uncertainty.Focus on consequential risks (rather than barriers), mostly of economic nature.
Stakeholders bring a critical lot to the tableForming realistic assumptions, assessing risks, substituting data unavailability, revealing hidden barriers/consequences.
The added value of decision support toolsBringing stakeholders and modellers closer, translating/exploiting expert knowledge, helping assess uncertainties.
From integrated to integrative: let’s build on this!Coupling IAMs with multicriteria analyses, portfolio optimisation, fuzzy cognitive maps andexpert knowledge provided a new level of insights.
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D ISCUSS ION
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Synergy science, policy, & implementation
[Biogas case in Indonesia/Kenya]
29/10/2018
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Policy Science Business
Toward 2 million biogas installation
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Needs for biogas = climate & non-climate targets
Clean energy
Sanitation
Health
Gender
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Policy Dialogue on H/H and electrification
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Crowdfunding Campaign
start from
November 29th 2018
http://su-re.coffee
As “a result” of EC projects
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Back up slides
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OutscalingOptimising technology, maintenance
Market-based approachCo-benefits with bio-slurry,
Upscaling
Strategic win-win
associationsMedium-scale
Biogas to electricity
Household Biogas
Low
erH
igh
er
Short-term Medium-term Long-term
Inve
stm
ent
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16
Science Policy Training Education
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biogas-coffee
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Millio
n tC
O2
-eq
GHG emission
Subsidy in billion $ 2020-40
LPG
Biogas
PV
Ethanol
Charcoal
Fuelwood
With land policy
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Stakeholder engagements with climate field school
Biogas digestor
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source: Statistik Nasional 2016 (BPS,2016)
0%
10%
20%
30%
40%
50%
60%
70%
80%
2001 2007 2008 2009 2010 2011 2012 2013 2014 2015
Electricity Gas/LPG
Kerosene Charcoal/Briquet
Firewood Others
Start of theKerosene to LPG
conversion program
National Trend in Household Cooking Fuel, 2001, 2007-2015
Electric stoves usage is decreased from 3% to 0.64% despite the increasing electrification ratio.
Accessibility is not the main factor in the transition.
More factors should be considered on analyzing energy transition.
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source: Group for the Environment, Renewable Energy, and Solidarity (2009)
Drivers of Household Fuel Choice
32%
26%
15%
2%
1%8%
8%8%
Government subsidy related to LPG
Cheaper
Easier to obtain
Cleaner
Better taste to food
Difficulty in obtaining previous fuels
Easier to use
Others
It is indicated that the reasons for switching fuel source are mostly related to cost.
Some are looking for a cheaper fuel and the rest are taking advantage of the government subsidy program.
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Biogas Diffusion Model