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

TRANSRISK PARTNERSHIP: WHO WE ARE

12 partners

70+ researchers

Interdisciplinary

team

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)

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?

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

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) :

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) :

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) :

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

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

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

▪ 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

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

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

▪ 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

▪ 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

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]

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

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]

-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]

-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]

▪ 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

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

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]

▪ (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

Thank you !

Andreas TürkKarl W. Steininger

Wegener Center for Climate and Global Cange

University of Graz

www.wegcenter.at

Back up slides

▪ 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

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

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

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

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

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

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?

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

STRESS-TEST ING RESULTS

SDG3: Good health & well-being

SDG7: Affordable & clean energy

SDG13: Climate action

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.

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

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.

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

• 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

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.

D ISCUSS ION

Synergy science, policy, & implementation

[Biogas case in Indonesia/Kenya]

29/10/2018

Policy Science Business

Toward 2 million biogas installation

Needs for biogas = climate & non-climate targets

Clean energy

Sanitation

Health

Gender

Policy Dialogue on H/H and electrification

Crowdfunding Campaign

start from

November 29th 2018

http://su-re.coffee

As “a result” of EC projects

Back up slides

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

16

Science Policy Training Education

biogas-coffee

Millio

n tC

O2

-eq

GHG emission

Subsidy in billion $ 2020-40

LPG

Biogas

PV

Ethanol

Charcoal

Fuelwood

With land policy

Stakeholder engagements with climate field school

Biogas digestor

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.

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.

Biogas Diffusion Model