Exploration of the SSP space with the ReMIND-MAgPIE … · LPJ (50x50 km grid) Bioenergy price, land use emissions MAgPIE – global land use optimisation model ... Please add SSP2

Potsdam Institute for Climate Impact ResearchResearch Domain Sustainable Solutions

Exploration of the SSP space with the ReMIND-MAgPIE

integrated assessment frameworkElmar Kriegler, Nico Bauer, Alexander Popp, David Klein

Marian Leimbach, Franziska Piontek, Lena Reuster, Gunnar Luderer, Jan-Philipp Dietrich, Benjamin Bodirsky, Lavinia Baumstark,

Jessica Strefler, Jana Schwanitz, Florian Humpenöder,Christoph Bertram, Christoph Schmitz, Hermann Lotze-Campen

SSP workshop, Snowmass, 25 July 2012

Elmar KrieglerPotsdam Institute for Climate Impact Research

Exploring the SSP space

• What IAM input assumptions tovary across SSP1, 2, 5 (beyondGDP & Pop)?

• What fossil fuel use / energydemand / emissions / forcingrange (relating to challenges formitigation) is spanned betweenSSP1 &5?

All results are preliminary!

Challenges to Adaptation

Challenges to

Mitigatio

n

SSP 2 SSP 4

SSP 3 SSP 1

Increasing

socio‐econo

mic

challenges fo

r mitigatio

n

Increasing socio‐economic challenges for adaptation

SSP 2

SSP 4SSP 1

SSP 5 SSP 3

Focus on SSP1, 2, 5 because of global market assumption in model framework


Cer

eals

Oils

eeds

Puls

es

Suga

r bee

ts

Crop yieldsLand & Water constraints

LPJ (50x50 km grid)

Bioenergy price, land use emissions

MAgPIE – global land use optimisation model• spatially explicit (0.5°), 10 economic regions• 30 production activities (13 crops, livestock,

irrigation, bioenergy, land conversion)• internal feed balances, international trade• endogenous land expansion• endogenous technological change

Bioe

nerg

y

Lotze-Campen , Popp et al. (2008), Agricultural Economics

LPJmL - global vegetation and hydrology model

Model framework

ReMIND - global energy-economy-climate model• Ramsey optimal growth model• 11 economic regions• detailed energy sector (~70 conversion techs)• international trade (capital, emissions allowances, oil, coal, gas, biomass)

Biophysical inputs

Bioenergy demand, emissions price

Climate projection

Leimbach, Bauer, Baumstark, Edenhofer (2009) Environ. Modeling and AssessmentBauer, Baumstark, Leimbach (2012) Climatic ChangeLuderer, Pietzcker, Kriegler, Haller, Bauer (2012) Energy Economics


Variation of SSP input assumptionsIndicator Parameter SSP1 SSP5 SSP 2

Population Population growth(IIASA scenarios)

Low Low Medium

Economy GDP growth (OECD scenarios) Very High High Medium

Convergence of per capitaincome (OECD scenarios)

Fast Fast Medium

Convergence of capital intensities Yes Yes No

Technology Resource extractionCoal/oil/gas

Low/low/low High/high/medium Medium/medium/medium

Solar (PV and CSP) and wind power

Optimistic / low cost

Pessimistic /high cost

Optimistic / medium cost

Environment Fossil fuel subsidies Phase out until2030

Constant Phase out until 2050

Petrol / diesel taxes Convergence to10 $/GJ by 2050

Constant Constant

Taxes on air pollutants High High Medium

Energy intensity Low High Medium

Forest/ecosystem protection High Low.-Medium Low-Medium

Behaviour Food demand incl food waste(Total calory per capita)

Low High Medium

Per capita demand for livestock products

Low High Medium

Globalization/Trade Free trade pool (MAgPIE) Medium High Low


SSP Population Scenarios (IIASA)

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

SSP1 AFR

LAM

MEA

OAS

USA

RUS

JPN

IND

CHN

EUR

ROW 0

2000

4000

6000

8000

10000

12000

2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

SSP2

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

SSP5

Population in million


SSP GDP scenarios (OECD)

0

50000

100000

150000

200000

250000

300000

350000

400000

450000

500000

2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

SSP1 AFR

LAM

MEA

OAS

USA

RUS

JPN

IND

CHN

EUR

ROW 0

50000

100000

150000

200000

250000

300000

350000

400000

2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

SSP2

0

100000

200000

300000

400000

500000

600000

700000

800000

2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

SSP5

GDP in billion USD(2005) at MER


Fossil resource assumptions

Oil

Preliminary


Assumptions on final energy intensity improvements

Preliminary


Energy intensity improvements – Regional pattern

SSAEU27

CHN

USA

Preliminary

SSA EU27

CHN

USA


Forest/ecoystem protection SSP1 vs SSP5


Assumption on food energy demand

SSP1 SSP2 SSP5


Preliminary results

from ReMIND-MAgPIE

based on these input assumptions


SSP1 SSP2 SSP5

Reference

Energy demand in SSP reference cases

Preliminary results


SSP1 SSP2 SSP5

450ppm

Reference

Energy demand in SSP reference and policy cases

Preliminary results

Elmar KrieglerPotsdam Institute for Climate Impact ResearchPreliminary results

Primary Energy Mix - 2030


Primary Energy Mix – 2050


Primary Energy Mix - 2070


Bioenergy consumption

Preliminary results


Global Regional (SSP2)

Bioenergy supply curve (MAgPIE emulation)

Purpose-grown energy crops only

Purpose-grown bioenergy use in year 2100 in 450 ppm scenario

Preliminary results


Land use - Global

Preliminary results

SSP1-bau SSP5-bau


Cell-specific share of total agricultural land (crop & pasture)

Preliminary results

SSP1-bau - 2095


Cell-specific share of total agricultural land (crop & pasture)

Preliminary results

SSP5-bau - 2095


Land use - Global

Preliminary results

SSP1-bau SSP5-bau


Land use - Global

Preliminary results

SSP1-450ppm SSP5-450ppm


Europe

Land use intensification - BAU

Northern America


Europe

Land use intensification - 450ppm

Northern America


CO2 emissions – ReMIND-MAgPIE SSP vs. RCP

Preliminary results

Please add SSP2 and update


CO2 emissions – Reference and 450 ppm

Preliminary results

Please add SSP2 and update


Radiative forcing – ReMIND-MAgPIE SSP vs. RCP

Preliminary results


Conclusions

• SSP5 reference scenarios reaches RCP8.5. SSP1 is in the area of RCP6. Can it go lower? Assumptions used were already very optimistic.

• Large variation in mitigation challenges between SSP1 & 5 already under 1st best climate policy assumptions, if a variety of SSP differences beyond GDP and population is implemented

• Key assumptions impacting emissions: fossil resources, energy intensity

• Further assumptions impacting mitigation costs: renewable energy costs (mediated via food demand and forest protection in case of bioenergy)

• Effect of assumptions on capital intensity convergence, globalization of food trade, fossil fuel taxes / subsidies need to be further explored.

• Land use assumptions likely will have increased significance for high population scenarios (SSP3 & 4). Effect on food prices need to be explored (ongoing work)


SSP TEAM AT PIKElmar Kriegler, Alexander Popp, David Klein, Lena Reuster, Jana Schwanitz, Nico Bauer, Gunnar Luderer, Marian Leimbach, Franziska Piontek, Benjamin Bodirsky, Lavinia Baumstark, Jessica Strefler, Jan Philipp Dietrich, Christoph Bertram, Hermann Lotze-Campen, Christoph Schmitz, Florian Humpenöder

Thank you


Backup Slides on Model Framework

Elmar KrieglerPotsdam Institute for Climate Impact Research 33

Welfare

LabourCapital

Energy system costs

Output

ConsumptionInvestments

Final energy

Energy transformations and conversion technologies

Fuelcosts

Investment costs

Operation and Maintenance

costs

Labour efficiency Emissions

Learning by doing

Ressource and potential

constraints

Macro Economy

Energy system

Exogenous Data

Energy efficiency

Trade

Trade

Trade

Climatemodule

Quick introduction to the models: ReMIND

Leimbach,Bauer, Baumstark, Edenhofer, O. (2009)


Quick introduction to the models used: ReMIND

ReMIND Energy System / Macro Interface


ReMIND regions

USA - USA EUR - EU27 JAP - Japan CHN - China IND - India RUS - Russia AFR - Sub-Saharan Africa (excl. Republic of South Africa) MEA - Middle East, North Africa, central Asian countriesOAS - Other Asia (mostly South East Asia)LAM - Latin America ROW - Rest of the World (Canada, Australia, New Zealand, Republic of South Africa, Rest of Europe).

Quick introduction to the models used: ReMIND


Cer

eals

Oils

eeds

Puls

es

Suga

r bee

ts


+200 mm-200 -100 0 +100

CCSR

ECHAM4

Climate

LPJ (50x50 km grid)

Biophysical inputs

Income vs. Food consumption

0

500

1000

1500

2000

2500

3000

3500

4000

0 5000 10000 15000 20000 25000 30000 35000 40000

GDP / Cap / Year

Kca

l / C

ap /

Day

kcal_cap (105 countries, 1990/2000) kcal_cap (fitted values)

kcal = 802 * gdp^(0.142327) [R^2 = 0.66]

0

2

4

6

8

10

12

14

16

1900 1920 1940 1960 1980 2000 2020 2040 2060 2080 2100

Bill

ion

Low fertility, low mortality High fertility, high mortality Central fertility, central mortality (Lutz et al. 2001)

Demography

Income and diet

Food demand, production costs

Socioeconomic inputs

MAgPIE – a global land use optimisation model• spatially explicit (0.5°), 10 economic regions• 30 production activities (13 crops, livestock,

irrigation, bioenergy, land conversion)• internal feed balances, international trade• endogenous land expansion• endogenous technological change

Bioe

nerg

y

Lotze-Campen , Popp et al. (2008), Agricultural Economics


Land

use

dyn

amic

s


0

500

1000

1500

2000

2500

3000

3500

4000

0 5000 10000 15000 20000 25000 30000 35000 40000

GDP / Cap / Year

Kca

l / C

ap /

Day


kcal = 802 * gdp^(0.142327) [R^2 = 0.66]

0

2

4

6

8

10

12

14

16

1900 1920 1940 1960 1980 2000 2020 2040 2060 2080 2100

Bill

ion


Cer

eals

Oils

eeds

Puls

es

Suga

r bee

ts


+200 mm-200 -100 0 +100

CCSR

ECHAM4

Climate change (GCM)Demography

Income and diet


LPJ (50x50 km grid)

Biophysical inputs


Bioe

nerg

y

Lotze-Campen et al. (2008), Agricultural Economics

Land use pattern

2035



0

500

1000

1500

2000

2500

3000

3500

4000

0 5000 10000 15000 20000 25000 30000 35000 40000

GDP / Cap / Year

Kca

l / C

ap /

Day


kcal = 802 * gdp^(0.142327) [R^2 = 0.66]

0

2

4

6

8

10

12

14

16

1900 1920 1940 1960 1980 2000 2020 2040 2060 2080 2100

Bill

ion


+200 mm-200 -100 0 +100

CCSR

ECHAM4


Income and diet


Biophysical inputs


Lotze-Campen and Popp, World Development Report 2010

Cer

eals

Oils

eeds

Puls

es

Suga

r bee

ts


LPJ (50x50 km grid)

Bioe

nerg

y



0

500

1000

1500

2000

2500

3000

3500

4000

0 5000 10000 15000 20000 25000 30000 35000 40000

GDP / Cap / Year

Kca

l / C

ap /

Day


kcal = 802 * gdp^(0.142327) [R^2 = 0.66]

0

2

4

6

8

10

12

14

16

1900 1920 1940 1960 1980 2000 2020 2040 2060 2080 2100

Bill

ion


+200 mm-200 -100 0 +100

CCSR

ECHAM4


Income and diet


Biophysical inputs


Popp et al. 2010, GEC

Cer

eals

Oils

eeds

Puls

es

Suga

r bee

ts


LPJ (50x50 km grid)

Bioe

nerg

y

2055 Agricultural N2O [Mt CO2-e]


MAgPIE world regions


Bondeau et al. 2007, GCB

Input: climate, CO2, soil, land-use

Natural vegetation

Managed grasland

Cropland

LPJmL


Agricultural yields Carbon content

Run-off

Müller, Bondeau, Popp et al. WDR 2010

Bondeau et al. 2006, GCB

Füssel, Popp, Heinke (2010)

Gumpenberg, Popp et al. 2010, ERL

LPJmL

Documents

Exploration of the SSP space with the ReMIND-MAgPIE … · LPJ (50x50 km grid) Bioenergy price, land use emissions MAgPIE – global land use optimisation model ... Please add SSP2