Overview of the PIK REMIND-MAgPIE-LPJml integrated ...Kriegler).pdf · REMIND-MAgPIE-LPJml integrated assessment framework Elmar Kriegler, ... Nico Bauer, Gunnar Luderer, ... 16 1900

Potsdam Institute for Climate Impact Research Research Domain Sustainable Solutions

Overview of the PIK REMIND-MAgPIE-LPJml

integrated assessment framework

Elmar Kriegler, Wolfgang Lucht

The World in 2050 Workshop IIASA, 11 March 2015

Elmar Kriegler Potsdam Institute for Climate Impact Research

IAM TEAM AT PIK

Nico Bauer, Gunnar Luderer, Christoph Bertram, Jerome Hilaire, Antoine Levesque, Ioanna Mouratiadou, Michaja Pehl, Robert Pietzcker, Jessica Strefler

Alexander Popp, Hermann Lotze-Campen, Anne Biewald, Benjamin Bodirsky, Florian Humpenöder, Ulrich Kreidenweis, Christoph Müller, Miodrag Stefanovic, Isabelle Weindl

Marian Leimbach, Franziska Piontek, Anselm Schultes, Niklas Roming, Gregor Schwerhoff

Jan Philipp Dietrich, Lavinia Baumstark, Anastasis Giannousakis, David Klein

Scientific coordination Laura Delsa

Energy Land use Economy M o d e l operations


Cer

eals

Oils

eeds

Puls

es

Suga

r bee

ts

Crop yields Land & 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 (2010) Environ. Modeling and Assessment Bauer, Baumstark, Leimbach (2012) Climatic Change Luderer, Pietzcker, Kriegler, Haller, Bauer (2012) Energy Economics

TRANSFORMATION PATHWAYS in REMIND-MAgPIE

Modeling the land-‐energy-‐water-‐climate nexus with climate change and mi6ga6on targets as star6ng point

Energy use

Land use

2°

4° 3°

Selected References •  Luderer et al. (2012) Energy Economics •  Popp et al. (2011) Environmental Research Letters

Energy use Land use

2°

4° 3°

•  Energy supply & demand changes

•  Energy prices

•  VRE integration1

•  International energy market effects2

•  Energy security

•  Technology development & policies3,4

Energy transforma6on in mi6ga6on pathways

Ø  Mitigation reduces fossil fuel rents. Ø  Overcompensated by emerging carbon rent

Bauer et al., Climatic Change (2014)

References 1.  Pietzcker et al. (2014) Energy 2.  Bauer et al. (2015) TFSC 3.  Bauer et al. (2012) PNAS 4.  Bertram et al. (2015) NCC

REMIND

Land use transforma6on

Energy use

Land use

2°

4° 3°

•  Land use changes and emissions •  Trade-offs between bioenergy,

afforestation, food production1,2

•  Agricultural productivity increases3

•  Land protection regimes & reduced deforestation2,4

•  Impact of dietary changes5

•  International agricultural trade6

•  Climate impacts on land References 1.  Humpenöder et al. (2014) Environmental Research Letters 2.  Popp et al. (2014) Environmental Research Letters 3.  Dietrich et al. (2014) TFSC 4.  Popp et al. (2014) Nature Climate Change 5.  Popp et al. (2010) Global Environmental Change, 6.  Schmitz et al. (2012) Global Environmental Change

Non-forest leakage due to REDD

Popp et al. 2014, NCC

MAgPIE


Further sustainability dimensions •  Water demand of energy and agricultural sector1, Environmental flow constraints2

•  Air pollution (joint work with IIASA & PBL, e.g. in LIMITS project) •  Material flows and resource requirements (ADVANCE project) •  Nitrogen pollution3

Agricultural water use

(Bonsch et al., 2014) 300 EJ/yr bioenergy in 2100

References 1. Bonsch et al. (2014) GCB Bioenergy 2. Bonsch et al. (2015) Global Environmental Change 3. Bodirsky et al. (2014) Nature Communications

Global quan8fica8on of the food-‐land-‐water nexus (LPJmL) (D. Gerten, W. Lucht and the LPJmL-‐4 team)

yield increase or LU expansion avoided

Example: Can water management improving efficiency in produc8ve water flows increase yield?

LPJmL4 agro-‐hydro-‐biosphere BGC model Low = 10% Med = 25% High = 50%

vapour shiW and rain water harves8ng

Water management:

Figures and preliminary results: J. Jägermeyr, D. Gerten, J. Heinke et al. PIK 2014

Elmar Kriegler: The Challenge of Mi8ga8ng Climate Change 9

Agreement in 2015 and 2oC

(LIMITS; Clim. Change Econ. 4(4)/5(1))

Role of Technology Availability

(EMF27; Clima8c Change 123(3-‐4) )

Global policy landscape & 6ming (AMPERE; Tech. For. &

Soc. Change 90A )

Recent studies of the energy-‐land transforma6on

Role of emissions drivers

(RoSE; Clima8c Change)

•  ca. 950 emissions scenarios

•  Major contribu8on to IPCC AR5 report of Working Group 3 AR5 Scenario Database: hips://secure.iiasa.ac.at/web-‐apps/ene/AR5DB

Forc

ing

leve

l (W

/m2 )

8.5

6.0

4.5

2.6

Shared Socio-economic Pathways

6 - 7.5 W/m2 >8 W/m2

5.2 – 6.2 W/m2 ~5.8 W/m2

Climate Policy Scenarios

Courtesy: Keywan Riahi

Development of new scenarios for climate change research

SSP1 Sustainability

SSP2 Middle of the Road

SSP3 Regional Rivalry

SSP4 Inequality

SSP5 Fossil-‐fueled Development

Marker scenario by REMIND-MAgPIE

PIK's Project "Co-‐Evolu8onary Pathways" (W. Lucht, J. Donges, J. Heitzig)

Dynamic Systems Analysis for t=2050: "Ask not what state you'll be in, but what flow you'll be in"

undesired states

desired states

Can we get into the desired flow by rowing?

(1) What are the proper8es of the flow? What are the system proper8es of BAU? (2) What is possible with rowing at 8me t? (3) What is the robustness of the path against an interrup8on of rowing? We could contribute a topological discussion of flow regimes and dilemmata faced


Considerations on TWI 2050

•  Embedding mitigation pathway work in broader sustainability context is area of active work and strategic interest at PIK

è excellent fit with TWI 2050, high interest in the project

•  Development of full modeling capabilities will be a multi-year multi-person project èscope of TWI contribution somewhat contingent on timeline and availability of funding

•  Large potential synergies with current SSP work by IIASA, PIK, PBL è would be very helpful to exploit them as much as possible in TWI50

•  Communication strategy of TWI 2050 è communication of model results coordinated by modeling teams


Backup Slides on Model Framework


Nitrogen pollution Only ambitious mitigation action may reduce nitrogen pollution below critical thresholds. Even then, the risk remains that thresholds are exceeded.

Bodirsky et al. (2014), Nature Communications


Variation of SSP input assumptions Indicator 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 capita income (OECD scenarios)

Fast Fast Medium

Convergence of capital intensities Yes Yes No

Technology Resource extraction Coal/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 until 2030

Constant Phase out until 2050

Petrol / diesel taxes Convergence to 10 $/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 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 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


SSP1 SSP2 SSP5

450ppm

Reference

Energy demand in SSP reference and policy cases

Preliminary results


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

Elmar Kriegler Potsdam Institute for Climate Impact Research 21

Welfare

Labour Capital

Energy system costs

Output

Consumption Investments

Final energy

Energy transformations and conversion technologies

Fuel costs

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

Climate module

Quick introduction to the models: ReMIND

Leimbach,Bauer, Baumstark, Edenhofer, O. (2010)


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 countries OAS - 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

Billion

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

Billion


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

Billion


+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

Billion


+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


Models can provide maps of the „solu8on space“.

Policy makers can use them to navigate through this space.

Mi6ga6on scenarios for the 21st century are of value to draw the link between short term ac6on and long term goals

How can global models inform the negotiation process?

Overview of the PIK REMIND-MAgPIE-LPJml integrated ...Kriegler).pdf · REMIND-MAgPIE-LPJml integrated assessment framework Elmar Kriegler, ... Nico Bauer, Gunnar Luderer, ... 16 1900

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