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Assessing low emissions
development pathways for
the agricultural
and land use sector
Ulrich Kleinwechter, Petr Havlík, Ni...
2
Introduction
3
 Potentially important role of AFOLU sector in climate change mitigation
 Potential for mitigation of emissions from a...
4
 Research questions:
 What is the geographical and sectoral distribution of AFOLU
emissions?
 What are the requiremen...
5
 Objectives
 Quantify GHG emissions from the AFOLU sector as a whole and in the
agricultural sectors specifically and ...
6
Method
Modeling approach
GLOBIOM – Global Biosphere Management Model
 Global scale agriculture and forest sector model based on ...
8
8
18 crops (FAO + SPAM)
Wheat, Rice, Maize,
Soybean, Barley, Sorghum,
Millet, Cotton, Dry beans,
Rapeseed, Groundnut,
Su...
Decomposition of mitigation mechanisms
 Global level decomposition
 Changes in crop area or livestock numbers within reg...
10
Scenarios
11
 Scenarios:
 Global GHG emission baselines
 2000-2050
 30 regions
 SSP1, SSP2, SSP3
 Mitigation scenarios
 RCP3....
12
Results
13
 AFOLU emissions
Source: GLOBIOM model projections.
Note: BRA = Brazil; CHN = China; CONGB = Congo Basin; EAF = Easter...
14
 Emissions from agricultural production (without LUC)
Source: GLOBIOM model projections.
Note: BRA = Brazil; CHN = Chi...
15
 Trade in emissions from agriculture
Source: GLOBIOM model projections.
Note: BRA = Brazil; CHN = China; CONGB = Congo...
16
 Mitigation of AFOLU emissions
Source: GLOBIOM model projections.
Note: BRA = Brazil; CHN = China; CONGB = Congo Basin...
17
 Mitigation of agricultural emissions
Source: GLOBIOM model projections.
Note: BRA = Brazil; CHN = China; CONGB = Cong...
18
 Mitigation pathways 1
Decomposition of global mitigation of emissions from agricultural production, by SSP, RCP
3.7, ...
19
 Mitigation pathways 2
Source: GLOBIOM model projections.
Note: BRA = Brazil; CHN = China; CONGB = Congo Basin; EAF = ...
20
Conclusions
21
Conclusions
 Emissions:
 Hotspots for AFOLU emissions: Brazil, Rest of South America,
Congo Basin, China, Western Afr...
22
Conclusions (ctd.)
 Mitigation
 Key role of LUC CO2 for global and regional AFOLU mitigation
 But agricultural mitig...
23
kleinwec@iiasa.ac.at
www.globiom.org
Thank you!
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Assessing low emissions development pathways for the agricultural and land use sector, Ulrich Kleinwechter, Petr Havlík, Nicklas Forsell, Mykola Gusti, Yuquan W. Zhang, Oliver Fricko, Keywan Riahi, Michael Obersteiner

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U. Kleinwechter (IIASA, Laxenburg, Austria), P. Havlik (IIASA, Laxenburg, Austria), N. Forsell, (IIASA, Laxenburg, Austria), M. Gusti, (IIASA, Laxenburg, Austria), Y. W. Zhang, (IIASA, Laxenburg, Austria), O. Fricko, (IIASA, Laxenburg, Austria), K. Riahi (IIASA, Laxenburg, Austria), M. Obersteiner (IIASA, Laxenburg, Austria)

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Assessing low emissions development pathways for the agricultural and land use sector, Ulrich Kleinwechter, Petr Havlík, Nicklas Forsell, Mykola Gusti, Yuquan W. Zhang, Oliver Fricko, Keywan Riahi, Michael Obersteiner

  1. 1. Assessing low emissions development pathways for the agricultural and land use sector Ulrich Kleinwechter, Petr Havlík, Nicklas Forsell, Mykola Gusti, Yuquan W. Zhang, Oliver Fricko, Keywan Riahi and Michael Obersteiner International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria Our Common Future Under Climate Change International Scientific Conference 7-10 July, 2015 Paris, France
  2. 2. 2 Introduction
  3. 3. 3  Potentially important role of AFOLU sector in climate change mitigation  Potential for mitigation of emissions from agriculture and land use change  Provision of biomass for bioenergy production  Carbon sequestration (afforestation, BECCS)  AFOLU has to be an integral part of any global low emissions development strategy for climate change mitigation  But:  Detailed yet comprehensive analyses of the low emissions agricultural development pathways are scant  It is not yet well understood where to set priorities for mitigation efforts  Geographically  Sectorally  Extent of AFOLU contribution to mitigation, its composition and the mechanisms leading to emissions reduction not yet well understood
  4. 4. 4  Research questions:  What is the geographical and sectoral distribution of AFOLU emissions?  What are the requirements for AFOLU mitigation to attain a given climate forcing target?  What is the geographical and sectoral distribution of AFOLU mitigation?  What are the mechanisms that lead to GHG mitigation in the agricultural sector?
  5. 5. 5  Objectives  Quantify GHG emissions from the AFOLU sector as a whole and in the agricultural sectors specifically and identify the most important emissions sources  Provide an account of global territorial AFOLU emissions and their distribution, identifying regions with highest emissions levels and highest contributions to global emissions  Quantify potentials for GHG mitigation in the AFOLU sector as a whole and in the agricultural sectors specifically and identify the most important regions and sectors for mitigation  Identify regional potentials for AFOLU and agricultural mitigation  Describe sectoral composition of mitigation  Identification of low emissions agricultural development pathways through a quantification of the mechanisms that lead to the reduction of GHG emissions  Decomposition of mitigation  Globally  At a regional level
  6. 6. 6 Method
  7. 7. Modeling approach GLOBIOM – Global Biosphere Management Model  Global scale agriculture and forest sector model based on detailed spatial resolution (>200k cells)  Partial equilibrium  Agricultural, wood and bioenergy markets  30 world regions  Bilateral trade  Bottom-up approach  Explicit description of production technologies a la Leontief  Technologies specified by production system and grid cell  Base year: 2000  Time step: 10 years, time horizon: 2050  Routines for activity based AFOLU emissions accounting 7
  8. 8. 8 8 18 crops (FAO + SPAM) Wheat, Rice, Maize, Soybean, Barley, Sorghum, Millet, Cotton, Dry beans, Rapeseed, Groundnut, Sugarcane, Potatoes, Cassava, Sunflower, Chickpeas, Palm Fruit, Sweet potatoes 3 different systems 7 animals (FAO + Gridded livestock) Cattle & Buffalo Sheep & Goat Pig Poultry 8 different systems Downscaled FAO FRA at grid level Area Carbon stock Age Tree size Species Rotation time Thinning Landuse Land suitable for Poplar Pillow Eucalyptus Productivity from literature Cropland Grassland Managed forest Global Land Cover 2000 Short rotation plantations Other natural land Natural forest Landcover ECONOMIC MARKET + Spatial equilibrium trade  PRICES Markets Food Fibers Energy Demand Industry Population, GDP, preferences BIOENERGY Processing  MJ biofuel  MJ bioelectric  Coproducts G4M Global Forest model  Harvestable wood  Harvesting costs EPIC Rain, Snow, Chemicals Subsurface Flow Surface Flow Below Root Zone Evaporation and Transpiration RUMINANT Digestibility model  Feed intake  Animal production  GHG emissions Production
  9. 9. Decomposition of mitigation mechanisms  Global level decomposition  Changes in crop area or livestock numbers within regions  Level effects in crops and livestock (LE)  Changes in emissions intensities through production system transitions within regions  Intensity effects in crops and livestock from management changes (IE Within)  Changes in average global emissions intensities from relocation of production across regions  Intensity effects in crops and livestock from inter-regional relocation of production (IE Reloc)  Regional level decomposition  Changes in crop area or livestock numbers within regions  Level effects in crops and livestock (LE)  Changes in emissions intensities through production system transitions and relocation of production:  Intensity effects in crops and livestock production (IE)  Emissions changes due to adjustments in trade  Trade effects (TE) 9
  10. 10. 10 Scenarios
  11. 11. 11  Scenarios:  Global GHG emission baselines  2000-2050  30 regions  SSP1, SSP2, SSP3  Mitigation scenarios  RCP3.7 (~2 degree limit), SSP1-3  Biomass demand and carbon prices from MESSAGE energy systems model  Full global policy collaboration
  12. 12. 12 Results
  13. 13. 13  AFOLU emissions Source: GLOBIOM model projections. Note: BRA = Brazil; CHN = China; CONGB = Congo Basin; EAF = Eastern Africa; EUR = Europe; FSU = Former Soviet Union; IND = India; NAM = North America; MENA = Middle East and North Africa; MEX = Mexico; PACI = Pacific Industrialized; PACIS = Pacific Islands; RCAM = Central America and Caribbean; RSA = South Africa; RSAM = Rest of South America; RSAS = Rest of South Asia; RSEA OPA = Rest of South East Asia / Other Pacific Asia; RSEA PAC = Rest of South East Asia / Planned Asia; SAF = Southern Africa; WAF = Western Africa. Emissions from agriculture, forestry and other land use, SSP 2, 2020, levels and composition
  14. 14. 14  Emissions from agricultural production (without LUC) Source: GLOBIOM model projections. Note: BRA = Brazil; CHN = China; CONGB = Congo Basin; EAF = Eastern Africa; EUR = Europe; FSU = Former Soviet Union; IND = India; NAM = North America; MENA = Middle East and North Africa; MEX = Mexico; PACI = Pacific Industrialized; PACIS = Pacific Islands; RCAM = Central America and Caribbean; RSA = South Africa; RSAM = Rest of South America; RSAS = Rest of South Asia; RSEA OPA = Rest of South East Asia / Other Pacific Asia; RSEA PAC = Rest of South East Asia / Planned Asia; SAF = Southern Africa; WAF = Western Africa. Emissions from agricultural production, SSP 2, 2020, levels and composition
  15. 15. 15  Trade in emissions from agriculture Source: GLOBIOM model projections. Note: BRA = Brazil; CHN = China; CONGB = Congo Basin; EAF = Eastern Africa; EUR = Europe; FSU = Former Soviet Union; IND = India; NAM = North America; MENA = Middle East and North Africa; MEX = Mexico; PACI = Pacific Industrialized; PACIS = Pacific Islands; RCAM = Central America and Caribbean; RSA = South Africa; RSAM = Rest of South America; RSAS = Rest of South Asia; RSEA OPA = Rest of South East Asia / Other Pacific Asia; RSEA PAC = Rest of South East Asia / Planned Asia; SAF = Southern Africa; WAF = Western Africa. Global flows of trade embodied agricultural emissions, in 100 Mt/CO2eq/yr, SSP 2, 2020.
  16. 16. 16  Mitigation of AFOLU emissions Source: GLOBIOM model projections. Note: BRA = Brazil; CHN = China; CONGB = Congo Basin; EAF = Eastern Africa; EUR = Europe; FSU = Former Soviet Union; IND = India; NAM = North America; MENA = Middle East and North Africa; MEX = Mexico; PACI = Pacific Industrialized; PACIS = Pacific Islands; RCAM = Central America and Caribbean; RSA = South Africa; RSAM = Rest of South America; RSAS = Rest of South Asia; RSEA OPA = Rest of South East Asia / Other Pacific Asia; RSEA PAC = Rest of South East Asia / Planned Asia; SAF = Southern Africa; WAF = Western Africa. Mitigation of emissions from agriculture, forestry and other land use, SSP 2, RCP 3.7, averages 2020-2050
  17. 17. 17  Mitigation of agricultural emissions Source: GLOBIOM model projections. Note: BRA = Brazil; CHN = China; CONGB = Congo Basin; EAF = Eastern Africa; EUR = Europe; FSU = Former Soviet Union; IND = India; NAM = North America; MENA = Middle East and North Africa; MEX = Mexico; PACI = Pacific Industrialized; PACIS = Pacific Islands; RCAM = Central America and Caribbean; RSA = South Africa; RSAM = Rest of South America; RSAS = Rest of South Asia; RSEA OPA = Rest of South East Asia / Other Pacific Asia; RSEA PAC = Rest of South East Asia / Planned Asia; SAF = Southern Africa; WAF = Western Africa. Mitigation of emissions from agricultural production, SSP 2, RCP 3.7, averages 2020-2050
  18. 18. 18  Mitigation pathways 1 Decomposition of global mitigation of emissions from agricultural production, by SSP, RCP 3.7, 2020, 2050 and averages 2020-2050 Source: GLOBIOM model projections.
  19. 19. 19  Mitigation pathways 2 Source: GLOBIOM model projections. Note: BRA = Brazil; CHN = China; CONGB = Congo Basin; EAF = Eastern Africa; EUR = Europe; FSU = Former Soviet Union; IND = India; NAM = North America; MENA = Middle East and North Africa; MEX = Mexico; PACI = Pacific Industrialized; PACIS = Pacific Islands; RCAM = Central America and Caribbean; RSA = South Africa; RSAM = Rest of South America; RSAS = Rest of South Asia; RSEA OPA = Rest of South East Asia / Other Pacific Asia; RSEA PAC = Rest of South East Asia / Planned Asia; SAF = Southern Africa; WAF = Western Africa. Decomposition of regional mitigation of emissions from agricultural production, SSP 2, RCP 3.7, averages 2020-2050
  20. 20. 20 Conclusions
  21. 21. 21 Conclusions  Emissions:  Hotspots for AFOLU emissions: Brazil, Rest of South America, Congo Basin, China, Western Africa, and parts of South East Asia  LUC CO2 most important source, esp. in tropical forest rich regions  Assessment based on emissions per capita accentuates role of Brazil and the Congo Basin and de-emphasizes, in particular, China  But need to bear in mind role of trade  Do not neglect emissions from agricultural production  Importance for individual regions  Hotspots: China, India, Europe, Brazil and North America  CH4 from enteric fermentation most important emissions source, but depending on region  Mitigation:  Global hotspots for AFOLU mitigation largely correspond to the regions which also have the highest emissions:  Brazil, Rest of South America, Congo Basin, Western Africa  Particularly high potentials in tropical forest regions and China
  22. 22. 22 Conclusions (ctd.)  Mitigation  Key role of LUC CO2 for global and regional AFOLU mitigation  But agricultural mitigation important role to play  Significant contribution to global AFOLU emissions and mitigation  High mitigation shares in some regions (e.g. China, Europe, India)  Rising importance in the future and with more stringent mitigation targets  Mitigation hotspot: China  Mixed impact of socio-economic development on emissions and mitigation  Difficult to generalize  Region specific analyses required  Role of level effects for global and regional mitigation  Dominant role for livestock sector  Contribution of intensity effects smaller, but still appreciable  But analysis with current technologies only -> higher potentials for intensity effects with technologies and management options designed specifically for GHG reduction
  23. 23. 23 kleinwec@iiasa.ac.at www.globiom.org Thank you!

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