low emissions development strategies (colombia feb 20, 2014)
DESCRIPTION
FROM GLOBAL TO LOCAL:MODELING LOW EMISSIONS DEVELOPMENT STRATEGIES IN COLOMBIA Globally, agriculture is responsible for 10 – 14% of GHG emissions and largest source of no-CO2 GHG emissions. Countries can choose among technologies with different emission characteristics and we believe it's less costly to avoid high-emissions lock-in than replace them, so EFFORT TO ENCOURAGE LEDS is key.TRANSCRIPT
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
FROM GLOBAL TO LOCAL:MODELING LOW EMISSIONS DEVELOPMENT STRATEGIES IN COLOMBIA
Dr. Alex De Pinto - Senior Research Fellow Dr. Tim Thomas - Research FellowDr. Man Li - Research FellowDr. Ho-Young Kwon - Research FellowMs. Akiko Haruna - Research Analyst
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
CLIMATE CHANGE BASICS
The drivers of food security challenges
Demand• The number of people,• Their control over financial and physical
resources,• Their dietary desires,• Their location.
Supply• Our capacity to sustainably meet these
demands.
Food security challenges are unprecedented
Page 4
On the demand side• More people
50 percent more people between 2000 and 2050
Almost all in fragile economies.• With more income
More demand for high valued food (meat, fish, fruits, vegetables).
• Climate change – exacerbates existing threats, generates new ones.
Greenhouse gas emissions have been rising
Page 5
From ‘agri-culture’
… and likely to rise more
Page 6
Figure 2 in Peters et al. (2012)
It has been getting warmer…
Page 7
… and could get a lot warmer!
Source: Figure 10.4 in Meehl, et al. (2007)
SRES scenario differences small until after 2050 (but GCM differences can be large)
Yield Effects, Rainfed Maize, CSIRO A1B (% change 2000 climate to 2050 climate)
Source: Nelson et al, 2010.
Yield Effects, Rainfed Maize, MIROC A1B (% change 2000 climate to 2050 climate)
Page 10
Source: Nelson et al, 2010.
And it gets much worse after 2050
Year Developed DevelopingRainfe
dIrrigate
dRainfe
dIrrigate
d2030 -1.3 -4.3 -2.2 -9.02050 -4.2 -6.8 -4.1 -12.02080 -14.3 -29.0 -18.6 -29.0
Climate change impacts on wheat yields with 2030, 2050, and 2080 climate (percent change from 2000)
Source: Nelson et al, 2010.
Income and population growth drive prices higher(price increase (%), 2010 – 2050, Baseline economy and demography)
Source: Nelson et al, 2010.
Climate change increases prices even more(price increase (%), 2010 – 2050, Baseline economy and demography)
Minimum and maximum effect from four climate
scenarios
Source: Nelson et al, 2010.
Maize price mean increase is 101 %
Rice price mean increase is 55%
Wheat price mean increase is 54%
Food security, farming, and climate change to 2050
Ag prices increase with GDP and population growth.
Prices increase even more because of climate change.
International trade is critical for adaptation.
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
GLOBAL FORCES, LOCAL REACTION:LOW EMISSION DEVELOPMENT STRATEGIES
Low Emission Development Strategies Globally, agriculture is responsible for
10 – 14% of GHG emissions and largest source of no-CO2 GHG emissions.
Countries can choose among a portfolio of growth-inducing technologies with different emission characteristics.
We believe that is less costly to avoid high-emissions lock-in than replace high-emissions technologies. EFFORT TO ENCOURAGE LEDS.
Main goal of USAID funded project: Create a tool for the objective evaluation of LEDS involving agriculture and forestry sectors.
Analysis and modeling based on IFPRI expertise and in-country knowledge coming from existing country programs in the CGIAR system and other local institutions
LEDS project includes four countries: Colombia, Vietnam, Bangladesh, Zambia
Low Emission Development Strategies
Since countries are part of a global economic system, it is critical that LEDS are devised based both on national characteristics and needs, and with a recognition of the role of the international economic environment.
Output • Simulations that show the long term effect on
emissions and sequestration trends of policy reforms, infrastructure investments and/or new technologies that affect the drivers of land use-related emissions and sequestration.
• Consistent with global outcomes.
Low Emission Development Strategies
Technical Approach
Combines and reconciles • Limited spatial resolution of macro-level economic models that
operate through equilibrium-driven relationships at a subnational or national level with
• Detailed models of biophysical processes at high spatial resolution.
Essential components are: • a spatially-explicit model of land use choices which captures the
main drivers of land use change • IMPACT model: a global partial equilibrium agriculture model that
allows policy and agricultural productivity investment simulations• Crop model to simulate yield, GHG emissions, and changes in soil
organic carbon
Output: spatially explicit country-level results that are embedded in a framework that enforces consistency with global outcomes.
Conclusion
This approach allows us to: Determine land use choices trends, pressure
for change in land uses and tension forest/ agriculture
Simulate policy scenarios, their viability and the role of market forces
Simulate the long term effect on emissions and sequestration trends of the identified policy reforms in relation to global price changes and trade policies
Page 20
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
MUCHIAS GRACIAS
THANK YOU
Page 22
IFPRI’s ApproachModeling Setting and Data
Technical Approach
Combines and reconciles • Limited spatial resolution of macro-level economic models that
operate through equilibrium-driven relationships at a subnational or national level with
• Detailed models of biophysical processes at high spatial resolution.
Essential components are: • a spatially-explicit model of land use choices which captures the
main drivers of land use change • IMPACT model: a global partial equilibrium agriculture model that
allows policy and agricultural productivity investment simulations;• Crop model to simulate changes in yields and GHG emissions given
different agricultural practices
Output: spatially explicit country-level results that are embedded in a framework that enforces consistency with global outcomes.
Parameter estimates for
determinants of land use change
Change in carbon stock and GHG
emissions
Policy scenario:Ex. land use allocation targets, infrastructure, adoption of low-emission agronomic practices
Land use change
Future commodity prices and
rate of growth of crop areas
IMPACT model
Macroeconomic scenario: Ex. GDP and population growth
Model of Land Use Choices
Model of Land Use Choices
Ancillary data:Ex. Soil type, climate, road network, slope, population, local ag. statistics
Satellite data
General Circulation Model
Climate scenario:Ex. Precipitation and temperature
Change in carbon stock and GHG
emissions. Economic trade-
offs
Land use
change
Baseline
Policy Simulation
Crop Model
Crop Model
Parameter estimates for
determinants of land use change
Model of Land Use Choices
Ancillary data:Ex. Soil type, climate, road network, slope, population, local ag. statistics
Satellite data
Future commodity
prices, yields, and
rate of growth of crop areas
IMPACT model
Macroeconomic scenario: Ex. GDP and population growthGeneral Circulation ModelClimate scenario:Ex. Precipitation and temperature
Parameter estimates for
determinants of land use change
Change in carbon stock and GHG
emissions
Land use change
Future commodity prices and
rate of growth of crop areas
IMPACT model
Macroeconomic scenario: Ex. GDP and population growth
Model of Land Use Choices
Model of Land Use Choices
Ancillary data:Ex. Soil type, climate, road network, slope, population, local ag. statistics
Satellite data
General Circulation Model
Climate scenario:Ex. Precipitation and temperature
Baseline
Crop Model
Parameter estimates for
determinants of land use change
Change in carbon stock and GHG
emissions
Policy scenario:Ex. land use allocation targets, infrastructure, adoption of low-emission agronomic practices
Land use change
Future commodity prices and
rate of growth of crop areas
IMPACT model
Macroeconomic scenario: Ex. GDP and population growth
Model of Land Use Choices
Model of Land Use Choices
Ancillary data:Ex. Soil type, climate, road network, slope, population, local ag. statistics
Satellite data
General Circulation ModelClimate scenario:Ex. Precipitation and temperature
Change in carbon stock and GHG
emissions. Economic trade-
offs
Land use
change
Baseline
Policy Simulation
Crop Model
Crop Model
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
The IMPACT Model
The IMPACT Model
Global, partial-equilibrium, multi-commodity agricultural sector model
Global coverage over 115 countries or regions.
The 115 country and regional spatial units are intersected with 126 river basins: results for 281 Food Producing Units (FPUs).
World food prices are determined annually at levels that clear international commodity markets
Global Food Production Units(281 FPUs)
The IMPACT Model
Economic and demographic drivers• GDP growth • Population growth
Technological, management, and infrastructural drivers• Productivity growth• Agricultural area and irrigated area growth• Livestock feed ratios• Changes in nonagricultural water demand• Supply and demand elasticity systems• Policy drivers: commodity price policy (taxes and
subsidies), drivers affecting child malnutrition, and food demand preferences, crop feedstock demand for biofuels
The IMPACT Model
Output:• Annual levels of food supply• International food prices • Calorie availability, and share and number of
malnourished children • Water supply and demand• For each FPU: area and yield for each
considered crop
Prices are used to determine where, due to changes in relative profitability, are going to occur,
Crop area predicted by IMPACT are spatially allocated by using the land use model
Page 34
Model of Land Use Choices
CocoaCoffeePalmPlantainOther Perennials
Model Structure: Two-level Nested Logit
Decision Maker
Pasture ForestPerennialCrops
AnnualCrops
Forest Other Uses
CassavaMaizePotatoRiceSugar CaneOther Annuals
Model Specification, Upper Level
Choice variable: land use at municipio level
Explanatory variables• Population density in 2005• Travel time to major cities• Elevation• Terrain slope• Soil PH• Annual precipitation • Annual mean temperature • Cattle density• Meat price
Model Specification, Lower Level
Lower level, choice variable: crop shares in provinces:• Crop suitability• Crop price• Soil PH• Elevation• Slope• Precipitation• Temperature
Assessment of Prediction Accuracy for Colombian Land Use ModelSummary Statistics of Municipal-level Predicted Percent Errors
Crop Mean Q1 Median Q3 Max
Perennial crop (N=927)
Cacao 2% 0% 2% 7% 52%
Coffee 3% -11% 1% 12% 90%
Palm 9% 0% 1% 12% 88%
Plantain -4% -16% 5% 15% 47%
Other crops -10% -12% 1% 5% 47%
Annual crop (N=1080)
Cassava -4% -9% 0% 3% 27%
Maize -4% -23% 0% 13% 68%
Potato 2% 0% 0% 2% 74%
Rice 7% 1% 5% 14% 94%
Sugarcane 4% -2% 2% 15% 88%
Other crops -4% -6% 1% 5% 44%
Land Categories (N=1121)
Perennial cropland 0% -1% 2% 3% 18%
Annual cropland 0% -1% 1% 3% 23%
Pasture 0% -12% -1% 12% 75%
Forests 0% -7% 2% 7% 61%
Other lands 0% -6% 4% 10% 53%
Assessment of Prediction Accuracy for Colombian Land Use ModelSummary Statistics of Municipal-level Predicted Percent Errors
Crop Mean Q1 Median Q3 Max
Perennial crop (N=927)
Cacao 2% 0% 2% 7% 52%
Coffee 3% -11% 1% 12% 90%
Palm 9% 0% 1% 12% 88%
Plantain -4% -16% 5% 15% 47%
Other crops -10% -12% 1% 5% 47%
Annual crop (N=1080)
Cassava -4% -9% 0% 3% 27%
Maize -4% -23% 0% 13% 68%
Potato 2% 0% 0% 2% 74%
Rice 7% 1% 5% 14% 94%
Sugarcane 4% -2% 2% 15% 88%
Other crops -4% -6% 1% 5% 44%
Land Categories (N=1121)
Perennial cropland 0% -1% 2% 3% 18%
Annual cropland 0% -1% 1% 3% 23%
Pasture 0% -12% -1% 12% 75%
Forests 0% -7% 2% 7% 61%
Other lands 0% -6% 4% 10% 53%
Assessment of Prediction Accuracy for Colombian Land Use ModelSummary Statistics of Municipal-level Predicted Percent Errors
Crop Mean Q1 Median Q3 Max
Perennial crop (N=927)
Cacao 2% 0% 2% 7% 52%
Coffee 3% -11% 1% 12% 90%
Palm 9% 0% 1% 12% 88%
Plantain -4% -16% 5% 15% 47%
Other crops -10% -12% 1% 5% 47%
Annual crop (N=1080)
Cassava -4% -9% 0% 3% 27%
Maize -4% -23% 0% 13% 68%
Potato 2% 0% 0% 2% 74%
Rice 7% 1% 5% 14% 94%
Sugarcane 4% -2% 2% 15% 88%
Other crops -4% -6% 1% 5% 44%
Land Categories (N=1121)
Perennial cropland 0% -1% 2% 3% 18%
Annual cropland 0% -1% 1% 3% 23%
Pasture 0% -12% -1% 12% 75%
Forests 0% -7% 2% 7% 61%
Other lands 0% -6% 4% 10% 53%
Assessment of Prediction Accuracy for Colombian Land Use ModelSummary Statistics of Municipal-level Predicted Percent Errors
Crop Mean Q1 Median Q3 Max
Perennial crop (N=927)
Cacao 2% 0% 2% 7% 52%
Coffee 3% -11% 1% 12% 90%
Palm 9% 0% 1% 12% 88%
Plantain -4% -16% 5% 15% 47%
Other crops -10% -12% 1% 5% 47%
Annual crop (N=1080)
Cassava -4% -9% 0% 3% 27%
Maize -4% -23% 0% 13% 68%
Potato 2% 0% 0% 2% 74%
Rice 7% 1% 5% 14% 94%
Sugarcane 4% -2% 2% 15% 88%
Other crops -4% -6% 1% 5% 44%
Land Categories (N=1121)
Perennial cropland 0% -1% 2% 3% 18%
Annual cropland 0% -1% 1% 3% 23%
Pasture 0% -12% -1% 12% 75%
Forests 0% -7% 2% 7% 61%
Other lands 0% -6% 4% 10% 53%
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
The results are still preliminary and subject to change. They should be interpreted as trends and pressure for change driven by global changes in supply, demand, and prices.
Page 42
Preliminary Results
Baseline Scenario Price Changes 2008-2030
Source: IMPACT.
Price (USD/ton) Yield (ton/ha) Area growth
(%) 2008 2030 Growth
(% )2008 2030 Growth
(%)
CACAO 2273 2462 8.32% 0.5 0.6 13.93% 3.41%COFFEE 2012 1957 -2.73% 0.9 1.1 17.15% 2.41%PALM 747 1213 62.38% 20.3 28.5 39.94% 5.87%PLANTAIN 404 507 25.50% 8.2 10.1 24.20% 21.24%OTHR_PERENNIAL 936 1168 24.79% 1.9 2.0 2.52% 18.40%CASSAVA 206 329 59.71% 10.9 15.0 37.51% 5.11%MAIZE 352 445 26.42% 2.8 3.3 19.99% -2.38%POTATO 396 252 -36.36% 17.6 20.6 16.97% 7.37%RICE 482 411 -14.73% 6.3 6.8 7.51% 2.91%SUGAR CANE 27 26 -3.70% 100.4 148.6 48.03% 13.38%OTHR_ANNUAL 938 1213 29.32% 9.5 11.5 21.35% 6.35%
Land Use Change 2008 - 2030 Baseline scenario
Land Use Category 2008 land area(Million
Hectares)
2030 land area (Million
Hectares)
Change in Area 2008 - 2030(Million ha)
Perennial cropland 2.1 2.2 0.2
Annual cropland 2.4 2.5 0.1
Pasture 35.6 42.8 7.2
Forests 39.2 29.9 -9.2
Other lands 37.2 38.9 1.8
Total 116.4 116.4
Land Use 2008-2030 Baseline Scenario
Land use conversion: Change in forested land. Year 2008 – 2030
Land use conversion: Change in pastureYear 2008 – 2030
Land Use 2030 – Baseline scenario
Page 46
Crops 2009 area
(1000 ha)
2030 area
(1000 ha)
Change in Area
2009 – 2030
(1000 ha)
CACAO 189 196 6
COFFEE 826 846 20
PALM 345 366 20
PLANTAIN 505 612 107
OTHR_PERENNIAL 191 226 35
CASSAVA 238 250 12
MAIZE 781 762 -19
POTATO 186 200 14
RICE 651 670 19
SUGAR CANE 391 444 52
OTHR_ANNUAL 155 165 10Total 4458 4735 277
Land Use 2030 – Baseline Scenario
Land use conversion: Change in agricultural land. Year 2009 – 2030
Carbon Stock – Changes 2009 - 2030
Land Use Category
Above Ground Biomass 2008(Tg C)
Below Ground Biomass 2008(Tg C)
Soil Organic Carbon 2008(Tg C)
Above Ground Biomass 2030(Tg C)
Below Ground Biomass 2030 (Tg C)
Soil Organic Carbon 2030(Tg C)
Net Change in Carbon Stock2009 - 2030 (Tg C)
Cropland - - 629.23 - - 670.27 41.04Pasture 226.35 72.43 4,491.46 272.08 87.07 5,409.77 978.67Forest 3,956.59 1,067.47 4,414.71 3,098.11 834.59 3,163.46 -2,342.61
Other Land Uses
- -2,683.84
- -2,750.88 67.04
Total 4,182.94 1,139.90 12,219.25 3,370.19 921.66 11,994.37 -1,255.87
GHG Emissions Changes 2008 - 2030
Crops Per ha GHG
emission in 2008
(Mg/ha)
2008 total GHG emission
(Tg CO2eq year-
1)
2030 total GHG emission
(Tg CO2eq year-
1)
Difference in total GHG
emission for 2008 - 2030(Mg CO2eq)
CACAOCOFFEECOFFEE 1.20 990,000 1,020,000 20,000
PALM PLANTAINOTHER PERENNIALCASSAVAMAIZEPOTATORICE 5.84 3,800,000 4,490,000 690,000
SUGAR CANEOTHER ANNUAL
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
WHAT TO DO WITH THIS INFORMATION
Policy Simulations – An Example from Vietnam
Land use policy scenario from Decision No. 124/QD-TTg and Decision on 3119/QD-BNN-KHCN and alternative agricultural management practicesScenario 1 Total forest cover increased to 45% of land area by
2030
Scenario 2 Cropland allocated to Rice cultivation kept constant at 3.8 million hectares.
Scenario 3 Adoption of Alternate Wet and Dry (AWD) in rice paddy:
Scenario 4 Replace conventional fertilizer in rice paddy with ammonium sulfate.
Scenario 5 Introduce manure compost in rice paddy in place of farmyard manure.
Time2009 2030
Em
iss
ion
s a
nd
Car
bo
n S
tock
(C
O2 e
q.)
A
B
C
Alternatives to baseline: 2009 - 2030
Carbon stock baseline
Emissions baseline
Carbon stock alternative policy
Emissions alternative policy
D
Policy Simulation Comparison
Page 53
Change C Stock (Tg CO2 eq)
Change in GHG Emissions
(Tg CO2 eq)
Change in Total Revenue
(Million USD)
Lower bound compensation for
gain in C stock and/or reduction of
emissions (USD)
Total forest cover increased to 45% of land area by 2030
513.8 -114.4 -6600 16.23Cropland allocated to Rice cultivation kept constant at 3.8 million hectares.
69.73 -68 -1800 27.53Adoption of Alternate Wet and Dry (AWD) in rice paddy:
0 -1550 -2700 2.27Introduce manure compost in rice paddy.
0 -260 -5300 25.58Replace conventional fertilizer in rice paddy with ammonium sulfate. 0 -102 1200 0.00
Conclusion
Where do we go from here: Need to validate current results and “fine tune”
the model Complete the computation of changes in carbon
stock and GHG emissions from agriculture Determine what policies should be the object of
simulation. These must be policies that the country is currently considering for implementation or are already scheduled to be implemented.
Page 54
Conclusion
All LEDS come with costs and benefits up to the local government to decide which one is the best option
We can help making educated decisions
Page 55
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
THANK YOU
MUCHIAS GRACIAS POR VUESTRA ATENÇION
Page 56