low emissions development strategies (colombia feb 20, 2014)

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

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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

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From ‘agri-culture’

… and likely to rise more

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Figure 2 in Peters et al. (2012)

It has been getting warmer…

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… 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)

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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

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THANK YOU

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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

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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.

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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

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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

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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.

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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

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INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

THANK YOU

MUCHIAS GRACIAS POR VUESTRA ATENÇION

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