INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
Technical Training for Modeling Scenarios forLow Emission Development StrategiesAlex De Pinto - Senior Research Fellow Dr. Tim Thomas - Research Fellow Dr. Man Li - Research Fellow Dr. Ho-Young Kwon - Research Fellow Ms. Akiko Haruna - Senior Research AssistantMs. Shahnila Islam - Senior Research AssistantMr. Daniel Mason – D’Croz - Research AnalystMs. Subhashini Mesipam - Administrative Coordinator
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
FROM GLOBAL TO LOCAL:AN INTEGRATED APPROACH TO LOW EMISSIONS DEVELOPMENT STRATEGIES (LEDS)
Alex De Pinto - Senior Research Fellow Dr. Tim Thomas - Research Fellow Dr. Man Li - Research Fellow Dr. Ho-Young Kwon - Research Fellow Ms. Akiko Haruna - Senior Research AssistantMs. Shahnila Islam - Senior Research AssistantMr. Daniel Mason – D’Croz - Research AnalystMs. Subhashini Mesipam - Administrative Coordinator
Introduction
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. NEED TO ENCOURAGE 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.
SCOPE OF WORK
Main objective: Identification and quantification of LEDS for agriculture/forestry/natural resources use.
Analysis and modeling based on IFPRI expertise and in-country knowledge coming from existing country programs in the CGIAR system and other local institutions
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 (global markets, trade).
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;• DNDC crop modeling tool to determine GHG emissions from crop
production
Output: spatially explicit country-level results that are embedded in a framework that enforces consistency with global outcomes.
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
Climate Change Mitigation in the Agricultural Sector
Agriculture is net emitter of GHG Agriculture emits about 14% of total
GHG emission
• Fertilizers (nitrous oxide or N2O)• Livestock (methane/CH4)• Rice production (methane/CH4)• Soil ‘mining’ (depleting soil C)/Land degradation• Drying of peat and wetlands for agriculture
Agriculture and Mitigation Potential Agriculture can also help reducing GHG
emission
MITIGATION:Climate change mitigation refers to actions that reduce the potentially harmful effects of global warming by reducing the atmospheric concentration of GHG.
Different ideas about Agriculture and Mitigation Potential MITIGATION:
Climate change mitigation refers to actions that reduce the potentially harmful effects of global warming by reducing the atmospheric concentration of GHG.
• Reduction of emission intensity • Reduction of total emissions• Actual carbon sequestration:
agroforestry and afforestation (REDD, REDD+)
A brief review of key terminology Adaptation Additionality Baseline Leakage Monitoring Reporting and
Verification Permanence
Adaptation: Climate change adaptation refers to a set of actions, strategies, processes, and policies that respond to actual or expected climate changes so that the consequences for individuals, communities, and economy are minimized.
Additionality: A project is said to meet the additionality criterion if the carbon sequestration or emission reductions achieved by the project would not have been obtained in absence of the project.
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A brief Review of key terminology
Baseline: The baseline scenario describes GHG emissions in the absence of a project or a policy.
Leakage: The adoption of certain agricultural practices may reduce emissions in a given area or region; however, these emission savings could be negated if the type of agricultural production a project is trying to prevent shifts to other regions.
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A brief Review of key terminology
Permanence: This concept is related to the length of time the carbon sequestration or reduction of emissions last. The objective is to assure that the mitigation service is provided for the entire length of a project. Permanence raises significant issues related to opportunity costs.
Measurement Reporting and Verification (MRV):• Measurement: “The process of data collection over time, providing
basic datasets, including associated accuracy and precision, for the range of relevant variables. Possible data sources are field measurements, field observations, detection through remote sensing and interviews.”
• Reporting: “The process of formal reporting of assessment results according to predetermined formats and according to established standards”
• Verification: “The process of formal verification of reports, for example, the established approach to verify national communications and national inventory reports to the UNFCCC.”
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A brief Review of key terminology
Total technical mitigation potentials (all practices, all GHGs: MtCO2-eq/yr) for each region by 2030.Note: based on the B2 scenario though the pattern is similar for all SRES scenarios.Source: Smith et al. (2007)
Global mitigation potential in agriculture
We can do much better now
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Agriculture can play a role in mitigating climate change Modifying and introducing agricultural
practices and crop patterns so that:• Sequester CO2 from atmosphere and store it soils (or
trees – agroforestry)• Reduce GHG emissions
It is obvious that most if not all modifications to agricultural practices will have effects, positive and negative, on output and will have effects, positive and negative, of the economics of these activities (i.e. profit)
Challenges and Opportunities
Potential Opportunities (????)• Provide farmers with an additional source of
income – carbon markets• Help small poor farmers dealing with the effects
of climate change (co-benefit)• Food security and resilience (co-benefit)
Challenges• Uncertainty in the amounts of GHG that can be
reduced, but also pemanence, MRV, lekeage• Uncertainty in the economic effects that
mitigation policies might have
Co-benefits of mitigation
Mitigation practices overlap considerably with sustainable use of resources. Could be interpreted as an increase of overall efficiency of the production system.
Positive correlation between soil C and crop yield. Some agricultural practices improve soil fertility and induce C sequestration
More efficient water use (reduces CO2 from fuel/electricity) and methane from rice paddy
Agricultural R&D, advisory services, and information systems
Consideration about mitigation and adaptation Many people make a clear
distinction between mitigation and adaptation.
In many instances mitigation practices are good adaptation practices.
Mitigation as a path towards adaptation.
Constraints to climate change mitigation using agriculture Growing literature on the difficulties for
agriculture to contribute to mitigation: • Defining the baseline• Evidence of additionality • Profitability• High transaction costs• Property rights• Leakage• Permanence
Biophysical Modeling
SocioeconomicAnalysis
Wider Scale Modeling
Biophysical Modeling• Defining the baseline • Evidence of additionality
Constraints to climate change mitigation using agriculture
Constraints to climate change mitigation using agriculture: Biophysical
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Em
issi
ons
– C
O2 e
q
Time
Emissions with business as usual
Emissions with mitigation option
Mitigation potentialAdditionality
BASELINE
Defining the Baseline
Forestry - one has to be able to “reliably” project the future of a forest in absence of a project.
Agriculture - most important cropping systems, production practices, need to be identified:• Easy for an American farmer who grows soybeans-
maize year after year• Easy for a Vietnamese farmer who only grows rice year
after year• Difficult for a smallholder who utilizes complex
cropping systems that change in time: an example from some African countries where cropping systems like maize-cocoyam-cassava-plantain-fallow are the norm
Carbon baseline
Current Ag. practices
Crop model
Mitigation potential
Most common/important crops
Geophysical characteristics
Climate scenarios
Carbon profile input package #1
Mitigation Ag. practices/input package
Crop model
Most common/important crops
Geophysical characteristics
Climate scenarios
Carbon profile input package #2
Carbon profile input package #n
.
.
A
B
A-B
Defining the Baseline
Time2009 2030
Em
iss
ion
s a
nd
Car
bo
n S
tock
(C
O2 e
q.)
Carbon stock baseline
Emissions baseline
Carbon stock and GHG emissions computed from simulations forbaseline
Growth in carbon stock and GHG emissions extrapolated from 2009 and 2030 values
A
B
C Increase in emissions 2009 – 2030 area ABC
Increase in Carbon stock 2009 – 2030
Assessing climate change mitigation potential in agriculture
Key Modeling Issues How good are crop models?
Knowledge / quantification of how different agronomic practices and different crops affect GHG emissions (DSSAT/Century, DNDC, CropSys, EPIC, APSIM).
How good is our ability to generate plausible scenarios of future land-use choices, crop choices, agronomic practices (surveys, models of land-use change).
Major obstacle: creating a baseline. Predicting smallholders behavior is challenging.
SocioeconomicAnalysis
• Profitability – Will farmers be better off?
• Society-wide benefits – Will society be better off?
Constraints to climate change mitigation using agriculture
CH
4 & N
2 O E
missio
ns
(CO
2 eq/h
a)
Net p
rofit (U
S$/h
a) co
mp
ared to
C
on
ventio
nal
1050
1100
1150
1200
1250
1300
1350
1400
1450
Conventional New variety Biochar Composting AWD irrigation
-300
-250
-200
-150
-100
-50
0
50
100
150
200
Conventional New variety Biochar Composting AWD irrigation
High labor cost to produce and spread
Some Economic Results - Vietnam
Manure/
Nitrogen
applications
(Kg/ha)
Carbon Accumulation,
end of 20 yr period
(Ton CO2 eq/ha)
Total Payment/Implicit
Cost of Carbon
Risk-neutral agent
(USD/ha)
Yield Gains
Maize/Cassava
Dry Weight
(Kg/ha)
250/0 0.31 207/670 40/162
500/0 0.45 456/278 58/290
750/0 0.81 732/247 74/406
1,000/0 1.16 1,000/235 88/509
250/60 2.16 73/34 215/950
500/60 3.40 220/65215/1,14
750/60 4.63 462/100214/1,071
1,000/60 5.85 719/123215/1,134
Some Economic Results - Ghana
Method NPV 1,000 US $ (r = 8%)
Necessary Investment (1,000 US$)
Yearly Average Carbon
Accumulation
Reduction in CO2
Emissions
Soft Wheat
Traditional 5.9 6.8
1.8 Tons CO2eq/year
0.9 Tons CO2eq/year Zero Tillage 13. 9 7.2
Potato
Traditional irrigation
8.1 3.2
- 0.3 Tons
CO2eq/year Drip irrigation 55.8 16.8
Onion
Traditional irrigation
5.0 3.2
- 0.4 Tons CO2eq/year
Drip irrigation 51.8 16.8
Some Economic Results - Morocco
Expanding the Analysis Using the Time Dimension - Ghana
Financial support can be removed and alternatives more profitable that baseline
Minimum incentive (USD, per ha), present value at the each payment period
Column1 1st cycle 2nd cycle 3rd cycle 4th cycle
No-till 101 192 322 566
to add 500 kg ha-1 farm yard manure to each crop (38.5 kg N ha-1) 309 0 0
to add 60 kg N ha-1 chemical fertilizer nitrate 0 0 0 0
500 kg manure +60kg nitrate + no-till 0 0 0 0
1000 kg manure +60kg nitrate + no-till; 332 0 0 0)
Expanding the Analysis Using the Time Dimension - Mozambique
Overall Lessons Learned: Pilot Study and Cost Benefit Analysis of Alternate Mitigation Practices
Productivity growth, increased sustainable production, reduction in GHG emissions, are not mutually exclusive choices.
Lack of credit, investments costs, and risk aversion create a substantial barriers to the adoption of mitigation practices.
Compensation for mitigation services facilitates the adoption of agronomic practices that allow sustainable intensification.
Role of Uncertainty and Risk
There is plenty of evidence that farmers are not likely to be neutral to risk and actually tend to be risk averse (Antle 1987; Chavas and Holt 1990; Bar-Shira, Just, and Zilberman 1997; Hennessy 1998; Just and Pope 2002; Serra et al. 2006; Yesuf and Bluffstone 2007)
and that risk considerations affect input usage and technology adoption (Just and Zilberman 1983; Feder, Just, and Zilberman 1985; Kebede 1992).
Risk considerations should not be ignored in the analysis of adoption of carbon sequestration practices.
Implementation Challenges Implementation challenges
• costs involved in organizing farmers (aggregation process)
• costs of empowering farmers with the necessary knowledge
• costs of Monitoring, Reporting and Verification (MRV)
Need for strong institutional support. Institutions should include the potential of various supply chains, producers of high value export crops, non-governmental organizations (NGOs), and farmer organizations as aggregators and disseminators of management system changes and measurement technologies
Your Opinion on Agriculture and Mitigation Potential MITIGATION:
Climate change mitigation refers to actions that reduce the potentially harmful effects of global warming by reducing the atmospheric concentration of GHG.
• Reduction of emission intensity • Reduction of total emissions• Actual carbon sequestration:
agroforestry and afforestation (REDD, REDD+)
Your Opinion on Agriculture and Mitigation Potential Form three groups and discuss which
one of the following options you think is the most likely to be accepted and why.• Reduction of emission intensity • Reduction of total emissions• Actual carbon sequestration:
agroforestry and afforestation (REDD, REDD+)
Page 38
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;
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 modelMacroeconomic 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 ChoicesAncillary 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 modelMacroeconomic scenario: Ex. GDP and population growth
General Circulation Model
Climate 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 modelMacroeconomic 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 modelMacroeconomic 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
Baseline: Land cover – MODIS 2009
Change in annual precipitation, 2009 to 2030 (CSIRO Climate Model)
Change in annual mean temperature, 2009 to 2030 (CSIRO Climate Model)
Climate change 2009 – 2030
GDP – IMPACT medium variant Population – UN pop medium variant Exogenous productivity and area growth
– IMPACT standard Climate – CSIRO GCM, A1B scenario
Assumptions in baseline scenarioTime period 2009 - 2030
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Typical Modeling Output
Baseline Scenario Price Changes 2009-2030
Source: IMPACT.
Price (USD/ton) a Yield (ton/ha) b Area growth
(%) c
2009 2030 Growth (% ) 2009 2030 Growth (%)
Bean 523 577 10.16% 0.95 1.01 6.69% 8.67%
Cassava 54 73 35.54% 16.80 20.40 21.40% 1.40%
Cotton 898 1132 26.02% 1.33 1.57 17.78% 0.00%
Groundnuts 512 577 12.73% 2.09 2.41 15.25% -0.63%
Maize 53 74 39.01% 4.10 5.74 39.93% 1.73%
Irrigated Rice
133 167 25.89% 5.19 5.90 13.69% -1.71%
Rainfed Rice 133 167 25.89% 2.87 3.27 13.69% -1.71%
Soybean 154 198 28.60% 1.46 1.41 -3.13% -2.18%
Sugar cane 13 17 27.32% 58.77 66.34 12.88% 43.68%
Sweet potato 433 531 22.72% 8.26 12.25 48.33% -3.33%
Coffee 806 897 11.39% 2.08 2.22 6.69% 8.67%
Land Use Change 2009 - 2030 Baseline scenario
Land Use Category 2009 land area(Million
Hectares)
2030 land area (Million
Hectares)
Change in Area 2009 - 2030(Million ha)
Cropland 5.05 5.14 0.10
Mosaic 6.52 6.72 0.20
Woody Savannas 6.22 5.29 -0.93
Forest 12.65 12.96 0.31
Shrub/Grassland 0.47 0.51 0.04
Other Land Uses 1.93 2.22 0.29
Total 32.84 32.84 0.00
Land Use 2030 – Baseline scenario
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Crops 2009 area
(ha)
2030 area
(ha)
Change in Area
2009 – 2030
(ha)
Beans 187,200 203,437 16,237
Cassava 507,800 514,894 7,094
Cotton 9,600 9,600 0
Groundnuts 245,000 243,468 -1,532
Maize 1,089,200 1,108,073 18,873
Irrigated Rice 6,892,600 6,774,615 -117,985
Rainfed Rice 546,000 536,654 -9,346
Soybeans 131,900 129,018 -2,882
Sugarcane 265,600 381,612 116,012
Sweet Potato 146,400 141,524 -4,876
Coffee 538,400 585,100 46,700
Total 10,559,700 10,627,996 68,296
Land Use 2030 – Baseline Scenario
Land use conversion: Change in forested land. Year 2009 – 2030
Land use conversion: Change in agricultural land. Year 2009 – 2030
GHG Emissions and Carbon Stock
Land Use Category
Above ground carbon stock a
Below ground carbon stock b
SOC c CO2 d N2O d CH4 d
Cropland
YES YES YES YES YES YES
MosaicYES YES YES YES YES YES
Woody Savannas
YES YES YES
ForestYES YES YES
Shrub/Grassland
YES YES YES
Other Land Uses
YES YES YES
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ANALYSIS OF THE RESULTS
Page 54
Carbon Stock – Changes 2009 - 2030
Land Use Category
Soil Organic Carbon 2009(Tg C)
Above Ground Biomass 2009(Tg C)
Below Ground Biomass 2009 (Tg C)
Soil Organic Carbon 2030(Tg C)
Above Ground Biomass 2030(Tg C)
Below Ground Biomass 2030 (Tg C)
Net Change in Carbon Stock2009 - 2030 (Tg C)
Cropland 286.3 - - 287.0 - - 0.7
Mosaic 294.0 135.6 33.5 298.9 148.0 36.4 20.2
Woody Savannas
246.2 29.1 15.0 208.8 25.7 13.9 -41.8
Forest 520.9 989.3 232.9 532.8 1,015.8 239.2 44.7
Shrub/Grassland
22.7 4.2 1.7 24.5 4.6 1.8 2.2
Other Land Uses
139.1 - - 161.0 - - 21.9
Total 1,509.2 1,158.2 283.0 1,512.9 1,194.1 291.4 47.9
GHG Emissions – Changes 2009 - 2030
Crops Per ha GHG emission
(ton/ha)
2009 total GHG emission
(Tg CO2eq year-
1)
2030 total GHG emission
(Tg CO2eq year-1)
Changes in total GHG emission
2009 - 2030(Tg CO2eq)
Beans 14.81 2.77 3.01 0.24
Cassava 14.87 7.55 7.64 0.09
Cotton 0.47 0.00 0.00 0
Groundnuts 10.62 2.60 2.58 -0.02
Maize 7.21 7.86 7.98 0.12
Irrigated Rice
21.21 146.17 143.82 -2.34
Rainfed Rice 21.37 11.67 11.47 -0.20
Soybeans 14.81 1.95 1.91 -0.04
Sugarcane 35.58 9.45 13.56 4.11
Sweet Potato
0.33 0.05 0.05 -0.002
Coffee 0.56 0.30 0.33 0.02
Total 190.37 192.35 1.98
Baseline Results
Carbon stock increases at an annual rate of 0.08% while GHG emission increase at a rate of 0.04% annually.
Increase in GHG emissions deriving from changes in crop production are counterbalanced by an increase in carbon stock, mainly due to increase in forested areas.
Page 57
Baseline Results
Carbon stock increases at an annual rate of 0.08% while GHG emission increase at a rate of 0.04% annually.
Increase in GHG emissions deriving from changes in crop production are counterbalanced by an increase in carbon stock, mainly due to increase in forested areas.
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Baseline Results - Economy
Page 59
Crops Change in Area
2009 – 2030
(ha)
2009 total revenue
(Million USD)
2030 total revenue
(Million USD)
Changes in total
revenue(Million USD)
Beans 16,237 93.2 119.0 25.8
Cassava 7,094 460.9 769.1 308.2
Cotton 0 11.5 17.0 5.6
Groundnuts -1,532 262.2 338.6 76.4
Maize 18,873 240.1 475.1 235.0
Irrigated Rice -117,985 4,768.7 6,708.7 1,940.0
Rainfed Rice -9,346 209.0 294.0 85.0
Soybeans -2,882 29.7 36.2 6.5
Sugarcane 116,012 211.0 435.8 224.8
Sweet Potato -4,876 523.7 921.5 397.8
Coffee 46,700 902.7 1,165.8 263.1
Total 68,296 7,712.7 11,280.8 3,568.2
Alternative Policies
Objective: increase forested area• Forest area would have to increase by an
additional 1.6 million hectares compared to the baseline - forest area increases by 310,000 hectares in the baseline projection – with a net change in carbon stock equivalent to 510 million tons of CO2eq (the increase in forest carbon stock of 800 million tons of CO2eq is counterbalanced a by loss in stock from other land uses). The increase in forested area causes a reduction in cropland area of about 500,000 hectares with a resulting reduction in annual GHG emissions from crop production estimated to be 10.2 million tons of CO2eq and a reduction in revenue from crop production of 670 million USD
Alternative Policies
Rice area at 3.5 million hectares – Carbon stock from all land uses increase by a total of 247 million tons of CO2eq (an additional 71 million tons of CO2eq compared to the baseline). Emissions from crop production decrease by 4.4 million tons of CO2eq a year by 2030 (baseline results project a yearly increase of 1.7 million tons of CO2eq). The reduction in rice area causes an estimated loss in revenue equivalent to 154 million USD per year compared to the baseline .
Alternative wet and dry management for irrigated rice – estimated reduction in GHG equivalent to 113 million tons of CO2eq and a loss in revenue of 271 million USD.
Alternative Policies
Use of compost manure - estimated reduction in GHG equivalent to 22 million tons of CO2eq and a loss in revenue of 530 million USD.
Ammonium sulfate - estimated reduction in GHG equivalent to 9 million tons of CO2eq and a gain in revenue of 130 million USD.
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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Conclusion
Next steps and important data needs: Include pasture and livestock in the analysis. Better inventory of existing carbon stock in
forests Include other crops: tea, cocoa, rubber Better prices at local markets Determine the policy and policy
reforms that Vietnam would like to explore and simulate
Page 64
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
THANK YOU
Page 65
UN-REDD and REDD+• Reducing Emissions from Deforestation and Degradation
(REDD)• REDD+ includes the role of conservation, sustainable
management of forests and enhancement of forest carbon stocks.
• http://www.un-redd.org/ Clean Development Mechanism (CDM)
• Exemples of Project accepted: Biogas recovery and electricity generation from Palm Oil Mill
Effluent Advanced swine manure treatment Methane capture and combustion from poultry manure
treatment• All GHG are considered, demanding and difficult to achieve• http://cdm.unfccc.int/index.html
Regulatory Markets
Voluntary Markets
Gold Standard (non-profit organization)• Examples:
Solar Cookers Biodigesters
• In essence as demanding as CDM• http://www.cdmgoldstandard.org/
Voluntary Carbon Standards (VCS)• Afforestation, Reforestation and Revegetation
(ARR)• Agricultural Land Management (ALM)• Improved Forest Management (IFM)
• No approved methodology for agricultural land management (ALM) yet
• http://www.v-c-s.org/• Supposed to be more accessible than CDM, GS
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Climate Action Reserve (CAR)• No protocols yet (last time I checked)• http://www.climateactionreserve.org/
Page 68
Voluntary Markets
Other Sources of Financing
• Nationally Appropriate Mitigation Actions (NAMAs)
• Locally Appropriate Mitigation Actions (LAMAs)
Page 69
Concluding Remarks
Farmers appear to have the potential to make a meaningful contribution to climate change mitigation.
Adoption of mitigation practices limited by lack of credit, required investments and risk reducing financial mechanisms.
At the farm level, the distinction between adaptation and mitigation goals becomes blurry.
There already are in place organizations that could facilitate the access to carbon markets. However, their involvement could be costly given their limited knowledge of the specifics involved in climate change mitigation in agriculture.
Functioning carbon market have the potential to help poor smallholder farmers.