Low Emissions Development Strategies (LEDS) Training Sept 9, 2013

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


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.


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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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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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

Page 15

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:


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:


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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;

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


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



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







Land use change







Satellite data



Baseline

Crop Model




Policy scenario:Ex. land use allocation targets, infrastructure, adoption of low-emission agronomic practices

Land use change







Satellite data



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


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.

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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


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.


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


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

http://www.un-redd.org/

http://cdm.unfccc.int/index.html

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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http://www.cdmgoldstandard.org/

http://www.v-c-s.org/

http://www.v-c-s.org/

Climate Action Reserve (CAR)• No protocols yet (last time I checked)• http://www.climateactionreserve.org/

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Voluntary Markets

http://www.climateactionreserve.org/

Other Sources of Financing

• Nationally Appropriate Mitigation Actions (NAMAs)

• Locally Appropriate Mitigation Actions (LAMAs)

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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.

Low Emissions Development Strategies (LEDS) Training Sept 9, 2013

Education