Low Emissions Development Strategies (Colombia Feb 20, 2014)

25 %
75 %
Information about Low Emissions Development Strategies (Colombia Feb 20, 2014)
Education

Published on February 26, 2014

Author: IFPRI-EPTD

Source: slideshare.net

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.

FROM GLOBAL TO LOCAL: MODELING LOW EMISSIONS DEVELOPMENT STRATEGIES IN COLOMBIA Dr. 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 - Research Analyst INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

CLIMATE CHANGE BASICS INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

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  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. Page 4

Greenhouse gas emissions have been rising From ‘agriculture ’ Page 5

… and likely to rise more Figure 2 in Peters et al. (2012) Page 6

It has been getting warmer… Page 7

… and could get a lot warmer! SRES scenario differences small until after 2050 (but GCM differences can be large) Source: Figure 10.4 in Meehl, et al. (2007)

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) Source: Nelson et al, 2010. Page 10

And it gets much worse after 2050 Climate change impacts on wheat yields with 2030, 2050, and 2080 climate (percent change from 2000) Year Developed Developing Rainfed Irrigated Rainfed Irrigated 2030 -1.3 -4.3 -2.2 -9.0 2050 -4.2 -6.8 -4.1 -12.0 2080 -14.3 -29.0 -18.6 -29.0 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) Maize price mean increase is 101 % Minimum and maximum effect from four climate scenarios Rice price mean increase is 55% Wheat price mean increase is 54% Source: Nelson et al, 2010.

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.

GLOBAL FORCES, LOCAL REACTION: LOW EMISSION DEVELOPMENT STRATEGIES INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

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 highemissions lock-in than replace high-emissions technologies. EFFORT TO ENCOURAGE LEDS.

Low Emission Development Strategies  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.

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 Pag e 20

MUCHIAS GRACIAS THANK YOU INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

IFPRI’s Approach Modeling Setting and Data Pag e 22

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.

Satellite data Model of Land Use Choices Ancillary data: Ex. Soil type, climate, road network, slope, population, local ag. statistics Macroeconomic scenario: Future commodity prices and rate of growth of crop areas IMPACT model Ex. GDP and population growth Model of Land Use Choices General Circulation Model Climate scenario: Ex. Precipitation and temperature Parameter estimates for determinants of land use change Land use change Baseline Crop Model Change in carbon stock and GHG emissions Policy Simulation Policy scenario: Ex. land use allocation targets, infrastructure, adoption of low-emission agronomic practices Land use change Crop Model Change in carbon stock and GHG emissions. Economic trade-offs

Satellite data Ancillary data: Ex. Soil type, climate, road network, slope, population, local ag. statistics Model of Land Use Choices Parameter estimates for determinants of land use change

Macroeconomic scenario: Ex. GDP and population growth General Circulation Model Climate scenario: Ex. Precipitation and temperature IMPACT model Future commodity prices, yields, and rate of growth of crop areas

Satellite data Ancillary data: Ex. Soil type, climate, road network, slope, population, local ag. statistics Macroeconomic scenario: Ex. GDP and population growth General Circulation Model Climate scenario: Ex. Precipitation and temperature Parameter estimates for determinants of land use change Model of Land Use Choices Future commodity prices and rate of growth of crop areas IMPACT model Model of Land Use Choices Land use change Baseline Crop Model Change in carbon stock and GHG emissions

Satellite data 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 Macroeconomic scenario: Future commodity prices and rate of growth of crop areas IMPACT model Ex. GDP and population growth General Circulation Model Model of Land Use Choices Climate scenario: Ex. Precipitation and temperature Land use change Baseline Crop Model Change in carbon stock and GHG emissions Policy Simulation Policy scenario: Ex. land use allocation targets, infrastructure, adoption of low-emission agronomic practices Land use change Crop Model Change in carbon stock and GHG emissions. Economic trade-offs

The IMPACT Model INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

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

Model of Land Use Choices Pag e 34

Model Structure: Two-level Nested Logit Perennial Annual Crops Crops Cocoa Coffee Palm Plantain Other Perennials Pasture Cassava Maize Potato Rice Sugar Cane Other Annuals Forest Forest Other Uses

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 Model Summary Statistics of Municipal-level Predicted Percent Errors Crop Mean Q1 Median Q3 Max Cacao 2% 0% 2% 7% 52% Coffee 3% -11% 1% 12% 90% Palm 9% 0% 1% 12% 88% -4% -16% 5% 15% 47% -10% -12% 1% 5% 47% 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% 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% Perennial crop (N=927) Plantain Other crops Annual crop (N=1080) Land Categories (N=1121)

Assessment of Prediction Accuracy for Colombian Land Use Model Summary Statistics of Municipal-level Predicted Percent Errors Crop Mean Q1 Median Q3 Max Cacao 2% 0% 2% 7% 52% Coffee 3% -11% 1% 12% 90% 9% 0% 1% 12% 88% -4% -16% 5% 15% 47% -10% -12% 1% 5% 47% 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% 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% Perennial crop (N=927) Palm Plantain Other crops Annual crop (N=1080) Land Categories (N=1121)

Assessment of Prediction Accuracy for Colombian Land Use Model Summary Statistics of Municipal-level Predicted Percent Errors Crop Mean Q1 Median Q3 Max Cacao 2% 0% 2% 7% 52% Coffee 3% -11% 1% 12% 90% Palm 9% 0% 1% 12% 88% -4% -16% 5% 15% 47% -10% -12% 1% 5% 47% Cassava -4% -9% 0% 3% 27% Maize -4% -23% 0% 13% 68% Potato 2% 0% 0% 2% 74% 7% 1% 5% 14% 94% Sugarcane 4% -2% 2% 15% 88% Other crops -4% -6% 1% 5% 44% 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% Perennial crop (N=927) Plantain Other crops Annual crop (N=1080) Rice Land Categories (N=1121)

Assessment of Prediction Accuracy for Colombian Land Use Model Summary Statistics of Municipal-level Predicted Percent Errors Crop Mean Q1 Median Q3 Max Cacao 2% 0% 2% 7% 52% Coffee 3% -11% 1% 12% 90% Palm 9% 0% 1% 12% 88% -4% -16% 5% 15% 47% -10% -12% 1% 5% 47% 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% 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% Perennial crop (N=927) Plantain Other crops Annual crop (N=1080) Land Categories (N=1121)

Preliminary Results 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. INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 42

Baseline Scenario Price Changes 2008-2030 Price (USD/ton) 2008 CACAO COFFEE PALM PLANTAIN OTHR_PERENNIAL CASSAVA MAIZE POTATO RICE SUGAR CANE OTHR_ANNUAL 2273 2012 747 404 936 206 352 396 482 27 938 Source: IMPACT. 2030 2462 1957 1213 507 1168 329 445 252 411 26 1213 Growth (% ) 8.32% -2.73% 62.38% 25.50% 24.79% 59.71% 26.42% -36.36% -14.73% -3.70% 29.32% Yield (ton/ha) 2008 0.5 0.9 20.3 8.2 1.9 10.9 2.8 17.6 6.3 100.4 9.5 2030 0.6 1.1 28.5 10.1 2.0 15.0 3.3 20.6 6.8 148.6 11.5 Area growth (%) Growth (%) 13.93% 17.15% 39.94% 24.20% 2.52% 37.51% 19.99% 16.97% 7.51% 48.03% 21.35% 3.41% 2.41% 5.87% 21.24% 18.40% 5.11% -2.38% 7.37% 2.91% 13.38% 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 pasture Year 2008 – 2030

Land Use 2030 – Baseline scenario 2009 area 2030 area Change in Area (1000 ha) Crops (1000 ha) 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 Total 155 165 10 4458 4735 277 Pag e 46

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 Stock 2009 2030 (Tg C) Cropland Pasture Forest Other Land Uses Total - - 629.23 4,491.46 226.35 72.43 3,956.59 - 1,067.47 - 4,182.94 2,683.84 1,139.90 12,219.25 4,414.71 - - 670.27 5,409.77 41.04 978.67 272.08 87.07 3,098.11 - 834.59 - 3,370.19 2,750.88 67.04 921.66 11,994.37 -1,255.87 3,163.46 -2,342.61

GHG Emissions Changes 2008 - 2030 Crops Per ha GHG emission in 2008 2008 total GHG emission 2030 total GHG emission (Tg CO2eq year-1) (Tg CO2eq year-1) (Mg/ha) CACAO COFFEE COFFEE PALM PLANTAIN OTHER PERENNIAL CASSAVA MAIZE POTATO RICE SUGAR CANE OTHER ANNUAL Difference in total GHG emission for 2008 - 2030 (Mg CO2eq) 1.20 990,000 1,020,000 20,000 5.84 3,800,000 4,490,000 690,000

WHAT TO DO WITH THIS INFORMATION INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

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 practices Scenario 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.

Emissions and Carbon Stock (CO2 eq.) Alternatives to baseline: 2009 - 2030 Carbon stock baseline Emissions baseline D Carbon stock alternative policy Emissions alternative policy C A B 2009 2030 Time

Policy Simulation Comparison Cropland allocated to Rice cultivation kept constant at 3.8 million hectares. Adoption of Alternate Wet and Dry (AWD) in rice paddy: Change in GHG Emissions (Tg CO2 eq) Change in Total Revenue (Million USD) 513.8 -114.4 -6600 16.23 69.73 -68 -1800 27.53 0 -1550 -2700 2.27 0 Total forest cover increased to 45% of land area by 2030 Change C Stock (Tg CO2 eq) Lower bound compensation for gain in C stock and/or reduction of emissions (USD) -260 -5300 25.58 0 -102 1200 0.00 Introduce manure compost in rice paddy. Replace conventional fertilizer in rice paddy with ammonium sulfate. Pag e 53

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. Pag e 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 Pag e 55

Page 56 THANK YOU MUCHIAS GRACIAS POR VUESTRA ATENÇION INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

Add a comment

Related presentations

Related pages

WHD 2014 – UN-Habitat

Urban low emission development strategies ; ... Objectives of World Habitat Day 2014 ... (+254) 20 7621234 infohabitat@unhabitat.org .
Read more

International Partnership on Mitigation and MRV

Low-Emission Development Strategies ... The International Partnership on Mitigation and MRV, ... Colombia. Around 60 ...
Read more

ICLEI Corporate Report 2014 - Home | ICLEI Global

International clearinghouse on sustainable development and environmental protection policies, programs, and techniques being implemented at the local level ...
Read more

Strategies for low-emission cultivation are being explored ...

... that in total includes more than 20.000 ... When SAMPLES is completed in 2014, ... For the latest updates on low-emission agriculture strategies in ...
Read more

Colombia | Data - World Bank

... 5,000 indicators from other collections such as Gender Statistics, African Development ... (2014, 2015 , 2016, 2017) ... other agencies in low and ...
Read more

Events – UN-Habitat

Urban low emission development strategies ; National Urban Policies; ... 17/10/2016 - 20/10/2016 All Day Habitat III Conference Quito, Quito :
Read more

Stockholm Environment Institute-U.S. Center

U.S. Center of the Stockholm Environment Institute, ... For more than 20 years, ... and low-emission development strategies.
Read more

Decision -/CP.20 Lima call for climate action

Decision -/CP.20 Lima call for climate action ... 1 In 2014 the Ad Hoc Working ... to develop and implement low-emission development strategies;
Read more

Research - Stockholm Environment Institute

SEI-FBDS Rio+20 Energy Assessment. ... Supporting Mitigation in Low Emission Development Strategies. ... Sustainable Development Forum 2014: ...
Read more

Low Carbon Technologies - European Commission | Choose ...

New technology development is essential to enable us to meet our EU and global climate change objectives, but is also a major contributor towards the EU's ...
Read more