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Projection, Policy Simulations, and Optimal Land Allocation

Projection, Policy Simulations, and Optimal Land Allocation. Man Li, Research Fellow International Food Policy Research Institute Technical Training for Modeling Scenarios for Low Emission Development Strategies, September 9 th –20 th , 2013.

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Projection, Policy Simulations, and Optimal Land Allocation

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  1. Projection, Policy Simulations, and Optimal Land Allocation Man Li, Research Fellow International Food Policy Research Institute Technical Training for Modeling Scenarios for Low Emission Development Strategies, September 9th–20th, 2013

  2. Section I: Projection and Policy Simulation with the Nested Land Use Model

  3. Things to Do Before Projection/Simulation • Research questions • Collect and clean data • Specify land use model • Run regression and then generate coefficient estimates • Model identification and robustness check • Evaluate model performance • Generate a baseline (or base year) scenario (t = 0), i.e.,

  4. Projection for the Future • Exogenous changes in • Upper level • Population growth (UN projection) • Climate changes (IPCC A1B) • Lower level • Growth of prices in various crops (IMPACT projection)

  5. Policy Simulation • Identifying Variables of Interests • Upper level • Market access, e.g., travel time to major cities • Institutional factors, e.g., land conservation • Lower level • Government subsidies/taxes that affect farmgate prices, e.g., crop prices • Improve land quality, e.g., crop suitability

  6. If at the Upper Level • Relatively easy because no changes occur in variables at the lower level

  7. If at the Lower Level • More complicated since changes occur in variables at the lower level and in INCLUSIVE VALUE variable at the upper level

  8. How to Interpret Probability? • Frequency: events occur during a given period • Ideal but difficult to summarize • Have to generate numerous maps • Winner-take-all: assign each pixel to the use with the highest probability • Easy to summarize • Aggregate areas are less consistent with the actual observations • Proportion: share of land use in any given pixel • Moderately easy to summarize • Aggregate areas are more consistent with the actual observations

  9. Summarizing the Results • Generate land use maps with fine resolution • Calculate land use changes • Combining land use changes and results from DNDC (or other crop models), analyze the effects of any given policy on agricultural emissions • Some further investigations …

  10. Exercise I • Assess the effect of conservation on land use in Vietnam • Step 1: Run regression, extract the coefficient estimates and save them • Step 2: Generate the baseline land use • Step 3: Assign all protected area dummies being zeroand calculate the simulated land use • Step 4: Compare the simulated scenario with the baseline (maps, tables, …)

  11. Land Use 2030 – Baseline scenario effect a policy that does not enforce protected area Designated and proposed protected areas. Land use conversions from forest to other categories. With protected area – Without protected area Page 11

  12. Section II: Optimal Land Allocation with Land Use Target

  13. When the Target is the Upper-level Use • Example: Increase forest cover from 41% to 45% in Vietnam • Insert a policy factor δ to forest “utility”

  14. When the Target is the Upper-level Use • Find a minimum δ such that • Initial guess δ0= .45/.41-1≈ .098; optimal δ* ≈ .295 • In this circumstance, the conditional probabilities at the lower-level would not change, i.e., • Hence, the logit probabilities of crop choice are

  15. When the Target is the Lower-level Use • Example: Decrease rice area from 4.131 Mha to 3.8 Mha in Vietnam • Insert a policy factor δ to rice “utility,” more complicated than the previous example

  16. When the Target is the Lower-level Use • Find a maximum δ such that • Under this circumstance, probabilities change at both levels • Initial guess δ0= 3.8/4.131-1 ≈ - .080; optimal δ* ≈ -.185

  17. Exercise II • Looking at the lower-level model: climate change affect crop suitability in Vietnam • Step 1: Run regression, extract the coefficient estimates and save them • Step 2: Generate the baseline land use • Step 3: Change explanatory variable at the lower level model and calculate the simulated land use • Step 4: Compare the simulated scenario with the baseline (maps, tables, …)

  18. Feedback: Questions and Discussions • Any suggestions to improve the land use model, especially on policy variables, institutional factors, etc.? Considering various dominate land cover in different countries, e.g., • Vietnam: Forest cover • Bangladesh: Cropland • Colombia: Pasture • Do you have any policies in mind that might affect land use (change) in your country?

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