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John Reilly, Niven Winchester, Adam Schlosser, Qudsia Ejaz MIT Joint Program on the Science and Policy of Global Change Massachusetts Institute of Technology. Climate and Economic Drivers of Land Use Change. North American Carbon Cycle Meeting, Albuquerque, New Mexico 4-6 Feb., 2013.

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climate and economic drivers of land use change

John Reilly, Niven Winchester, Adam Schlosser, QudsiaEjaz

MIT Joint Program on the Science and Policy of Global Change

Massachusetts Institute of Technology

Climate and Economic Drivers of Land Use Change

North American Carbon Cycle Meeting,

Albuquerque, New Mexico

4-6 Feb., 2013

Questions or comments?

Contact: John Reilly

[email protected]

http://globalchange.mit.edu/

slide2
2

Drivers

  • Population and Income Growth—greater demand, changing consumption patterns—more meat, more land.
  • Agricultural productivity growth—more food on less land.
  • Price driven intensification of production—pasture to feedlots, dry-land to irrigated, more fertilizer, low value to high value
  • Climate: Wetter in dry climates, drier in wet climates, or vice versa, warmer in cold climates, warmer in hot climates
  • Atmospheric composition: CO2 and crop increase but also weeds; ozone—damage; nitrogen deposition.
  • Energy and Climate Policy: higher energy prices, higher fertilizer and energy costs for agriculture, costs of reducing methane from rice and livestock, nitrous oxide from use of fertilizers
  • Land policy: Carbon sequestration/reforestation, forest protection
slide3

Economy-Global Land System Interactions

GHG and Other Pollutants

from energy and agriculture/land use

Land use shares for crops,

livestock, bioenergy, forestry

Coupled Ocean,

Atmosphere

Crop, pasture,

bioenergy, forest

productivity

CH4, N2O, Net CO2

from land use

Biogeophysical Land

Processes

Spatial data (0.5º x 0.5º) for

land use

Temperature, Precipitation,

Solar Radiation

CO2, Tropospheric Ozone,

Nitrogen deposition

MIT EPPA, 16 Region, multi

Sector CGE model

Downscaling Technique/

Spatial disaggregation

algorithm

DYNAMIC

TERRESTRIAL ECOSYSTEMS

MODEL (TEM)

slide4
4

Climate, CO2Concentrations, and Changes in Land Carbon Storage

  • Land policy gets us another ½ degree of avoided warming.
  • Not much difference between the no biofuels and biofuels policy.
  • With land incentives, land is a major sink.
  • The climate policy also helps largely because we need less land for crops because of less environmental (ozone) damage.
slide5
5

Energy Results

  • Energy-Only: All liquid fuels from biomass, large reduction in use, coal w/CCS
  • Energy+Land: Less biofuels, some petroleum, less use.
  • No-biofuels: similar, more petroleum
slide6

Land Use

  • Main difference in Energy only from No-Policy is more land biofuels, less to cropland.
    • Reason is mostly less damage to crops from ozone.
  • With Energy+Land significant reforestation, with biofuels further push into crop and pasture land.
    • This at cost of higher commodity prices.
slide7

Regionalpatterns of change in land carbon

Much loss, especially Africa, Asia

Mostly gain

slide8

Big Effects on FoodPricesWhen Land Carbon priced

Surprising result:

No-policy and energy only about the same.

Less environmental damage w/ energy policy, but higher energy and GHG control costs offset benefit to crops.

Energy+Land has big price impacts. w/o biofuels some pressure off.

Solid: no-policy Short Dash: Energy-Only

Dots: No biofuels Long Dash:Energy+ Land

slide9

Summary

  • This is a complex system with many interactions
    • Climate policy affects climate/atmospheric composition
    • Which affects crop/forest/pasture productivity
    • And, energy and fertilizer inputs
    • And, costs of controlling methane and nitrous oxide
    • It also affects incomes and food prices.
    • And these determine both the amount of land used and where crops are grown.
    • Biofuels and land carbon sequestration are still another element.
  • Understanding these linkages is critical to designing policies that don’t have unpleasant surprises.
  • Data and models well calibrated to data are needed.
  • Models must represent physical processes and economic/social
  • response that will occur through markets and policy
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