LURNZ Simulations Example output and methodology Alex Olssen, Levi Timar, Simon Anastasiadis, Suzi Kerr.

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Presentation transcript:

LURNZ Simulations Example output and methodology Alex Olssen, Levi Timar, Simon Anastasiadis, Suzi Kerr

Outline What is LURNZ Land use change module Allocation module Intensity module Greenhouse gas module Experiments

LURNZ Land Use in Rural New Zealand (LURNZ) Can simulate national ETS policy Outcomes include –Land use change –Land use intensity –Greenhouse gas emissions –Cost to communities (forthcoming)

Land use change module Based of econometric work in Olssen and Kerr (forthcoming) Inputs –Coefficients of land use response to commodity prices –Price projections –Carbon price

Land use change $25 carbon price

Actual prices in LURNZ and SONZAF

Converting carbon prices to effects on commodity prices Milk solids (2008) –Estimate emissions per kg of milk solids –Median lifespan 6.31 years (LIC) –5.31 * 323 * 6.14 = kg co2-e per cow MILK ONLY –Carcass weight 203 kg (SNZ) –203 * = 3589 kg co2-e per cow MEAT ONLY –( ) / 5.31 * 323 = 8.23 (NO FERT)

Forestry and scrub

A problem with dairy

Allocation module Based on econometric work by Timar (2011) Inputs –Probabilities from a multinomial choice model –2008 land use map –National level land use change (first module) Allocates changes in land use (understates)

Allocation module heuristic

Allocation algorithm example Typical scenario –Dairy up, sheep-beef down, forestry up, scrub down 1. Best land to dairy 2. Worst sheep-beef to scrub 3. Scrub to forestry

Intensity and GHG Modules Guiding principles Exogenous land use intensity (but dynamic) Spatial heterogeneity in simulated intensity and emissions On aggregate, consistency with NZ National Inventory

Methods Calculate IEF for a relevant production process from the Inventory Use spatial variation in production process to estimate heterogeneous emissions Project forward components via trends fitted to historical changes Ensure results consistent with Inventory and other data sources

Equations

Possible experiments ETS scenarios –Agriculture in or out Water quality policy that affects price Productivity or price shocks