Market-Based Policies for Carbon Trading: A Simulation Study in the SA Murray Darling Basin

1. Travellers dilemma (Ariel Rubenstein 2004) • Imagine you are one of the players in the following two-player game: • Each of the players chooses an amount between $180 and $300 • Both players are paid the lower of the two chosen amounts • Five dollars are transferred from the player who chose the larger amount to the player who chose the smaller one. • In the case that both players choose the same amount, they both receive that amount and no transfer is made. • How much would you choose?

2. Ex ante testing of carbon trading policies in the SA Murray Darling Basin John Ward, Brett Bryan, Darran King, Neville Crossman June 2007

3. Context • The SA region of the Murray Darling Basin has seen over 80 years of land clearance and agricultural production • Prominent signs of environmental degradation – Surface and ground Water, Water quality, Land, Biota • Government policy - Integrated Natural Resource Management • INRM plan based on resource condition targets and actions • SA MDB Integrated NRM targets are multi-objective and include: • Biodiversity • River Salinity • Wind Erosion • Establish on-ground investment priorities for NRM actions on private land • Evaluate the potential of market based Instruments

4. Research programme: calibrating multi-agent models for policy optimisation in the SA MDB • Identify and evaluate market based incentives to encourage revegetation • At the farm scale, estimate the economic viability and contribution to resource targets of biomass energy and carbon trading • Survey dryland farmers to elicit farming styles and describe relationships between current community attitudes and land management actions. • Use experimental economics to quantify behavioural responses by landholders to market incentives in revegetation decision environments • Use the survey and experimental data to calibrate a multi-agent dynamic simulation of revegetation actions over fifty years • Implement four simulation scenarios of revegetation policy which estimate carbon, natural resource and economic outcomes • Describe the relationships between policy variables and NRM and economic outcomes to inform policy making processes prior to implementation.

5. Economic Viability Carbon • Mallee community - €10 / tonne • Mallee community - €20 / tonne • Mallee community - €30 / tonne • Mallee community - €40 / tonne • Viable areas - €10 / tonne • Viable areas - €20 / tonne • Viable areas - €30 / tonne • Viable areas - €40 / tonne One € = A$1.62

6. Economic Viability of Carbon

7. The quest for a behavioural epsilon:which version of “rational behaviour” to model? H.reciprocans H.economicus H.psychologicus Chi squared test of experimental cluster frequencies cf with survey sample= 0.659 (not sig dif α =0.05)

8. Principle components factor analysis and hierarchical cluster analysis innovative farm business managers socially influenced farmers time and capital constrained conservation managers life style hobby farmers

9. Cluster spatial (centroid) distribution • RBi = Atti + Ii + Sni + PCi + Oppi + wBj • Where for land holder i: RB: represents current revegetation behaviour • Atti: represents vector of attitudes • Ii: represents intended reveg action • Sni: represents influence of social norms on i decision making • PCi: represents a vector of perceived controls • Oppi: represents current opportunity cost • wj: represents decayed weighted influence of nearest neighbour j for behaviour  and • w = 1/distance i-j

10. Experimental design and metrics Experimental metrics Individual and aggregate carbon production Individual and aggregate income: Player payments Decision making Market behaviour

11. Carbon trading experimental sessions

12. Farm decision making: visual cue (map) of all catchment decisions

13. Experimental results

14. Dynamic simulations of SA MDB revegetation • Four farm scale decision making scenarios • Where an individual agent selects one cleared ha per annum to revegetate for a period of 50 years Random Lowest opportunity cost to highest Highest biodiversity value to lowest According to social diffusion. • If neighbour revegetates, then agent revegetates (influence is a decaying distance function) • assumes 5% are innovators and the probability of revegetation for all agents increases with time.

15. Dynamic decision outcomes: 50 years

16. Bringing empirically based behavioural data into NRM policy testing • Calibrate the social diffusion model based on empirical data • Higher initial levels of innovation (31% not 5% as previously assumed) • Quantify policy that includes dissemination of catchment wide decisions • Quantify variable learning capacities, responses and transaction costs of novel choices • Complement pre-existing norms and institutions • Effect on policy performance by targeting observed farming segments • Enumerate the effects of policy that matches the motivations of cluster segments: attitudinal and temporal sequencing • Ex ante modelling of policies that address the likely effects of global warming: addressing regional vulnerability and resilience

17. Waikerie carbon trading

18. Murray Bridge carbon trading

19. Total recharge results a a ac d cd e e Dunnett’s T3 post hoc test: Homogeneity of variance (Levine statistic) p < 0.05; ANOVA coefficients: F (7, 142) = 98.600; p< 0.05; Treatment means with the same letter were not statistically different at =0.05

20. “Life is animated water” (Vernadsky 1986) • Email: j.ward@csiro.au • Phone 83038685