multi criteria comparison of fuel policies renewable fuel mandate emission standards and ghg tax n.
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Multi-criteria comparison of fuel policies: Renewable fuel mandate, emission standards, and GHG tax. Deepak Rajagopal (UCLA), Gal Hochman (Rutgers) , David Zilberman (UCB). Objective of this paper.

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multi criteria comparison of fuel policies renewable fuel mandate emission standards and ghg tax

Multi-criteria comparison of fuel policies: Renewable fuelmandate, emission standards, and GHG tax

Deepak Rajagopal (UCLA),

Gal Hochman(Rutgers),

David Zilberman (UCB)

objective of this paper
Objective of this paper
  • A simple framework to compare different policies based on multiple attributes recognizing heterogeneity, unintended consequences, leakage, and uncertainty with the aim of answering
    • How can we improve policy outcomes while satisfying the constraints?
economics of biofuel policies
Economics of biofuel policies
  • A large literature that evaluates biofuels from different perspectives
    • Market surplus/Terms of trade
      • Cui et al. , De Gorter et al., Khanna et al.
    • Cost effectiveness of GHG reduction/oil use reduction
      • Holland et al., Jaeger et al.
    • Land use change
      • Searchinger et al., Hertelet al. , Tyner et al.
      • Bercket al.
    • Food price impact
      • Hochman et al., Ujjayant et al.
    • Indirect land use change
      • Searchinger et al., Hertel et al., Tyner et al.
    • Petroleum or fuel use change
      • Rajagopal et al., Drabik and de Gorter
    • Energy Security
      • Leiby ORNL (2010)
this paper
This paper
  • We compare five policies
    • biofuel market-share mandate (similar to EU policies)
    • corn market-share ethanol mandate (similar to RFS)
    • Emission intensity standard (similar to LCFS)
    • Fuel carbon tax
    • biofuel market-share mandate + fuel carbon tax
  • In terms on several attributes or criteria
    • GHG
      • Domestic and Global
    • Energy imports
    • Fuel market surplus
      • Fuel consumer
      • Oil producer
      • Biofuel producer
    • Quantity of biofuel – (a proxy for food market surplus)
    • Share of different types of biofuel
    • Government revenue
  • Basically, it brings together issues which have been considered before by different authors with in single unified framework
  • Builds on Fischer et al. (2010)
  • Two region (Home and ROW) non-spatial partial equilibrium model of the world market for liquid fuel
  • Open economy and competitive markets
  • Two types of fuel – fossil and renewable
  • Two types of fossil fuel (crude oil and synthetic crude) and two types of renewable fuel, specifically, (corn ethanol and cane ethanol)
  • Each of the four fuels have a fixed GHG intensity
  • GHG emission intensity of biofuel
    • It is the sum of two components
      • zbiofuel= zlca+ ziluc
    • However, we assume regulations and tax are based only direct life cycle emission
      • This way we show the impact of ignoring ILUC
simplifying assumptions
Simplifying assumptions
  • No fuel products sector
    • We assume biofuel is a substitute for oil
    • So given a gasoline blend mandate, we can compute a oil blend mandate as follows
      • Oil distillation yields about 40% gasoline (energy fraction)
      • Ethanol is 2/3rd the energy content of gasoline
      • A gasoline blend mandate of αgasoline

=> oil blend mandate αoil = αgasoline* 0.4 * 2/3

  • No blend wall or cost to increasing biofuel consumption
simplifying assumptions1
Simplifying assumptions
  • Another limitation of the model from a welfare perspective is we do not model food sector explicitly
    • However, we use quantity of biofuel as a criterion
    • Since there is a consensus that food market surplus declines due to biofuel, the quantity of biofuel is a proxy for food market impact
no policy
No policy

This can be rewritten as


















policy simulations
Policy simulations

We simulate each of five policies (three above and in addition a corn market share, and biofuel market share+ Carbon Tax) for the different levels of stringencies 5000different randomly chosen combinations of the parameters shows earlier

We first graphically show the trade-offs based on the mean outcome for the five trials

summary of results
Summary of results
  • Carbon tax ranks highest under more number of criteria while corn ethanol mandate ranks lowest on more criteria compared to the other policies;
  • a combination of tax and mandate ranks higher under more number of criteria than biofuel mandate, corn ethanol mandate, and emission intensity standard;
  • emission intensity standard and biofuel mandate each rank higher on an equal number of criteria relative to the other but both policies rank higher under more number of criteria than corn ethanol biofuel mandate.
  • From a greenhouse gas (GHG) perspective, a corn ethanol mandate ranks the lowest (i.e., most pollution) and is the only policy that is expected to accelerate GHG emissions compared to the baseline while also entailing a two to three fold larger increase in global biofuel consumption relative to the other biofuel-based regulations.
  • A corn ethanol mandate ranks highest with respect to fuel import outlay
  • US RFS (which is similar to a corn ethanol share mandate) driven mainly by energy independence
  • CA LCFS (similar to emission standard) driven by GHG – but at a national level this not beneficial for energy independence
  • EU approach, which is based on biofuel mandates better than RFS as a policy. Howevergiven that biodiesel from veg. oils is more land intensive than ethanol from corn/cane it may be even less cost-effective
  • Under any type of regulation, subsidies and tariffs increase reduce home fuel price, increase emissions and benefit domestic biofuel producers
future work
Future work
  • This framework can be used to analyze cellulosic biofuels and with some extension electric cars/CNG etc.
  • Planned extensions
    • Oil products sector
    • Spatial aspects – transportation cost
    • Food sector
    • Non-competitive behavior
  • Larger extensions
    • Dynamic issues – innovation etc

We thank the Energy Biosciences Institute for funding this research