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Research Report

The national bioenergy investment model: Technical documentation

Eric Kemp-Benedict
Copyright Date: Jan. 1, 2012
Pages: 39
OPEN ACCESS
https://www.jstor.org/stable/resrep02320
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Table of Contents

  1. (pp. 1-1)

    The model described in this working paper, the National Bioenergy Investment Model (NBIM), simulates the decisions of domestic and international direct investors on whether to invest in biofuel projects in a developing country. The model can inform scenarios that assess the potential contribution of biofuel production to national development goals. It can also be run interactively, with users specifying policy packages and trajectories for highly influential but uncertain factors, such as fossil fuel prices.

    The design constraints for the model were set in a series of engagements with the South African Development Community (SADC) Biofuels Task Force in the context...

  2. (pp. 2-3)

    The core logic of the model is that investment provides capital that is then combined with other factors, such as labour and land, to produce feedstocks and fuels that are sold on domestic and international markets. Investment allocations are determined by prices and perceived risks, which can be influenced, but not determined, by policymakers. Prices, demands, investment and production, are calculated using a dynamic non-equilibrium model (Ferguson 1998) that operates at a quarterly timestep. In the model, prices adjust, after a lag of one time-step, in the direction of their equilibrium level, depending on the gap between supply and demand....

  3. (pp. 4-10)

    Investors in the model are either domestic investors or multinational enterprises (MNEs) engaging in foreign direct investment (FDI). The question the model seeks to answer is, which of a set of business models are likely to receive domestic investment funds or FDI, and in what quantities? In contrast, most research on FDI focuses on other factors, including: FDI flows to countries (rather than projects) (Asiedu 2002, Sethi et al. 2002, Akinkugbe 2003, Ahlquist 2006, Blonigen et al. 2007, Busse and Hefeker 2007, Jinjarak 2007, Lim 2008, Dippenaar 2009); mode of entry (Kogut and Nath 1988, Hennart and Park 1993, Li...

  4. (pp. 11-13)

    Domestic energy demand in the model comes from the household and transport sectors, where per capita energy demand is determined by incomes and fuel prices, subject to the possible constraint placed by a mandatory blend ratio. International demand is assumed to be so large that national production has no effect on prices (fuel prices, both domestic and international, are discussed in Section 5). Rural and urban populations can have different demand parameters, as well as different income levels and fuel prices.

    Population growth in rural and urban areas, and economic growth, are given exogenously. From these, average income (as gross...

  5. (pp. 14-17)

    Prices are determined within a dynamic equilibrium-seeking framework. In this equilibrium-seeking model, at any given time, there are potentially distinct prices for each type of consumer – rural, urban and international. Within the country, prices for each consumer category can be affected by transport costs, local demand patterns, taxes and subsidies. International prices are determined, fundamentally, by the free on-board (FOB) price of fuels as determined in international markets, but are further influenced by costs at port, domestic taxes, subsidies and tariffs.

    Feedstock producers respond to the domestic price. Biofuel producers respond to an average of international and domestic prices,...

  6. (pp. 18-20)

    In the model, risk is captured entirely in the expression for the expected return, copied here from Equation 16,

    rE = rrf + β(raverrf) + γrcurr + ρmacro + ρmicro − θ. (68)

    In this expression, the risk factors γ and ρmacro can, in principle, be gathered from data. The systematic risk coefficient β can be calculated for many investments, but not for biofuel investments in most countries, as there are insufficient historical data to support the calculation. The micro term ρmicro depends to some extent on subjective factors, and so it cannot be estimated from historical data;...

  7. (pp. 21-22)

    Scenario models are useful tools for exploring options under uncertainty. The most important types of uncertainty in a scenario are the highly uncertain and high-impact external factors that can significantly affect the success of a policy. However, ordinary parameter uncertainty is also present. The NBIM is particularly afflicted by parameter uncertainty, as it simulates a poorly understood (although well studied) phenomenon, investor decision making. To communicate this uncertainty to the model user, the model is run in a ‘sensitivity mode’, in which parameter values are sampled from statistical distributions. Typically, the literature provides plausible minimum, maximum and nominal values, but...

  8. (pp. 23-23)

    The NBIM is a non-equilibrium dynamic model that features ‘boom and bust’ cycles, as actually experienced in biofuel feedstock and other cash crop operations. The model is intended to be used in an interactive setting, complimented by a narrative scenario process.

    As with any model, the outputs are only as good as the inputs and the model assumptions. Simulation models, which attempt to anticipate human behaviour, require particular caution. The intended use of the model is to quickly try out a variety of options in an environment where any surprising outcomes can be investigated in detail. Policy analysts and policymakers...