Nakhon Kokkaew

Carbon-to-nitrogen ratio affects the biomass composition and the fatty acid profile of heterotrophically grown Chlorella sp. TISTR 8990 for biodiesel production

Chlorella sp. TISTR 8990 was cultivated heterotrophically in media with various initial carbon-to-nitrogen ratios (C/N ratio) and at different agitation speeds. The production of the biomass, its total fatty acid content and the composition of the fatty acids were affected by the C/N ratio, but not by agitation speed in the range examined. The biomass production was maximized at a C/N mass ratio of 29:1. At this C/N ratio, the biomass productivity was 0.68gL(-1)d(-1), or nearly 1.6-fold the best attainable productivity in photoautotrophic growth. The biomass yield coefficient on glucose was 0.62gg(-1) during exponential growth. The total fatty acids (TFAs) in the freeze-dried biomass were maximum (459mgg(-1)) at a C/N ratio of 95:1. Lower values of the C/N ratio reduced the fatty acid content of the biomass. The maximum productivity of TFAs (186mgL(-1)d(-1)) occurred at C/N ratios of 63:1 and higher. At these conditions, the fatty acids were mostly of the polyunsaturated type. Allowing the alga to remain in the stationary phase for a prolonged period after N-depletion, reduced the level of monounsaturated fatty acids and the level of polyunsaturated fatty acids increased. Biotin supplementation of the culture medium reduced the biomass productivity relative to biotin-free control, but had no effect on the total fatty acid content of the biomass.

A modeling government revenue guarantees in privately built transportation projects: a risk-adjusted approach

Abstract Countries around the world have welcomed Public Private Partnerships (PPPs) as an alternative to finance infrastructure. For strategic projects with high demand uncertainty, a government may decide to provide a concessionaire with a Minimum Revenue Guarantee (MRG) to mitigate revenue risk and to help enhance the projects credit, thereby reducing the financing costs of the project. However, government revenue guarantees can pose fiscal risks to the issuing government if too many significant claims are redeemed at the same time. This undesirable circumstance can be exacerbated during an economic recession in which tax revenues are low and the costs of subsidies are potentially higher than expected. This paper presents a new model of government revenue guarantees by which revenue guarantee thresholds are adjusted over time to reflect the inter-temporal risk profiles of the project. Revenue risk is modeled using a stochastic process called the Variance Model. Then, revenue shortfalls and revenue exc...

Alternative to Government Revenue Guarantees: Dynamic Revenue Insurance Contracts

AbstractPublic private partnerships (PPPs) are arrangements under which the private sector supplies infrastructure assets and services that traditionally have been provided by the public sector. Public authorities may enhance the marketability of PPP projects by offering revenue guarantees. However, government revenue guarantees can pose significant fiscal risks for the issuing government, particularly during economic crises. This paper presents a new type of revenue risk hedging contract, the dynamic (flexible) revenue insurance contract, which can be offered as an alternative to the conventional government guarantees. This new contract gives PPP stakeholders other than the government the opportunity to participate in the revenue risk coverage. Potential revenue risk insurers include international financial institutions, export credit agencies, and private insurance companies. The key feature of these new contracts is that they facilitate the pooling of project revenue insurers by accommodating insurer f...

Stochastic Optimization of Capital Structure in PrivatelyFunded Infrastructure Projects

Capital structure optimization is a key aspect to ensure the success of infrastructure financing. Interest in capital structure optimization in infrastructure projects has been growing rapidly because of the prevalence of public-private partnerships in the U.S. and private finance initiative in the United Kingdom. Even though it is recognized that there are three types of financial sources (i.e., equity, mezzanine, and debt capital) in funding an infrastructure project, the traditional capital structure optimization method either did not consider the existence of mezzanine finance or treat it to be debt or equity instruments. This assumption is not valid in the new era when more and more inflows of capital into infrastructure development projects are from all kinds of institutional investors and multilateral development finance institutions through private equity-style funds. These new equity investors are willing to take advantage of mezzanine financial instruments and common shares as a vehicle to invest in infrastructure assets, which makes a huge difference with traditional equity providers. The frequent implementation of convertible security as one kind of mezzanine financial instrument makes the prediction of the evolution of capital structure impossible due to the dynamic stopping time of the contingent claim embedded in convertible security. Thus, the traditional method for capital structure optimization in the new era is not tenable any more. The principal objective of this article is to present a new model from the perspective of project promoters, which considers convertible security in infrastructure financing and identifies optimal mix of equity, debt, mezzanine capital by incorporating stochastic dynamic programming into the traditional approach.

Copula-Based Portfolio Credit Risk Assessment in Infrastructure Project Financing

Project lenders are concerned about credit risk management in infrastructure project financing. According to the maxim in risk management, “you cannot manage what you cannot measure,” so project lenders want to know the exposure of their debt loss. Although many sophisticated credit risk models have been developed for corporate financing, only some relevant literature has focused on credit risk assessment for infrastructure projects. However, the models developed assume that project debt lenders invest only in one single project at a time. In fact, project lenders are inclined to be simultaneously involved in a portfolio of assets around the world rather than a single local project asset in order to diversify away idiosyncratic risks of an individual infrastructure project. Consequently, in order to meet project lenders’ needs, this article presents a copula-based model to measure the credit risk of a portfolio of projects by implementing the variance model and the double stochastic intensity model. A numerical example of two interdependent projects is shown as a case study to illustrate how to quantify this joint default risk in infrastructure project financing.