Ekundayo Shittu

Optimal Energy R&D Portfolio Investments in Response to a Carbon Tax

In this paper, we deal with a very timely issue-R&D strategies needed for compliance with a climate policy in an economically optimal way. We provide interesting insights into the composition of R&D portfolios across the main mitigation options for decision makers and policy makers. We address the optimal R&D investment response of a decision maker or an engineering manager-at the firm level with a portfolio of alternative technologies-to a rising carbon tax. Understanding the optimal allocation of investments in these technologies is crucial because like most economic resources, there is a limitation on the investment capabilities of a firm to undertake these innovative efforts. In addition, environmental R&D spending is irreversible and investment decisions made today have multiperiod consequences on the energy technologies landscape. Thus, we explore the reaction of a firms optimal investment in an energy R&D portfolio comprising four different technologies to increases in a future carbon tax. We find that investment allocation depends on the elasticity of substitution between fossil and nonfossil energy inputs, and the relative costs and efficacy of the R&D programs; and that overall investment tends to decrease in risk depending on firm flexibility and specifications.

A control model of policy uncertainty and energy R&D investments

Using an optimal control model, we explore the reaction of a firms optimal investment into an RD first, that the influence of risk on investment decisions depends on model formulation. Second, near-term investments decrease in risk in the magnitude of a carbon tax, but increase in uncertainty in the timing of a carbon tax.

Prescriptive measures for environmental performance: emission standards, overcompliance, and monitoring

This paper studies optimal regulation when a regulator can exploit two levers: traditional enforcement and certification. The objective is to demonstrate how regulation can be adapted by combining theory and empirical regularities in the existing literature. The key result is that a regulatory scheme that allows the regulator to exploit overcompliance certification as well as traditional enforcement can achieve substantively greater environmental performance: a firm now has clear incentives to overcomply, and the others have to improve environmental performance through more stringent optimal standards.

Energy Technological Change and Capacity Under Uncertainty in Learning

This paper explores the role of learning in managing the capacities of existing and emerging energy technologies. Specifically, we address how uncertainties in learning rates impact R&D investments into a range of electricity generating technologies. Understanding managerial strategic response under learning uncertainty is particularly important as decision makers face competing R&D portfolios, dwindling and unstable financial resources, and an imminent energy policy. We develop a risk-minimizing optimization model of an abridged global energy system to investigate the effects of uncertainties in technological learning on electricity capacity additions. Addressing the risks associated with uncertainties in technological learning is relevant to distill cost-effective decisions, and to develop risk-hedging strategies. We find that 1) the willingness to hedge against the inherent risks associated with uncertainty in energy technological learning is positively correlated with the risk premium; 2) near-term or early investments are required to achieve a mix of sustainable energy technological portfolios as a hedge against R&D uncertainty; and 3) the management of electricity generation capacities under learning uncertainty allows decision makers to chart a more prudent intermediate path for energy technological growth through the creation of a more diversified technological portfolio.

Reorganizing Nigeria’s Vaccine Supply Chain Reduces Need For Additional Storage Facilities, But More Storage Is Required

One of the major problems facing Nigerias vaccine supply chain is the lack of adequate vaccine storage facilities. Despite the introduction of solar-powered refrigerators and the use of new tools to monitor supply levels, this problem persists. Using data on vaccine supply for 2011-14 from Nigerias National Primary Health Care Development Agency, we created a simulation model to explore the effects of variance in supply and demand on storage capacity requirements. We focused on the segment of the supply chain that moves vaccines inside Nigeria. Our findings suggest that 55 percent more vaccine storage capacity is needed than is currently available. We found that reorganizing the supply chain as proposed by the National Primary Health Care Development Agency could reduce that need to 30 percent more storage. Storage requirements varied by region of the country and vaccine type. The Nigerian government may want to consider the differences in storage requirements by region and vaccine type in its proposed reorganization efforts.

Envelope modeling of renewable resource variability and capacity

To unify the analysis of both renewable and conventional fossil-fuel generating resources in electricity systems, we develop an envelope-based modeling method. Built on Network Calculus theory (NetCal) for deterministic queuing systems from the field of telecommunications engineering, this method characterizes the variability of electricity supply and demand by upper and lower envelopes and their respective Legendre conjugates. Differing from all other modeling methods, this method not only quantifies variability across different time scales, but also captures the intrinsic tradeoff between capacity and the corresponding Quality-of-Service (QoS) performance. In particular, the QoS measures represent matching/mismatching patterns between power supply and demand and provide an intuitive interpretation of the role of storage resources. The concept of QoS leads to two QoS-based capacity metrics - guaranteed capacity and best-effort capacity - whose conceptual and numerical properties are analyzed and compared against existing capacity metrics for validation purpose. As illustration, the proposed methods are applied to data from the California Independent System Operator (CAISO), which allows us to explicitly quantify the capacity contribution (via the notion of best-effort capacity) of wind during peak hours and its negative system impact at night, and demonstrates the positive capacity contribution of storage resources even though they are net energy consumer. HighlightsDeveloped a worst-case oriented variability modeling called envelope method.Characterized variability of renewables across multiple time scales.Characterized capacity - QoS tradeoffs.Proposed two QoS-based capacity definitions and compared with existing ones.Implemented envelope analysis with 1-min data from CAISO and gained new insights.

Energy technology investments in competitive and regulatory environments

The goal of this paper was to develop a better understanding of how energy firms might respond to competitive pressures in the context of regulatory risk. We model how competitive pressures affect capacity investments that firms make into their portfolio of technologies. Using comparative statics, we characterize energy firms’ incentives to invest in different energy technologies under imperfect competition in outputs and prices and within different environmental regulatory regimes. We find that under Cournot competition, firms that invest in renewable technologies benefit from both strategic and spillover effects on their overall profits. Under Bertrand competition, these benefits are enjoyed only by firms that invest in conventional technologies. Our findings can provide guidance for policy makers. Notably, even relatively weak targets set by regulators are likely to spur additional investment into renewable technologies. Regardless of policy type, strategic interactions and spillover benefits drive the optimal management of energy technology R&D activities. Our results also suggest ways for regulators to exploit the inherent benefits of imperfections in competitive markets to stimulate firms’ efforts at improving on the technologies in their portfolios. Overall, our results describe how technology investment incentives are shaped by the strategic interactions between firms, market structures, and environmental policy choices.

Market structure and the enforcement of emissions taxes

This paper presents a theoretical analysis of the nature of an optimal emissions tax when firms’ emissions are not perfectly observable, specifically in two types of market structure: perfect competition and Cournot competition with and without free market entry. The purpose is to examine how the optimal tax is affected by enforcement costs and the market structure. We find that market imperfections and enforcement costs push the optimal tax lower than the marginal damage to society when the number of firms in the market is exogenous. However, when the number of firms is determined endogenously, enforcement costs generate two countervailing effects on the optimal tax. The direct effect is that higher marginal enforcement costs push the optimal tax lower. The indirect effect of enforcement costs results from the role of the tax as a deterrent to entry. Limiting entry and hence the resources expended on enforcement improves social welfare. Thus, the overall effect of enforcement costs on the optimal tax dep...

Evaluating scenarios of locations and capacities for vaccine storage in Nigeria

Many developing countries still face the prevalence of preventable childhood diseases because their vaccine supply chain systems are inadequate by design or structure to meet the needs of their populations. Currently, Nigeria is evaluating options in the redesign of the countrys vaccine supply chain. Using Nigeria as a case study, the objective is to evaluate different regional supply chain scenarios to identify the cost minimizing optimal hub locations and storage capacities for doses of different vaccines to achieve a 100% fill rate. First, we employ a shortest-path optimization routine to determine hub locations. Second, we develop a total cost minimizing routine based on stochastic optimization to determine the optimal capacities at the hubs. This model uses vaccine supply data between 2011 and 2014 provided by Nigerias National Primary Health Care Development Agency (NPHCDA) on Tuberculosis, Polio, Yellow Fever, Tetanus Toxoid, and Hepatitis B. We find that a two-regional system with no central hub (NC2) cut costs by 23% to achieve a 100% fill rate when compared to optimizing the existing chain of six regions with a central hub (EC6). While the governments leading redesign alternative - no central three-hub system (Gov NC3) - reduces costs by 21% compared with the current EC6, it is more expensive than our NC2 system by 3%. In terms of capacity increases, optimizing the current system requires 42% more capacity than our NC2 system. Although the proposed Gov NC3 system requires the least increase in storage capacity, it requires the most distance to achieve a 100% coverage and about 15% more than our NC2. Overall, we find that improving the current system with a central hub and all its variants, even with optimal regional hub locations, require more storage capacities and are costlier than systems without a central hub. While this analysis prescribes the no central hub with two regions (NC2) as the least cost scenario, it is imperative to note that other configurations have benefits and comparative tradeoffs. Our approach and results offer some guidance for future vaccine supply chain redesigns in countries with similar layouts to Nigerias.

Improving communication resilience for effective disaster relief operations

The objective of this paper is to identify strategies to improve the resilience of interagency communication between relief organizations and the community when dealing with an emergency. This research draws from frameworks including information theory, organization design, and how the private sector has learned and evolved from the challenges of information flow to provide guidance to disaster relief agencies. During times of emergency, private organizations as well as public authorities must coordinate in real time to create an effective response. When coordination is absent, failure results, as was seen after Hurricane Katrina and the Haiti Earthquake. Using data that the authors collected immediately after these disasters, two case studies of systemic failure are presented to extract lessons that might be used to improve communication resilience through coordination between parties in humanitarian relief operations. Recent emergency response trends are identified, and the paper argues that the persistence of response failures is not surprising, in part because response organizations normally operate independently, and their operations evolve at different rates. As a result, the organizational interfaces that enable rapid integration during a disaster naturally degrade and may be weak or absent. Integrating the literature on information processing theory and organization design with the data from the two case studies, the paper proposes that increasing the resilience of disaster response systems can be achieved by (1) improving the interoperability and information flow across organizational boundaries; (2) increasing the synergies between organizations on adapting new technology such as social media for the coordination of structured and unstructured data for use in decision-making, and (3) increasing the flexibility of relief organizations to use external resources from areas not affected by disasters on an opportunistic basis. The paper concludes by discussing resilience enhancing solutions including boundary spanning investments and argues that effective emergency response does not result from sporadic or intermittent efforts but rather requires sustained investment, continuous monitoring, and data collection.