Strategic Environmental Policy, Clean Technologies and the Learning Curve

In the political discussion, it is often emphasized that the environmental service industry (which produces a clean factor of production) benefits from an early and strong environmental policy. This is especially likely if the costs of production are decreasing over time due to learning curve effects. Surprisingly, the environmental service industry has not been integrated into the theory of strategic environmental policy yet. Our main question is whether a national leadership in environmental policy can pay off if profits of the environmental service industry are taken into account. We consider a two-period model with one firm in each country competing on a third market. Emissions can be substituted by the clean factor when deciding upon the production technology. The unit costs of producing the clean factor in the second period are decreasing in the quantity produced in the initial period. We derive the optimal environmental policy for both periods from a national point of view and show that the presence of the environmental service industry can indeed lead to a national leadership in pollution control.     

Safety regulation and monitor liability

We propose a simple liability rule when several agents are jointly responsible for monitoring a risky economic activity or certifying its security. Examples are safety controls for drugs or technical systems, environmental liability, or air safety accidents. The agents have private knowledge of their monitoring or avoidance costs. We adopt a mechanism design approach to ensure optimal monitoring incentives. Our innovation is to focus on information that is available or can be proxied when harm has occurred and when typically regulators and/or courts deliberate over fines and damages. By contrast, earlier proposals require more estimations of hypothetical accident scenarios and their ex ante probabilities. We argue that our rule promises substantial savings in information costs for courts and regulators and excludes likely sources of errors. Received: 11 October 2000 / Accepted: 14 September 2001 We are grateful to Doug Bernheim, Lewis Kornhauser, Kai-Uwe Kühn and an anonymous referee for helpful comments and discussions, and to an associate editor in particular for very valuable suggestions. Financial support from the European Commission (TMR Grant ERBFMBICT 972827) and from Thyssen Foundation is gratefully acknowledged.     

Monetäre Anreize zur Steuerung der Ladelast von Elektrofahrzeugen – eine modellgestützte Optimierung

Electric mobility is supposed to contribute to climate policy targets by reducing CO₂-emissions in the transportation sector. Increasing penetration rates of electric vehicles (EV) can lead to new challenges in the electricity sector, especially with regard to local distribution networks. Thus the management of charging loads is discussed as a key issue in energy economics. Due to their long parking times, high electricity and power demand, EV seem to be predestined for load management. Monetary incentives as dynamic pricing can be suitable for that: They reflect the current supply situation, pass the information to the consumers and can thus lead to a corresponding charging behaviour. In this article we analyse this interaction between dynamic pricing and charging loads. For this reason we have developed the optimization model DS-Opt+. It models a total number of 4,000 households in two residential areas of a major city with regard to its electricity demand, its mobility behaviour and its equipment of photovoltaic systems. Four different pricing models are tested for their effects on charging behaviour and thus the total load of the residential area. The results illustrate that only fairly high penetration rates of EV lead to remarkably higher electricity demand and require some load management. The tested dynamic pricing models are suitable for influencing charging loads; load-based tariffs are best in achieving a balanced load curve. In our analysis uncontrolled charging strategies are superior regarding a balanced load curve than controlled strategies by time-varying tariffs. Our results lead to several implications relevant for the energy industry and further research.     

Why costless auditing may reduce social welfare

We analyze a model where both a regulator and a firm may detect and stop bad projects. We show that full auditing by the regulator may be socially suboptimal even with zero auditing costs. The reason is that the firm's own auditing incentive may be crowded out when protected by limited liability. The optimal policy depends on the firm's wealth.     

International Technology Transfers and Competition*

This paper analyzes North–South technology transfers in a model of oligopolistic competition and spatial product differentiation. Two firms in the North supply a high‐tech good and a technically related low‐tech good. They decide about licensing the low‐tech good to suppliers in the South. With the license Southern firms get access to technology from the North, which enables them—with a certain probability—to enter the market for the high‐tech good. Northern firms may therefore license strategically to influence the competitive environment in the high‐tech market. In this setting, multiple equilibria with and without licensing may arise, and the resulting outcomes may be inefficient from the viewpoint of the Northern firms.     

REVENUE SHARING IN PROFESSIONAL SPORTS LEAGUES

We employ a model of n heterogenous profit-maximizing clubs to analyze the impact of revenue sharing in professional sports leagues on competitive balance. Revenues of each club depend on absolute quality, relative quality and on competitive balance itself so that our model captures much of the preceding literature as special cases. We show that revenue sharing always increases competitive balance if clubs differ only with respect to the impact of absolute quality on revenues. On the contrary, revenue sharing reduces competitive balance if only clubs' relative qualities play a role for revenues or if only two teams are considered.     

The limits of leverage

When trading incurs proportional costs, leverage can scale an asset's return only up to a maximum multiple, which is sensitive to its volatility and liquidity. In a model with one safe and one risky asset, with constant investment opportunities and proportional costs, we find strategies that maximize long‐term returns given average volatility. As leverage increases, rising rebalancing costs imply declining Sharpe ratios. Beyond a critical level, even returns decline. Holding the Sharpe ratio constant, higher asset volatility leads to superior returns through lower costs.