Elsevier

Energy Policy

Volume 38, Issue 4, April 2010, Pages 1734-1740
Energy Policy

Auctioning wind power sites when environmental quality matters

https://doi.org/10.1016/j.enpol.2009.11.046Get rights and content

Abstract

In this work, we propose an index that allows a public authority to order different projects for the construction of onshore wind energy plants and that explicitly takes into account their environmental quality. Wind farm projects are defined as vectors of four attributes: the technical properties of each project, its social impact, its environmental impact, and the share of earnings that proponents offer to the collectivity in compensation for the negative externalities of the wind plant. We define an absolute index that allows the ordering of different proposals and evaluation of the acceptability of each project, providing the monetary value of each point and inducing a truthful revelation of firms' private information. Moreover, we calibrate the index on the basis of data referring to wind plants in Southern Italy and derive the corresponding iso-scoring curves.

Introduction

Wind power is one of the most important sources of renewable energy.1 It is generally widely accepted that the exploitation of onshore wind power sites can be efficiently undertaken in a market setting; however, this development implies significant market failures, which justifies the need for planning and regulation by the public authority. Indeed, there are relevant local negative environmental externalities that are associated with the visual and sound impact of onshore windmills and their possible negative interaction with local wildlife and other working activities.2 From a theoretical point of view, it is well known that a project is socially efficient whenever the social benefits overtake social costs. Therefore, the public body in charge of authorizing the exploitation of wind power sites should approve a specific project only after an adequate evaluation of its net benefits and then, having internalized the externalities with the proper instruments, let the market choose the characteristics of the investment project. However, in the real world, investment decisions about the exploitation of onshore wind power are not easy to make. There are asymmetries in information about the precise location of the sites, technologies that should be adopted and their costs, preemptive moves by investors, short-sighted investment problems, local resistance by local communities that bear the cost of local externalities, and imprecise or unclear selection rules. All of these aspects are intercorrelated.

Public approvals are often constrained by some minimal requirement, including, generally, the obtaining of a positive judgment in the environmental impact assessment. Unfortunately, such procedure can only highlight those proposals that are insufficient with respect to one or more specific aspects, but neglects to capture the interaction among all elements that characterize a specific project. A second relevant point that has to be taken into account when dealing with the exploitation of onshore wind power sites relates to the scarcity of resources. Indeed, even if the wind supply can be assumed to be a public good, is not true for the land, which is a scarce and rival good. Therefore, in order to reach an efficient allocation of the investments in wind power production in a given area, the public authority has to decide on the highest number of wind parks that should exist in the area. Moreover, if the number of projects that are to be developed is higher than the desired number (for instance, the amount of investments that maximize social welfare, or the number of wind turbines that can be physically implemented in that specific place), the public body has to call on some decision making criterion to order and select the various rival proposals.

In this paper, we show that this problem can be tackled by a specific tool, namely a single scoring rule that measures the net benefit associated with each project. A threshold of the scoring rule can be set in such a way as to separate projects that have net positive social benefits from those whose costs exceed their benefits. Moreover, for those investment projects whose score is above the threshold, the rule allows them to be ranked according to their social welfare. Indeed, such a rule works as an (implicit) auction for the exploitation of onshore wind power sites. Afualo and Mcmillan (1998) have shown that auctions are widely used to allocate public resources, since they give the auctioning public body a positive revenue from the procedure and also because (if properly run) they can efficiently allocate scarce resources. In economic theory, it is widely recognized that well-designed auctions usually achieve better results than alternative allocation methods, such as the first-come-first-served principle, beauty contests, negotiations, and lotteries.3 In our context, an auction can help public regulators to choose the best proposals, even if they do not hold reliable information with regard to both the costs of alternative technologies and the average wind power of different sites.4 The most profitable projects usually allow private proposers to submit a higher royalty. However, the royalty should not be considered as the unique reliable signal of the social value of each project. The existence of relevant environmental and social externalities makes it hard to identify the best projects from a social welfare standpoint. For this reason, we set a multidimensional scoring rule5 that ranks the projects, taking into account the various aspects related to the building and running of a wind park. The crucial element of the scoring rule is the definition of the scoring function, i.e., the algorithm that allows the attachment of a single numerical value to the vector of the elements that describe the project. Economic theorists usually cope with this kind of problem by using the optimal mechanism approach. The existing literature shows that the optimal scoring rule, i.e., the one that maximizes the ex-ante expected welfare, consists a distortion of the true welfare function (Che, 1993, Naegelen, 2002). Therefore, this kind of optimality requires deviation from the ex-post efficiency, in the sense that the optimal scoring rule cannot rank alternative projects according to the results of a cost-benefit analysis. Moreover, as shown by Asker and Cantillon (2006), when suppliers' private information is multidimensional, the definition of an optimal scoring function is quite a complex task.

In our work, we do not follow the optimal mechanism approach, as we think that public authorities are not usually able to implement complex mechanisms. On the contrary, we define and characterize a viable scoring function that is ex-post efficient, i.e., it ranks alternative projects according to the associated welfare, and moreover it has the merit of being easily implemented by the public authorities. Dini et al. (2006) show that the scoring function enables the translation of the impact that every element has on the social welfare into figures. Therefore, our scoring rule can be seen as a proxy of the net social welfare correlated to each specific project, providing a (implicit) cost-benefit assessment of each of them.

Our work is grounded in real-life situations, where often a public authority6 needs to allocate rights to exploitation or usage of some common resource according to some practical rule. This has been the case of the Italian Regione Basilicata (RB from now onward), that has set a joint research team, together with the GSE s.p.a.7 to define its new Environmental and Energy Plan. Under this framework, RB identified the need to define the allocation rules that would allow it to discriminate across wind plant projects. The surface of the region has been differentiated in four different categories, according to the environmental quality level of the land.8 RB has then expressed the need for an objective ranking that evaluates each wind farm proposal in order to maximize wind power exploitation within each category, and that takes into account at the same time the negative local externality and the economic return for each municipality involved. RB has highlighted the importance of all these aspects to evaluate wind plans and the opportunity to measure each of them individually and their trade-offs, which means that it has (indirectly) expressed complete and independent preferences.9 This paper's scoring rule solves the problem faced by RB. However, we believe that, far from being just an ad hoc solution, the theoretical basis and the properties of the scoring rule justify it as a useful tool that can be applied to several similar situations.

The paper proceeds as follows: in the next section, we show the proposed scoring rule, analyzing it in terms of the multidimensional auction theory; in the subsequent section we calibrate the index, showing an example of its possible implementation. References follow. The mathematics of the scoring rule is reported in the appendix.

Section snippets

The scoring rule for the selection of wind power projects

Generally, when the public authority runs a multidimensional auction, it applies the “best economic offer” as the decision making criterion, attaching a score to each proposal according to the following summation:Ij=iθivijwhere Ij is the total score assigned to the jth proposal, θi is the weight attached to the element i, vij is the image of a function fi that associates a specific score to the ith element of the proposal j, i.e., vij=fi(xi1, …, xiN), and xi is the figure that is proposed by the

Calibration of the scoring rule

It is natural to interpret the scoring rule in Eq. (8) as a social welfare function whose weights are θi, i={N, R, E, S}. The public authority should assess the weights according to its preferences, in order to reflect the importance that each component has in the public decision (i.e., welfare) function. Then it should state the EVP by fixing the minimum and maximum acceptable royalty as compensation. On the basis of these choices, we can calculate the EVP according to the following formula:EVP

Concluding remarks

In this paper, we have defined a scoring rule that allows the selection of both windmill sites and projects in an efficient way. Clearly, we do not claim that our scoring rule corresponds to the optimal solution of an allocation problem in a first-best world. On the contrary, we have argued that information asymmetries (both in costs of projects and in their economic return), externalities (both local negative and global positive), and targets' multiplicity (i.e., dealing with externalities and

Acknowledgement

The authors have participated in the GSE research team that has been set to support the Regione Basilicata by defining its Environmental and Energy Plan (PIEAR). The team has been coordinated by Alberto Biancardi. It has delivered a draft of the Plan, written in cooperation with Regione Basilicata, and a technical addendum; both are now undergoing the strategic environmental evaluation by the Regione Basilicata. Some material in this article has been derived from the technical addendum. We

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