The Case of Municipal Decision Making and Wind Power Development in Sweden

Democratic principles and the energy

transition

The case of municipal decision making and wind power development in Sweden

By: Sania Valivand

Supervisor: Mats Nilsson Södertörn University | Department of Economics

Master’s dissertation 30 credits Economics | Spring semester 2021 (Economics Master´s Programme 120 credits)

Abstract

The purpose of this study is to empirically investigate if the municipal decision on wind power development can be explained by a model including socioeconomic variables and proxies for the natural environment, using a pooled cross-section data set for Swedish municipalities for the period 2010-2019.

The study poses the question whether politicians' decisions-making can be explained by socioeconomic factors. In order to analyse the approving or denying of wind power development in Swedish municipalities, three models are used: the linear probability model, the probit and the logit model.

The results show that the Green political party (positively affecting wind power development) and that the unemployment rate, income, population density, protected areas and the affiliations with the Sweden Democrats (negatively affecting the approval rate), has a statistical significant effect on the permission process. Installed capacity of wind power plants seemingly have no impact. Our findings suggest that the municipal decision making is less random than the critics of the municipal veto proposes.

Keywords: Municipal decision, wind power, municipal veto, environment, MB, socioeconomic variables, Swedish muncipalities, accepting wind power, deny project, Probit, Logit

Acknowledgement

I would like to express my special thanks of gratitude to my supervisor, Mats Nilsson, whose expertise and support was invaluable throughout this study. I could not have imagined a better advisor and mentor for my master thesis.

My gratitude goes also out to Fredrik Dolff from Swedish wind power and Thomas Hallberg from Swedish wind energy, for valuable inputs and discussions. However, all views expressed, and any errors, remain with me.

Finally, I would like to thank my family and friends for valuable support and comments.

Table of Contents 1 Introduction ...... 3 1.1 Aim of study and method ...... 6 1.2 Disposition ...... 7 1.3 Contribution ...... 7 2 The municipal veto...... 8 3 Theoretical background ...... 10 3.1 Public choice theory ...... 10 3.2 Competition among pressure groups for political influence ...... 12 4 Previous studies...... 15 4.1 Wind power development and the local context ...... 15 4.2 Local decision-makers and the democratic process ...... 17 4.3 Nimby and externalities ...... 18 4.3 The municipal veto...... 19 5 Data ...... 21 5.1 Veto data ...... 21 5.2 Control variables ...... 22 6. Empirical models ...... 26 6.1 The Linear probability model ...... 26 6.2 The Probit and Logit regression models ...... 27 6.2.1 Probit model ...... 27 6.2.3 Logistic model...... 30 7 Results ...... 31 7.1 Econometric models ...... 31 7.2 Two case studies ...... 34 7.3 Interest groups impact on the veto decision ...... 37 8 Conclusion ...... 39 References ...... 41 APPENDIX ...... 48

1 Introduction

Just like the rest of the world, Sweden is facing challenges when it comes to converting to become a fossil-free society by 2045.1 The wind power development is often said to be a central part to reach this goal (e.g. Göteborgs-Posten, 2020). The Swedish wind power association2 (2021) reported that during 2020 a total of 4,363 wind turbines were installed with an installed capacity of 10 GW. Thus a 27.9 TWh of electricity was produced by wind power. That is roughly 20 % of domestic demand.

The expansion of wind power continues at a rapid rate. In 2021, 2.9 GW is estimated to be built, and in a few years wind power is expected to be Sweden's second largest power source (Svensk Vindenergi, 2021). With growing wind power development it would not be surprising if we encounter increasing conflict when siting new projects. For example, the conservation of pristine mountain areas versus renewable and climate friendly power production. Other areas where the development of wind power conflicts with different interests are for example the Sami’s reindeer herding, where the development impacts negatively on endangered species, or when the Swedish Armed Forces find the development impeding Swedish national security interests.3 In Sweden, the approval process includes an environmental impact assessment but also the necessity of getting an approval from the local municipality, see figure 1.

1 Sweden adopted a climate policy framework in 2017.The framework consists of a climate law, climate goals and a climate policy advice. The long-term goal means that Sweden should not have any net emissions of greenhouse gases in 2045 (Naturvårdsverket, 2020) 2 Svensk Vindkraft 3 Vindval (2018) did a research that showed, where wind farms are centrally located within a grazing area, decreased the reindeer's use of the area by 57 %. Another research done by Vindval (2017) regarding wind turbines effect on different bird species, showed that some turbines kill only few birds while others can cause the deaths of up to approximately 60 birds per year. The Swedish Armed Forces used their veto in 2010 and imposed wind power bans in a radius of 40 kilometres around their airports, restrictions have been broadened which includes shooting areas and areas around weather radar stations exclude wind power (Svensk Vindenergi, 2020). 3

Figure 1 Roles and division of responsibilities regarding probation of wind power establishment. Source: Energimyndigheten (2015)

Figure 1 illustrates the main actors and roles in the probation regarding wind power establishment in a municipality. Briefly, the first role means that the municipality has the opportunity to bring up standpoints about wind power establishment in the permit review, in order to keep environmental and public interests within the municipality (Energimyndigheten, 2015). This can be done at different times, such as in the case of early dialogue between the developer, the municipal representatives and the county administrative board, during the consultation with the authorities for which the developer is responsible (Energimyndigheten, 2015). Further, the construction planner/planning managers are responsible for consulting with relevant municipalities, county administrative boards and others affected by the planned wind power establishment. They should at an early stage initiate dialogue with those affected by the establishment and also have good knowledge of the municipality's planning of land and water resources. The information must be provided to the county administrative board, the municipality and the individuals who are particularly affected. The construction planner is also responsible for producing a permit application and environmental impact statement that highlights the issues that need to be analyzed in order to make decisions on permits in accordance with the environmental legislation (Energimyndigheten, 2015).

The second role of the municipality is to determine whether wind projects that is subject to a permit should be approved or not, which brings us to the environmental legislation (Energimyndigheten, 2015). The environmental legislation, Swedish environmental law (Miljöbalken, hereinafter MB), changed in 2009. This now included the need for the municipality to approve wind power projects. The municipality does not, however, need to qualify their decisions. The development of these approval/denials are shown in figure 2.

Approved Denied (Veto)

40 37 37 35 35 30 29 30 26 25 21 19 20 15 14 13 13 15 12 12 11

TOTAL SUM OF DECISONS OF TOTALSUM 10 8 6 5 4 5 3

0 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 YEAR

Figure 2 Reported decisions on wind power projects in all Swedish municipalities 2010- 2019 Source: Data on decisions collected from municipalities and the county administrative board (2021).

As seen in figure 2, as a proportionate amount of decisions, the approvals have decreased. There were only 12 wind projects approved in 2019 when the approval rate was at its lowest. The highest number of rejections of wind power was in 2015 and 2017.4 From the viewpoint of the developers the possibility to stop projects that has advanced far, in for example the environmental approval, is problematic. For that reason the “municipal veto”5 has been criticised for being “random”

4 One issue that requires a different approach than is used in this study are all the cases where the threat of a municipal veto have made wind power companies withdraw an application. As there is no public data available this would require collecting data directly from the wind power developers. In this study we acknowledge the bias in the data set that the omission of this data produces. 5 This is not actually a veto, but chapter 16, 4 § Environmental code addresses what is required for a license request to operate wind power activities, which will be dealt with by the licensing authority. It requires an application, a technical description, an environmental impact assessment, documentation that consultations were carried out, and municipal approval.. Thus, it is not a "veto", but an approval (an agreement from the municipality) is required (Geijer & Essen, 2017). 5 and giving the municipalities too much power over the development of a crucial factor in the energy transformation (SVD, 2021).

On October the 14th 2020, the Swedish government appointed the former parliamentarian Lise Nordin (the Green party), to review the possibility of removing the wind power veto (Regeringen, 2020). The Environmental Code has been criticized, mainly from the wind power lobby, for the rules on the municipal decision-making process concerning wind power. According to critics, the wind power veto lacks decision criteria, requirements on justification for the decisions and a time limit for the municipal choice (Svensk Vindenergi, 2020). Many agents therefore consider the municipal veto to be unpredictable and argue that it constitutes an unnecessary obstacle to develop wind power. Nordin is expected to submit her investigation and recommendations on the 30th of June 2021. However, in an interview published the 11th of March 2021, she outlines suggestions in order to constrain the municipal decision-making to only concern land use and to force the municipalities to make early decisions (DN, 2021).

According to Eklund (2017) all right wing parties6 except the Liberals support the municipal veto. This means that the Sweden Democrats are also behind the veto. It is still unclear how the Social Democrats view the municipal veto, while the Green Party and the Left Party are willing to remove the provision (SR, 2017). However, the question remain whether the municipal decisions are “random” and if they include non-important (to wind power development) factors?

1.1 Aim of study and method

The aim of this study is to investigate if the municipal decision on wind power development can be explained by a model including socioeconomic variables and proxies for the natural environment. The decision is a binary variable, yes or no to wind power development. Therefore, we use a model that is designed for binary dependent variables. A nonlinear regression model that estimates the probability of a specific observation with multiple characteristics is also used. In all, three variants of models are used to deal with binary outcomes, the Linear probability model (LPM), the Probit and the Logistic/Logit model. These models will be further explained in section 5.

Some caveats should be stated at this point. There exist two clear interests when discussing the municipal veto. One group, including the wind power developers, are clearly against the veto and want to either remove, or restrict it. The other group, view the municipal veto as an important democratic

6 Right wing parties including the Moderate party, Christian democrats, Centre party and Sweden Democratic party. 6 instrument, and a way to insert local knowledge into the decision-making. In this thesis we will not be able to conclude whether either of these two interests are more correct than the other. Neither will we be able to evaluate whether a municipal veto, or for that matter an approval, is socioeconomically correct. Our aim is instead to investigate whether it is possible to explain the municipalities’ decisions. The accuracy of the decisions is for future investigations to decide.

1.2 Disposition

The remaining part of the thesis is structured as follows: Chapter 2 offers some background on the municipal veto. Chapter 3 outlines the theoretical framework used in this study, including economic theories about political decisions. Chapter 4 explains and reviews some previous studies that have been conducted in the field. Chapter 5 elaborates on the data and the variables used in the empirical chapter, and chapter 6 outlines the empirical models employed. Chapter 7 presents the results and two case studies, and in chapter 8 concluding remarks are put forward.

1.3 Contribution

This study offers a good opportunity to shed some light on whether it is possible to explain the municipalities’ veto decisions by socioeconomic and environmental variables from a Swedish perspective.This study have contributed to existing literature. The use of this unique data set, have contributed to new information and insights. The analysis of this study added to existing research by identifying important characteristics of muncipalities that should be considered in the wind power development process. The empirical study of the municipal veto, has not been done before. The study confirmed results of existing studies e.g. Waldo et. al. (2012), Söderholm et. al., (2005), Ek (2017), Moe (2017) and Liljenfeldt (2017), that also emphasized the importance of socioeconomic factors regarding wind power development. These studies point out that resistance on a local level towards wind power is often an obstacle in the deployment in Sweden. Khan (2003) highlights conflict between the national goals for wind power, and the application of the MB at the municipal level. Moreover, deployment of wind power have mostly been studied with country-specific data, mainly as panel data studies. One of the contributions of this study will therefore be that the analysis is based on municipality level data.

2 The municipal veto

In recent years over 350 wind turbines have been stopped by the so-called municipality veto (Dagens Samhälle, 2017). The purpose of the 2009 change of the MB was to remove double testing (two separate tests by both the municipalities and county administrative boards) of wind power development. The expressed purpose was to create a clearer and more foreseeable legislation for the companies that develop and operate wind power generation (Riksdagen, 2020).

The rule in chapter 16, section 4, in MB states (authors translation): Permission for a wind power plant may only be given if the municipality in which the facility is intended to be, approves.7

This is called the "municipal veto" (we will refer to this as the approval, denial, rejection or veto interchangeably). To get a permit an active and positive decision from the (municipal) City Council is required. It gives the municipality an option to reject applications without the developer being given the opportunity for reconsideration. Also, the municipality does not need to qualify why they reject a project proposal. In addition, there is no time limit set for the municipal decision-making process concerning this matter.

For all the above reasons, the municipal veto has endured sharp criticism from some media and wind power developers.8 In many cases, the veto will be announced late in the process, which means that resources added during the design and environmental assessment phases may be lost (Energimyndigheten, 2021). In many cases, the experienced unpredictability of the municipalities' decision- making causes great uncertainty and financial loss for the stakeholders.

However, there are also supporters of the municipal veto. Local Social Democratic politicians in Åre, Berg and Härjedalen, believe that a removal of the veto would undermine the municipal self- government and strengthen the imbalance between city and country. The local context may not be conducive to wind power development, and thus local resistance may reflect legitimate concerns. Reducing the municipal self-government by removing municipal veto also risks undermining the confidence in the local democracy (ÖP, 2020). According to the Sweden Democrats in Avesta, wind turbines have a large significant local consequence, such as corrupted values of nature and culture

7 Miljöbalk (1998:808): 4 § Tillstånd till en anläggning för vindkraft får endast ges om den kommun där anläggningen avses att uppföras har tillstyrkt det. 8 For example, Svensk Vindenergi (2020) implies that the Environmental Code have to be changed so that the municipalities do not use their veto rights for economic gain. Among other critique is the lack of decision criteria, requirements for justification and the absence of a time limit. These are all reasons why Svensk Vindenergi (2020) support the possibility of removing the municipal approval requirement. For example, Avesta newspaper (2021) reports that investors had wished that the municipality asked for supplements in the form of suggestions for adaptations of wind power before using its veto.

(Avesta tidning, 2017). Many may have residential properties that would suffer when the wind turbines are built, from for example noise and sun reflexes. The Sweden Democrats believe that municipalities' inhabitants are best suited to make decisions on this issue (Avesta tidning, 2017). Sweden’s municipalities and regions (SKR), are also against the government proposition to remove the municipal veto of wind power. To remove the veto could be against democracratic and the fundamental principles that people affected by a decision should also be able to influence the decision (Dagens Samhälle, 2017).

Tekniska verken (2021) imply that the municipal veto is a good idea, since wind power establishment is undoubtedly an intervention in the local environment for the residents of the municipality and something that the inhabitants naturally should have a saying in. But, this does not mean that the municipal veto works well in its current form. For example, the municipalities can deny wind power establishments without any formal justification (Tekniska verken, 2021). The decision can also come at any time during the establishment process, which creates great uncertainty for both the wind power actors and the local landowners affected. A recent example is a project started by Tekniska verken in 2016 that was canceled on January 18, 2021. That project had been going on for almost five years and with an associated cost of about SEK 2.6 (Tekniska verken, 2021). Tekniska verken pleads for a process where the municipality is forced to make an earlier decision. That could prevent some of the resulting in resources being wasted.

According to Jan Hedman (2018), Chairman of the Association Swedish Landscape Protection (Föreningen Svenskt Landskapsskydd), citizens at a municipal level have more knowledge and insights about local conditions and environment, and that is why the approval of wind power development should remain at the municipal level. One example being when, in Flen and Gnesta, both city councils said no to wind power in the area of Ånhammar, due to a particularly pristine untouched area with environmental values to conserve. The city council further stated that they could accept other areas in the municipality being used for wind power establishment (Föreningen Svenskt Landskapsskydd, 2018).

3 Theoretical background There is a lack of studies in this field. The aim is to investigate if the municipal decison on wind power development can be explained by socioeconomic and environment variables. Therefore, theories are applied to understand and interpret the results. Therefore we start with a brief description of theories explaining why political decisions of the type studied in this thesis is made. Becker’s framework allows for a deeper understanding of different interests role in the decisions at a municipality level.

3.1 Public choice theory

First of all, theories that could explain different political decisions, are e.g. Public interest theory which claims that regulation seeks to maximize and protect the society's benefit at large, decision-makers operate to reflect the general interest (Croley, 1998).9 We can directly investigate the impact of different ideologies (via the proxies of the local politicians’ affiliations). We assume public interest theory, politicians and decision-makers would make choices based on what is best for the society. With this in mind the different political parties could explain the political decision-making.

In economics, the public interest theory have developed from assuming benevolent bureaucrats and politicians to a more self-interested actor in bureaucracy and politics, which is called Public choice theory. Theories of Public choice explains political decisions at different levels of society, by assuming rational choice and self-interested decision-makers. It emphasizes the impact of various socio-economic variables to explain political decision-making. Thus, a decision- maker’s choice will reflect how she perceives the will of the electorate, and how her decision-making affects her possibility to be re-elected. Hence, if we understand the electorate we can predict how politicians will vote and act regarding the municipal veto. Public choice is therefore an economic theory of political decision-making, which applies the theories and methods of economics to the analysis and explanation of political behaviour (Shughart, 2001).

The basic principles and ideas of public choice was introduced by Buchanan and Tullock (1965) in their seminal work the Calculus of consent. The approach is based on individualism and that the collective action is based on individuals’ decision-making. The theory assumes that no government thinks or acts

9 Croley (1998) also discusses the Neopluralist theory, focuses on the general public's ability to supervise decisions concerning regulation. This view also takes organized groups into account to explain regulation, but unlike public interest theory focuses on interest competition (Croley, 1998). A third example, Civic republican theory, rejects the premise public interest states instead that decision making involve shared regulatory values, of those who hold the stake in decision (Croley 1998). Each of these theories have benefits in different research endeavours. For the Neopluralist account we think it is partly included in our discussion of Gary S. Becker and the impact of interest groups. We see these two theories (Neopluralist and Becker) as important possible explanations of things that are missing in our formal modelling endeavour. 10 instead, actions are the result of individuals making decisions in their roles as elected officials, appointed officials, or bureaucrats (Buchanan & Tullock, 1965). To understand on which basis the government decides, we need to analyse how individuals make choices. Buchanan and Tullock (1965) makes distinct assumptions about politicians, bureaucrats, and decision- makers. Earlier an enlightened altruism (public interest) was assumed but, with public choice the political market is rational and self- interested, and actors make sure to maximize their own benefit instead of maximizing the utility for the commonwealth (Buchanan & Tullock, 1965).

Olson (1965) states that the main reason for individuals joining interest groups is due to self-interest. Lobbying and forwarding the interests of one group could lead to some groups will benefitting at the expense of others. According to Olson (1965), interest groups tend to aggravate and extend political decision-making, which can affect society's capacity and willingness to adapt to new technologies in a country, and overall complicate the political processes and the ability to change society.

Public choice theory analyses the political institutions where decisions are traditionally made, concerning the distribution of welfare and the setting of the rules of society (Shughart et. al., 2003). What can be discerned from public choice theory is that the pursuit of self-gain by individual actors in the public sector, could be inefficient and fruitless. For example, a politician suggests a wind power project. The political decision will depend on the actions at least of some homeowners nearby the project. There is no guarantee that everyone in the community would gain welfare from this, due to the fact that benefits may accrue to a few, while costs may be shared by everyone. The support will mainly be from those who live far from the project, that benefit the most. Thus, owners that live nearby the wind power areas will rationally vote against the proposal. Which could harm or even make politicians relocate the issue to another jurisdiction. A conclusion that can be drawn is that when decisions may have a direct effect on individuals, agendas and motivations will change (Buchanan & Tullock, 2003). This however does not mean that economic theory assumes that people are irresponsible. Rather, what “self-interested” means depends upon what people consider to be in their own interest, and people have wide ranges of interest (Becker, 1976). The ideology assumes that individuals are rational when they choose, which is the main argument of the analysis. Some critics claims that rational choice theory itself is limited.10 Simon (1972) proposed bounded rationality as a cognitive limitation which affects decision-makers. This means that decisions will be taken with satisficing information rather than

10 Compare for example Herbert Simon’s (1972) Theories of bounded rationality Decision and organization, 1(1), 161-176. 11 complete one. This implies that the municipal decisions may not be rational, but rather boundedly rational.

3.2 Competition among pressure groups for political influence

Public theory, exemplifies Becker's (1983) point regarding the application of economic analysis to the research of government decisions. Public choice theory and Becker gives an insight for the emergence of regulations. It is the existence of the political sphere that allows different interests groups to use political power for self-interest at the expense of the national economy. The state becomes an arena for interest-seeking activities. The interest groups that are best organised and who will be most affected by a political decision spend the most money to promote their own interests. With guidance of Becker's theory we can shed light on decisions why different decison regarding the wind power development is done.

Becker's (1983) model is based on competition between different interests or pressure groups seeking political influence. His framework allows for a deeper understanding of the mechanism of different decisions on both local and national level. The model depends on individuals belonging to different groups based on occupation, income, education, geography, age etc. City councils are assumed to use political influence to increase the welfare of their members. Political influence is not entirely determined by the political process but can be increased by the expenditure on time and money spent on political pressure. In a democratic political system, organised citizens are more likely to influence political decisions than disorganised citizens. A group that becomes more efficient at producing political pressure would be able to impact the decision for development of wind power in their municipality. Economists have traditionally explained political behavior not by the power of interest groups but by market "failure". Governments produce public goods, reduce externalities, and overcome other failures. Although these political activities raise rather than lower aggregate efficiency, they can be readily incorporated into the previous analysis of competition among pressure groups for political influence. Becker's (1983) theory explains why one single voice can’t influence decisions. Rather, it's when individuals come together, they can affect politics. Becker (1983) provides a theoretical explanation for incentives to become well informed about political issues. Although rational political behaviour has appeared to be contradicted by widespread voter ignorance and apathy, the opposite conclusion is justified because rational voters do not invest much in political information. A reasonable explanation for the upcoming of interest groups trying to influence the political process is that the voter is likely to be ill-informed and his evaluations are likely to be based on a few selected issues relating to everyday life. By joining forces in organizations, the individual can gain influence in the political process. Becker

(1983) therefore chooses to include several interest groups in his model of political influence. Organised interest groups are actively trying to influence the decision-making process by persuading policy- makers to put their particular issues on the political agenda. Different special interests groups specialise in collecting and disseminating information about political activities and also try to influence them. Decisions are therefore influenced by the wishes of specific special interests and may not be primarily an expression of the public interest according to Becker (1983). He argues that there is always some competition between different interest groups. These special interests will balance each other through the solution chosen. The intensity of an interest group in pressure (or compensation for being disadvantaged by a decision) is proportionate to what the group has to gain. The size of the organisation is often important for political influence. Alliances between different interest groups can therefore pay off. At best, this means that redistributions, at a high socio-economic cost, are being shifted away within the interest groups.

According to him what matters in determining which groups will be benefited and by how much is the relative ability to produce pressure in political decisions. Political influence is not simply fixed by the political process but can be expanded by expenditures of time and money on campaign contributions, political advertising, and in other ways that exert political pressure. Which could explain government decisions and that an unified approach is possible because whereas groups harmed by activities that reduce efficiency have the intrinsic advantage in the competition for influence, groups benefiting from activities that raise efficiency have the intrinsic advantage relative to groups harmed by decisions. Political decisions have been subject to pressures from special interest groups that try to use influence to enhance their welfare. Becker (1983) points out that; a group that becomes more efficient at producing political pressure would be able to impact the decision at a local level.

Building on this basic model of political decision which does not distinguish between different types of interest groups, one objection that can be made to the model is that only financial interests are taken into account. For example, the model does not consider the fact that there are groups that try to influence the political process on an ideological basis (Kalt & Zupan, 1984). There is no distinction between different types of interests. Coughlin et. al., (1990) points out that Becker’s (1983) model provides an explanation of how interest groups act as a group but argues that the model should be supplemented by some kind of weighted sum of the benefit to each member of an interest group. Empirical studies have shown that homogeneous and geographically concentrated interest groups are easier to organise than large and differentiated ones (Olson, 1965).

Within Becker’s (1983) framework, the interest group is central and what matters is how much pressure in the form of lobbying each group exerts on decision-makers. When legislatives only provide to the interest of the minority at the expense of the majority is reinforced by the logic of collective action. Which can lead to small homogenous groups with strong communities of interest within the municipality, tend to pressure or support politicians and guide them to the desirable direction (Becker, 1983). The members of smaller groups have greater individual stakes in favourable policy decisions, organize at lower cost, and can more successfully control the free riding that otherwise would undermine the achievement of their collective goals. Hence, politicians getting influenced by an interest group with special motives is effective. Because the vote motive provides re-election-seeking politicians with strong incentives to respond to the demands of small, well-organized groups, representative democracy frequently leads to a control of the minority. Further, as active seekers, interest groups seek decision makers that can e.g. help them to change a legislation. Politicians faces pressure from interest groups, and some disadvantages may occur when legislators are affected by some interest group with unpopular interests.

To conclude, the theoretical framework underlying this study generally implies that there is a relationship between political decision and socioeconomic factors. However, one should keep in mind that there are limitations as well, as these models may still miss out on some important aspects such as individuals with financial resources or leadership skills, can respond quickly to political decisions and why politicians vote against their parties interest (Pressman, 2004). In addition, some research on economic and political behavior find an important distinction between the individual acting as a consumer and as a citizen (e.g. Lewinsohn-Zamir, D., 1998). Thus, some of the interpretations of the result in this study takes this, at least superficially, into account.

4 Previous studies

The expansion of wind power has been the subject of many recent studies both globally and in Sweden (e.g. Lauf et. al., 2020 & Darpö, 2020).There are several studies that have done research about for example, the location and deployment of wind power. The deployment of wind power is affected by several different factors, as suggested by Wolsink (2000), such as not-in-my-back-yard motives (Nimby),which means that most people are in favour of wind power, but are opposed to having wind power close to them and their homes. In addition, both forward-looking and backward-looking case studies have been conducted for several different regions and local areas with regard to actual wind power establishments. Previous research has shown how different socioeconomic factor influence the wind power projects. The general attitude in Sweden is positive towards wind power (SOM institute 2019). Waldo et.al. (2012) and Söderholm et. al. (2005) point out that higher density of the population have a negative impact on the deployment of wind power. This is due to facts that visual impacts and noise affects more people.

On the other hand, the municipal veto itself and what may be behind this political decision in the municipalities has not been studied to the same extent. The relationship between wind power and which socioeconomic variables affect the location have been mostly studied in a country-specific data, mainly as panel data studies. In this brief survey of earlier studies, we disregard studies on profitability and technical specifications of the wind power locations. We make the heroic assumption that the applications dealt with would all have been profitable from the developer’s viewpoint, and that location with respect to for example wind speed is satisfying.

4.1 Wind power development and the local context

Waldo et al. (2012), finds that municipal specific factors such as, demographic and socio-economic conditions, is probably determining factors explaining the difference in the deployment of wind power. They use data on the amount of installed wind power on municipality level between the years 2003- 2012. To address the issue in a local context, the role of physical and social municipal factors was analysed with a cross section analysis of all Swedish municipalities. The results showed that the local context has an impact on the deployment of wind power. Variables for population density (and growth), business climate, environmental concerns (green values) and tourism are analysed. Waldo et. al., (2012) expected population density and population growth to have a negative impact on the deployment of wind power. The authors find a significant and negative effect of population density on the deployment of wind power. Their results show that it is more likely to establish wind power in a municipality with 15 extensive leisure tourism (Waldo et. al., 2012). Thus, it should be noted that this does not say anything about if tourism is actually affected by wind power, only that municipalities with a large proportion of tourism also have tendency to have wind power installed (Waldo et. al., 2012). However, they found no support for the hypothesis that deep environmental values of the constituency or that a good business climate helped wind power development.11

Söderholm et al. (2005), finds that on a local level a strong opposition due to visual impacts and noise negates a general attitude positive towards wind power in Sweden. To achieve wind power expansion it is necessary, according to the authors, to attain a stronger political commitment at a local level. The study shows a tendency for inefficient diffusion of wind turbines due to citizens lack of participation at the local level.

A more recent panel data analysis on municipalities in Portugal showed that the deployment of wind power had negative a correlation with unemployment, during the construction phase of the establishment unemployment decreased (Costa & Viega, 2019). Ek et. al. (2017) indicates that during the construction phase, production and services are necessary and have to be available in these areas during wind power establishment for employment to increase. However, an econometric analysis compared impacts between Sweden and Germanys probability of wind power development by Lauf et. al. (2020), did not find any correlation between unemployment rates and wind power. This could be an artifact of the time period the data were collected from (2008-2012). This outcome could be explained due to a boom in the mineral commodity markets, where regions in these areas during this period had a significant growth which decreased the employment, and crowded out other productive activities (Lauf et. al., 2020).

Hedberg (2008), shows that differences in attitudes regarding wind power is of importance. It is repeatedly found that left-leaning individuals in Sweden are more positive towards wind power than right-leaning individuals. Assuming that people reveal their true preferences by voting and vote according to a traditional left/right scale, the political governance in a municipality may have an effect on the deployment of wind power. Lundmark (2008) finds similar results, left leaning constituencies is more supportive of “environmental values”.

Moe (2017) did a comparative case-study of five countries investigating if politics matters for wind power development. The main purpose where to explain swings in wind power installations and how the

11 Superficially this could be a consequence of the green vs green conflict. You may be engaged in improving the local biotopes. Wind power development may conflict with that aim. 16 politics behind the scenes matter. While economic, technological, or geographic explanations all provide useful insights neither can, according to Moe, explain wind power development. He finds it to be politically determined. According to him politics has been more important to explain the success (or failure) of wind power than non-political variables. He showed that in Denmark depending on which political party that is in charge, different policies caused severe swings in wind power establishment.

4.2 Local decision-makers and the democratic process

Khan’s (2003) study showed that an important determinant is the attitudes among politicians and local government officials. He finds a conflict between the national goals for wind power, and the application of the MB at the municipal level. He finds that Sweden is characterized by a large municipal autonomy. The combination of an ambiguous national wind policy has given the municipalities extensive decision- making power over the development of wind power. Through interviews with planners, politicians, and officials in three municipalities12, Khan (2003) found that the local involvement in the ownership and civic influence are decisive for the acceptance of wind power. The study noted that a small degree of civic influence can influence opinions about wind power establishment. Another conclusion from Khan’s (2003) work is that the choice of planning approach influences the structure of economic ownership in wind power projects. This is important since local involvement in such projects can effectively increase the acceptance of wind power. An important determinant is the attitudes among politicians and local government officials. In higher income municipalities, the ability to mobilize resistance to projects within the municipality is bigger, which can make it harder for infrastructure projects. Ek (2005) also studied the impact of income upon wind power development. They showed that individuals with higher incomes were on average more negative towards wind power establishment within their area. A possible explanation for this relationship according to Söderholm et. al. (2007), could be that the positive effect related to wind power projects, such as employment opportunities, are less important for those with a higher income.

Liljenfeldt (2017) showed that local participation within a municipality could help support the planning process of wind power establishment. The study points out the importance to take any local resistance group into account. If these individuals are excluded from the planning process, they could manage to become influential through lobbying and networking. This can eventually hinder wind power development at the local and policy level.

12 The three municipalities are: Laholm, Halmstad and Falkenberg 17

Thus, there is support in earlier studies that the demographics of the population in a municipality can have a direct or indirect connection to the development of wind power.

4.3 Nimby and externalities

Previous research shows that citizens may view a technology as good for society on a national level but object to projects in their own vicinity. This phenomenon has been dubbed Nimby (Wolsink, 2000). This could be one factor explaining local resistance in the municipalities. However, these explanations may be too simplistic.

Ek (2005) found that attitudes towards wind power where not different between locals without visual experience and those with installations visible from their residence. Thus, instead the reason behind local resistance could be due to ineffective communication and planning between wind developers and local politicians. Wolsink (2007) showed that a positive general attitude is mainly due to environmental benefits, but when it comes to the visual impact of wind power it tends to overcome the initial benevolent view. Kraft and Clary (1991) found that concerns regarding health, safety risks and a distrust of project sponsors matters. They also find that lack of necessary knowledge can lead to rejection of wind power.

Devine-Wright (2005) showed that nimbyism failed to explain local opposition to wind power. It may be incorrect and misleading when describing community resistance to proposed wind power projects with only nimbyism. When motives are selfish, a social dilemma could occur, where Nimby behaviour can be one explanation of why blocking wind power development could lead to suboptimal socioeconomic investment decisions. Although previous research suggests other factors such as decision-making processes, mistrust of government or Nimby dilemma, the occurrence of actual externalities may play a dominant role regarding the potential objections against wind power.

The external effects of wind power is important to account for when considering the decisions on wind power. Externalities arise when a third party is affected by the wind power project. These effects are divided into negative and positive effects. Examples of negative external effects are primarily environmental pollution, but it can also be noise or other disturbances to the environment. Negative externalities from wind power is for example consequences for the wildlife (birds, etc) but also through noise and an aesthetic deterioration of landscapes (Zerrahn, A., 2017). A survey done by Bixia (2015) showed a strong support for wind power in Sweden, but also found that public support might decrease due to various characteristics such as, ugliness, noise, harm to birds and other wildlife, and so forth. The impact on user values arises, for example, if a wind power establishment affects the landscape in a way 18 that makes it perceived as less appealing to watch or stay in, or if it becomes less pleasant to stay in an area due to noise or flashing lights from wind power (Ek et. al., 2007). Externalities are only problematic if they are not considered by decision-makers (Pihl, 2007). Thus the green values of a renewable electricity source that should be part of the transition to a climate friendly economy are sometimes opposed to local green values such as pristine nature, biodiversity and the preservation of native culture.

4.3 The municipal veto

There is a certain gap concerning research on the Swedish municipal veto on wind power development. Fridolfsson et al. (2013) did studies on renewable electricity policy in Sweden. They proposed that one solution to underinvestment in renewable energy sources is to replace the municipal veto with a centralized decision procedure, which grants construction permits based upon the arguments presented by the interested parties. Thus they discuss that the asymmetric distribution of cost and benefits lead to Nimby outcomes, where local politicians block “undesirable” wind project with the municipal veto. They recommended two solutions. Share the benefits of these projects with local members of the community and/or through local ownership (Fridolfsson et al., 2013). A study by Darpö (2020) analysed licensing of wind turbines and species protection under the Environmental Code (1998: 808, MB). He collected decisions on permits concerning wind turbines on land and in water during the period 2014- 2018. Results from this study showed that the protection of threatened species is not a significant hinder of development of wind power. Total 8 -11 % of turbines have been stopped for this specific reason (Darpö, 2020). Further, the populist resistance that drives public opinion against the "central power" can be decisive for investment of wind turbines.

Beside the studies mentioned above, there are some bachelor and master thesis work from several Swedish colleges and universities. A study by Hallbeck (2011) analysed what the new rule (MB) meant and how this affected the permitting process for developers and different local authorities. The result from the study showed, among other things, that the municipal veto gave rise to side effects. For example, to approve wind power establishment, some municipalities required financial compensation. Petterson (2014), focused on three different wind farms in two municipalities (Åre and Rätan). She investigated the question “which factors are behind the municipalities' decisions in refusing or approving the parks?”. The author argues that to get local support, communication is important between the developer and the public. Through this a wind power project can get an approval from the city council. Another study by Strömstedt (2019) examined the MB´s effects and its actual outcomes. The author shows that MB have some legal uncertainty, lack of predictability and uncertainties around the

19 application. However, the municipal veto also indicates some practical problems such as delayed processes and a more complicated authorization process for individuals.

In conclusion, we will include several socioeconomic and demographic factors that previous studies had found to be determinants of wind power development in our investigation, such as income, unemployment, population density, installed effect of wind power and politics. Yet, few studies have examined the municipal veto from an empirical point of view, so more research is needed on the subject.

5 Data

This section outlines the data on decisions to approve or reject wind power development on the municipal level. The pooled cross section dataset used in this study consists of annual data from 146 Swedish municipalities that have made a political decision regarding wind power development, for the period 2010–2019.13 The dataset contains 350 observations.14

5.1 Veto data

The municipal veto data consists of a binary variable taking the value of 0 (=rejection) or 1 (=acceptance). It has been collected through a manual search of articles in the news article database Retriever, and email contact with all 290 Swedish municipalities. The motive behind the use of articles to get a broader view of the reasons why rejections occur, rather than minutes from the meeting where the decision is made. However, checking minutes from meetings will be the next step for future study to develop a data set.

In order to be able to explore the research question based on political decisions, 144 municipalities have been removed from the dataset due to email responses stating that they have not made a decision on wind power development during 2010-2019. Thus the data set consists of 350 decisions in the 146 municipalities remaining. The “veto” was used 90 times (value 0) and at 260 times wind power development was approved.

The data on reported political decisions most likely does not fully reflect the total number of rejections and approvals. Some projects would have been approved of but did not get started, others understood that the likelihood of a veto was high and thus did not pursue the development project. At least 19 occasions have occurred where we have received confirmations that the project has been withdrawn but not due to the direct use of the veto.15 These occurrences (would have been approved/implicit use of veto) is for natural reasons excluded from the data set.

An issue worth mentioning is that seven municipalities have both decided to reject and support wind power development projects within the same year. This impairs the model’s outcome as the decision

13 See Table A1 in the Appendix for the list of municipalities. 14 Which to our knowledge is the complete data set on municipal decisions on wind power for the studied time frame. 15 There is no clear pattern discerned from these cases. One possible reason may be that project managers realizes that the project will not be approved and thus withdrew its application (Wallberg & Göthe, 2015). 21 made in the same context lead to two different outcomes. A brief analysis of the concerned municipalities are given in chapter 7.

5.2 Control variables

The data used in this study mainly comes from the Swedish Central bureau of statistics (in Swedish abbreviated SCB), which includes the variables unemployment rate, income and protected areas and population density and the share of seats of green political (MP) and right wing conservative (SD) parties. Installed wind power in Swedish municipalities is taken from the Swedish Energy Agency (Energimyndigheten). Table 1 summarize the characteristics of the variables.

Table 1 Descriptive statistics of all dependent and independent variables16

Variable Obs Mean Std. Dev. Min Max Political decision (yes=1) 350 0.74 0.43 0 1 Megawatt (installed capacity) 350 33.10 64.78 0 721 Unemployment rate 350 13.81 3.68 6.4 22.3

Income (SEK in thousands) 350 232.13 25.27 185.5 324.3

Protected area (%) 350 7.65 9.17 0.1 51.6 Population density (people/km2) 350 35.40 53.74 0.2 430.3 The Green party (%) 350 4.2 2 0 15 The Sweden Democrats (%) 350 7 4.9 0 29

Source: SCB (2021), our data on decisions, and Energimyndigheten (2021)

The variable installed effect of wind power (Megawatt) is the sum of the installed capacity of all wind power plants in a municipality. It is the theoretical standardized maximum capacity of a plant (Energimyndigheten, 2019). The cumulative number of invested wind power in a municipality in the year a municipality decide on additional investments, may play a significant role in testing two hypotheses: There is a saturation in how much wind power the citizens in a municipality would accept within an area. The second hypothesis is that municipalities that already experienced wind power development have a better understanding of wind power projects and therefore hypothetically allow more development (Ek, 2005).

Unemployment rate is based on the proportion of registered unemployed in percent. The variable consists of persons who at some point during the year have been registered as unemployed according to the Swedish Public Employment Service's register from the age 20-64 years old (SCB, 2020). During

16 See table A2 in the Appendix for correlation matrix. 22 the work phase of constructing the wind power plant unemployment could shrink. Employment increases during the building phase and if local labour is used, a positive economic effect on the entire local community will appear (Ek et. al., 2017). Thus we expect based on earlier literature that unemployment rate should cause an increase in the likelihood of municipal approval of wind power projects.

Income, is measured as the median income in thousand, from the age group 20 years and above. More precisely, a measure called total earned income is applied, which includes income from services (salary income, pension, sickness benefit and tax-related contributions provided by the Social Insurance Agency) (SCB, 2018). The motive for including the variable is given in for example Ek (2005). She suggests that attitudes towards renewable energy is negatively related to income, wind power development support declined with higher incomes. Superficially there may be several explanations of this. Higher income is often a good proxy for education and resourcefulness. Thus it is more likely that if there are reasons to object to infrastructure projects, this will happen in municipalities with a higher median/mean income (Ek, 2005).

Protected area includes nature reserves that are formed by the county administrative boards and municipalities with the support of Chapter 7.Section 4-6 of the Environmental Code (MB). In Sweden and many other countries, the formation of nature reserves is one of the most common ways to protect valuable nature long-term. The protection includes national parks, nature reserves, forest habitat protection and other habitat protection areas. The variable contains the total sum of these and is measured in total areal share in percentage. A conflict that may arise is between wind power and protected nature as a nature reserve. This is a "Green vs. Green "-dilemma where nature conservation values comes into conflict with renewable energy (Burch et. al., 2020). 17 The reason for citizens that oppose wind farms could be a wish to preserve a natural environment and wildness (Warren, 2005).

Previous research by Waldo et. al., (2012) has also found population density and the degree of urbanization to have a negative relationship with wind power development. This may have various underlying reasons. This variable consists of number of inhabitants per square kilometre (km2) at the municipal level from 2010 to 2019. The reason behind the inclusion of the variable is that the population affects the development of wind power. Wind turbines are often established in places that are sparsely populated, where residents don’t live nearby wind turbines. There may be more veto decisions in

17 See also a similar conflict reported from SVT (2021) regarding Europe’s largest solar cell facilities that may be built near a protected areas in Skåne. 23 densely populated areas as there are more individuals who may be affected by e.g. visual impact, noise and light.

There could be several reasons why a municipality chooses to stop wind power projects. For example, a different valuation of environmental concern than they put forward in the environmental process. We remind the reader that the wind developer submits the environmental assessment statement for evaluation in a separate (from the municipality) process. There may also be ideological preferences for or against wind power. Thus the political majority in a municipality could matter.

To make a superficial test we ran the decision variable against the share of seats for all the political parties in the municipal council in each municipality, see table 2.

Table 2 Result including only the political parties

VARIABLES Probit(MLE)

Moderaterna 1.104 (1.453) Centerpartiet -0.377 (1.388) Liberalerna -3.641 (2.690) Kristdemokraterna -0.741 (2.287) The Green party (MP) 10.23*** (3.376) Socialdemokraterna 0.914 (1.436) Vänsterpartiet 0.0988 (1.765) The Sweden Democrats (SD) -7.384*** (1.822) Constant 0.601 (1.098)

Observations 350 Pseudo R-squared 2 0.1149

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 The dependent variable is Political Decision (0,1)

Two parties stand out, the Green party and the Sweden Democrats. These will be used as proxies for two different ideological views pertaining in the municipalities.18 They are the only significant variables in table 2.

Further, earlier research has sometimes included the share of the green party as a proxy for the support of a “green” ideology (eg. Hedberg, 2008; Lundmark, 2008). We would also like to address the possibility of an ideology which would at least counter local renewable initiatives. This could reflect for example a more nuclear friendly transition to a fossil free future, but also a distancing from the idea that politicians “in Stockholm” decides on how the local environment should be developed.19 Hedberg (2008) and Lundmark (2008) indicates that left-leaning individuals are more positive towards wind power and more supportive of “environmental values” than right-leaning individuals. To include these two ideological differences in wind power development we include both a Green party (MP) and a Sweden Democratic (SD) variable. 20 MP will symbolise a complex left and green national values and SD a right wing and locally rooted values.

To avoid the risk of increased multicollinearity, this study will not include all of the political parties. However, a tentative econometric test using only the party affiliations support our choice. Thus the Green party (MP) will represent pro wind power establishment and the opposition is represented by the Sweden Democrats (SD). These two variables have been converted to a percentage share of the total mandate in respectively municipality and will also be a proxy for wind establishment.

In addition, our foreknowledge from the collecting of the data set (among other things reading articles in Swedish newspapers) points to the choice of these two as good representatives of different ideologies concerning local wind power development.

18 The green party variable has been used in earlier research. However, the Sweden Democrats did not enter the parliament before the 2010. This makes it possible for us to use them as a proxy for a somewhat less wind power friendly energy policy than the green party. However, the political variables should be used and interpreted with discretion as they signify a set of complex values apart from just the simple pro or con of wind power. 19 This would rather ad hoc connect to the ideas of Goodhart’s (2017) “anywheres “and “somewheres”. He explains “anywhere”, as the highly educated, high-paid liberally minded people in the big cities that he calls the liberal elite and “somewhere”, who have lower education, lower pay and more conservative values, such as against the EU and extensive immigration as the Sweden Democrats. However, it is “anywhere” that holds the political power (in the parliament). Thus, there is an underlying force of the voters that is irritated on the "Stockholm elite", because they do not represent them when it comes to the wind power projects. This could be a contributing factor to SD´s strong position regarding wind power expansion. However, this should only be treated as superficial evidence as we have not investigated this in-depth. 20 Several newspaper articles shows the Sweden Democrats clear position regarding wind power development. For example, Hela Hälsingland (2017), mention that SD has argued that they are the only political force trying to save Sweden's nature from wind power. In the article from Klimatupplysningen (2012), a member of parliament (SD) addresses current local issues such as the environmental and noise problem of wind power, as well as falling property values and local citizens problems not being a part of the decision. A local paper from Avesta (2021) writes that SD don’t want any wind power in Avesta and believes that it is better to use nuclear power. Again, this must be treated only as tentative ideas of the underlying reason for the correlation we find in our data set.

6. Empirical models

In pursuit of our investigation whether politicians' decisions can be explained by socioeconomic factors to analyse the decision of approving or denying wind power development in Swedish municipalities, three models are used: A Linear Probability (LPM), Probit and in order to assess the reasonableness of the model's estimate, a Logit model is also estimated.

6.1 The Linear probability model

A linear probability regression could be used when the dependent Y variable is binary. Using ordinary least squares (OLS) , the model can be estimated with a binary dummy as the dependent variable (Stock & Watson, 2015). This model estimates the likelihood that the dependent variable assumes the value 1, given a certain value of 푋𝑖 according to the least square method. The dependent variable regression yields the LPM: a binary dependent model of the probability of a ”successful” outcome, that is, Y =1 if wind power is accepted, Y =0 when municipalities use the veto. In the LPM model, the parameter estimates of a given variable is the change in the probability of success when changing that variable, holding all other factors fixed. Using annual values of a pooled cross-sectional data on the municipality level for the period 2010- 2019, we begin with specifying the following LPM equation with all variables:

Pr(푌 = 1|푋1 ,푋2 , … , 푋푘 ) = β0 + 훽1푋1푖 + 훽2푋2푖 + ⋯ + 훽푘푋푘푖 + 푢푖 (1)

퐴푐푐푒푝푡𝑖푛푔 푤𝑖푛푑 푝표푤푒푟푖 =

훽0 + 훽1MW푖푡 + 훽2Unemployment 푖푡 + 훽3Income 푖푡 + 훽4Protectedarea푖푡

+ 훽5Pop. Density푖푡 + 훽6MP푖푡 + 훽7SD푖푡 + 푢푖 (2)

Where Accepting wind poweri is the estimated probability that wind projects will be approved. 훽0 is the intercept 훽1 is the coefficient for MW, 훽2 is the coefficient for Unemployment and so on. The control variables are included to minimize the bias. Further, 푢푖 is the error term and i stands for observation i=1..., n. We have used heteroskedasticity robust standard errors when estimating a linear probability model. The 훽1 − 훽7 can be interpreted as the change in the probability 푌푖 = 1, holding the rest constant k-1 regressors. An inherent major problem with this model is that it estimates probabilities outside the actual probability space, because it assumes that conditional probability function to be linear and does not restrict the probability, Pr(푌 = 1|푋1 ,푋2 , … , 푋푘 ) to lie between 0 to 1 interval (Wooldridge, 2012). In other words, nothing that forces Ŷ predictions to be in the relevant interval. Further, due to the nature

26 of the dependent variable being binary, the distribution would not be normal, and the model would exhibit heteroskedasticity unless the probability does not depend on the independent variables at all.

6.2 The Probit and Logit regression models

The purpose of the model with a binary outcome dependent variable is to estimate the probability that observations with particular characteristics will fall into a specific one of the categories; accepting wind power projects or deny projects through veto; moreover, classifying observations based on their predicted probabilities is a type of binary classification model.

For both the Probit and Logit model, the dependent variable is a discrete variable i.e., binary, which takes a value of either 1 or 0:

1 (푎푝푝푟표푣푒)푤𝑖푡ℎ 푝푟표푏푎푏𝑖푙𝑖푡푦 푝 푦 = { 0 (푑푒푛푦)푤𝑖푡ℎ 푡ℎ푒 푝푟표푏푎푏𝑖푙𝑖푡푦 (1 − 푝) (3)

When assuming that the conditional probability:

Pr(푦|푥) = Φ (푥훽), (4) where Φ described as a parametric function that takes the value between 0 and 1. Both logit and the probit are derived from the cumulative density function. For the logit it’s from the logistic log normal distribution and for the probit it’s the standard normal distribution.

6.2.1 Probit model

The Probit model is used to explain why different political decisions are made in Swedish municipalities. This nonlinear model forces the outcome to lie between 0 and 1, unlike LPM which assumes the parameters to be strictly linear. The probit model is derived from a cumulative density function and has a standard normal distribution, and ensures that predicted probabilities to be within the range 0-1 (Wooldridge, 2012).

The conditional probability:

E (Y|X) = Pr (Y = 1|X) = Φ (Xβ). (5)

Equation (3) shows the probabilities of Y=1 given a X.

푍 1 푍2 Φ (Xβ) = ∫ exp (− ) 푋훽 , (6) −∞ √2휋 2 where, Φ (Xβ) is the cumulative standard normal distribution function and have the range between 0 and 1 for all values of 푋훽, hence errors follow a standard normal distribution.

The probit model with multiple regressors will be:

Equation (7) specifies the potential variables that determine the decision behind deployment of wind power, where important factors such as installed wind capacity, economic incentives of wind turbines, of unemployment rate, median income in the municipality, protected areas, population density and the political stance in terms of green or right wing mandate could play an important part in the decision. In combination with the cumulative functional form, the model predicts the effect of an estimated change in X on the probability of Y=1 (Stock & Watson, 2015). If 훽1 is positive, an increase in 푋1 , holding constant 푋2 , … , 푋푘 , increases the z-value and thus increases the probability that 푌 = 1. The effect of a change in X will depend on the value of X.

The pooled cross-sectional data deals with time series of cross sections, but observes different subjects in different time periods, where as a panel data is used to observe changes over time in different variables (Wooldridge 2010). What’s important to control for when the data consist of different time periods is if we can apply the same model in each time period. This could be captured by a time dummy (Wooldridge 2010). Thus we tested a probit model with time effects.

Pr(Y = 1|MW , Unemployment, Income, Protectedarea, Pop. Density, MP, SD)

= Φ (훽0 + 훽1MW푖푡 + 훽2Unemployment 푖푡 + 훽3Income 푖푡 + 훽4Protectedarea푖푡 + 훽5Pop. Density푖푡 + 훽6MP푖푡 + 훽7SD푖푡 + 훽8 푇𝑖푚푒푒푓푓푒푐푡푠) (8)

In addition, equation (8) with time effects, that is "time dummies" for each year, allows us to capture some exogenous variation in the dependent variable that happens over time that is not directly related to the explanatory variables. These time fixed effects, control for unobserved variables (omitted variable bias) that are constant across municipalities but change over time (Stock & Watson, 2015). In terms of time, we would like to know if time contributes to decisions since the last ten years have seen a

28 lot of development, like an increase in awareness of the environment. This time variables did not change the results in any meaning or way. The outcome of this is only reported in the appendix A3.

One drawback of the probit model, however, is that it is not as easy to interpret as LPM. Regression estimation is no longer linear in the parameters, so we need to use the method Maximum Likelihood Estimation (MLE) for both probit and logit models. MLE is a method to o find the probability distribution and parameters that best explains the observed data, where the function is the joint probability of a chosen data, which is treated as a function of the unknown coefficients (Stock & Watson, 2015). The basic idea is to choose the values of those 훽̂ parameters that maximize the joint probability of observing the outcomes.

The maximum likelihood estimator: 푁

푌푖 (1−푌푖) 21 퐿 = ∏ 푃푖 (1 − 푃푖 ) (9) 푖=1 Taking the logs, we get the “log likelihood”: 푁

ln 퐿 = ∑ 푌푖 ln(푃푖) + (1 − 푌푖)ln (1 − 푃푖) (10) 푖=1 Where the function is the same for probit and logit models. The only difference is that the conditional probability, equation (4) going to have different cumulative distribution depending on the model.

However, the regression coefficients in the probit model cannot be interpreted as the marginal effect without further calculations as the coefficient is the partial effect on all the independent variables (Stock & Watson, 2015). Marginal effects depend on X:

푑푃(푌 = 1|푋1) ′ = 훽1G (X훽), (11) 푑푋1 Hence, G′(훽) will change as X changes. Allows for nonlinear relationships and diminishing returns.

21 푌푖 The product of each observations probability of observing “what we see” from the data. 푃푖 is 0 (deny) or 1 (approve), and when it takes 1 (1−1) 1 the value of 1 we get 푃푖 but (1 − 푃푖) but disappears due to (1 − 푃푖) = 0 = 푃푖 , so the probability to observe 1 when 1 actually occurs. 29

6.2.3 Logistic model

To be able to assess the robustness of the model's estimate, we also estimated a logistic model. Logit models are also used for discrete outcome modelling. The main difference from a probit model is the logit cumulative density function, is derived from the log normal distribution function (Wooldridge, 2010). Thus, the differences in the estimates tend to be marginal. The biggest difference between the LPM and the logit and probit models, is that the LPM assumes constant marginal effects for all explanatory variables, while the logit and probit models imply diminishing marginal magnitudes of the partial effects (Wooldridge, 2010). When comparing the models, probit and logit regressions frequently produces similar results and there is no right answer for which model to use. However, since the conditional logit model is a standard approach in the location choice literature (Stock & Watson, 2015), the logit regression model will also be used in this study.

The logistic regression model is shown in equation 12:

Pr(Y = 1|MW , Unemployment, Income, Protectedarea, Pop. Density, MP, SD) = Φ (훽0 + 훽1MW푖푡 + 훽2Unemployment 푖푡 + 훽3Income 푖푡 + 훽4Protectedarea푖푡 + 훽5Pop. Density푖푡 + 훽6MP푖푡 + 훽7SD푖푡) = 1 (12) 1 + 푒−(훽0+훽1푀푊+훽2푈푛푒푚푝푙표푦푚푒푛푡+훽3퐼푛푐표푚푒+훽4푃푟표푡푒푐푡푒푑푎푟푒푎+훽5푃표푝.퐷푒푛푠푖푡푦+훽6푀푃+훽7푆퐷)

푒푋훽 Φ (Xβ) = (13) 1 + 푒푋훽

The Φ (Xβ) denotes the cumulative logistical distribution function. Since 푒-() ranges between [0, ∞], 1 1 must range between [0,1]. 1+푒−(… )

One advantage of the logit model is that it has some additional interpretation called the odds ratio (OR) The variables' odds ratio (OR) is the probability that an outcome occurs, divided by the probability that 푃 it does not occur . Further, the marginal effects will be reported alongside the OR in the result. The (1−푃) marginal effects can be interpreted in the same way as the coefficients in the linear regression model.

7 Results

This section presents the empirical results of the three models we have chosen to employ in our investigation of which factors are related to a political decision regarding establishment of wind power in a municipality. In addition, two case studies will be interpreted for some further understanding.

7.1 Econometric models

It should also be taken into account that there may be other variables that can explain the outcome which are not included. Thus variables that are not included in the regression can also affect other variables. Some of the following examples are things that affect politicians’ decisions: corporate cultures, tourism, the market value of real estates and so on. The availability of these data is lacking thus the results from our study must be interpreted with a possible omission biases in mind.

Before we explain some further results, one main concern is that the dependent variable is within the range 0-1. A linear approximation could result in estimates outside of that range. In other words, the 푌̂ predictions will not lie between 0 and 1. The results from a linear regression (LPM) imply that some municipalities have a maximum above 100 % probability that accepting wind power will occur, see table 3. The Probit and the Logit models solve the problem and the resulting 푌̂ lies between 1 and 0.

Table 3 Result of 푌̂ in the LPM and of 푌̂ in the Probit and Logit model

Variable Obs Mean Std. Dev. Min Max Political Decision Ŷ (LPM) 350 0.74 0.28 -0.9 1.3

̂ 350 0.76 0.26 0 1 Political Decision Y (Probit and Logit) Source: Our estimates using Stata (2021)

Further, with the help of a classification table we can see how successful our model was in predicting outcomes in the probit and logit model.22 The model predicts 253 out of 276 possible approvals correctly and 67 out of 74 denials.

Table 4 below, summarize the results from the econometric exercises. Model 1 presents the results from the LPM with all the control variables. Model 2 shows the results from the Probit model and model 3 gives us the Logit estimations. Both model 2 and 3 is estimated using MLE instead of OLS, which aims

22 See A3 what percentage of those observations we did correctly classify in our predicted model, that a municipality was going to approve wind power and that they actually did. 31

to minimize the variance. Considering that MLE do not give us the marginal effects, we have for this reason the additional models 4 and 5. In terms of marginal effects, the LPM, Probit and Logit are largely equivalent and should give similar results. To check if the explanatory variables in our model are significant, we did a Wald test and based on the results of this we are able to reject the null hypothesis, indicating that the coefficients for all control variables are not simultaneously equal to zero (Stock & Watson, 2015). This means when including these variables we create a significant improvement in the fit of our models. 23

In order to measure the degree of explanation in a logit or probit model we use the so-called pseudo-R 2s. These are not calculated in the same way as the usual R 2, but are similar as 0 shows that model has a bad fit and 1 a perfect fit. In this study, McFadden’s pseudo-R 2 is used. This value describes how much of the variation in the model is explained and indicates to what degree the model outperforms the baseline model (Stock & Watson, 2015). Table 4 Municipal decision the dependent variable. Yes=1, No=0 (1) (2) (3) (4) (5) (6) VARIABLES LPM(OLS) Probit(MLE) Logit(MLE) Probit(dy/dx) Logit(dy/dx) Logit(OR)24

Megawatt -0.000272 -0.000851 -0.00850 -0.00325 -0.00326 0.9974006 (0.000321) (0.00122) (0.00120) (0.00022) (0.00023) (0.0021236) Unemployment -0.0152** -0.0720** -0.0719** -0.0205** -0.0207** 0.81919** (0.00541) (0.0258) (0.0256) (0.0058) (0.0069) (0.0498876) Income -0.00036** -0.0106** -0.0105** -0.0066** -0.00067** 0.9796** (0.000773) (0.00357) (0.00347) (0.00055) (0.00073) (0.0075711) Protected area -0.00133*** -0.0361*** -0.0349*** -0.0145*** -0.0148*** 0.9162*** (0.00228) (0.00911) (0.00906) (0.00313) (0.00315) (0.0196564) Population density -0.00082*** -0.0151*** -0.0148*** -0.0091*** -0.0093*** 0.9358*** (0.00041) (0.00179) (0.00175) (0.00058) (0.00061) (0.0082611) The Green party % 0.0986*** 0.1615*** 0.1410*** 0.1013*** 0.1021*** 1.2528*** (0.0731) (0.3800) (3.600) (0.1800) (0.1827) (0.6372369) The Sweden Democrats % -0.1393*** -0.5345*** -0.5341*** -0.3445*** -0.3448 0.7810*** (0.0418) (0.1961) (1.957) (0.0934) (0.0940) (0.0946199)

Constant 1.701*** 4.938*** 4.921*** 5.991*** (0.213) (0.995) (0.905) (0.876)

Observations 350 350 350 350 350 350 R-squared 0.418 Pseudo R-squared 0.574 0.575 0.554

Robust standard errors in parentheses Note: *** p<0.01, ** p<0.05, * p<0.1 The dependent variable is Political Decision (0,1)

23 See A4 in Appendix, for the result of the Wald-test, 24 When calculating OR, we need to convert an odds ratio into percent, this is easily done through the formula: 100 × (푂푅 − 1). 32

According to this study's hypotheses, Megawatt (installed capacity), Unemployment, Income, Protected Area, Population density, the Green party and the Sweden Democrats influences the extent to which the city council complies with the Swedish authorities' recommendations. All models in table 4 shows that all independent variables besides “megawatt” are significant. This supports the hypothesis of socioeconomic factors having an effect on political decisions. The significance level for income and unemployment is somewhat lower, 5 per cent. Unemployment also has an unexpected sign. Costa & Viega (2019) showed that the deployment of wind power had negative a correlation with unemployment, during the construction phase of the establishment unemployment. However, if Megawatt was excluded the results would not have change significantly. 25

In the LPM model (1) higher income has a negative effect on the decision of accepting wind power. This supports the result by Ek (2005), it was found that individuals with higher incomes were, on average, more negative towards wind power development in their area. According to Söderholm et. al (2007), the positive effects of wind power projects, such as employment opportunities, are less important for those with a higher income. As for the unemployment variable, a one percentage point increase in the unemployment rate in a municipality would decrease the probability for accepting wind with 0.15 %. This is somewhat counterintuitive as the development of wind power would at least in the short run create some jobs. Both income and unemployment have a lower p-value (5 % significance level) than the other variables in this model. Protected areas and the population density have a negative impact on the decision. However, Waldo et. al., (2012) found no support for the hypothesis that deep environmental values of the constituency helped wind power development.

Our two “political” variables used as proxies for pro and con wind power development ideologies yields the expected signs. An increase of Sweden Democrats in the city council increases the use of the veto against wind power. Lastly, the green politics seems to affect accepting further wind establishment, where a one percentage point increase of the Green party would imply an 9 % increase in deciding to approve the project. This result is in line with the previous study of Hedberg (2008), shows that left- leaning individuals in Sweden are more positive towards wind power than right-leaning individuals.

The result from model 4 and 5 shows that the unemployment, income, protected area, population density and the Sweden Democrats are more likely to effect political decision to not approve deployment of

25 See A5 in appendix for result. 33 wind power in a municipality. Waldo et.al. (2012) and Söderholm et. al. (2005) point out that higher density of the population have a negative impact on the deployment of wind power. This could be due to that visual impacts and noise affects more people. Note that the marginal effects between probit and logit in model 4 and 5, are also virtually identical. For both models, the reason behind these results could be that the likelihood of accepting wind power is decreasing in populated areas with a higher density to be affected by noises, lights, and visual impacts. The variable protected areas may lead to a conflict of environmental values that hinders wind power development. This result also implies that the environmental ambition of the Green majority party definitely have a positive impact and is in line with the national support, while the Sweden Democratic party has a negative impact and will affect the political decision to deny wind power development.

However, this don’t have to be the situation for every municipality. There could be some latent differences between the municipality and national support in deciding green energy development. But our results show that the Green party is the only variable in the model that increases the probability to approve wind power development.

The last model 6, which shows the logit model with some additional interpretations, with the help of odds ratio of the logit probability predictor. First, a value greater than 1 indicates odds increase as X increases. The values less than one, tells us that the ratio is less than one so as X goes up the odds falls, or the probability falls. Thus, if the odds ratio is 1.0 this means that the predictor X has no correlation with Y since an increase / decrease in X does not affect the odds of the event. The Sweden Democrats increases for each unit, the decision of accepting wind power decreases with 22 %, but when the Green party increases by a unit, the odds of not using a veto increases with 25 %. The Green party have a significant effect on accepting wind projects, which is in line with Lundmark (2008), who found that politics with a greener stance are more willing to wind power parks. Income, protected area and population density shows that for each unit the predictor X increases, the odds accepting wind power development is reduced approximately by 10 %. The population density seems to have a significant effect on the veto, a result partially in line with previous studies (Waldo et. al., 2012).

7.2 Two case studies

The data has some peculiarities which we will explore in two case studies.

The first case includes seven municipalities that have decided to both accept wind power and use the veto during the same year. According to our probit model, three of the municipalities would have used the veto and the remaining four would accept the wind projects.26 These are shown in table 5.

Table 5 The characteristics of the seven municipalities with both accepted and declined projects Municipal Megawatt Unemployment rate Income Protected Population MP SD % KSEK area % density % % Hylte 41 13,1 224,5 1 10,6 2 5 Piteå 146 12,6 237,5 5,6 13,7 6 4 Tranemo 0 10 230,7 1,7 15,7 5 11 Ulricehamn 6 9,6 242,3 2,3 23,4 4 12 Sundsvall 1 14,1 250,3 2,3 30,4 5 6 Vetlanda 169 11,3 274,8 0,9 18,3 0 11 Svenljunga 0 12,3 230,8 3,3 11,2 3 6 Mediana 10 12,4 230,45 2,8 18,2 4 6,5 Source: SCB (2021) and Energimyndigheten (2021) a This is the median of all municipalities in the study.

Though these municipalities have chosen to both approve and deny wind power development, the simple explanation is that one project was objectively better suited, and that one project did not fit the local context. In other words, the approved projects have a nice fit in the local planning.27 However, our model predicts the split as almost 50-50, (see appendix A7), i.e. we would have predicted approvals in 4 cases

A simple analysis of newspaper articles discussing foremost the vetoing of project may give some hints or superficial explanations. These indicate some common denominator why these muncipalities used the veto. In Piteå and Vetlanda, the Swedish Armed Forces said no to parts of the wind power expansion planned. We consider that as part of projects that never really was feasible. The suggested wind power development sites would clash with national interests of the nation’s security (SR, 2012). In Hylte and Sundsvall the city council decided to use the municipal veto and rejected several wind projects, as some of the turbines were suggested to be placed outside the priority area (Hallandsposten, 2010 & Sundsvall newspaper, 2019). Another reason could be pressure from local citizens, for example, in residences in Svenljunga and Tranemo have protested against the plans for wind power in the disapproved area. They

26 See A6 in appendix for the result. 27Wind farm plan is part of a master plan drawn up in a municipality with the intention of facilitating the control of the establishment of wind turbines to the most suitable locations. The plan shall guide decisions on issues relating to land and water areas in the field of wind power (Tranemo kommun, 2020). 35 believed that wind power would have a negative impact on the development of the area (Borås tidning, 2012). Further, the distance between residential property and the wind plants, is one reason for their decision not to grant a building permit for wind power in Ulriceham (SR, 2012).

Moving on to the second case where 10 municipalities changed the political decision over the years, see table 6. These municipalities have in 9 cases first accepted wind power development, but later further development gets stopped. Political stance seems to play role in these cases. The green party share MP, shrinks and the Sweden Democrat variable, SD, increases. However, these are cases that should be investigated further as our analysis does not rigorously account for the complexity of the changes in the constituencies’ views. But it does seem that the Sweden Democrat proxy for non-wind power development ideology is worth further research. These municipalities tend to have higher income and protected area gets larger.

Table 6 The characteristics of the ten municipalities with changing decisions over the years (arrows indicate changes28) Muncipality Year Political Megawatt Unemployment Income Protected Population MP SD decision rate % KSEK area % density % % Bräcke 2012 Yes 0 11,5 201,8 0,6 2 3 2 Bräcke 2016 No ↑92 ↑19,4 ↑223 ↑0,8 1,9 ↓2 ↑8 Dals-Ed 2014 Yes 48 13,4 203,2 6,4 6,6 3 6 Dals-Ed 2019 No 48 ↑16,2 ↑236,6 ↑7,3 6,6 ↓ 0 ↑13 Degerfors 2010 Yes 0 11 205,6 2,4 24,9 2 2 Degerfors 2014 No ↑5 ↑20 ↑220,3 ↑2,5 24,9 ↑3 ↑6 Falkenberg 2012 Yes 65 13,6 225,8 1,5 37,3 4 6 Falkenberg 2017 No ↑173 ↑17,2 ↑258 ↑1,6 ↑39,9 4 ↑12 Hultsfred 2014 Yes 5 10,8 213,5 0,5 12,4 2 11 Hultsfred 2019 No ↑28 ↑19,4 ↑243,9 0,5 ↓12,2 ↓0 ↑13 Kristianstad 2014 Yes 60 11 233,8 3,5 66,8 5 19 Kristianstad 2018 No ↑98 ↑18,7 ↑264,3 ↑4,2 ↑68,9 ↓0 ↑23 Kalmar 2013 Yes 24 14,4 236,5 2 67,6 7 3 Kalmar 2017 No ↑35 ↑16,6 ↑269 2 ↓65,3 7 ↑10 Mariestad 2014 Yes 12 9,9 229 5,8 39,7 4 6 Mariestad 2018 No ↑83 ↑16,4 ↑264,6 5,8 ↑40,5 ↓2 ↑8 Tingsryd 2010 No 0 17,3 194,8 2,3 11,7 6 5 Tingsryd 2017 Yes 0 ↓13 ↑237,4 2,3 ↑11,9 2 ↑12 Tranås 2013 Yes 4 9,7 220,5 1,9 45,2 5 5 Tranås 2017 No ↑13 ↑17,4 ↑249 1,9 ↑46,9 5 ↑15 28 It is measured as an increase or a decrease from the previous value of the variable. Source: SCB (2021) and Energimyndigheten (2021)

The majority of the municipalities have gone from accepting wind development to deny further expansion, except for Tingsryd. One can ask the question whether the experiences of wind power (the first project) have led to the rejection of further projects. This, if true, is contrary to the findings of Ek (2005). There could also be that the municipalities reached a saturation point. For example, one reason

36 why Falkenberg chooses to reject further wind power establishment is that there is already a lot of wind power in this area (Hallandsnyheter, 2017).

Further, a casual analysis of the newspaper discourse indicates that a process will lead to a veto when developers do not account for protection values. For example, in Mariestad wind turbines are not allowed to be closer to houses than 900 meters according to their municipality policy. When this seems violated they have used their veto (SVT, 2020). The Sweden Democrats said no to the plans in Hultsfred since the location is not considered suitable for the wind turbines (Dagens Hultsfred, 2019). As well for the city council in Kristianstad decided to not accept higher wind turbines, the reasons behind are that it affects the view from coastline and that it destroys the marine environment (SR, 2018).

7.3 Interest groups impact on the veto decision

It is part of the result that cannot be explained by the econometric models, which brings us to Becker’s thoughts regarding interest groups and their impact on political decisions. We believe that an in-depth case study of the cases which have been denied would point to actions from organized resistance to the wind power projects. Thus, our model suffer from omitted variable biases.29 Even if the in-depth case study is outside the scope of this this study some cursory evidence may shed some light on this bias.

Swedish landscape protection is one of the groups that has had an impact on development of wind power in some areas. This group focusses on the negative externalities such as noise and visual deterioration of the landscape. For example, the Swedish landscape protection in Ölme tries to stop the planned wind turbines in the municipality with help of an open letter directly to the City Council (NKP, 2020). This group indicates that the municipality have already fulfilled its share of the national goal and expect that the municipality uses its veto rights and rejects the request for further development (NKP, 2020).

There are various resistance groups that expanded their membership with help of social media, where they express that from an environmental point of view wind power is not good for the nature (e.g. Vilda Västra, 2021, Bloomberg, 2020). These groups are increasingly vocal and become more efficient at producing political pressure to impact the decision at a local level. The protests from interest groups have become common and wind power is increasingly seen as something controversial (already noted in Åstrand & Neij, 2006). A veto can, as in the case with Tekniska verken in Avesta also be an effect from

29 Suggestions for potential proxy for tourism variable that could potentially solve this; a variable for hotel nights and for the interest groups statistics on local commitment in the form of memberships. However, this data is not publicly available. 37

“contagion” of a neighbouring municipality where the political consequences was undesirable for the ruling parties (Avesta tidning, 2021).

Even a wind power friendly group like the Conservation society (Naturskyddsföreningen) can potentially become hinder for potential wind establishment areas and a reason for the use of the municipal veto. According to the report (2021) about “wind power as an important part of energy in the future”, the Conservation society indicates that a major expansion of wind power is necessary, but that it must never take place at the expense of the wildlife.

Becker’s theory indicates that a transparent development process in areas with strong and active resistance groups that obstruct heavily, could complicate for future development of wind power. We believe that Becker’s theory gives some insights to understand the residual, the unexplained part of our empirical results. Further research efforts should investigate in more details what for example the above mentioned interest groups impact may have been.

8 Conclusion

Wind power development is expected to be an important piece in transforming to a circular economy and a fossil free society. Nevertheless, wind power requires land use. Land can be “used” for several different purposes. Additionally, wind power has some external effects on the local environment. Wind power developers must therefore pass processes ensuring that the local impact is acceptable. Part of this process, in Sweden, is that the wind power developers have to get their projects approved by the municipalities. Lately, this power of the municipalities has been questioned.

This study has investigated the relationship between municipal decision making and whether determinants of the decision can be found in different environmental, political, geographical, and economic indicators for municipalities in Sweden. The econometric analysis was based on a pooled cross section dataset, covering 146 of Sweden’s 290 municipalities for the period 2010-2019. The theoretical framework was mainly based on the economic theories of public choice (foremost represented in the seminal works by Buchanan and Tullock), which aim to explain the impact of various socio-economic variables for political decision-making.

The econometric models showed that the Green political party (positively affecting wind power development) and that the unemployment rate, income, population density, protected areas and the affiliations with the Sweden Democrats (negatively affecting the approval rate), has a significant effect on the permission process. Installed capacity of wind power plants seemingly have no impact.

Political stance has a strong and significant effect on the veto decision and partially confirms results from earlier studies. Higher income in a municipality decreased the probability of accepting wind. This could be due to Khan’s (2003) hypothesis that the ability to exert influence is higher which makes it harder to develop infrastructure projects that has no direct benefit for the concerned citizens. Further, results showed that municipalities with a higher percentage of protected natural areas tend to be more prone to use the veto. We can only speculate on the reason for this. Assuming that the protected areas in a sense must be “discovered” this may be a proxy for the interest in environmental conservation and thus we measure a green vs green conflict. Thus, unemployment rate did have a unexpected sign, this is somewhat counterintuitive as the development of wind power would at least in the short run create some jobs.

As the study by Waldo et. al. (2012) we also estimated that population density has a negative impact on the deployment of wind power. This result is intuitive as more people will get affected by the wind power turbines. One indication that this variable is important is that an increasing number of new projects are proposed in the sparsely populated northern part of Sweden.

In conclusion, this study has shown that the municipal decision-making is less random than the critics of the municipal veto proposes. However, our simple model only captured approximately 50 % of the possible explanations (pseudo R-square approx. 0.5). There are factors outside the model that can explain this phenomenon even more thoroughly, which has not been adressed by this specific model. We believe that further inquiries into the actions, motives and goals of local interest groups is warranted. Our study also points to the case of a more in-depth case study answering the question: Do the municipal veto capture values that are important to local citizens, which are not captured in the complementary processes already in place (e.g. environmental impact assessments)?

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APPENDIX

Table A1. List of municipalities

1. Alingsås 50. Kristinehamns 99. Staffanstorps 2. Aneby 51. Krokoms 100. Stenungssunds 3. Arjeplog 52. Kumla 101. Storumans 4. Askersund 53. Kungsbacka 102. Strömstads 5. Avesta 54. Köpings 103. Strömsunds 6. Bergs 55. Laholms 104. Sunne 7. Bengtsfors 56. Laxå 105. Sundsvalls 8. Bjurholm 57. Lerum 106. Svalövs 9. Bollnäs 58. Lekeberg 107. Svenljunga 10. Borgholm 59. Lilla Edet 108. Säffle 11. Bollebygd 60. Ljungby 109. Söderhamns 12. Borlänge 61. Ljusdal 110. Sölvesborgs 13. Borås 62. Lomma 111. Tanums 14. Bromölla 63. Ludvika 112. Tidaholms 15. Bräcke 64. Lunds 113. Tingsryds 16. Dals-Ed 65. Lysekils 114. Tjörns 17. Degerfors 66. Malung-Sälens 115. Torsås 18. Eskilstuna 67. Malå 116. Tranemo 19. Eslövs 68. Mariestad 117. Tranås 20. Falkenbergs 69. Marks 118. Trelleborgs 21. Falköpings 70. Melleruds 119. Töreboda 22. Falun 71. Mjölby 120. Uddevalla 23. Flens 72. Mora 121. Ulricehamns 24. Färgelanda 73. Motala 122. Umeå 25. Gislaveds 74. Munkedals 123. Uppsala 26. Gnesta 75. Mönsterås 124. Uppvidinge 27. Gnosjö 76. Nordanstigs 125. Vadstena 28. Gotland 77. Norberg 126. Vansbro 29. Gullspångs 78. Nordmalings 127. Vara 30. Gällivare 79. Norrtälje 128. Varbergs 31. Götene 80. Norsjö 129. Vetlanda 32. Hallsbergs 81. Nybro 130. Vilhelmina 33. Halmstads 82. Nässjö 131. Vindelns 34. Hedemora 83. Ockelbo 132. Vårgårda 35. Helsingborg 84. Orust 133. Västerviks 36. Hjo 85. Ovanåker 134. Växjö 37. Hofors 86. Pajala 135. Åmåls 38. Hultsfreds 87. Piteå 136. Ånge 39. Hylte 88. Ragunda 137. Åre 40. Härjedalens 89. Robertsfors 138. Årjängs 41. Härnösands 90. Ronneby 139. Åsele 42. Höganäs 91. Rättviks 140. Älvdalens 43. Hörby 92. Sandvikens 141. Ängelholms 44. Jönköpings 93. Simrishamns 142. Örebro 45. Kalmar 94. Skara 143. Örnsköldsviks 46. Karlstads 95. Skellefteå 144. Östersunds 47. Kiruna 96. Smedjebackens 145. Österåkers 48. Kramfors 97. Sollefteå 146. Övertorneå 49. Kristianstads 98. Sorsele

Table A2. Correlation matrix

(1) (2) (3) (4) (5) (6) (7) (8)

(1) Political decision 1.0000 (2) Megawatt -0.0354 1.0000 (3)Unemployment rate -0.1462 0.0341 1.0000 (4)Income -0.1734 -0.0304 0.0189 1.0000 (5)Protected area -0.2521 0.0324 -0.0469 -0.0129 1.0000 (6)Population density -0.5093 -0.0776 -0.0271 0.0421 0.1142 1.0000 (7)The Green party 0.2049 -0.1226 -0.2376 -0.0620 -0.0178 0.1068 1.0000 (8)The Sweden Democrats -0.2987 0.0133 -0.0471 0.0471 0.0059 0.2398 -0.1403 1.0000

Table A3. Model with time dummy Probit model with time dummies (1) VARIABLES Probit

Megawatt -0.000903 (0.00223) Unemployment -0.0651** (0.0203) Income -0.0119** (0.00389) Protected area -0.0381*** (0.00921) Population density -0.0156*** (0.00172) The Green party % 0.1567*** (0.3790) The Sweden Democrats % -0.7445*** (0.00122) 2011 -0.0531 (0.538) 2012 -0.520 (0.430) 2013 -0.871** (0.440) 2014 -0.380 (0.432) 2015 -0.328 (0.437) 2016 0.329 (0.519) 2017 -1.037** (0.473) 2018 0.400 (0.584) 2019 0.331 (0.568) Constant 5.374*** (1.114)

Observations 350 Robust standard errors in parentheses Note: regression with time dummies *** p<0.01, ** p<0.05, * p<0.1 The dependent variable is Political decision (0,1)

Table A4. Classification

Logistic and Probit model for Political Decision True -- Classified D ~D Total + 253 23 276 - 7 67 74 Total 260 90 350

Classified + if predicted Pr(D) >= .5 True D defined as Political Decision !=0

Sensitivity Pr( + D) 97.31% Specificity Pr( -~D) 74.44% Positive predictive value Pr( D +) 91.67% Negative predictive value Pr(~D -) 90.54%

False + rate for true ~D Pr( +~D) 25.56% False - rate for true D Pr( - D) 2.69% False + rate for classified + Pr(~D +) 8.33% False - rate for classified - Pr( D -) 9.46%

Correctly classified 91.43%

Table A5. Wald-test

(1) [PolDecision]MW = 0 (2) [PolDecision]Unemployment = 0 (3) [PolDecision]Income = 0 (4) [PolDecision]Protectedarea = 0 (5) [PolDecision]PopDensity = 0 (6) [PolDecision]MP = 0 (7) [PolDecision]SD = 0 chi2( 7) = 112.31 Prob > chi2 = 0.0000

Table A6. Without the variable Megawatt

Binary dependent variable analysis

VARIABLES Probit(MLE)

Unemployment -0.0723** (0.0319) Income -0.0105** (0.00343) Protected area -0.0362*** (0.00931) Population density -0.0150*** (0.00565) The Green party % 0.1650*** (0.3967) Sweden Democrats % -0.5330*** (0.1868) Constant 4.872*** (1.228)

Observations 350 Pseudo R-squared 0.581

Robust standard errors in parentheses Note: regression without variable Megawatt *** p<0.01, ** p<0.05, * p<0.1 The dependent variable is Political decision (0,1)

Appendix A7.Results from the probit model

Hylte: (4.937598 +(-0.000851)*41+(-0.0720)*13,1+(-0.0106)*244,5+(-0.0361)*1+(-

0.0151)*10,6+(0.1615)*0,02+(-0.5345)*0,05)= 1.218994

Transformation with CDF =0.88857677 = 1

Piteå:

(4.937598 +(-0.000851)*146+(-0.0720)*12,6+(-0.0106)*237,5+(-0.0361)*5,6+(-

0.0151)*13,7+(0.1615)*0,06+(-0.5345)*0,04)= 1.216424 Transformation with CDF = 0.88808828= 1

Sundsvall:

(4.937598 +(-0.000851)*1+(-0.0720)*14,1+(-0.0106)*250,3+(-0.0361)*2,3+(-

0.0151)*30,4+(0.1615)*0,05+(-0.5345)*0,06)= -0.704004 and after transformation with CDF =

0.20398309 = 0

Svenljunga:

(4.937598 +(-0.000851)*0+(-0.0720)*12,3+(-0.0106)*230,8+(-0.0361)*3,3+(-

0.0151)*11,2+(0.1615)*0,03+(-0.5345)*0,06= -0.328277 Transformation with CDF =0.37135112 = 0

Tranemo:

(4.937598 +(-0.000851)*0+(-0.0720)*10+(-0.0106)*230,7+(-0.0361)*1,7+(-

0.0151)*15,7+(0.1615)*0,05+(-0.5345)*0,11= 1.185608 Transformation with CDF = 0.88211143 = 1

Vetlanda:

(4.937598 +(-0.000851)*169+(-0.0720)*11,3+(-0.0106)*274,8+(-0.0361)*0,9+(-

0.0151)*18,3+(0.1615)*0+(-0.5345)*0,11= -0. 264776 Transformation with CDF=0.39559101 = 0

Ulricehamn:

(4.937598 +(-0.000851)*6+(-0.0720)*9,6+(-0.0106)*242,3+(-0.0361)*2,3+(-

0.0151)*23,4+(0.1615)*0,04+(-0.5345)*0,12= 1.105722 Transformation with CDF = 0.86557657 =1