Case Selection - Simple Learning Complex Learning

Simple Learning Complex Learning

5.1 Case Selection

This thesis aims to understand local energy policy-makingthe important actors, salient issues and actor constellationsin a broad range of counties. The German counties dier tremendously concerning their socioeconomic, geographical and political preconditions.

Yet, even counties with similar preconditions vary distinctively concerning their outcome in implementing REs (see below). Thus a careful case selection is necessary in order to make sure that observed dierences are not based solely on these preconditions, and to carve out whether actors, issues and policy networks might actually make a dierence in the policy process. Following the idea of Cosmi et al. (2015, 695), a holistic approach is necessary in order to understand the regional potentials towards a sustainable energy development. The research design of this thesis keeps the physical, environmental, in-frastructural and socio-economic characterisation[!] as well as the legislative reference framework as constant as possible in order to identify the main challenges and expec-tations of the local community as well as the main stakeholders and their relationships (Cosmi et al., 2015, 695). Aiming to nd comparable cases, a comprehensive data base of preconditions was collected for the 97 counties in the two German states BW and NRW.

The data collection, correlations of the variables with the implementation ofREs as well as the matching and case selection are described in this section.

The study aims to identify four countiestwo of which are located in the rural and two in the urban area. The two county pairs should each be comparable in their preconditions but distinct in the implementation success ofREs. Many of the comparative case studies identied in the literature review selected best-practice examples. It is noteworthy, that neither the best-practice examples nor the other cases where selected in a transparent way.

This problem is encountered in many qualitative studies, which are often accompanied by an intransparent case selection based on pre-knowledge of the authors. Case selection thus seems to appear from nowhere. But in qualitative research more than anywhere else the cases you choose aect the answers you get (Geddes, 1990, 131). Thus, in order to pro-duce a replicable analysis it is most important, that the case selection is replicable itself (Dafoe, 2014; Nielsen, 2014b). Statistical matching methods are a good way of choosing

the best available cases and in the notion of Nielsen (2014b, 23), they oer substantial improvements over traditional practices of case selection: they ensure that most similar cases are in fact most similar, they make scope conditions, assumptions, and measurement explicit, and they make case selection transparent and replicable. Furthermore, match-ing improves the credibility of case selection by providmatch-ing comparisons to the cases not selected (Nielsen, 2014b, 18).

Moreover, to answer the research question of this paper and testing the hypothesis a proper case selection is crucial. This paper studies counties which dier in the extent to which they implement REs. When analyzing important actors, salient issues and actor constellations as well as their relation to the implementation of REs in a county, it is important to account for varying preconditions. For example, an area that has only a low potential to install REs cannot be seen as following a worse policy path per se. Thus, it is necessary to select cases that are most similar in the preconditions which drive local energy policy-makingpolitical, economic and geographicaland most dierent in their implementation ofREs, to evaluate the role of relational drivers. Matching of cases helps hereby to account for the various preconditions. To fulll this task, the following quanti-tative approach was taken:

To compare rural areas with urban areas, two out of the sixteen German federal states where chosen: BW and NRW. BW is rural in many areas, has a concentrated (car) in-dustry in the greater area of the state capital Stuttgart and a low overall unemployment rate. The state was long led by a conservative government (Christian Democratic Union [Christlich Demokratische Union] (CDU)), but the government changed in 2011 when the nuclear catastrophe in Fukushima fostered the success of theGreens. The government cur-rently consists of theGreensand the conservative partyCDU, and is led by the rst green prime minister of the state in Germany. Geographically, the state is coined rurally and by forest. Tourism is an important source of income, especially in southern BW. There are areas with high potentials ofREs. Not rarely these potential areas coincide with touristic and/or forest areas. This might lead to potential controversies between fostering the en-ergy transition and harming the local tourism industry as well as between fostering the energy transition and harming the environment.

Contrary,NRWincludes the most industrial areas in Germany. Especially the Ruhr region was coined by coal mining and is under permanent structural change since the beginning of the coal crisis. Unemployment rates are high in many areas. Through the energy tran-sition the area experiences a double burden due to rising energy prices and decreasing employment in the industrial sector, caused by the closing of coal power plants. The Social Democratic Party Germany [Sozialdemokratische Partei Deutschlands] (SPD) is the ma-jor political force in the state, representing the workers interests. Potential areas forREs are present. Here, the topics around the energy transition are expected to be framed more

Mecklenburg-Vorpommern

North Rhine-Westphalia

Rhineland-Palatinate

Baden-Württemberg

Saxony-Anhalt Lower Saxony Brandenburg

Thuringia Hamburg

Saarland

Bremen

Bavaria

Saxony Hesse

Berlin

-1K 270K

CO2 emissions (in t)

Figure 5.1: State Level GHG-Emissions in Germany (Total in t) (own Visualization based on the data provided by Statista, 2013)

economically, as generally the themes of developing the industry and decreasing the unem-ployment rate are very present. Therefore, one major conict line in the public discourse may be either seeing the energy transition as a threat or as an economic chance. Electric-ity has to be aordable also for low income households, nevertheless the energy transition and the related opportunities could be a great chance for the regions development.

Figure5.1depicts the CO²emissions in Germany at the state level. The map demonstrates that, in absolute numbers,NRWemits by far the most, while BWis in the medium range of states. Although the relative gures are better for both states when comparing CO² emissions per Gross Domestic Product (GDP) and CO² per capita (see gure A.1 in the appendix), the emission performance ofNRW is still considerably lower than the perfor-mance ofBW.

To identify cases according to Mill's (1882) method of dierence, data was collected for all 44 counties belonging to the federal state ofBW and for all 53 counties belonging to the federal state of NRW. For these 97 counties, data was collected on general

precon-ditions (size of the county, number of inhabitants), on economic preconprecon-ditions (average income per inhabitant, unemployment rate, agriculturepercentage of GDP produced in this sector), on political preconditions (election results of the last local elections for the four major parties) and on geographical preconditions (land-usepercentage of residential and trac area; potential of installable solar and wind power). The data on size, number of inhabitants, average income, election results, percentage of agriculture at the GDP and land use were collected through the data bases of the statistical of-ces of the two federal states, the Statistisches Landesamt Baden-Württemberg (2014) and theLandesbetrieb Information und Technik Nordrhein-Westfalen(2014). The data of the unemployment rates is based on the statistics of the Federal Agency of Employment (Bundesagentur für Arbeit, 2014). The measurement of the potential of installable solar and wind power is based on the studies of potentials of the Regional Oce for the Envi-ronment, Measurement and Nature Conservation [Landesanstalt für Umwelt, Messungen und Naturschutz Baden-Württemberg] (LUBW) (LUBW, 2014) and the State Oce for Nature, Environment and Consumer Protection [Landesamt für Natur, Umwelt und Ver-braucherschutz Nordrhein-Westfalen] (LANUV) (LANUV, 2012,2013). Furthermore, the data of currently installed renewable energy power (wind, solar, hydro, biomass and gas from purication plants) was collected for all counties. This extensive data collection was possible due to the EEG obligation for net operators to disclose their data (Bundesge-setzblatt, 2009a, 45-52). The data utilized in this article is based on the conditioned version of this raw data from the `EnergyMap' (Deutsche Gesellschaft für Sonnenenergie e.V. (DGS), 2014).¹⁸ The data set includes 12 independent variables describing the pre-conditions of the counties and their installed renewable energy power.¹⁹ The next section describes the identication of comparable cases based on the collected data set.

The data set includes the installedRE power of the counties. To evaluate the amount of installed power, two measures are possible. The rst possible measure is to understand the relation between the installedREpower and the power consumption within a county.

Counties that are able to produce a high percentage of their consumption withREs should be seen as more successful in the implementation, than counties with a low percentage (% of REon consumption). The second possible measure accounts for a county'sRE po-tential. It measures the actual installed power of REs in percentage out of the potential power that could be installed (% installed of potential). The following examples show that the two measures are complementary and equally important for the evaluation of the

18In an updated version of the web page, the German Society of Solar Energy [Deutsche Gesellschaft für Sonnenenergie e.V.] (DGS) claims that information on the registration of the construction of RE plants are completely concealed since the new decree on the registration of REplants [`Anlagenreg-isterverordnung'] took eect on the 1st of August 2014 (BMWi, 2014b). This development does not interfere with the analysis of this thesis, since the data was collected prior to this date. However, it is a noteworthy development towards less rather than towards more transparency.

19The full data set will is provided on the attached CD and will later be available for download.

success of a region. For example, the county Alb-Donau-Kreis (ADK) in BW has a very high potential of wind energy. While the county is trying harder, installing many more wind power plants than the surrounding counties, it will be very dicult to reach a high percentage of installed power out of the potential power. Thus, to evaluate this county fair, the rst measure will be better. Comparing the percentage of the power installed out of the consumption with other counties shows the eort the county is making. How-ever, the implementation of REs is highly dependent on the potential: Urban counties may have a lower potential of REs due to space constrains and a higher consumption.

A low percentage of REs on the consumption is thus not per se equal to policy failure.

Measuring the installed power, as compared to the potential power, will give a fairer t of the eort of urban areas. These examples show that both measurements are important in order to fairly evaluate the eorts of rural and urban counties. The results of these measures are depicted in the gures 5.2 and 5.3. Interestingly, in BW the two measures are almost mirroring each other, while inNRWpatterns between the counties look rather similar with both measures.

In order to identify signicant preconditions for the implementation of REs, two linear regression analyses are conducted with the 12 independent variables and the two dierent dependent variables (% of potential and % of consumption), applying a backward variable selection. The results of these models are summarized in table5.1. Studying the eect of the independent variables on the rst Dependent Variable(DV)₁, percentage of (wind and solar) potential realized, we observe a positive signicant eect of the agriculture share of the Gross Value Added (GVA) and of the solar potential. This means that the more the county is dependent on agriculture and the more solar potential a county has, the higher is the share ofREs installed from the (wind and solar) potential. Contrary, the inhabitants perkm² have a strong negative eect on the implementation of this potential. The more people live in a county, the less likely is the county to have implemented a higher share of its potential. The wind potential also has a strong negative eect on the percentage of the realized potential. Thus, while the potential for solar increases the overall share, the potential for wind decreases this share. This phenomenon could be explained by the dierent characteristics of theREs. While solar energy is not controversial and investment decision are often made by individuals, the decision-making for the construction of wind power plants is much more complex. Thus, keeping the installations up with the potential is more challenging.

The results show, that the signicance of variables changes, when observing the second DV2 percentage of the consumption that is gained through REs. The agriculture variable is again signicantly positive related to theDV2. The more agriculture production exists in a county, the more likely is the county to produce a high share of their energy consump-tion withREs. Contrary to the other model, the wind potential has a signicant positive

Freiburg im Breisgau

Alb-Donau-Kreis Bodenseekreis

Mannheim Stuttgart -1,5%18,0%% of REs installed from potential (a)%ofRealizedREPotential

Freiburg im Breisgau

Alb-Donau-Kreis Bodenseekreis

Mannheim Stuttgart -5,5%62,5%% of REs from total energy consumption (b)%ofEnergyConsumptionProvidedbyRE Figure5.2:REinBaden-Württemberg(ownvisualizationbasedoncombineddataprovidedbyDeutscheGesellschaftfürSonnenenergie e.V.(DGS)(2014),LUBW(2014)andStatistischesLandesamtBaden-Württemberg(2014))

Düsseldorf

Dortmund

Cologne

Hagen

Bonn

-1,5% 18,0%

% of REs installed from potential

(a) % of Realized RE Potential

Düsseldorf

Dortmund

Cologne

Hagen

Bonn

-5,5% 62,5%

% of REs from totalenergy consumption

(b) % of Energy Consumption Provided by RE

Figure 5.3: RE in North Rhine-Westphalia (own visualization based on the data provided by Deutsche Gesellschaft für Sonnenenergie e.V. (DGS) (2014), LANUV (2012), LANUV (2013) and Landesbetrieb Information und Tech-nik Nordrhein-Westfalen (2014))

Table 5.1: Linear Regression Models for Two DVs Based on the 97 Counties

Model 1 Model 2

DV₁ DV₂

% of potential % of consumption

(Intercept) 8.951^∗∗ 33.86533^∗∗

(3.058) (10.21844) inhabitants_sq −0.001725^∗∗∗

(0.000445)

income −0.000182 −0.00089^∗

(0.000129) (0.00041)

agriculture 1.800^∗∗ 9.53407^∗∗∗

(0.6134) (1.93727)

election_GRUENE −0.51216^∗∗

(0.19087)

land use −0.11117.

(0.06486) wind potential −0.0005101^∗∗∗ 0.00141^∗∗

(0.0000989) (0.00032) solar potential 0.0008798^∗

(0.0004241)

R² 0.43 0.70

Adj. R² 0.40 0.68

Num. obs. 97 97

Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1

eect on the percentage of consumed power that is produced byREs. More surprising are the other two eects observed: The average income in a county, as well as the last election results of the Greens, are signicantly negative correlated with theDV2. The signicant correlation of the election results of theGreenscould be explained by two contrary mech-anisms: Either the Greens in the county are emphasizing the necessity to protect the environment against the construction ofREs and thus hinder the implementation ofREs in their county, or the residents are not satised with the current low implementation of REs and are thus supporting the Greens in their county, in order to increase the share of REs. This puzzle cannot be solved with the regression results alone. This shows once more that signicant results within a regression analysis can only explain parts of the puzzle and more in-depth analysis is necessary in order to understand the mechanisms behind the correlation.

Therefore, cases are selected for a more in-depth analysis of the underlying mechanisms.

Instead of taking one or the otherDVfor the case selection, all counties are ranked accord-ing to their performance in both measures. For both states separately, the county with the highest percentage ofREs of the consumption gets the ranking_RE= 1, the second best gets the ranking_RE = 2 and so forth. Accordingly, the county with the highest percentage of installed power out of the potential power gets theranking_installed= 1, the second best gets theranking_installed= 2 and so forth. The two rankings will then be summed up, leading to aranking_sum ≥2 for all counties. This procedure leads us to theDVranking_sumthat neither favors rural nor urban areas. The ranking approach has the huge advantage, that both factors are weighted similar, in order to not overesti-mate one of the factors due to dierences in their range of percentages.²⁰

The case selection has the aim to select cases, that are similar in their various precon-ditions, yet vary greatly in the policy outcome. Therefore, the method of dierence is applied (Mill,1882, 483).²¹ The method of dierence aims at nding cases, that are most similar in their preconditions and dier signicantly in their outcomes. The (to be studied) explanatory aspect of policy network structures can then be seen as the explanatory vari-able for the dierence in outcomes. The method of dierence can easily be confounded with the most-similar-systems design. The most-similar-systems design aims at nding cases, that are most similar in their preconditions and vary in their explanatory variable, analyzing whether this has an eect on the outcome. This certainly preferable procedure

20In the urban case, the % of RE on consumption varies between 0.2% and 20%, while the percentage of

% installed of potential varies between 0.4% and 6.9%. In the rural case, the % of RE on consumption varies between 3% and 54%, while the percentage of % installed of potential varies between 1% and

21The method of dierence as dened by16%. Mill (1882, 483) as following: If an instance in which the phenomenon under investigation occurs, and an instance in which it does not occur, have every circumstance in common save one, that one occurring only in the former; the circumstance in which alone the two instances dier, is the eect, or the cause, or an indispensable part of the cause, of the phenomenon.

Table 5.2: Overview of Components for the Matching Algorithm

% of GDP produced in the

agri-culture sector agriculture

% the residential and trac area land.use potential to install wind power windpotential potential to install solar power solarpotential

was not feasible in the present case, because the explanatory variable of policy networks is very costly to access and can thus not be accessed a priori for all possible cases. How-ever, based on the method of dierence the case selection was not done arbitrary. The matching was conducted by using the R package caseMatch by Nielsen (2014a). The in-dependent variables as well as the outcome, with which the matching was performed, are summarized in table 5.2. The cases were matched with the Mahalonobis algorithm.

Based on Nielsen (2014b) the Mahalanobis matching appeared to be most meaningful, because it identies units located close together in the k-dimensional space dened by the covariates (Nielsen, 2014b, 10).²² Moreover, it identies similar units by minimizing pairwise Mahalanobis distance, a generalization of Euclidean distance that accounts for correlations between variables (Rubin, 1973 inNielsen, 2014b, 8). The outcome variable is the ranking_sum. Applying the algorithm to the 97 cases in leads to nd matches, that are most similar in their 12 covariates and at the same time have a highly dierent outcome. In other words, the algorithm results in pairs, that have the same preconditions (based on the data collected for the 12 independent variables) appearing to be meaningful

22Formally, the Squared Mahalanobis distance is dened for twop×1 vectors xand y as D² = (x− y)⁰P−1

(x−y)where Pis the covariance matrix of the p×pdistribution. (Nielsen,2014b, 24)

for the implementation of REs (compare to section 3.1), and yet show a very dierent implementation success according to their ranking_sum. Although not identical to the preferable most-similar-systems design, this procedure is clear, veriable and reproducible.

Im Dokument Local Policy Networks : Actors, Challenges and Cooperation in the Case of the German Energy Transition (Seite 108-119)