Cultured Construction: Global Evidence of the Impact of National

Existing data for 66 nations (representing an equal number of developed and developing economies) are used to fuel the analysis. Somewhat dependent on...
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Cultured Construction: Global Evidence of the Impact of National Values on Renewable Electricity Infrastructure Choice Jessica A. Kaminsky* Department of Civil and Environmental Engineering, University of Washington 201 More Hall, Seattle, Washington 98195, United States S Supporting Information *

ABSTRACT: Renewable electricity is an important tool in the fight against climate change, but globally these technologies are still in the early stages of diffusion. To contribute to our understanding of the factors driving this diffusion, I study relationships between national values (measured by Hofstede’s cultural dimensions) and renewable electricity adoption at the national level. Existing data for 66 nations (representing an equal number of developed and developing economies) are used to fuel the analysis. Somewhat dependent on limited available data on controls for grid reliability and the cost of electricity, I discover that three of Hofstede’s dimensions (high uncertainty avoidance, low masculinity-femininity, and high individualism-collectivism) have significant exponential relationships with renewable electricity adoption. The dimension of uncertainty avoidance appears particularly appropriate for practical application. Projects or organizations implementing renewable electricity policy, designs, or construction should particularly attend to this cultural dimension. In particular, as the data imply that renewable technologies are being used to manage risk in electricity supply, geographies with unreliable grids are particularly likely to be open to renewable electricity technologies.

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INTRODUCTION In this paper, I seek to determine if there are statistically significant relationships between cultural dimensions and the diffusion of renewable electricity at the national unit of analysis, after controlling for economic factors and grid reliability. The contribution of this research is to study global, aggregate implications of national values for global infrastructure construction and policy, specifically considering the case of renewable electricity. I define renewable electricity as electricity produced from geothermal, solar, tidal, wind, biomass, or biofuel sources.1 While these technologies have certainty existed for a long time, they are theoretically framed here as an innovation being taken up (or not) in national electricity grids. In other words, they are an innovation in a local sense;2 something different that is both adding to and replacing more traditional fossil fuel generation. The factors driving or preventing the diffusion of renewables are diverse.3 Technical reasons are commonly cited in the literature; some of these are challenges with grid integration,4 with storage and batteries,5,6 or with technology efficiencies.7,8 Economics are also a challenge to the wide diffusion of renewable technologies.9 In some key locations renewables are cost competitive;10 in most, however, they are still more expensive when externalities are neglected.11,12 Despite the joint challenges of cost and technical issues, however, renewable penetration is increasing as time passes.13 One known reason for this is enabling regulation and policy that encourages uptake;14−16 a linked constraint is the strong institutional environment that dominates the energy sector. © XXXX American Chemical Society

Similarly, increased local knowledge of renewable options, another competing explanation for increased renewable penetration, has been shown to explain a significant but still small portion of renewable electricity diffusion.17 However, uptake still occurs unevenly. To contribute to the literature on the global diffusion of renewable electricity infrastructure, I claim and test if there is also a cultural values component. A large number of studies18−24 have suggested that cultural and social factors variously definedare important factors in energy infrastructure design and construction. However, we lack research that tests for these relationships at the national level and that can thereby determine the importance of cultural factors for energy infrastructure at an aggregate level. This research contributes to filling this gap in the body of knowledge. Hofstede’s Framework. To test hypotheses on this topic, we need a widely available, quantitative instrument for crosscultural comparison. To meet this theoretical need, I use Hofstede’s cultural dimensions as a measure of national difference.25 Hofstede conceptualises culture as a deeply held “collective programming of the mind. . .rooted in value systems of major groups of the population. . .stabilized over long periods in history.25” His dimensions are numeric representaReceived: November 22, 2015 Revised: January 5, 2016 Accepted: January 6, 2016

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B

23.0 67.6

21.3

112

59.3

“expresses the degree to which the members of a society feel uncomfortable with uncertainty and ambiguity”30 uncertainty avoidance (UAI)

a

The theoretical scale for all dimensions is 0−120.

8

11 “the degree to which the less powerful members of a society accept and expect that power is distributed unequally. The fundamental issue here is how a society handles inequalities among people”30 power distance (PDI)

104

19.3 49.5 5 “a preference in society for achievement, heroism, assertiveness, and material rewards for success. Society at large is more competitive” vs a “preference for cooperation, modesty, caring for the weak and quality of life. Society at large is more consensus-oriented”30 masculinity vs femininity (MAS)

110

45.2 6 “a preference for a loosely-knit social framework in which individuals are expected to take care of only themselves and their immediate families“ vs. a “preference for a tightly-knit framework in society in which individuals can expect their relatives or members of a particular in-group to look after them in exchange for unquestioning loyalty”30 individualism vs collectivism (IDV)

91

minimuma definition dimension

Table 1. Hofstede’S Cultural Dimensions

maximuma

mean

std. dev.

tions of national culture, developed from 88 000 employee surveys at IBM between 1967 and 1973. These dimensions have been enormously influential in the study of global projects. For example, Hofstede’s dimensions have been used to explain phenomena like information seeking in global social networks,26 modes used by firms to enter new markets,27 and sanitation technology choice.28 In energy research, Hofstede’s framework has been used as a measure of cultural difference in climate change policy18 or energy conservation work.29 This paper considers Hofstede’s original four dimensions, summarized in Table 1. I acknowledge and deeply appreciate the many serious criticisms that have been made of Hofstede’s framework.31−34 Many of these detractors note that cultures change over time and that the dimensions (which have not been updated since collection) are outdated. Others call attention to the reductionist improbability of being able to describe something as complex as culture with four numbers, or even challenge the methodology of the original study. These latter criticisms note that relatively wealthy and well-educated employees of a single corporation are not representative of national culture. They also claim that in many cases the sample size for each nation is too small for generalization, doubt that the questions asked were an appropriate operationalization of the dimensions Hofstede generalizes to, or note the arbitrary nature of the nation-state unit of analysis. Any modeland Hofstede’s is no exceptionis necessarily an imperfect reflection of the reality it represents. However, these imperfect reflections may still help us discover useful knowledge. Indeed, many scholars have found Hofstede’s dimensions to be an analytically useful representation of crossnational difference.35 For the purposes of the current analysis, I claim only that if this rough operationalization of cultural comparison shows significant relationships to infrastructure technology choice, more precise and nuanced local measurements of culture will likely be even more highly significant. The Diffusion of Innovation and Hofstede. A limited number of researchers have linked Hofstede’s framework to diffusion theory, which is concerned with the adoption of innovations like the renewable electricity technologies of interest here. More broadly, diffusion theory has described (for example) the stages in the generation of new innovations, the innovation decision process, categories of attributes of innovations and how those attributes impact their rate of adoption, and attributes of adopter categories. Importantly for this study, the insights from diffusion theory have been found to apply cross-culturally, and to organizations such as governments as well as individuals.2 For this research, the most important knowledge gained from the diffusion of innovation literature regards the characteristics of categories of adopters of innovations, ranging from the first to adopt to the last to adopt.36 As renewable electricity is new to many grids, characteristics of innovators are of particular interest. Innovators have been differentiated from later adopters in a number of ways. Some of these are particularly relevant to Hofstede’s dimensions. The first I will discuss here is the claim that “earlier adopters have greater empathy than do later adopters.”2 The diffusion literature credits this characteristic to the ability to be able to think of oneself in a different situation in order to note and solve problems for others. This characteristic links to Hofstede’s masculinity−femininity (MAS) dimension (greater empathy corresponding to low MAS scores), and leads us to

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Environmental Science & Technology the first hypothesis to be tested in this research. This hypothesis is in contrast to recent research that finds a positive relationship between MAS scores and cross-national product adoption37 and other recent work that found no relationship38 between MAS and the adoption of innovation. H1: Nations’ MAS scores will show an inverse relationship to the diffusion of renewable electricity. In other words, more feminine nations (low MAS) will be more likely to have adopted renewable electricity. The second insight from the diffusion of innovation literature that is of particular interest here is the claim that “earlier adopters are better able to cope with uncertainty and risk than are later adopters.2” The literature notes a number of potential reasons for this. For example, innovators tend to be wealthier and better educated than are later adopters, enabling them both to withstand failed innovations and to manage technical change. This insight from the diffusion literature links to Hofstede’s uncertainty avoidance (UAI) dimension and leads us to the second hypothesis to be tested in this research. In this case, the existing literature for communication technology,39 product adoption,38 and IT infrastructure40 supports the hypothesis of an inverse relationship between UAI scores and innovation adoption. H2: Nations’ UAI scores will show an inverse relationship to the diffusion of renewable electricity. In other words, nations that are more comfortable with uncertainty (low UAI) will be more likely to adopt renewable electricity. Past work has presented mixed results for the impact of individualism-collectivism (IDV) on the diffusion of innovation. For example, Hofstede39 finds a positive relationship between IDV and diffusion, while Dwyer37 finds a negative relationship. More recent research has explained these contradictions by finding that high IDV is an advantage for invention, while low IDV is an advantage for the commercialization of innovation.41 Following this insight, I note that renewable electricity technologies are well past the invention stage despite continuing technical advances. As such, I hypothesize that nations’ IDV scores will show an inverse relationship to the diffusion of renewable electricity. In other words, more collective societies (low IDV) will be more likely to adopt renewable electricity (H3). Finally, the diffusion of innovation literature also presents mixed results regarding the relationship between power distance (PDI) and innovation adoption. For example, Erumban and de Jong42 found an inverse relationship between these two variables; Dwyer37 finds a direct relationship; and both Hofstede39 and Png40 find there is no relationship. As such, and as I do not find a convincing reason in the wider diffusion literature to do otherwise, I hypothesize that there is no relationship between PDI and the diffusion of renewable electricity (H4). As I test H1−H4, I control for national wealth (GDP), the cost of grid electricity, and a measure of grid reliability. These are described in more detail in the data collection section. In addition, I test the various cultural dimensions together so that I may determine the significance of each while controlling for the impact of other cultural dimensions. This allows us to determine the relative practical importance of the contribution of each dimension. As described below, the inclusion of the control variables limits the number of nations I was able to analyze, and as such I present results for three analyses; one that considers just the combined cultural dimensions (66 nations), a second that considers the cultural dimensions and that also controls for grid reliability (39 nations), and a third

that considers the cultural dimensions and the cost of electricity from the grid (27 nations).

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MATERIALS AND METHODS Research Design and Limitations. This research was designed to discover relationships between cultural values and renewable electricity adoption, taking advantage of publicly available data30,43,44 summarized in the SI for this paper. These data were available at the national level, which led to the national unit of analysis used here. Nations are not culturally homogeneous; more detailed models would be ideal. However, the present analysis is limited by what data are available. As discussed below, limitations in data availability also limited the number of nations that could be included in this analysis. As described and explained in the analysis section, nonlinear methods were required for this analysis, which used an exponential growth relationship and required p values below 0.05 for statistical significance. Data Collection. The data used for this analysis comes from three sources. The first source is Hofstede’s Web site, where he makes his cultural dimensions available for researchers.45 The second source is electricity cost data from Eurostat.43 The third data source is the World Bank Indicators.44 This data source provided national energy mix; the key indicator of interest here is 2010 Electricity Production f rom Renewable Sources, Excluding Hydroelectric (% of Total). In addition, the World Bank provided two variables to be controlled for: gross domestic product for each country (2010 GDP (Constant 2005 US$)) and a measurement of electrical grid outages. This last variable merits some discussion. The available metric is not a technical measure of grid reliability such as the global standards of CAIDI (Customer Average Interruption Duration Index), SAIDI (System Average Interruption Duration Index) or SAIFI (System Average Interruption Frequency Index). These various measures would certainly provide a better description of grid reliability. However, these statistics are not widely available at the national level. As such they cannot be used for the research undertaken here. Assuming these become available in the future, it would be worth repeating the current analysis to better understand the impacts of grid reliability. In lieu of these unavailable data, I use the World Bank indicator of Value Lost Due to Electrical Outages (% of Sales). These values are taken from the World Bank Enterprise Surveys.46 The Enterprise Surveys interview individuals within multiple randomly sampled businesses within individual economies; the number of businesses surveyed depends on the size of the economy. Some countries include data from surveys of informal (unregistered) enterprises. In all cases the primary sectors of interest are manufacturing and services; the specific questions asked are available online.47 As part of these surveys, businesses are asked to determine lost sales due to electrical outages. This figure is used as the best widely available indicator of grid reliability. These figures are not collected every year for every nation. As such, in order to get the largest possible coverage of nations, I used the most recently reported figure available from the World Bank. The oldest value used was from 2006; the most recent from 2014. Even with this broad criterion, these data are only available for 136 nations. I matched these sources and developed three data sets while eliminating missing values. The first represents the Hofstede and World Bank percent renewable electricity data, and has figures for 66 nations. Moving forward, this is called the f ull C

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Environmental Science & Technology data set. The second adds the grid reliability measure; we have both this reliability measure and Hofstede dimensions for a set of 39 nations. Moving forward, this is called the reliability data set. The third data set includes the Hofstede measures and an index of national electricity cost.48 The cost data was only available for 27 of the nations in the full data set. Moving forward, this is called the cost data set. A notable limitation of these three data sets stems from the lack of Hofstede values for African nations; as such, Morocco is the only nation from that continent represented in any of the data analyzed here. However, the full data set does represent nations from Europe, Asia, North America, South America, and Australia. Of the 66 nations in the full data set, 30 are classified as developing, three are classified as in transition, and 33 are classified as developed.49 Data Analysis: Testing for Relationships. A review of the data showed that the renewable electricity measurement was not appropriate for linear regression. I tested a wide range of transformations, but both a visual review and the resultant chi squared values showed that these did not result in an acceptable distribution in either the transformed data or in the residuals. As such I turned to a nonlinear least-squares model. Relying on diffusion theory and a visual inspection of the data, I suspected that the appropriate nonlinear model for this data would be exponential, reflecting an early stage of technology diffusion. In the diffusion literature, the rate of technology adoption is frequently, though not always, found to follow a normal distribution, with the cumulative distribution following an S-shaped curve.2,50 In the early stages of adoption, such as the current stage of renewable electricity uptake, the distribution represents a tail of the normal curve. As such, I fit an exponential growth curve to the data. To check this theoretical intuition, I also tested several other common nonlinear functions such as different forms of exponential equations, logistic functions, and Gompertz functions. As theorized, the exponential growth curve (shown in eq 1, where b1 is a constant) provided the best fit for the data, validating the selection of the exponential model. renewable electricity percentage = b1cultural dimension

some deviance observed for nations with particularly high percent coverage. As this mirrors the prediction from diffusion theory, which suggests the exponential relationship will fade as uptake increases and a more complete S-curve is sketched, the exponential form was accepted for the analysis undertaken here. Data Analysis: Model Building. A review of the first order correlation table for the full data set shows a high correlation between PDI and IDV (−0.64). A similar relationship was also observed in previous work examining sanitation infrastructure and Hofstede’s cultural dimension.28 A review of the final selected models (described below) show an improvement in the R2 values when IDV is included rather than PDI. This was an anticipated result as the review of the literature (as described in the hypotheses) led the author to prefer the inclusion of IDV for theoretical reasons. The nonlinear least-squares procedure is an iterative model fit. When the standard deviation of a derivative across all observations for any term is very small as compared to the mean of the observations, that term is assumed to be a constant. As discussed below, the GDP term (which was intended as a control for wealth) emerges as a constant term with a zero coefficient when included in the model as an exponential, and as insignificant if included as a multiplicative term. Similarly, when tested as the sole predictor of the diffusion of renewable electricity (without any cultural dimensions), the GDP coefficient resulted as constant term, with coefficient value of zero. As such it was dropped from the models presented below. I discuss theoretical explanations for this in the discussion. The first set of relationships (eq 2) uses the cultural dimensions as sole predictors for the renewable electricity percentage; this allows us to determine the impact of each of the cultural dimensions on renewable electricity coverage while controlling for the other cultural dimensions. As described in the data collection section, this test takes advantage of a larger data set; the subsequent tests add control variables that considerably reduce the sample size due to data availability. As discussed previously, the PDI dimension was dropped due to its high correlation with IDV. eq 2 (full data set, 66 nations, where bn are constants) is shown below.

(1)

As a final check, I examined quantile-quantile plots that compared the measured distribution of Renewable Electricity Percentage against a theoretically generated exponential distribution (Figure 1). The resulting plot was linear, with

renewable electricity percentage = b1UAI + b2 IDV + b3 MAS (2)

Next, eq 3 adds a control variable for electrical outages. As described in the data collection section, this reduces the number of nations in the analysis from 66 to 39. Considering just these 39 nations, Outages is correlated with IDV at p = 0.03; the other variables are not significantly correlated. As such IDV was dropped from eq 3. Dropping IDV does not change the significance of the other variables. When better or more outage data is available it would be valuable to run this analysis on the larger data set, which might also permit the inclusion of the IDV variable. eq 3 (reliability data set, 39 nations, where bn are constants) is shown below. renewable electricity percentage = b4 UAI + b5 MAS + b6 outages (3)

Finally, eq 4 introduces an index of national electricity cost to the analysis as a control variable. The data availability for this analysis is less than the other two, with data for just 27 nations.43 It must be emphasized that these nations are member states in the Organization of Economic Cooperation and Development.51 This suggests cultural similarities that, as

Figure 1. Quantile−quantile plot. D

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Environmental Science & Technology Table 2. Predictors of the Diffusion of Renewable Electricitya R2

b

(SE)

Full Data set: Controls for Cultural Dimensions Only. PDI Excluded from Analysis. n = 66 countries 0.43 UAI (b1) 1.02 IDV (b2) 1.02 MAS (b3) 0.97

0.01 0.01 0.20

Reliability Data set: Controls for Cultural Dimensions and Grid Reliability. PDI and IDV Excluded from analysis. n = 39 countries 0.37 UAI (b4) 1.02 0.01 MAS (b5) 0.94 0.59 outages (b6) 1.10 0.21 Cost Data set: Controls for Cultural Dimensions and Grid Electricity Cost. PDI, IDV, UAI Excluded from Analysis. n = 27 countries 0.76 MAS (b7) 0.97 0.124 cost (b8) 77415.9 49,697 a

t

p

148.4 162.0 4.8

0.000*** 0.000*** 0.000***

high UAI high IDV low MAS

149.6 1.61 5.28

0.000*** 0.12 0.000***

high UAI low MAS high outages

7.81 1.56

0.000*** 0.132

low MAS high grid cost

drives more renewables

*p < 0.05 **p < 0.01 ***p < 0.001. Nonlinear least-squares analysis. All coefficients initialized at 1.

above this may be an artifact of the more culturally homogeneous set of nations for which cost data was available. This likely also drives the observed increase in the R2 value. It is important to reemphasize that both the reliability and the cost data sets are much smaller than the full data set, and this likely impacted the significance of the MAS variable in these two models. However, as discussed below, being above or below one is an important difference for exponential equations. It is worth noting that none of the cultural variables moved across this threshold across the three analyses. As noted above, before considering control variables all four of the cultural dimensions show a significant relationship to the adoption of renewable electricity. I discuss each cultural dimension individually below. In opposition to the hypotheses, each exhibits a direct relationship (although the MAS relationship takes the hypothesized inverse form when I control for the contributions of the other variables). When interpreting the findings in combination, the exponential form of the observed relationship must be considered. When the coefficients for the cultural dimensions are below one, the resulting contribution to the model outcome ranges between one (for example, 0.9^0) and effectively zero (for example, 0.9^120) (as Hofstede’s cultural dimensions range from 0 to 120). This is the case for the MAS dimension, and this means that nations with low MAS dimensions tend to generate more renewable electricity. Alternatively, if the model coefficient is above one, the resulting contribution to the model ranges between about one (for example, 1.1^1) and about 90 000 (for example, 1.1^120). This is the case for the UAI and IDV cultural dimensions. For these two dimensions, higher values (more individualistic and higher uncertainty avoidance) correlate with higher rates of renewable electricity. The exponential nature of these relationships means that higher UAI or IDV scores contribute exponentially more to renewable electricity outcomes. This means that they quickly obscure the contribution of the (still statistically significant) MAS dimension. As discussed below, the UAI dimension in particular seems particularly robust and well suited for practical application. As

discussed below, likely bias the results of the analysis. Supporting this are statistically significant observed correlations between the cultural dimensions in this data set that are not observed in the larger data sets (specifically, between UAI and IDV (−0.58), and between both of these dimensions and the cost index). Due to these correlations, only the MAS dimensions was included in the analysis presented here. However, as this is the best available data, the analysis is included here. eq 4 (cost data set, 27 nations, where bn are constants) is shown below.

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renewable electricity percentage = b7 MAS + b8cost

(4)

RESULTS AND DISCUSSION Using eq 1 (no control variables, each dimension analyzed individually), all four cultural dimensions are significantly and directly related to renewable electricity coverage at the p < 0.000 level. This level of significance and positive directionality are observed regardless of which of the three data sets is considered. The next step in the analysis controlled for the other cultural dimensions and for the other hypothesized control variables. As described above, because the introduction of these controls drastically reduces the number of nations considered in the analysis, and because first order correlations within each of these data sets reduced the number of variables that could be included, these results are presented separately below. In each case, the model R2 of the variable combination below is within 0.01 of the R2 when all the cultural dimensions are included. Table 2 shows strongly significant relationships for cultural dimensions in each of the models. When using the full data set, all three cultural predictors (UAI, IDV, and MAS) are strong predictors of change in the diffusion of renewable electricity. When using the smaller reliability data set, the relationship between UAI and renewable electricity is almost unchanged. In addition, the relationship between outages and the diffusion of renewable electricity is also highly significant. However, MAS is no longer significant. In contrast, for the cost data set MAS is the only variable that is still significant, although as described E

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services, rather than just the services themselves. In this analysis, the controls for electrical reliability and cost suggest similar electrical service. However, the method of generation (increased renewable electrical supply) was a particular driver in nations with low MAS scores, presumably because of concern regarding externalities and impacts on others. Individualism−Collectivism. There is a significant, direct relationship between IDV and the diffusion of renewable electricity (Table 2). This finding is robust to control for the other cultural variables, but the other control variables could not be tested due to significant correlations between the control variables and IDV. The direction of the observed relationship is surprising because by nature infrastructure is collectively used by many people; as such, I hypothesized that more collective contexts would have higher coverage of renewable electricity. In contrast, the data show that more individualistic contexts were correlated with more renewable electricity. It is possible this is due to renewable electricity’s (existing) smaller organizational size. While coal may be brought in from across the globe, renewable energy is not yet so portable and may appeal to those who like to control energy supply themselves rather than trusting to a larger collective. Future case study work is needed to examine this proposed explanation of the surprising directionality of the relationship between IDV and the diffusion of renewable electricity. Similarly, when more data is available on grid reliability and cost, the IDV relationship should be reevaluated to see if it is robust to electricity cost and grid reliability controls. Power Distance. There is a significant, direct relationship between PDI and the diffusion of renewable electricity when it is considered as the sole variable explaining the adoption of renewable electricity, per eq 1. I cannot evaluate if this finding is robust after consideration of the hypothesized controls or to the other cultural dimensions due to significant correlations between PDI and UAI. As discussed in the literature review, previous studies suggested that this cultural dimension would not be significantly related to renewable electricity coverage. When data is available for a larger number of nations, this relationship should be re-evaluated. Control Variables: Grid Reliability. From the reliability data set, the electrical Outage variable (which as a reminder is defined as the percentage of sales lost due to electrical outages) is importantly different from the others in the model (Table 2). Specifically, the theoretical maximum contribution of UAI is 10.8 (1.02^120), the theoretical maximum contribution of MAS is 1 (0.94^1), and the theoretical maximum contribution of Outages is 13 780 (1.1^100% outages). However, as the maximum reported value for Outages in the model is 9.2%, the maximum value seen here is 2.4 (1.1^9.2), making it an important but not dominating factor. In contrast, the theoretical maximums suggest that under conditions of extreme unreliability in the grid, of the variables considered here the Outages factor would be the most important contributor to renewable electricity adoption, though I emphasize again that that level of unreliability was not observed in the data. This sort of unreliability could conceivably be from an unreliable national grid, or from future concerns about climate change and fossil fuel availability. It is at about 25% loss of sales that the Outage variable would begin to dominate the UAI variable at its maximum value. At the median UAI value of 60, the Outage variable begins to dominate at about 12%. These relationships suggest that the cultural dimensions are important only after technical performance has been achieved; energy technologies

major infrastructure decisions are often made at the national level, analyses at that same level (such as that presented here) are particularly relevant to policies that are necessarily broadly applied. Similarly, these findings can help policy makers identify contexts that are well suited for renewable energy adoption. In aggregate, the findings empirically show (for example, and discussed further below) that renewable electricity adoption is related to tendencies to avoid uncertainty. This provides a theoretical basis for building support for renewable electricity projects; specifically, policy, designs, and outreach campaigns should address uncertainty in electricity supply. While some contexts (with high UAI scores) may be more strongly aligned with these strategies than others (with low UAI scores), I would suggest that in virtually every case people prefer reliable, rather than unreliable, electricity supply. Uncertainty Avoidance. There is a significant, direct relationship between UAI and the diffusion of renewable electricity (Table 2). The observed relationship is robust to control for the other cultural variables and the grid reliability variable. I could not investigate if the relationship was robust when controlling for cost as these two were significantly correlated in the available data set. The observed relationship shows that nations that are more uncomfortable with uncertainty are more likely to have adopted renewable electricity technologies. This suggests that, at least among innovators, renewable electricity is perceived as more reliable than the nonrenewable counterparts. This is true in contexts with both high and low grid reliability. The direction of this relationship is in opposition to my hypothesis; the literature expects any new technology to be seen as less reliable.25 However, renewable energy supply (which can more easily be locally implemented and managed) could reasonably provide a real or perceived improvement to the national grid. Similarly, in the context of uncertainty about the future supply of fossil fuels, renewable electricity may be seen as hedging energy supply bets. This suggests an opportunity for the diffusion of renewable electricity. Nations may be particularly receptive to renewable electricity promotion focused on reducing real and perceived unreliability of electrical supply. While this effect is expected to be strongest in contexts with high UAI scores, it is reasonable to think that virtually all nations prefer reliable electricity supply. Masculinity−Femininity. There is a significant relationship between MAS and the diffusion of renewable electricity (Table 2). The raw relationship appears to be direct, but resolves into an inverse relationship after controlling for the other cultural dimensions. This inverse relationship is robust after controlling for grid reliability and grid electricity cost. I hypothesized that feelings of empathy (related to low MAS) would lead to renewable electricity adoption because of a concern regarding the impact of electrical generation on others. This hypothesis was founded in both diffusion theory and in Hofstede’s definitions of the MAS dimension. The results of this analysis supports this hypothesis, which is opposed to some literature that has suggested higher MAS scores correlate with the diffusion of innovation37 and other work that finds no relationship.38 By diversifying findings regarding MAS and the diffusion of innovation, this research suggests that the nature of the innovation is important to cultural analysis. Specifically, environmentally targeted technologies may diffuse more easily to contexts with low MAS scores. Environmental technologies are inherently concerned with the broader implications and consequences of the methods of delivery of infrastructure F

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electricity’s early stage of diffusion. In contrast, and as stands to reason, the data show that extremely high grid variability could eclipse the joint contributions of the cultural variables in terms of driving renewable electricity diffusion. Cultural drivers for the diffusion of renewable electricity are statistically significant across 66 nations, and are also fundamentally different than what the existing literature would predict. Case study research is needed to validate the hows and whys of the observed relationships. More generally, the literature on diffusion of innovation has not often considered civil infrastructure, and relatively few studies have attempted to use the Hofstede dimensions to better understand the diffusion of innovation. In addition to these contributions to diffusion theory, this work contributes to the literature on the social sustainability of civil infrastructure.53−55 This growing body of literature seeks to understand the technology-society nexus in order better regulate, design and construct infrastructure that better meets the needs of society. To this body of knowledge, the current paper presents quantitative evidence that cultural tendencies impact energy technology choice on the national and global scale. Future research is needed to expand the ways that engineers and technical policy makers understand social sustainability of infrastructure; for example, the author’s ongoing research investigates the impacts of cultural dimensions on water infrastructure. In addition, I have proposed and shown the utility of established theories (Hofstede’s cultural dimensions and diffusion theory) that can help address research questions and build new theory in this field. Finally, these findings can help us better understand the specific case of energy infrastructure policy and suggest the utility of Hofstede’s framework for understanding the adoption of renewable electricity. Organizations or individuals who are interested in the spread of renewables should develop policy intended to appeal to empathy regarding the impacts of electricity generation on others, and the drive to avoid uncertainty and increase individual control of the electrical supply. Future research is needed to understand how to localize these findings to the project or community level. The coefficients of the findings suggest that in most cases appeals to empathy will have a smaller impact than strategies targeting the other factors. The particular Hofstede scores for specific nations may be used to help select which (or what combination) of these strategies will be most effective. However, even nations with apparently unpromising scores in these dimensions may still be receptive to these strategies. For example, even cultures with high tolerance for uncertainty likely prefer resilient electrical supply, and even highly collective groups may like to have electrical supplies that are locally resilient against national grid shortcomings or that are controlled by a collective other than the nation. More generally yet, the insights gained from the analysis presented here indicate the importance of research that blends social, technical, and economic factors to promote the uptake of technological innovation that can protect the wellbeing of our environment and the people who live in it.

(renewable or traditional) are only tolerated when they work, and a culturally driven choice is dependent on reliable performance. Control Variables: GDP and Electricity Cost. It seems counterintuitive that neither GDP nor the grid electricity cost index were significant in this analysis, although some existing research supports this finding.17,52 As of the date of this article, when disregarding externalities renewable electricity is on average still more expensive than the more traditional coal, oil, or gas. 11,12 While the perception of economic costs, opportunities, and barriers vary depending on context, these higher costs might lead us to suppose that wealthier nations would be more likely to install renewable technologies, or that a high local grid cost would drive renewable adoption. However, these relationships are not observed in this data. This may be because the small set of nations for which there is an electricity cost index for are not the cases in which I would expect to see particularly high grid costs (indeed, the standard deviation of the cost index is just 0.05 Euros/kWh). For example, on many small island nations renewables are now the least cost option, but these nations are not represented in the cost analysis due to a lack of data. Still, in the available data set, a cultural variable (MAS) is significant and the cost index is not. An explanation of the missing relationship between cost and the diffusion of renewable electricity is founded in diffusion of innovation theory. That body of knowledge tells us that different types of technology adaptors are different than each other.2 Early adopters are actively interested in trying new things, and research suggests that innovators are typically better able to absorb financial losses related to failed innovations, can understand complex technical knowledge, and are willing to accept those failures. In contrast, mainstream adopters are more cautious, waiting until many or most others have adopted before switching themselves. As mainstream adopters are less willing to accept financial losses from trying new ideas, this leads us to suggest that measures of wealth (and relative energy cost) will become an important factor for adoption as renewables become more prevalent in the electricity mix. This explanation is supported in our data, as only the nations with the highest coverage of renewables were somewhat deviant from the exponential model. From this point of view, recent drops in cost are particularly welcome as they are occurring in advance of mainstream adoption patterns that theory suggests will likely be more heavily influenced by cost considerations. Policy Implications. The analysis presented here considers data from 66 nations across the globe and discovers that there are exponential and statistically significant relationships between three of Hofstede’s cultural dimensions (high IDV, high UAI, and low MAS) and the diffusion of renewable electricity. The value of the various coefficients means that the contribution of MAS is much less than those of IDV and UAI. These findings help us better theorize forces driving the uptake of renewable electricity in a form that is useful for policy. For example, the findings regarding the UAI dimension show that renewable electricity is being adopted in order to reduce uncertainty. Second, the findings regarding MAS suggest that empathy for others drives the construction of environmentally protective infrastructure such as renewable electricity. Third, higher individualism (IDV) correlates with more renewable electricity, perhaps because these technologies can exist at smaller scales that enable energy independence. GDP and grid cost are not currently statistically significant factors in the spread of renewable electricity, possibly due to renewable

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(18) Alló, M.; Loureiro, M. L. The role of social norms on preferences towards climate change policies: A meta-analysis. Energy Policy 2014, 73, 563−574. (19) Friebe, C. A.; von Flotow, P.; Täube, F. A. Exploring technology diffusion in emerging markets − the role of public policy for wind energy. Energy Policy 2014, 70, 217−226. (20) García, C. Grid-connected renewable energy in China: Policies and institutions under gradualism, developmentalism, and socialism. Energy Policy 2011, 39 (12), 8046−8050. (21) Sovacool, B. K. Rejecting renewables: The socio-technical impediments to renewable electricity in the United States. Energy Policy 2009, 37 (11), 4500−4513. (22) Walker, G.; Devine-Wright, P.; Hunter, S.; High, H.; Evans, B. Trust and community: Exploring the meanings, contexts and dynamics of community renewable energy. Energy Policy 2010, 38 (6), 2655− 2663. (23) Wirth, S. Communities matter: Institutional preconditions for community renewable energy. Energy Policy 2014, 70, 236−246. (24) Wüstenhagen, R.; Wolsink, M.; Bürer, M. J. Social acceptance of renewable energy innovation: An introduction to the concept. Energy Policy 2007, 35 (5), 2683−2691. (25) Hofstede, G. Culture’S Consequences: Comparing Values, Behaviors, Institutions, And Organizations Across Nations; Sage Publications: Thousand Oaks, CA, 2001. (26) Zaheer, S.; Zaheer, A. Country effects on information seeking in global electronic networks. J. Int. Bus. Stud. 1997, 28 (1), 77−100. (27) Kogut, B.; Singh, H. The Effect of National Culture on the Choice of Entry Mode. J. Int. Bus. Stud. 1988, 19 (3), 411−432. (28) Kaminsky, J. A. Cultured Construction: Global Evidence of the Impact of National Values on Sanitation Infrastructure Choice. Environ. Sci. Technol. 2015, 49 (12), 7134−7141. (29) Chiu, M.-S. Tensions in implementing the “energy-conservation/carbon-reduction” policy in Taiwanese culture. Energy Policy 2013, 55, 415−425. (30) Hofstede, G. Dimensions - Geert Hofstede http://geerthofstede.com/dimensions.html (accessed February 18, 2014). (31) Baskerville, R. F. Hofstede never studied culture. Account. Organ. Soc. 2003, 28 (1), 1−14. (32) Kirkman, B. L.; Lowe, K. B.; Gibson, C. B. A quarter century of culture’s consequences: A review of empirical research incorporating Hofstede’s cultural values framework. J. Int. Bus. Stud. 2006, 37 (3), 285−320. (33) McSweeney, B. Hofstede’s model of national cultural differences and their consequences: A triumph of faith-a failure of analysis. Hum. Relat. 2002, 55 (1), 89−118. (34) Myers, M. D.; Tan, F. B. Beyond models of national culture in information systems research. Adv. Top. Glob. Inf. Manag. 2003, 2, 14− 29. (35) Taras, V.; Kirkman, B. L.; Steel, P. Examining the impact of Culture’s consequences: A three-decade, multilevel, meta-analytic review of Hofstede’s cultural value dimensions. J. Appl. Psychol. 2010, 95 (3), 405. (36) Rogers, E. Categorizing the Adoptors of Agricultural Practices. Rural Sociol. 1958, 23 (4), 346−354. (37) Dwyer, S.; Mesak, H.; Hsu, M. An exploratory examination of the influence of national culture on cross-national product diffusion. J. Int. Mark. 2005, 13 (2), 1−27. (38) Yeniyurt, S.; Townsend, J. D. Does culture explain acceptance of new products in a country? - An empirical investigation. Int. Mark. Rev. 2003, 20 (4), 377−396. (39) Hofstede, G. J. Adoption of communication technologies and national culture. Systèmes D’Information Manag. 2001, 6 (3), 55−74. (40) Png, I. P. L.; Tan, B. C. Y.; Wee, K. L. Dimensions of national culture and corporate adoption of IT infrastructure. IEEE Trans. Eng. Manage. 2001, 48 (1), 36−45. (41) Cerne, M.; Jaklic, M.; Skerlavaj, M. Decoupling management and technological innovations: Resolving the individualism-collectivism controversy. J. Int. Manag. 2013, 19 (2), 103−117.

Renewable electricity metrics and Hofstede’s cultural dimensions (PDF)

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*Phone +1.206.221.3058; fax +1.206.543.1543; e-mail [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS I thank University of Washington for financial support of this work.

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REFERENCES

(1) World Bank. Electricity production from renewable sources, excluding hydroelectric (% of total) | Data | Table http://data. worldbank.org/indicator/EG.ELC.RNWX.ZS (accessed December 17, 2014). (2) Rogers, E. M. Diffusion of Innovations, 5th ed.; Simon and Schuster, 2003. (3) Miller, D. Selling Solar: The Diffusion of Renewable Energy in Emerging Markets; Earthscan: London; Sterling, VA, 2009. (4) Hu, Y.; Cheng, H. Development and Bottlenecks of Renewable Electricity Generation in China: A Critical Review. Environ. Sci. Technol. 2013, 47 (7), 3044−3056. (5) Denholm, P.; Hand, M. Grid flexibility and storage required to achieve very high penetration of variable renewable electricity. Energy Policy 2011, 39 (3), 1817−1830. (6) Hiremath, M.; Derendorf, K.; Vogt, T. Comparative Life Cycle Assessment of Battery Storage Systems for Stationary Applications. Environ. Sci. Technol. 2015, 49 (8), 4825−4833. (7) Daly, H. E.; Scott, K.; Strachan, N.; Barrett, J. Indirect CO2 Emission Implications of Energy System Pathways: Linking IO and TIMES Models for the UK. Environ. Sci. Technol. 2015, 49 (17), 10701−10709. (8) Green, M. A.; Emery, K.; Hishikawa, Y.; Warta, W.; Dunlop, E. D. Solar Cell Efficiency Tables (Version 39). Prog. Photovoltaics 2012, 20 (1), 12−20. (9) Thiam, D. R. An energy pricing scheme for the diffusion of decentralized renewable technology investment in developing countries. Energy Policy 2011, 39 (7), 4284−4297. (10) Dornan, M. Access to electricity in Small Island Developing States of the Pacific: Issues and challenges. Renewable Sustainable Energy Rev. 2014, 31, 726−735. (11) Borenstein, S. The Private and Public Economics of Renewable Electricity Generation, Working Paper 17695; National Bureau of Economic Research, 2011. (12) del Río, P.; Cerdá, E. The policy implications of the different interpretations of the cost-effectiveness of renewable electricity support. Energy Policy 2014, 64, 364−372. (13) Jacobsson, S.; Johnson, A. The diffusion of renewable energy technology: an analytical framework and key issues for research. Energy Policy 2000, 28 (9), 625−640. (14) Zhao, Y.; Tang, K. K.; Wang, L. Do renewable electricity policies promote renewable electricity generation? Evidence from panel data. Energy Policy 2013, 62, 887−897. (15) Beery, J. A.; Day, J. E. Community Investment in Wind Farms: Funding Structure Effects in Wind Energy Infrastructure Development. Environ. Sci. Technol. 2015, 49 (5), 2648−2655. (16) Johnson, J. X.; Novacheck, J. Emissions Reductions from Expanding State-Level Renewable Portfolio Standards. Environ. Sci. Technol. 2015, 49 (9), 5318−5325. (17) Popp, D.; Hascic, I.; Medhi, N. Technology and the diffusion of renewable energy. Energy Econ. 2011, 33 (4), 648−662. H

DOI: 10.1021/acs.est.5b05756 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

Article

Environmental Science & Technology (42) Erumban, A. A.; de Jong, S. B. Cross-country differences in ICT adoption: A consequence of Culture? J. World Bus. 2006, 41 (4), 302− 314. (43) Eurostat. Prices - Eurostat http://ec.europa.eu/eurostat/web/ energy/methodology/prices (accessed Dec 18, 2014). (44) World Bank. Indicators | Data http://data.worldbank.org/ indicator (accessed May 1, 2012). (45) Hofstede, G. Geert Hofstede | Hofstede Dimension Data Matrix http://www.geerthofstede.com/dimension-data-matrix (accessed November 12, 2013). (46) World Bank. Enterprise Surveys - What Businesses Experience World Bank Group http://www.enterprisesurveys.org/ (accessed December 12, 2014). (47) World Bank. Survey Methodology for Enterprise Surveys World Bank Group http://www.enterprisesurveys.org/methodology (accessed December 12, 2014). (48) IEA. Energy Prices and Taxes: Quarterly Review http://www. eia.gov/totalenergy/data/monthly/previous.cfm (accessed December 18, 2014). (49) UN. Statistical Annex: Country Classification http://www.un. org/en/development/desa/policy/wesp/wesp_current/2012country_ class.pdf (accessed November 18, 2014). (50) Ryan, B.; Gross, N. C. The diffusion of hybrid seed corn in two Iowa communities. Rural Sociol. 1943, 8 (1), 15−24. (51) OECD. OECD.org http://www.oecd.org/ (accessed December 18, 2014). (52) Johnstone, N.; Hašcǐ č, I.; Popp, D. Renewable Energy Policies and Technological Innovation: Evidence Based on Patent Counts. Environ. Resour. Econ. 2010, 45 (1), 133−155. (53) Kaminsky, J.; Javernick-Will, A. The Internal Social Sustainability of Sanitation Infrastructure: A Set Theoretic Approach. Environ. Sci. Technol. 2014, 48 (17), 10028−10035. (54) Valdes-Vasquez, R.; Klotz, L. Social Sustainability Considerations during Planning and Design: A Framework of Processes for Construction Projects. J. Constr. Eng. Manag. 2013, 139 (1), 80−89. (55) Walters, J. P.; Chinowsky, P. S. Planning rural water services in Nicaragua: A systems-based analysis of impact factors using graphical modeling. Environ. Sci. Policy 2016, 57, 93−100.

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DOI: 10.1021/acs.est.5b05756 Environ. Sci. Technol. XXXX, XXX, XXX−XXX