If it ain’t broke, don’t fix it: how the public’s economic confidence in the fossil fuel industry reduces support for a clean energy transition

ABSTRACT

We expand on ongoing debates about the role of economic losses and benefits for a clean energy transition. Rather than focusing on the potential economic benefits of alternative industries and energy sources, we highlight the role of economic optimism people display towards the fossil fuel industry. We argue that people’s confidence in the fossil fuel industry to remain an important economic driver in the future can undermine support for climate policies because people do not perceive a need to turn to alternative industries for economic prosperity. Instead, they continue to support the status-quo. We test our argument using survey level data collected in the spring of 2019 in the Canadian province of Alberta, an ideal case due to the province’s economic dependence on the fossil fuel industry. The results support our argument, highlighting the need for policymakers to develop communication strategies on future economic benefits of energy transition.

KEYWORDS:

• Energy Transition
• Climate Policies
• Public Opinion
• Alberta, Canada
• Economic Benefits

Introduction

Over the past decade, climate change has become one of the most important issues to citizens in countries around the world (Pew Research Center 2019). At the same time, political debates continue to take place in most parts of the world to decide if and how policy measures should be implemented to meet goals, such as those formulated in the 2015 Paris Agreement. Ongoing efforts often result in a constant back and forth between implementation and retraction of measures (cf. Mildenberger 2020, Chapter 1), as economic and electoral upheavals change what preferences over climate change policies are represented in key institutions. Trailblazing academic efforts to explain if, when, and how climate policies are implemented often point towards the role of institutions and interest groups (Madden 2014, Mildenberger 2020, Stokes 2020). Yet, a key variable in this process remains public opinion, as it plays a crucial role in determining if and how governments at all levels implement climate policies (Shwom et al. 2010, Meckling et al. 2015, Bernauer and McGrath 2016, Drews and van den Bergh 2016, Anderson et al. 2017, Egan and Mullin 2017, Bromley-Trujillo and Poe 2020). Despite the increase in the salience of climate change as a public issue in many places around the world, support for policies designed to limit carbon emissions remains mixed, thus raising the question: What shapes individuals’ preferences for climate policies?

Existing work on individual support for climate policies has included a host of factors such as political orientations, climate change perception, or costs and benefits associated with implementing these policies (cf. Drews and van den Bergh 2016). In this paper, we build on the literature of costs and benefits associated with transitioning to low-carbon energy policies. Even though we acknowledge the role of other individual underpinnings, we build on the premise that the distribution of the economic costs and benefits is at the roots of cross-cutting preferences that are the primary driving force behind (un-)successful climate policy implementation (Michaelowa 2005, Zahran et al. 2008, Aklin and Urpelainen 2013, Mildenberger 2020). Most of this literature focuses on the costs and benefits associated with implementing climate policies (see Drews and van den Bergh 2016, p. 861–826 for an overview). In contrast, we focus on peoples’ future economic expectations for their communities and regions (i.e., sociotropic considerations) delivered by the fossil fuel industry.¹ Our central argument is that some individuals are less likely to support low-carbon policy measures because they expect the existing fossil fuel industries to remain economically beneficial and trust them to deliver wealth to and benefits for peoples’ communities and regions in the foreseeable future. In turn, these people have less incentive to support policies geared towards transitioning to a low-carbon society. We argue this is especially relevant where countries or regions are economically dependent on the fossil fuel industry.

We provide an empirical test of our argument, relying on survey level data collected in the Spring of 2019 in the Canadian province of Alberta. Alberta is the largest greenhouse gas emitter amongst the Canadian provinces and territories and a ‘reluctant actor’ (Boyd 2019, p. 184) in support of climate change policy. This case of a jurisdiction where the fossil fuel industry plays a dominant role can provide valuable insight into the public’s thinking in regions with a crucial role in the energy transition. The findings support our argument. Across a set of eight different policies, we find that confidence that Alberta’s oil and gas industry will remain the major economic driver in the province can lead to peoples’ resistance to energy transition policies. Thus, this paper’s primary contribution is two-fold. First, it expands the theoretical discussion about costs and benefits by accounting for future benefits associated with the status-quo, that is, the benefits people expect from the fossil fuel industry in the long-term future. Second, we make an empirical contribution to understanding support for climate policies, particularly in regions which have historically relied on extractive industries as their main economic driver, by testing our argument in the context of the Canadian province of Alberta.

The paper is structured as follows. In the next section, we discuss the relevant literature and develop our central argument. Based on the argument, we then derive a set of theoretical propositions that we set out to test empirically. We contextualize our case and discuss our research strategy. We present our results and, in the concluding section of the paper, discuss the implications for future research and policy.

Theoretical background

A rapidly growing literature investigates the variation in preferences for climate and energy policies (for a comprehensive literature review, see Drews and van den Bergh 2016). One branch of this research examines how support for climate policies is driven by the expected economic costs imposed on people and the expected economic gains from transitioning to renewable sources of energy (Shwom et al. 2010, Bidwell 2013, Tvinnereim and Ivarsflaten 2016, Stokes and Warshaw 2017, Doran et al. 2019, Bergquist et al. 2020). Individuals whose income depends, for example, on the oil and gas sector are generally expected to be less likely to favor climate policies as they stand to lose economically from a transition (Stokes and Warshaw 2017, Mayer and Malin 2019). However, employment in the oil and gas industry may not be a necessary condition for opposition to climate policies (Tvinnereim and Ivarsflaten 2016). Such opposition may also stem from sociotropic evaluations that an industry economically benefits one’s surrounding community, as research on attitudes toward nuclear power facilities has shown (Freudenburg and Davidson 2007). This argument is supported by empirical evidence from studies investigating support for fracking in the U.S., which highlight the role of community considerations in shaping support for energy industries (Boudet et al. 2016). Similarly, Olson-Hazboun et al. (2018) demonstrate how individuals residing in U.S. counties dependent on natural gas and mining are less likely to support renewable energy sources.

Here, we examine these economic and climate costs and benefits from a different perspective. Research shows that if individuals perceive the need for economic restructuring, they are more likely to support policies favouring new industries that they anticipate might bring about the necessary economic development in their communities. The central argument in the literature is that if new (low-carbon) industries, such as the wind industry, can revitalize a community’s economy, its residents are more receptive to it (Michaelowa 2005, Slattery et al. 2012, Bidwell 2013, Lindén et al. 2015, Olson-Hazboun et al. 2016). What unites these studies is their primary focus on the potential or expected economic benefits to local or regional communities from transitioning away from fossil fuels to a new, more renewable energy industry.

Yet, the picture remains incomplete without paying attention to the expected economic benefits, through jobs or tax revenues (Mayer and Malin 2019), to a region from the continued presence of incumbent companies in the fossil fuel sector. The core of our argument is that as long as people are confident that the incumbent fossil fuel companies can continue to deliver wealth and economic benefits to their communities or regions by retaining their fossil fuel business, the incentive to support policies that would facilitate a transition to a low-carbon economy remains low. In other words, a positive belief in the future economic benefits of existing fossil fuel industries manifests itself in the support of the status-quo and the opposition to a low-carbon industry transition.

To clarify, we do not imply that those who hold these beliefs automatically support climate policies if they were not to expect any future economic gains. Rather, these beliefs lead to more general support of the status quo over change. This implication is consistent, for example, with evidence from the United Kingdom, which suggests that residents of regions with high employment levels in the oil and gas sector do not support the expansion of fracking in their regions (Roddis et al. 2019, p. 9). The results thus point towards a broader resistance to change in general, including expanding both high-carbon and low-carbon industries. Considering how the fossil fuel industry has been at the core of economic development in many countries, regions, and communities alike, the rejection of change should reduce support of an energy transition, including one towards a low-carbon economy.

The argument we advance here closely relates to studies that show how economic gains (Tvinnereim and Ivarsflaten 2016, Mayer and Malin 2019) of entire communities can shape climate policy preferences (Boudet et al. 2016, Olson-Hazboun et al. 2018, Mayer and Malin 2019). Similar to these studies, our argument rests on individuals’ sociotropic considerations whereby they consider the economic benefits – here, the anticipated future economic benefits from the oil and gas industry – for their communities or regions as relevant when forming their attitudes towards climate policies. The role of sociotropic considerations is well documented in related fields. For example, economic voting is based on sociotropic more often than egocentric considerations (Lewis-Beck and Stegmaier 2019, p. 251). Likewise, scholars argue that individual trade policy preferences can result from sociotropic considerations. For example, individuals may form their trade preferences based on national information cues (Mansfield and Mutz 2009). Alternatively, individuals’ trade policy preferences can also originate from other-regarding behavior (altruism), such as in cases where people residing in areas with geographically concentrated industries consider how trade policies may affect the local/regional industries and, in turn, the general well-being of the region (Schaffer and Spilker 2019, p. 1271). Considering the historical role that fossil fuel industries have played in the economic developments of entire regions through their direct (employment) and indirect (taxes, economic benefits for ancillary businesses) effects (Mildenberger 2020, p. 39), it is conceivable that sociotropic economic considerations are at the core of our proposed link between confidence in these industries and support for climate policies.

Despite the joint focus on sociotropic considerations, our argument also differs from existing studies in the following aspects. First, we argue that confidence in the industry’s ability to sustain economic benefits – that is, a prospective sociotropic assessment – plays a crucial role, too. Second, our argument differs in that we focus on economic optimism about the future of the oil and gas sector, rather than the economic allure of new technologies. We see our argument not as a rival explanation for the role of economic benefits. Instead, we consider these arguments as complementary in explaining preferences for climate policies.

This is where our emphasis on prospective evaluations is especially important. If people are resistant to change because they do not anticipate a reduction in the economic benefits yielding from the fossil fuel industry, it is unlikely that the economic allure of alternative industries, such as solar or wind energy, can sway them to support renewable energy policies. However, when people come to the belief that a change in the economic structure is required to secure future economic benefits, they should also be more open to policies favoring low-carbon industries. This underscores the importance of expected future gains of existing fossil fuel industries to support for (or opposition to) climate policies. This is admittedly a challenging prospect, as established industries advocate for their own persistence, and new energy technologies are often presented with some degree of skepticism about their future benefits (c.f. Blair et al. 2015). Yet, evidence shows that news about anticipated economic changes and benefits structure public perceptions and preferences, even if more objective measures show those changes have not yet materialized (c.f. Soroka et al. 2015). Without narratives or perceptions of any future need for economic change, citizens are unlikely to support any energy transition.

Scope conditions and context

Before we outline our research strategy to test our argument, we highlight some scope conditions. First, our argument is based on perceived, anticipated economic benefits rather than projected objective expected economic outcomes. This is rooted in the literature that shows that perceived economic conditions are more important to understand political attitudes than objective indicators because objective indicators cannot account for the heterogeneity in economic perceptions (cf. Kenny 2018, p. 583). The differences in perceptions may arise, for instance, from partisanship, different personal experiences and demographics, or from media coverage and narratives (c.f. Soroka et al. 2015).

Second, we argue that optimism about the future economic gains from the fossil industry is particularly relevant in areas and communities where it is the dominant industry. Economic dependence on the oil and gas industry and strong beliefs in economic benefit derived from the industry tend to go hand in hand. One factor that explains such high levels of confidence in these areas is trust in the oil and gas industry (Mayer 2016).² Moreover, in areas where particular industries are of historical importance, residents generally tend to overestimate these industries’ economic importance (Blaacker et al. 2012). These biases may result from various different process (e.g., status quo bias or endowment effects), but, regardless of their source, they reflect that peoples’ general rule of induction is that they ‘expect the future to be like the past’ (Eidelman and Crandall 2012, p. 270). We expect a similar perceptual bias to operate in geographical regions where fossil fuels have traditionally played a dominant role in the economy, leading to high degrees of confidence that the incumbent industry will deliver economic benefits for the foreseeable future.

In sum, we expect that resistance to implementing low carbon policies is, in part, driven by the belief in the fossil fuel industry to deliver future economic benefits, particularly in areas that are highly dependent on a single industry. Empirically, we expect to see that greater confidence in the oil and gas industry to retain its economic importance and deliver economic benefits should lead individuals to support policies favoring the incumbent industries while objecting to those that are geared towards transitioning to a low-carbon society.

Research strategy

In our study, we focus on the Canadian province of Alberta. Like West Virginia (U.S.), Texas (U.S.), or Queensland (Australia), Alberta’s economy is intrinsically linked to energy production, with the energy sector directly responsible for just over 22% of the province’s GDP in 2019 (Alberta Government 2020). This number does not include indirect economic benefits as a result of the energy industry. The industry is dominated by the oil and gas business, with coal playing only a small role. Energy policy continues to be a highly salient and politicized issue, and the carbon tax was a key issue in the 2019 provincial (April) and federal (October) elections. The left-leaning New Democratic Party (NDP) government, formed following the 2015 provincial election, introduced a broad based carbon levy. After winning the 2019 provincial election, the right-wing United Conservative Party (UCP) followed through on election pledges to abolish the carbon tax (Macneil 2020) and set up a taxpayer-funded centre dedicated to defending and promoting Alberta’s oil and gas industry. Similar to efforts in other regions with a concentration of extractive resource industries such as West Virginia (Bell and York 2010), Northern Minnesota (Kojola 2019) or the Central Appalachia region (Lewin 2019), the Centre is part of a continuous effort by government and industry in the province to foster an economic identity and culture closely linked to oil and gas.

Regarding their attitudes towards energy production, research shows that Albertans are aware of alternative energy sources. However, these are often considered welcome additions rather than alternatives to the existing oil and gas industry (Marshall et al. 2018). Research from 2011–2015 also shows that Albertans, compared to all other Canadian provinces and territories, are among the most climate change skeptical Canadians. Not surprisingly, they are also less likely to support climate policies, such as a carbon tax (Mildenberger et al. 2016). Taken together, Alberta represents an interesting and important case to test our argument at a time where the issue of energy policy received ample attention due to the ongoing provincial elections.

Data

The data for our analyses comes from a survey that was fielded between 31 March 2019 and May 3, 2019 in Alberta, Canada, by the survey agency Vox Pop Labs.³ We use a random sample of all adult Albertans who participated at least once in the agency’s Vote Compass panel leading up to either the previous federal (2015) or the 2019 provincial election. Respondents participating in the Vote Compass chose to do so voluntarily and, upon completition, were given the option to opt-in to participating in other studies, including the present study. Participants were recruited using a single email, sent immediately after selection into the sample. Respondents did not receive incentives for completing the survey. The total number of respondents in our data is 2,634 (including respondents with missing data on key variables). However, the sample differs from the target population in that respondents are more highly educated, contain a greater share of middle-aged persons, and are more likely to support the NDP. Appendix A1.3 provides more information on how our sample compares to other studies fielded in close proximity to our survey as well as the official 2016 Canadian Census data. To inspect for potential biases arising from the discrepancies in key demographic variables, we conduct a series of robustness checks. We detail the tests in the results section (see also Appendix A3.3).

Measures

Our dependent variable is comprised of eight measures that gauge support for various policies relevant to the public debates in Alberta and Canada around the time of the survey. All of the items are meant to capture the respondents’ attitudes towards a low-carbon energy transition (Table 1). Of these eight items, six (Policies 1 through 6) are measures that are geared towards reducing carbon emission and facilitate an energy transition. The other two items (Policies 7 and 8) imply support for maintaining and expanding the status-quo in Alberta by supporting the incumbent fossil fuel industry. Each policy is a likert type item for which respondents were asked to indicate their support in four categories: ‘strongly support,’ ‘support,’ ‘oppose,’ and ‘strongly oppose.’ To construct our dependent variable, we fit a graded response model from the family of item response models. We use the information obtained from the model to score respondents along a continuum ranging from strong opposition to transition policies to strong support for transition policies as IRT scores are generally preferable to other methods of scoring, such as summed scores (Edwards 2009). The final variable is standardized and normaly distributed.⁴

Table 1. List of policies respondents are asked about.

Policy	Wording	Category
1	Regulating the amount of carbon dioxide that can be produced.	Low-carbon policies (Transition)
2	Taxing carbon dioxide as a pollutant.
3	Provide tax rebates as incentives for people to buy energy efficient vehicles, appliances, or solar panels.
4	Provide money to people and corporations who want make their homes or businesses more energy efficient.
5	Build more mass transit.
6	Subsidize renewable energy source such as solar and wind.
7	Increase Alberta’s capacity to refine oil sands in the province.	High-carbon policies (status quo)
8	Build more pipelines.

Note: Items were asked in a single block in the questionnaire (with two additional policy items not included here) and the question wording was: ‘Now we’d like to ask you about some policies about energy and the environment. For each statement below, please indicate if you strongly support, support, oppose, or strongly oppose it.’; Item order was randomized to avoid systematic question order effects;

To measure respondents’ confidence in the oil and gas industry, we use two variables, each capturing a distinct aspect of economic optimism based on prospective, sociotropic considerations related to Alberta. The first item is meant to capture the confidence that respondents have in Alberta’s incumbent oil and gas industry – as currently constructed – to remain Alberta’s most viable source of economic benefits in the long-term future. In the survey, we asked respondents about the extent to which they agree with the following statement: ‘Twenty-five years from now, oil and gas will still be Alberta’s most important industry’ (Likert item with answer categories ‘strongly agree,’ ‘agree,’ ‘disagree,’ and ‘strongly disagree’). In the data, about 46% of respondents either agreed or strongly agreed with the item. While the number might appear low given Alberta’s historical context, it is important to consider that the survey took place at the heel of a historic slump in oil prices that started in the midst of 2014. The second indicator is meant to capture respondents’ optimism for the oil and gas industry by asking them about the extent to which they would like Alberta to rely on the oil and gas industry as one of the primary sources of economic benefits to in the future. The indicator in our survey asks about respondents’ preferences for diversifying the economy. The likert type item includes the level of agreement with the following statement: ‘Alberta’s economy is too dependent on oil and gas’ (Likert item with answer categories ‘strongly agree’, ‘agree,’ ‘disagree,’ and ‘strongly disagree’). About 18% of respondents either disagree or strongly disagree with this statement which reflects confidence in the oil and gas industry.

In contrast to the first indicator which captures peoples’ confidence in the oil and gas industry’s future economic dominance, the second variable captures the extent to which Albertans feel comfortable with relying on the oil and gas industry as the primary source of Alberta’s economic wealth. Economic diversification beyond the dominant oil and gas industry, with its history of boom-bust cycles, has been on the political agenda in Alberta since the 1970s (Smith 1991), and is is particularly salient when low oil and gas prices lead to concerns about the economic health of Alberta. Thus, even though the indicator may theoretically capture respondents’ preferences for economic diversification that are not linked to the economic health of the oil and gas industry, we are confident that the measure captures respondents’ economic optimism for the industry given Alberta’s historical context. Hence, the two variables are related, but they capture two different aspects of economic optimism.

Our expectations for both key items are similar. The more confident people are that the oil and gas industry can and will remain the dominant industry in Alberta, the less likely they should favor policies geared towards reducing carbon emissions. Likewise, the more respondents agree that Alberta’s economy does not require diversification, the less likely they should be in favor of transition policies.

Methods and covariates

We combine bivariate and multivariable analyses. Since our dependent variable is continuous, we use a linear regression model. We implement our analyses in a Bayesian framework to accurately reflect the uncertainty of the potentially small relationships we might expect given the explantory power of a vast amount of other factors (Drews and van den Bergh 2016, Marquart-Pyatt et al. 2019). In all models, we include a set of covariates derived from the literature which, theoretically, may affect both our explanatory variables as well as our dependent variable. We include a measure of peoples’ pride in the oil and gas industry. While pride and identity are not the same, the latter is often the cause of the former (Edwards 2005, p. 220), and as such helps to adjust for what the literature has determined as a local or community-based identity with an extractive industry (e.g. Mayer 2018). Similarly to Tvinnereim and Ivarsflaten (2016), we account for whether respondents or a household member of theirs have a job directly tied to the oil and gas industry to capture expected short-term economic benefits.⁵ Furthermore, we also adjust for respondents’ ideological left-right self-placement, whether or not they identify with the left-leaning NDP, and whether or not they believe in climate change.⁶ Finally, we include a set of demographic variables (university education, gender, age, urban/suburban/rural area of living).⁷ We implemented all analyses in R (R Core Team 2020) and used weakly information priors to stabilize computation and moderate regularization.⁸

Results

We begin our presentation of the results with a bivariate analysis, using raincloud plots. The panels in Figure 1 show the distribution of support, captured by the scores based on the eight policies analysed here, across the two explanatory variables. In Figure 1A, we see that the more respondents agree with the statement that Alberta’s oil and gas industry will retain its dominant economic position in Alberta, the lower the support in the data for an energy transition. For example, the average support for a transition is just below one standard deviation above the mean of our estimated support scores among respondents who strongly agree that oil and gas will remain Alberta’s most important industry in 25 years. In contrast, among respondents who strongly disagree with the statement, the average support is about one standard deviation below the average. In other words, greater confidence in the oil and gas industry to deliver future economic benefits for Alberta is associated with less support for changing the status quo. In Figure 1B, we observe that the less comfortable respondents are with Alberta relying on the oil and gas industry for Alberta’s economic wealth, the higher their support for transition policies. In other words, the more Albertans agreed that their provinces economy relies too much on the oil and gas industry, the more likely were they to support energy transition policies. Taken together, this initial descriptive, bivariate exercise provides some support to our central theoretical argument. These findings are supported further by the regression analyses, which we present in Table 2 and Figure 2.

Figure 1. Raincloud plots showing the relationship between confidence in Alberta’s oil and gas industry (Figure 2A), perception of economic dependence on Alberta’s oil and gas industry (Figure 2B), and the support for energy transition policies.

Note: The value ‘3’ on the y-axis indicates strong support, ‘-3’ indicates strong opposition; Brighter shadings indicate stronger support, darker shadings indicate opposition.

Table 2. Effects of confidence in Alberta’s oil and gas industry and perception of economic dependence on Alberta’s oil and gas industry on the support for energy transition policies.

	Estimates
OandG to remain AB’s most important industry	−0.15
	[−0.18; −0.12]
AB too dependent on OandG	0.23
	[0.19; 0.26]
Intercept	0.59
	[0.38; 0.79]
Covariates	yes
N	2,393

Note: Estimates (posterior medians) from Bayesian linear model with .95 Credible Intervals (Highest Density Interval, HDI) in parentheses; Results based on 8,000 MCMC samples; Covariates adjust for household employment in oil and gas sector, pride in oil and gas industry, belief in climate change, political left-right self-placement, party identification with the NDP, gender, education, age, and urban/suburban/rural living area.

We report the posterior medians and 95% Credible Intervals from our Bayesian linear model in Table 2.⁹ Generally speaking, the estimates show that greater confidence in Alberta’s oil and gas industry is associated with lower support for a transition. For example, given the data there is a 95% chance that support for climate policies will decrease between 0.12 and 0.18 standard deviations for every additional one-step increase in agreement with the statement that the ‘Oil and gas industry will be Alberta’s most important industry in 25 years.’ Likewise, we see that the more respondents agree that Alberta is economically too dependent on the oil and gas industry, the higher their support, on average, for policies geared towards a transition. For a one-step increase on this variable, there is a 95% chance that the support for a transition increases between 0.19 and 0.26 standard deviations that is, roughly a quarter of a standard deviation. For further illustrations of the uncertainty of our parameters, we use quantile plots to display the posterior distributions in Figure 2.

Figure 2. Posterior distributions of estimates (confidence in Alberta’s oil and gas industry and perception of economic dependence on Alberta’s oil and gas industry) based on Model shown in Table 2

Note: Intervals are .89 and .95 Highest Density Intervals.

In addition to the primary analyses, we also conducted a series of robustness checks. First, we fit our main linear model, using only our main explanatory variables to ensure that our findings are stable and do not emerge only when additional covariates are added. The results remain the same (Appendix A3.1). Second, while we focus on general support here and thus chose a continuous dependent variable capturing the response patterns to all of our policy measures, Tvinnereim and Ivarsflaten (2016, p. 365) argue that the type of policy matters due to variance in cost concentration. Hence, we conducted additional analyses, using the support for each policy as separate dependent variables (Appendix A3.2). By and large, we find that the indicators consistently show the same effect across all policies. Lower confidence in the oil and gas industry’s prospective dominance and respondents’ desire for economic diversification are consistently related to higher support for transition policies and lower support for status quo policies (e.g., building more pipelines).¹⁰ This speaks to our initial argument that, contrary to short-term economic benefits, the belief in sustainable economic benefits from existing fossil fuel industries leads to a general opposition towards change, including energy transition. Regardless of whether we examine measures of general policy support or specific policy support, by and large, both yield the same results. Lastly, we tested how the discrepancies between our data and the target population with respect to age, education, gender, and support for the provincial NDP might affect our conclusions if we had a true random sample of the target population available. The analyses (Appendix Section A3.3) show that the effect for the confidence in Alberta’s oil and gas industry to remain economically relevant in the future on climate policy support might be smaller (due to the overrepresentation of NDP supporters) but that the overall substantial conclusions we drew here are unlikely to change.

Overall, the results indicate that, even after adjusting for a wide range of potential confounders, future expected economic benefits matter to respondents. Confidence in the oil and gas industry to continue providing Canada’s province of Alberta with economic benefits in the future leads to general opposition to climate policies. These results are in line with our core argument. Without perceiving the need to move away from the status quo, people lack the incentive to fix what they perceive will not be broken any time soon.

Discussion and conclusion

In this paper, we asked what future expected benefits from the fossil industry, i.e., maintaining the status quo, means for the support of an energy transition towards a low-carbon economy. Our central argument was that peoples’ confidence in the fossil fuel industry to deliver economic benefits for and wealth to their regions in the future reduces support for energy transition. We conducted an initial test of our core argument, using survey data from an oil and gas-dependent region, the Canadian province of Alberta. The results provide empirical support to our idea. Overall, our paper adds, both theoretically and empirically, to the existing literature, which has remained mostly quiet on the benefits of retaining the status quo. We argue here that this omission is crucial because people may only be swayed to support a transition once they see the need to change the status quo. Absent the perceived need for economic change, parts of the public are adverse to any change. The implications are relevant, both to the academic and policy debates.

First, in light of the opposition to change more generally, it appears that certain segments of the public will be less receptive to the potential economic benefits of renewable industries, which may provide equally important direct and indirect economic benefits for regions. Future research could explore under which conditions these ‘non-movers’ become ‘potential supporters’ and, subsequently, support a change to a low-carbon economy. Like a sequencing policy approach (Meckling et al. 2015), the sequencing conversion approach could be the key in moving even the most unlikely climate policy supporters. At the very least, it might be possible to move them into a neutral state, similar to what Tvinnereim and Ivarsflaten (2016) find for oil and gas workers in Norway. Thinking about these questions as a sequence should also allow for developing clearer expectations about when and why some people would be willing to support a transition to, for example, renewable industries while others support fracking. Such an analysis was not within this paper’s scope due to data availability, but it would well connect both to our argument and to existing arguments and shed further light on the relationship between costs/benefits and climate policy support. Experimental studies may lend themselves well to tackle these questions.

Second, our study also speaks to the role of industry concentration in communities, regions, and countries. Here, we find empirical evidence that such a concentration can lead individuals to focus on prospective, sociotropic perceptions, rather than on egocentric economic considerations. This in turn shapes peoples’ attitudes towards climate policies. At the same time, our argument can be interpreted through an egocentric lens as well as through a sociotropic lens, with the former implying that people use their personal expected economic benefits as a reference point for their evaluations.¹¹ Generally, it is conceivable that both mechanisms may work as, for example, both employment in the oil and gas sector as well as benefits for communities have been linked to policy support for alternative energy industries (Kallbekken and Sælen 2011, Boudet et al. 2016, Tvinnereim and Ivarsflaten 2016, Olson-Hazboun et al. 2018, Mayer and Malin 2019, Beiser-McGrath and Bernauer 2020). In this study, we were limited to testing the sociotropic version of the argument but future studies may explore both, egocentric and sociotropic considerations and also, how the two are linked (e.g., Schaffer and Spilker 2019, p. 1271–72).

Our third contribution speaks to the importance of context. We conducted our empirical analysis in a most-likely scenario, the oil and gas-dependent province of Alberta, and we are confident that our findings would emerge in contexts similar to our case. However, in other regions (e.g., other Canadian provinces), without the force of economic identity and a history of economic dependence on the industry to shape their future expectations, the general public may be far less confident in the fossil fuel industry’s future. As such, the findings of our study likely generalize to regions, such as West Virginia’s coal regions, in which public opinion towards energy production is shaped in similar ways, including by industry led PR efforts (e.g., Bell and York 2010). Further, while we are confident that the general mechanism of economic confidence affecting peoples’ attitudes towards climate policies holds in regions outside of these contexts, the extent to which they play out empirically is likely lower. However, we cannot test this assumption given our data. This limitation presents an open question for future research to explore.

Lastly, our study bears implications beyond academia. Advocates for climate policies will first have to convince some parts of the public that change is necessary at all. Without this step, some members of the public are unlikely to be convinced, regardless of how economically attractive other industries might appear to them. This implication may be especially focussed on two sets of actors: the fossil fuel industry itself, and the news media. With respect to industry, we currently see a push by various fossil fuel companies to diversify their business portfolio, including investing in renewable energy (Kungl 2015). These same companies have long conducted public campaigns to ensure sufficient public support for their fossil fuel business (e.g., Supran and Oreskes 2017). Now they may have to convince at least part of their investors not only of the economic benefits provided by a more diversified or, in some cases, completely overhauled business portfolio. To achieve this, they may first have to convey that the fossil fuel industry may not yield the future benefits they have long been advertising. For news media, the considerations here are similar to those they would encounter with other major economic changes (e.g., recession) or new technologies, in that how their frames relating to fossil fuels, renewable energy, and energy transition changes will affect how the public views each. The effects of these framing choices are not neutral, and future research could explore how language used by the fossil fuel industry and the media alike structure the public’s expectation for future economic benefits and energy transition.

Acknowledgments

The authors gratefully acknowledge support from the University of Alberta Future Energy Systems - Canada First Research Excellence Fund (FES - CFREF) and the University of Calgary Global Research Initiative - Canada First Research Excellence Fund (GRI - CFREF). We presented an earlier version of this paper at the 2020 Future Energy Systems Digital Research Showcase. We thank all participants for their helpful feedback. Furthermore, we want to thank Robert Huber, the editor, and the three anonymous reviewers for their valuable comments and feedback.

Data availability statement

The complete replication material (Data and R-Scripts) is available from the Harvard Dataverse: https://doi.org/10.7910/DVN/JIPTBC.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed here

Additional information

Funding

This work was supported by the Canada First Research Excellence Fund.

Notes

1. Breetz et al. (2018, p. 493), use the term ‘incumbent industries’ to describe a similar set of actors.

2. Related research also finds that high levels of trust in the oil and gas industry can lead to outright objection to climate policies. See Dietz et al. (2007) and Rhodes et al. (2017).

3. The survey was approved by University of Calgary Conjoint Faculties Research Ethics Board (REB190219) and by the University of Alberta Research Ethics Board (Pro00089470).

4. See Appendix A2.1 for the details of the IRT analyses.

5. Tvinnereim and Ivarsflaten (2016) only ask if the respondent is employed in the oil and gas sector. This explains why, relative to the actual employment in the industry of Alberta, the number of respondents answering with yes in the survey is relatively high at 26%.

6. The survey included a skip pattern: Only respondents who believed in climate change were asked about the extent to which they are worried about. Including the latter would thus a) exclude respondents who do not belief in climate change and, relatedly, b) capture only the degree of worriness amongst those believing in climate change. Thus, even though the dummy is a more crude measure, we opted for the latter, confident that it captures the very essence of the underlying concern about climate change.

7. The survey also included a survey experiment, detailed in Thomas et al. (2021). However, since the randomized treatments were administered after measuring our central explantory variables and before our set of policy measures, we argue that it does not confound the relationship between the two; The university dummy is meant to capture different world views as a result of going through university. However, the variable may theoretically also function as a proxy for income which is highly correlated with education; We acknowledge that the set of possible confounders could be larger. However, we believe that we capture at least the most essential variables that may affect both our explanatory variables and the dependent variable.

8. For detailed wording of the covariates and descriptive statistics of all variables, see Appendix A1.1 and A1.2; For a list of all relevant R packages, see Appendix A1.4. We also implement the model in the frequentist framework, using a more commonly used OLS regression model. Due to our choice of weakly informed priors, the estimates from the OLS models match those from the Bayesian model. Note, however, that the interpretation and estimation of uncertainty differ between the two approaches.

9. Tests of model convergence showed that the model converged without issues (Appendix A2.2); Full table with estimates for covariates are shown in Appendix A2.3.

10. The single outlier we detect is for the expansion of oil sand refinery capacities which does not change depending on whether or not respondents perceive that Alberta is too dependent on the oil and gas industry. This may reflect that, to some, the expansion of crude oil refinery capacities in and by itself is a process of diversification which reduces the risk of economic dependence on crude oil exports.

11. Shwom et al. (2010, p. 476) make a similar distinction, using the terms ‘personal costs’ and ‘costs to others and economy.’