RESEARCH ARTICLES Risk Perceptions of Natural Gas Development ...

RE S E ARCH A R T I C L E S

Risk Perceptions of Natural Gas Development in the Marcellus Shale Kathryn J. Brasier, Diane K. McLaughlin, Danielle Rhubart, Richard C. Stedman, Matthew R. Filteau, Jeffrey Jacquet

Exploration and extraction of natural gas from the Marcellus Shale have created considerable controversy. At the core of these debates are differing perceptions of the level and types of risks involved with the extraction activities, such as hydraulic fracturing, truck traffic, air emissions, and population growth. Risks described include the potential for human and environmental health implications, as well as community change and economic gain. This article explores the nature of perceived risks associated with Marcellus Shale development by using data from a household survey (N 5 1,917) conducted in 2009–10 in Pennsylvania and New York counties

ginia, Ohio, and Maryland!. The United States ~US! Energy Information Agency released an assessment in 2012 that the Marcellus Shale contains 141 trillion cubic feet ~TCF! of unproved technically recoverable reserves of natural gas, making this source one of the largest in the world ~US Energy Information Agency, 2012!.1 Natural gas from unconventional geological formations ~such as tight sands and shales! like the Marcellus Shale is typically extracted by using a combination of horizontal drilling and hydraulic fracturing.2 Although hydraulic fracturing is the technology most visibly debated, multiple dimensions of the development process ~including drilling, truck traffic, jobs, temporary oilfield workers, population growth, and change! have been linked to potential risks to the natural environment and local communities. In this article, we explore the dimensions of risk as applied to this natural resource–based activity. This article draws from previous risk perception research to quantitatively measure perceptions of risk related to development of natural gas in the Marcellus Shale region and to explore the ways in which these perceptions vary across critical individual and geographic characteristics.

located in the core areas of the Marcellus Shale region. The article describes a quantitative measure of risk perceptions. Statistical analyses of the data indicate that trust in institutions responsible for managing the risks associated with development and attitudes related to relationships between people and nature are associated strongly with perceptions of risk. Other associated variables include reported knowledge of environmental, social, and economic impacts, mineral rights ownership, demographic characteristics (gender, income), and state of residence. Implications of these findings are discussed, particularly as related to improving local discourse surrounding Marcellus Shale development. Environmental Practice 15: 108–122 (2013)

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he Marcellus Shale is a geological deposit containing significant natural gas reserves beneath portions of five northeastern states ~Pennsylvania, New York, West Vir-

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Environmental Practice 15 (2) June 2013

Affiliation of authors: Kathryn J. Brasier, Associate Professor of Rural Sociology, Department of Agricultural Economics, Sociology, and Education, Pennsylvania State University, University Park, Pennsylvania. Diane K. McLaughlin, Professor of Rural Sociology and Demography, Department of Agricultural Economics, Sociology, and Education, Pennsylvania State University, University Park, Pennsylvania. Danielle Rhubart, Graduate Research Assistant, Department of Agricultural Economics, Sociology, and Education, Pennsylvania State University, University Park, Pennsylvania. Richard C. Stedman, Associate Professor of Natural Resources, Department of Natural Resources, Cornell University, Ithaca, New York. Matthew R. Filteau, Department of Agricultural Economics, Sociology, and Education, Pennsylvania State University, University Park, Pennsylvania. Jeffrey Jacquet, Assistant Professor of Sociology and Rural Studies, Department of Sociology and Rural Studies, South Dakota State University, Brookings, South Dakota. Address correspondence to: Kathryn J. Brasier, Department of Agricultural Economics, Sociology, and Education, Penn State University, University Park, PA 16802; ~phone! 814-865-7321; ~fax! 814-865-3746; ~e-mail! [email protected]. © National Association of Environmental Professionals 2013

doi:10.10170S1466046613000021

tive measure of risk perception from natural gas extraction in the Marcellus Shale.

Definitions of Technological Risk Social science approaches to technological risk assessment began to grow in the 1980s and emphasized the role of psychological ~Slovic, 1987! and sociological ~Freudenburg, 1988! processes in both lay and expert assessments of technological risk ~Krimsky and Golding, 1992!. This work emphasized that public perception of risks are not simply irrational or ignorant, as often described by technical experts, but instead represent complex social and psychological processes that consider multiple dimensions of the technology being assessed ~Freudenburg and Pastor, 1992; Slovic, 2000! and broad social and cultural orientations to technology, society, and nature ~Dake, 1991; Douglas and Wildavsky, 1982!. Of particular importance are those technological risks “where the planned or actual implementation of technological development produces benefits for some, or even for society at large, but does so while imposing a burden of risks on others” ~Freudenburg and Pastor, 1992, p. 390!. Much of the social science literature on technological risk has examined how individuals define risk and how their perceptions vary by social and psychological factors ~Alario and Freudenburg, 2010!. One approach has become known as the psychometric paradigm ~Fischhoff et al., 1978!, which focuses on describing the relative levels and types of risk associated with various activities and technologies and identifying the dimensions of these technologies that influence how people perceive them. Starr ~1969! provided one of the first risk measurements by comparing risks and benefits against each other to create risk-benefit patterns. He found that greater risks were tolerated when accompanied by greater benefits: voluntary risks ~such as skiing! would be tolerated at roughly 1,000 times the risks of those from involuntary hazards ~such as food preservatives! that provided the same level of benefits. Slovic, Fischhoff, and Lichtenstein ~1980! built from Starr’s work to develop measures and methods to quantify and model public perceptions of risks associated with technologies prevalent in society. Attributes of the risks were measured, including voluntariness, equity in the distribution of harm, uncertainty about the likelihood and degree of harm, catastrophic potential, level of control over the technology, the degree to which the harm is known, the “observability” of the harm, familiarity with the technology, number of people exposed, benefits, and the timeliness of the manifestation of harm ~Fischhoff et al., 1978; Slovic 1987, 1993; Slovic, Fischhoff, and Lichtenstein, 1980!. It is these dimensions we incorporate into our development of a quantita-

Influences on Risk Perception There are a number of important influences on a person’s perception of the level of technological risk. Here we highlight three sets of influences found to be critical in risk perception literature: ~a! perceived knowledge of effects of the activity, ~b! trust in the institutions responsible for managing risk, and ~c! critical demographic and geographic characteristics.

Perceived Knowledge of Effects of Technologies The social amplification of risk framework ~Kasperson et al., 1988; Renn et al., 1992! suggests that people develop their perception of risk through a two-step process: “First, individual and social ‘amplification stations’—including the scientist who communicates the risk assessment, the news media, politicians and government agencies, cultural groups, interpersonal networks, and others—process and amplify signals about the risk. The amplified risk then leads to behavioral responses, which, in turn, have secondary impacts or what we term ‘ripples’” ~Kasperson, 2012, p. 60!. Individuals develop their beliefs about a potential risk by taking in available information, filtering it based on characteristics of the source, assessing the credibility of the information, rating the meaning of that information in relation to other experiences, and comparing the information to their preexisting beliefs and those of critical reference groups ~Renn et al., 1992, p. 142!. As a result, even those without direct experience with the technology can develop knowledge of the risk based on the individual’s interaction with these “amplification stations.” Purported knowledge therefore may relate to greater awareness and interaction with these information sources and are likely to either amplify or attenuate perceptions of the risks.

Institutional Trust The risks posed by modern technology are no longer immediately perceptible to our senses and can act as hidden dangers ~Erikson, 1994!. People have become increasingly distant from the creation of the technologies with which we interact daily, creating the need for groups of specialists to manage these technologies. As a result, we increasingly rely on a complex set of social and technological systems to understand and manage risks ~Carolan, 2006; Erikson, 1994; Freudenburg, 1993, 2000!. However, our reliance on these

Risk Perceptions of Natural Gas Development in the Marcellus Shale

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specialized sociotechnical systems creates the potential for enhanced vulnerability to failure of institutions entrusted with effectively managing the risks. Freudenburg ~1993, p. 916! created the term recreancy ~“a retrogression or failure to follow through on a duty or trust”! to capture this concept. Signal events ~e.g., spills, contamination, accidents! can reveal such failures and lead to decreased trust in the managing institutions and their ability to manage the risks ~Kasperson, Golding, and Tuler, 1992; Slovic, 1987!. Consequently, the perceived trustworthiness of the organizations responsible for managing the risks and conveying information about those risks is a critical predictor of the level of perceived risk ~Freudenburg, 1993; Kasperson et al., 1988; Norgaard, 2007; Sapp et al., 2009!.

Demographic and Geographic Characteristics of Respondents Risk literature has outlined demographic characteristics that can play a role in the development of one’s risk perceptions. For example, Flynn, Slovic, and Mertz ~1994! describe a White male effect, in that White men tend to have lower perceptions of risk across a number of different types of risk. Finucane and colleagues ~2000! find that White men also exhibit attitudes related to “individual achievement, initiative, and self-regulation, trust in experts and risk proponents, and intolerance of community-based decision and regulation processes. As a consequence, we speculate that the world seems safer and hazardous activities seem more beneficial to white males than to other groups” ~p. 170!. Other studies that examine gender have also found that women tend to perceive modestly higher levels of risk, particularly related to more localized environmental risks ~Davidson and Freudenburg, 1996; Freudenburg and Davidson, 2007; Flynn, Slovic, & Mertz, 1994; Stern et al., 1993!. The most frequently cited reason for this relatively consistent finding relates to the socialization of women into traditional gender roles in which women develop greater concern for health and safety, particularly for that of children ~Davidson and Freudenburg, 1996!. These concerns make them more attuned to environmental risks, especially risks within their own local communities ~Freudenburg and Davidson, 2007!. Further, women express less trust in the organizations that manage technology ~Davidson and Freudenburg, 1996!. Risks may also differentially activate norms among men and women; Stern, Dietz, and Kalof ~1993! found that norms related to social welfare were

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activated among women but norms related to economic concerns were activated among men. As already noted, race is also a critical influence on risk perceptions. Minorities tend to report higher perceptions of risk because of historical patterns of racism and differential exposure to environmental problems ~e.g., Pellow and Brulle, 2005!, as well as differing attitudes and worldviews. Although race is an important influence on risk perceptions, it is not included in this study because of the racial homogeneity of the target population. In addition to social demographic influences on risk perceptions, geographic characteristics are important considerations. Physical proximity can increase the perceived risk of exposure to environmental and health problems associated with the technology ~Freudenburg, 2006; Norgaard, 2007; Renn et al., 1992!.

Research Objectives Based on their place of residence and the area’s level of development, residents in the Marcellus Shale region report differences in potential risks and benefits associated with natural gas development ~Brasier et al., 2011; Stedman et al., 2012!. While some people point to the economic benefits received by local businesses and the local economy, others draw attention to the environmental and social threats related to extraction. These benefits and risks are not easily predicted nor measured. Further, scientists have publicly debated both the risks and benefits, revealing uncertainty about the science of the Marcellus Shale. The hydraulic fracturing technology used in the natural gas extraction lies at the crux of the debate on Marcellus Shale development. Concerns about hydraulic fracturing include the volumes of fresh water drawn from surface and groundwater resources, migration of methane or hydraulic fracturing fluids through the geological strata into groundwater, earthquakes, chemical spills, and treatment and disposal of the wastewater. These possible threats from hydraulic fracturing, as well as the larger suite of attendant activities and negative effects ~truck traffic, noise, population change, economic change, etc.!, have been documented in earlier work on the Marcellus Shale ~Brasier et al., 2011!, as well as other regions ~Anderson and Theodori, 2009; Theodori, 2009!. However, these reports did not measure the extent of perceived risk nor how these perceptions differ among individuals.

The empirical analysis that follows describes the development of a quantitative measure assessing perceived risk related to natural gas development in the Marcellus Shale region and ascertains the causes/concomitants of these differing perceptions. We focus specifically on the following individual characteristics: self-reported knowledge of the impacts of Marcellus Shale activity, trust in relevant institutions and agencies, and demographic and geographic characteristics shown to be influential in previous research.

Data, Measures, and Methods We used information from a 2009–10 survey mailed to 6,000 households in 21 Pennsylvania and 8 New York counties located in the core areas of Marcellus natural gas resources. A total of 521 addresses were incorrect or incomplete and were returned by the post office as undeliverable. Of the remaining 5,479 cases, 1,917 returned completed survey forms ~a 35% effective response rate!. A comparison of the demographic characteristics of the sample to those of the adult population in the surveyed counties 3 suggests that the sample overrepresents men ~53.5% in the sample; 50.5% in the population!, older residents ~57.5% of the sample is 55 years of age or older; 34.9% of the population is in the same age group!, and those with more education ~32.2% of the sample has a bachelor’s

degree or more; 21.6% of the population is in this category!. This suggests the need to be somewhat cautious in generalizing the survey results to the extent these characteristics ~gender, age, and education! influence perceptions of risk.

Measures of Risk Perception Six items on the questionnaire were used to measure respondents’ perceptions of risk in regard to Marcellus Shale and natural gas production. Each of these items reflected one dimension of risk. These are preventability of risks, knowledge of risks, weighing of costs and benefits, catastrophic potential, reversibility of harm, and inequitable distribution of benefits ~see Table 1!. Responses to these items were measured on a scale of 1–5, with 1 indicating “strongly disagree,” 2 5 “disagree,” 3 5 “neutral,” 4 5 “agree,” and 5 5 “strongly agree.” To obtain scores with high values meaning “high risk,” the coding on items 1, 2, 3, and 5 was reversed. A composite score was calculated by summing the scores for each of the six items. Thus, possible scores ranged from 6 to 30. In the few cases where an individual did not respond to every item, the mean sample value for that item was substituted in the summation.

Table 1. Risk perception scale items and descriptive statistics

Item 1. Negative impacts of natural gas extraction from the Marcellus region can be prevented if it proceeds carefully. 2. We already know enough about the potential impacts of natural gas extraction to move forward with development in the Marcellus Shale. 3. All in all, the benefits to this region of natural gas extraction from the Marcellus Shale will outweigh the costs. 4. I worry that there will be some sort of catastrophic accident involving natural gas extraction in the Marcellus Shale area. 5. Any negative impacts of natural gas extraction in the Marcellus Shale can be fixed. 6. Only a few people in the area will receive any benefits from the natural gas development. Scale statistics ~N 5 1,917!

Strongly disagree (%)

Disagree (%)

Neither agree nor disagree (%)

Agree (%)

Strongly agree (%)

Mean (SD)

4.1

9.7

28.0

42.2

16.0

2.44 ~1.01!

10.1

19.7

41.8

20.7

6.0

3.06 ~1.02!

8.7

15.3

45.2

22.8

8.0

2.93 ~1.02!

8.4

24.0

37.7

21.6

8.2

2.97 ~1.06!

10.8

26.5

40.8

18.0

3.9

3.22 ~0.99!

4.3

15.4

32.3

33.6

14.3

3.37 ~1.04! 18.02 ~4.36!

Reliability: Cronbach’s alpha 5 .824 ~six items!.


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A factor analysis of the items indicated that they comprised a single dimension. The summative scale of these items showed satisfactory reliability, with a Cronbach’s alpha of .824. The mean of the scale of these items was 18.02 ~very close to the exact midpoint of the scale!, with a minimum value of 6 ~at least one respondent selected the lowest risk response for each item! and a maximum value of 30 ~at least one person selected the highest risk response for each item!.

Independent Variables The independent variables expected to be associated with our composite risk perception measure were grouped into three categories: self-reported knowledge of the impacts of natural gas extraction, trust in institutions and agencies, and demographic and geographic characteristics of the respondents. In addition, we include control variables of specific importance to this issue and this region. Detailed descriptions of the variables are listed in Table 2; descriptive statistics are listed in Table 3.

Self-Reported Knowledge of Impacts of Natural Gas Activities Two variables indicate the self-reported knowledge level of the respondent related to the effects of natural gas activity. One reflected their knowledge of potential economic and social effects, and another their knowledge of potential effects on the natural environment and drinking water. The measurement and calculation of each measure are described in Table 2.

Trust in Institutions The survey included questions asking respondents to report their level of trust in organizations and agencies that communicate and manage risk related to natural gas development. Four separate variables are included in the analysis, indicating level of trust in ~a! the natural gas industry, ~b! the relevant state-level permitting agencies @New York Department of Environmental Conservation ~DEC! and Pennsylvania Department of Environmental Protection ~DEP!#, ~c! scientists and researchers, and ~d! local environmental groups. All of these organizations have been active in public discourse related to defining the risks associated with Marcellus Shale development. Respondents were asked to rate how much trust they had in each of these institutions/organizations on a scale from 0 ~no trust! to 3 ~a great deal of trust!. Respondents also had the choice

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of “don’t know”; those who selected this response were coded as missing for these items.

Environmental Attitudes Because of the salience of the potential environmental consequences of Marcellus Shale development, a general measure of environmental attitudes was developed by using six items patterned after the widely used New Ecological Paradigm ~NEP! scale ~Dunlap and Van Liere, 1978!. The NEP scale indicates the extent to which respondents exhibit attitudes reflective of humanity’s interconnectedness with nature rather than dominance over nature. Individual items are listed in Table 2. A scale indicating adherence to the NEP was created by summing the items. The scale exhibits acceptable reliability ~Cronbach’s alpha 5 .663!.

Demographic and Geographic Characteristics Multiple variables are used to indicate physical proximity to development and demographic characteristics. Physical proximity is indicated by respondents’ reported awareness of wells or pipelines within 10 miles of the respondents’ homes. A variable to indicate whether they had friends or family with drilling on their land is included as a measure of indirect experience and engagement with one type of amplification station.4 A variable to indicate ownership of mineral rights is also included to control for their potential ability to benefit directly from natural gas extraction. Demographic variables include gender, age, income, education, and length of residence in their current county. State-level differences in policy approaches necessitate the inclusion of an indicator of the state of residence of the respondent.5

Missing Values To maximize the number of cases in the multivariate model, we substitute the mean ~for interval level variables! or median ~for ordinal variables! for those variables with a significant number of missing cases. The variables with the highest number of missing cases were the four trust variables and household income. Dummy variables identifying those cases for which missing values were substituted are included in the bivariate and multivariate results to test whether the respondents with missing responses to those items have differing risk perceptions than those respondents who completed the questions.


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Survey questions and answer categories a

Self-reported knowledge of natural gas extraction impacts Knowledge of environmental impacts How much do you know about each of the following? Effects of gas drilling on the natural environment ~streams, rivers, fish, wildlife! Implications of natural gas drilling for water quality and/or quantity 0 None or almost none 1 Very little 2 Some but not much 3 A good bit 4 A great deal Knowledge of economic and social How much do you know about each of the following? impacts Economic impacts of the natural gas industry Social impacts of natural gas well development on communities 0 None or almost none 1 Very little 2 Some but not much 3 A good bit 4 A great deal Trust in institutions and agencies Trust in natural gas industry How much trust do you have in the natural gas industry? 0 No trust 1 Very little trust 2 Some trust 3 Great deal of trust 4 Don’t know Missing: trust in natural gas industry A dummy variable to identify missing cases on “trust in natural gas industry” 1 5 Case with missing value Trust in permitting agencies How much trust do you have in state departments of environmental protection/conservation 0 No trust 1 Very little trust 2 Some trust 3 Great deal of trust 4 Don’t know Trust in science institutions How much trust do you have in scientists/researchers? 0 No trust 1 Very little trust 2 Some trust 3 Great deal of trust 4 Don’t know

Variable name

Table 2. Independent-variable definitions and measurement

~continued!

“Don’t know” coded as missing; cases with missing values assigned the median value ~2!.

“Don’t know” coded as missing; cases with missing values assigned median value ~2!.


Scale is sum of responses to both items

Scale is sum of responses to both items

Calculation

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Environmental Practice 15 (2) June 2013 Survey questions and answer categories a

Trust in institutions and agencies ~continued! Trust in local environmental groups How much trust do you have in local environmental groups/organizations? 0 No trust 1 Very little trust 2 Some trust 3 Great deal of trust 4 Don’t know Environmental attitudes Indicate whether you strongly agree, agree, are neutral, disagree, or strongly disagree with each of the following items: The balance of nature is very delicate and easily upset by human activities Most environmental problems can be solved by applying more and better technology. ~Reversed.! Nature exists primarily to be used by humans. ~Reversed.! Ecological rather than economic factors must guide our use of natural resources When humans interfere with nature, it often produces disastrous consequences The state should lower environmental standards to keep and attract industry ~Reversed.! Demographic and geographic characteristics of respondents Proximity to gas wells/pipelines Are you aware of any Marcellus Shale gas wells or pipelines anywhere within 10 miles of where you live? 0 5 No; 1 5 Yes Friends/family have gas drilling on Have any of your friends or family members living outside your household their land had Marcellus gas drilling on their land? 0 5 No; 1 5 Yes Ownership of mineral rights Do you own the mineral rights to any land in the Marcellus Shale region of Pennsylvania or New York? 0 5 No; 1 5 Yes Gender What is your gender? 0 5 Female; 1 5 Male Age In what year were you born? Education What is the highest level of education you have completed? 1 Did not graduate from high school 2 High school graduate/GED 3 Some college or other post–high school education 4 Completed a four-year college degree 5 Graduate work or graduate degree

Variable name

Table 2. Continued

Age in years

~continued!

Missing values on individual items are replaced with the item mean prior to summation. Scale calculated as a sum of all six items. Higher values indicate congruence with ecological paradigm.


Calculation


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How long have you lived in this county? 1 1 year or less 2 2–5 years 3 6–9 years 4 10–19 years 5 Not all my life, but 20 years or more 6 All my life What was the total income of your household ~before taxes! last year? 1 Less than $15,000 2 $15,000–$24,999 3 $25,000–$34,999 4 $35,000–$49,999 5 $50,000–$74,999 6 $75,000–$99,999 7 $100,000 or more A dummy variable to identify missing cases on “household income” 1 5 Case with missing value

Years living in current county

Cases with missing values are assigned the median value ~4!.

Calculation

a “Reversed” means the responses are reversed such that answers of disagreement are coded with higher values so that the interpretation of all items is consistent. In the environmental attitude scale, higher values all indicate greater agreement with the New Ecological Paradigm.

Missing: household income

Household income

Survey questions and answer categories a

Variable name

Table 2. Continued

Table 3. Descriptive statistics for independent variables ~after mean substitution! Variable name Self-reported knowledge of natural gas extraction impacts Knowledge of environmental impacts Knowledge of economic and social impacts Trust in institutions and agencies Trust in natural gas industry Missing: trust in natural gas industry ~dummy variable for cases with missing values! Trust in permitting agencies Trust in science institutions Trust in local environmental groups Environmental attitudes Demographic and geographic characteristics of respondents Aware of gas wells/pipelines within 10 miles Friends/family have gas drilling on their land Ownership of mineral rights Gender Age Education Did not graduate from high school High school graduate/GED Some college or other post–high school education Completed a four-year college degree Graduate work or graduate degree Years living in current county 1 year or less 2–5 years 6–9 years 10–19 years Not all my life, but 20 years or more All my life Household income ~before taxes! last year Less than $15,000 $15,000–$24,999 $25,000–$34,999 $35,000–$49,999 $50,000–$74,999 $75,000–$99,999 $100,000 or more Missing: household income ~dummy variable for cases with missing values! State

N

1,887 1,891 1,917 1,917

1,869 1,874 1,890 1,879 1,839 1,869

Minimum–maximum

3.7; 4.0 ~2.4! 3.4; 4.0 ~2.2!

0–8 0–8

1.6; 2.0 ~0.8!

0–3

1.7; 2.0 ~0.7! 2.0; 2.0 ~0.7! 1.8; 2.0 ~0.7! 20.6; 20.6 ~3.9!

0–3 0–3 0–3 7–30

Yes 5 39.3 Yes 5 13.9 Yes 5 20.2 Male 5 53.5 57.0; 58.0 ~15.6!

18–107

5.9 27.9 34.0 15.0 17.2 1,891 1.5 6.3 4.0 9.0 30.5 48.7 1,917

1,917

9.7 12.7 11.0 28.3 19.1 8.5 10.7 Missing 5 13.5

1,917

New York 5 23.7

Risk Perception Items and Scale The six items in the risk perception scale are listed in Table 1. Mean scores for the risk perception items cluster around the neutral category ~value of 3!, with actual per-item mean


Mean; median (SD)

Missing 5 15.9

1,917 1,917 1,917 1,917

Results

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Percentages

scores ranging from 2.44 for item 1 to 3.37 for item 6. With two exceptions, the modal category for these items is neutral. The first item reflecting preventability had a mean of 2.44 ~where 5 means strongly disagree!, with just over half, 58.2%, agreeing that negative impacts are preventable and 13.8% disagreeing. The sixth item, “Only a few people in the area will receive any benefits from the natural gas develop-

ment,” has a mean of 3.37 ~where 5 is strongly agree!, with 47.9% agreeing with this statement and 19.7% disagreeing. For the other items, among those who have formed opinions, respondents are generally equally split related to the dimensions of knowledge of the risks ~item 2! and the potential for a catastrophic accident ~item 4!. For those respondents who have formed opinions, a larger percentage indicates a concern that harm caused by development cannot be reversed ~item 5!. It is worth noting that the modal response for item 3, that the benefits outweigh the costs, is neutral; respondents do not seem to have come to a conclusion about the trade-offs of natural gas development.

Bivariate Relationships The bivariate correlations ~Pearson’s R! between risk perception and several independent variables were statistically significant ~ p , .05; see Table 4!. Self-reported knowledge of economic and social impacts was negatively ~but weakly! related to risk perception ~R 5 2.091; p 5 .000!. Higher

trust in the gas industry was strongly associated with perception of lower risk ~R 5 2.542; p 5 .000!. More modest significant relationships exist between risk perceptions and trust in state regulatory agencies @Department of Environmental Protection/Department of Environmental Conservation ~DEP/DEC!# ~R 5 2.147; p 5 .000! and in science institutions ~r 5 2.128; p 5 .000!. In contrast, greater trust in local environmental groups was associated with higher risk perception ~R 5 .119; p 5 .000!. Respondents with attitudes more consistent with the NEP perceived higher risks than did those with lower scores on the environmental attitudes measure ~R 5 .542; p 5 .000!. Awareness of nearby gas wells/pipelines was associated weakly with lower risk perception ~R 5 2.090; p 5 .000!, whereas having friends or families with gas drilling on their land was associated weakly with higher risk perception ~R 5 .090; p 5 .000!. Ownership of mineral rights was associated with lower perceptions of risk ~R 5 2.144; p 5 .000!. Men ~R 5 2.189; p 5 .000!, older respondents ~R 5 2.068; p 5 .003!, and those reporting higher incomes ~R 5 2.120; p 5 .000!

Table 4. Correlation coefficients and standardized ordinary least-squares regression coefficients with risk perception scale as dependent variable ~N 5 1,794!

Independent variables Self-reported knowledge of impacts Knowledge of environmental impacts ~higher 5 greater felt knowledge! Knowledge of economic and social impacts ~higher 5 greater felt knowledge! Trust in institutions and agencies Trust in natural gas industry ~higher 5 greater trust! Missing: trust in natural gas industry ~missing 5 1! Trust in state DEP/DEC ~higher 5 greater trust! Trust in scientists/researchers ~higher 5 greater trust! Trust in local environmental groups ~higher 5 greater trust! Environmental attitude scale ~higher 5 ecological paradigm! Characteristics of respondents Aware of gas wells/pipelines within 10 miles ~1 5 yes! Friends/family have gas drilling on their land ~1 5 yes! Ownership of mineral rights ~1 5 own mineral rights! Gender ~1 5 male! Age in years Education Years living in current county Household income ~before taxes! last year Missing: household income ~missing cases 5 1! State ~1 5 New York! F ~significance! Adjusted R 2

Bivariate correlation

Multivariate regression

Pearson’s R (significance)

b coefficients (significance)

.043 ~.064! −.091 ~.000!

.193 ~.000! −.175 ~.000!

−.542 ~.000! .023 ~.311! −.147 ~.000! −.128 ~.000! .119 ~.000! .542 ~.000!

−.394 ~.000! .079 ~.000! 2.027 ~.214! −.071 ~.001! .122 ~.000! .337 ~.000!

−.090 ~.000! .090 ~.000! −.144 ~.000! −.189 ~.000! −.068 ~.003! .036 ~.123! 2.045 ~.052! −.120 ~.000! .085 ~.000! .130 ~.000!

2.015 ~.422! .043 ~.017! −.091 ~.000! −.070 ~.000! 2.022 ~.224! .009 ~.657! .022 ~.217! −.052 ~.009! .077 ~.000! .047 ~.007! 98.949 ~.000! .496

DEP/DEC, Department of Environmental Protection/Department of Environmental Conservation.


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perceived less risk than did their opposites. Those who did not report their income perceived higher risks ~R 5 .085; p 5 .000!, as did respondents living in New York ~R 5 .130; p 5 .000!.

Multivariate Models of the Risk Perception Scale To assess the relationships of the independent variables with the risk perception scale, adjusting for the effects of other variables, we developed an ordinary least-squares regression model that incorporates self-reported knowledge, trust in institutions and agencies, environmental attitudes, and respondents’ geographic and demographic characteristics ~Table 4!. The largest standardized coefficients were for trust in the natural gas industry ~b 5 2.394; p 5 .000! and environmental attitudes ~ b 5 .337; p 5 .000!. These relationships indicate that trust in the natural gas industry was associated with lower risk perception, and proenvironmental attitudes were associated with higher risk perception. The perception of higher risks associated with self-assessed knowledge of environmental impacts ~ b 5 .193; p 5 .000! was countered by the perception of lower risks among those with greater selfassessed knowledge of economic and social impacts ~ b 5 2.175; p 5 .000!. Three other trust variables were weakly associated with perception of risk. Trust in local environmental groups was associated with increased risk perception ~ b 5 .122; p 5 .000!, and trust in science institutions was associated with lower risk perception ~ b 5 2.071; p 5 .001!. Respondents with missing values related to trust in the natural gas industry ~i.e., they either did not answer or indicated they did not know their level of trust! had higher risk perception than those who completed this question ~ b 5 .079; p 5 .000!. Trust in the DEP/DEC was not statistically significantly related to risk perception in the multivariate model. Geographic and demographic characteristics were modestly associated with risk perception and contributed little to the overall fit of the model. Among the significant respondent characteristics, those who have friends or family with drilling activity had higher perception of risk ~ b 5 .043; p 5 .017!, and those who own their mineral rights had lower perception of risk ~ b 5 2.091; p 5 .000!. Men also had lower risk perception ~ b 5 20.070; p 5 .000!. Respondents with higher household income had lower risk perception ~ b 5 2.052; p 5 .009!. Those who did not report household income perceived higher risk from natural gas extraction ~ b 5 .077; p 5 .000!, as did those who live in New York ~ b 5 .047; p 5 .007!. The combined effect

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of all of these variables explained almost half of the variance ~adjusted R 2 5 .496! in respondents’ risk perception. In general, the relationships among the independent variables and risk perception are consistent between the bivariate and multivariate results, with most relationships significant in the bivariate results still significant ~though attenuated! in the multivariate results. A few exceptions, however, are worth noting. Three variables become significant or have larger coefficients in the multivariate model as compared to the bivariate results: knowledge of environmental impacts, knowledge of economic and social impacts, and the dummy variable indicating missing values for trust in the natural gas industry. This suggests the need to use structural equation models to explore potential interactions that can identify both direct and indirect relationships to perception of risk. The patterns of associations reflect the complicated nature of assessing risk and the multiple factors that enter into individual’s assessments of risk. The largest effects—those associated with environmental attitudes and trust in the natural gas industry—suggest the tension over the uncertainty of the environmental impacts of natural gas extraction and whether the gas industry will proceed carefully. The offsetting effects of knowledge of environmental impacts and knowledge of economic and social impacts seem to reflect the other tension often expressed—that between the economic opportunities that may be provided by natural gas extraction and, again, the potential for negative environmental impacts.

Summary and Conclusion This article drew from existing theories related to technological risk to examine the perception of risk specifically in the case of natural gas extraction from the Marcellus Shale. A quantitative measure of risk perception was developed and tested, and correlates of the risk perceptions were examined. Drawing on the psychometric paradigm, the measures developed here reflect six dimensions of risk perceptions: preventability of risks, knowledge of risks, weighing of costs and benefits, catastrophic potential, reversibility of harm, and inequitable distribution of benefits. For all but two of these dimensions, most respondents answered in the neutral categories. The exceptions are preventability and inequitable distribution of benefits, suggesting that these dimensions are those around which respondents have formed perceptions of risk.

The dimension of preventability tied directly to institutional trust, particularly the perceived ability of the natural gas industry to minimize problems effectively and the ability of state and federal agencies to develop and enforce protective policies and regulations. The question of how many people ~and who! will benefit from development, and who will not, has been a consistent concern raised by local and state leaders in Pennsylvania ~Brasier et al., 2011!. Economic impact studies that describe varying levels of aggregate economic benefits ~e.g., Considine et al., 2009; Kelsey et al., 2011! are countered by other reports and media accounts of negative effects on those who have been unable to benefit from either leasing and royalty income ~such as renters or those not owning their mineral rights! or those unable to work in the industry ~e.g., Williamson and Kolb, 2011!. The distribution of costs and benefits has also been an important component of political discussions, including legislation passed in 2012 enacting an impact fee in Pennsylvania ~Pennsylvania General Assembly, 2012!. Clarifying the magnitude and likely recipients of benefits—and the balance of these benefits against potential costs and who is harmed—is an area in need of further research. The relatively large percentages of respondents giving neutral responses to the risk dimension items raise an important line of future inquiry. It is unclear the extent to which respondents were truly neutral versus uncertain about their beliefs, as they were not afforded a “not sure” response option. Further, we did not examine whether there are important differences between those who have formed a risk perception and those who have not. Further research needs to explore the meaning of the “neutral” response and the important influences on the development of an opinion such as those suggested in the social amplification of risk framework ~e.g., direct experience, interaction with information sources, signal events!. The relative importance of trust in the natural gas industry raises a number of additional questions. Both Pennsylvania and New York have long histories of natural resource extraction, particularly oil, natural gas, coal, minerals, and timber. It is unclear the extent to which histories of extraction in particular localities might affect the development of worldviews related to natural resource extraction and economic imperatives such as those found by Freudenburg and Davidson ~2007!. Our preliminary analyses suggested that individual employment in these industries was not related to perceptions of risk, but we did not test how community-level dependence on natural resource activities might influence individual attitudes and worldviews about technologies common within rural, resource-dependent

communities ~e.g., Brown et al., 2011; Freudenburg and Gramling, 1993!. Further research might explore differences in risk perceptions as related directly to these regionally-specific histories. The inverse relationship between perceived knowledge on economic/social issues and environmental issues suggests that those who perceive themselves as more knowledgeable are drawing information from amplification stations that either amplify or attenuate the risks, and that these stations are likely linked to specific messages about the environmental risk or the economic/social benefits of development. The results of previous research suggest that individuals who already have an opinion about an issue are more likely to believe the information that is commensurate with their beliefs and less likely to believe information that is contrary to their beliefs ~Renn et al., 1992!. This suggests the need to further investigate the messages coming from amplification stations related to Marcellus Shale and how individuals take in this information, evaluate the information and its source, and develop their own perception of risk. As in previous research, gender is one of the few demographic variables that is significant, albeit relatively weakly, in this analysis. This finding adds to the number of studies that raise questions about the reasons for this difference between men and women ~Freudenburg and Davidson, 2007!. However, additional research is needed to understand the mechanisms by which gender is relevant related to natural gas development in the Marcellus Shale. The state variable has a significant but relatively modest relationship with risk perception in the multivariate analysis. The persistence of this relationship suggests that the policy differences between New York and Pennsylvania create differing contexts for the development of risk perceptions. New York respondents tend to have heightened perceptions of risk, perhaps related to public policy discussions that question the safety of natural gas extraction using hydraulic fracturing ~Stedman et al., 2012!. The public discourse in New York has centered on regulatory systems to protect public and private water supplies, raising and reinforcing concerns about the environmental and human health risks of extraction. Further, very few economic benefits have been realized by New York residents to date. By contrast, Pennsylvania residents have experienced increased economic activity and personal wealth, without the same level of reinforcement of potential risks by state agencies and politicians.


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One limitation of this study is the relatively small number of the dimensions of risk tested. Future studies could examine other dimensions, such as familiarity and voluntariness, that are relevant with this type of technological risk. Some areas within the region have a long history of oil and gas development, but it is unclear the extent to which this history makes natural gas development from the Marcellus Shale familiar. Additional research might also attempt to disentangle the multiple types of risks involved with development of Marcellus Shale. Although the approach described here examines a generalized risk associated with Marcellus Shale development, risks could also be examined independently related to specific technologies or to potential effects on health, environment, and community. One of the central findings—that distrust of the natural gas industry is strongly related to higher perception of risk—is commensurate with previous literature on the relative importance of trust in institutions. As Slovic ~1999! notes, trust is fragile, more easily lost than gained. Reports recount how landmen ~individuals who negotiated with landowners to lease their subsurface rights! took advantage of landowners’ lack of knowledge about the Marcellus Shale to sign leases for very small amounts ~Alter et al., 2010; Brasier et al., 2011!. Regulatory violations and accidents ~such as methane migration in Dimock, PA! in the early stages of development, near the time of this survey, also may have acted as signal events that the industry could not be trusted to manage this new technology. This finding also suggests that those who want to have a constructive dialogue related to the risks related to extraction of natural gas from unconventional resources need to work with the public to develop participatory processes in which mutual respect among lay and expert knowledge sources can grow and trust can be built over time ~Freudenburg and Pastor, 1992; Slovic, 2000; Wynne, 1992, 1996!. These processes can help to recognize and document the multiple dimensions in which people think about a potential risk and how those risks are shaped by psychological processes and social structural factors. Doing so can lead to greater participation of the public in decision making, oversight of technologies, and improved analysis of the risks involved.

Acknowledgments Funding for this research was provided by the Appalachian Regional Commission, Penn State University College of Agricultural Sciences, and Cornell University. We also acknowledge the valuable contributions of our partners on this project, Ted Alter and Fern Willits at Penn State and Teri Ooms and Sherry Tracewski at the Institute for Public Policy and Economic Development.

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Notes 1. “Unproved technically recoverable reserves” are “those quantities of oil and gas which . . . can be estimated with reasonable certainty to be commercially recoverable . . . from known reservoirs and under current economic conditions, operating methods, and government regulations but have not been proven to exist based on accepted geologic information, such as drilling or other accepted practices per SEC @Securities and Exchange Commission# guidelines” ~Marcellus Center for Outreach and Research, n.d., p. 2!. 2. Hydraulic fracturing, which is a technique used to extract oil and natural gas from unconventional sources such as shale and tight sand formations, involves the injection of water, sand, and chemicals under high pressure to fracture the rock and prop the fractures open to allow the gas to travel up through the well ~Ground Water Protection Council and ALL Consulting, 2009!. 3. Population data are drawn from the 2006–8 American Community Survey ~ACS! for all counties except Cameron and Sullivan in Pennsylvania and Schuyler in New York ~US Census Bureau, 2008!. These counties have population counts under the threshold for the three-year estimates available in the ACS. Data for these counties are from the 2000 US Census of Population and Housing ~US Census Bureau, 2000!. 4. The issue of leasing is complicated in New York and Pennsylvania by the separation of surface from subsurface rights and the history of leasing in the region. Of the 1,917 respondents, 382 indicated they own their subsurface rights. Of these, 333 had been approached about a lease and 183 had already signed a lease. Only 26 reported activity on their land. Because this is such a small proportion of the sample, we chose to use a measure of indirect experience ~friends and family with activity!. 5. New York and Pennsylvania have taken very different policy approaches: New York effectively halted permitting for hydraulic fracturing, pending review of state environmental regulations, whereas Pennsylvania has encouraged development ~see Stedman et al., 2011!.

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Submitted October 1, 2012; revised December 7, 2012; accepted December 18, 2012.

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