The structure of attitudes towards shale gas extraction in the United Kingdom

Andersson-Hudson, Jessica; Rose, Jonathan; Humphrey, Mathew; Knight, Wil; O’Hara, Sarah L.

doi:10.1016/j.enpol.2019.02.056

Cited by 12 publications

(4 citation statements)

References 20 publications

(20 reference statements)

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“…e results have suggested that information has an important role in regulating responses to shale gas. is factor has implications for how governments approach policies related to shale gas extraction [5]. However, they did not study the intelligent evaluation model of pressure relief gas drainage in goaf nor did they study the effect of intelligent evaluation.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Evaluation Method of Pressure Relief Gas Drainage in Goaf Based on IoT Perception

Cao

2022

Scientific Programming

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The pressure relief gas drainage in goaf is the main control method of mine gas. This paper has been designed to study how to analyze and study the gas drainage of goaf pressure relief based on the perception layer of the Internet of Things. The intelligent evaluation of pressure relief gas drainage in goaf is described. This paper has raised the problem of gas extraction, which is based on the Internet of Things, so it has elaborated on the data-level fusion-related algorithms for sensing coal mine safety, and the case design and analysis of the prediction model and intelligent evaluation have been carried out. Aiming at the problem of intelligent grading of gas drainage evaluation in goaf, data preprocessing is performed on the drainage metering data. Using a deep learning evaluation method based on a convolutional neural network (CNN), an intelligent evaluation model is constructed for gas extraction. Compared with the classification model of the shallow neural network, the CNN classification model is more suitable for gas intelligence evaluation and has higher accuracy due to the good learning ability and accuracy of the deep neural network. When the learning rate is 0.1 and the batch is 256, the prediction effect of the CNN pressure relief gas intelligent classification model is the best, which can effectively provide classification results.

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Section: Related Workmentioning

confidence: 99%

Intelligent Evaluation Method of Pressure Relief Gas Drainage in Goaf Based on IoT Perception

Cao

2022

Scientific Programming

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“…Howell (2018) found that as self-assessed knowledge about hydraulic fracturing decreased, negative views about the technology increased. Moreover, higher levels of actual knowledge also seem to produce more consistent views about support for hydraulic fracturing (e.g., Andersson-Hudson et al 2019). As a result, knowledge of hydraulic fracturing may influence perceived risks.…”

Section: Methodsmentioning

confidence: 99%

The Role of Institutional Trust in Industry, Government, and Regulators in Shaping Perceptions of Risk Associated with Hydraulic Fracturing in the United Kingdom

Stretesky

Short

Stamford

2022

Sociological Perspectives

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This study draws upon concepts of institutional trust and expendability to examine perceptions of risk associated with hydraulic fracturing or “fracking.” To study trust and risk, we collected data from a nationally representative sample of U.K. residents and analyzed it using multivariate regression. Perceptions of trust are measured for the oil and gas industry, central government, local government, and regulators while perceived risks are measured for seismicity, water quality, and hydraulic fracturing in general. Participants with high levels of trust in the oil and gas industry tend to perceive lower levels of risk associated with hydraulic fracturing. Levels of government and regulator trust are, however, largely unrelated to perceived risks. Importantly, trust in the oil and gas industry appears to mediate the relationship between political affiliation and perceptions of risk. Implications for theories of recreancy and environmental justice are explored.

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“…2016 Baptie et al (2016) Unconventional Oil and Gas Development -Understanding and Monitoring Induced Seismic Activity.…”

Section: Tfsg (2015)mentioning

confidence: 99%

Fracking bad language – hydraulic fracturing and earthquake risks

2021

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Abstract. Hydraulic fracturing, or fracking, is a borehole stimulation technique used to enhance permeability in geological resource management, including the extraction of shale gas. The process of hydraulic fracturing can induce seismicity. The potential to induce seismicity is a topic of widespread interest and public concern, particularly in the UK where seismicity induced by hydraulic fracturing has halted shale gas operations and triggered moratoria. Prior to 2018, there seemed to be a disconnect between the conclusions of expert groups about the risk of adverse impacts from hydraulic-fracturing-induced seismicity and the reported level of public concern about hydraulic fracturing induced seismicity. Furthermore, a range of terminology was used to describe the induced seismicity (including tremors, earthquakes, seismic events, and micro-earthquakes) which could indicate the level of perceived risk. Using the UK as a case study, we examine the conclusions of expert-led public-facing reports on the risk (likelihood and impact) of seismicity induced by hydraulic fracturing for shale gas published between 2012 and 2018 and the terminology used in these reports. We compare these to results from studies conducted in the same time period that explored views of the UK public on hydraulic fracturing and seismicity. Furthermore, we surveyed participants at professional and public events on shale gas held throughout 2014 asking the same question that was used in a series of surveys of the UK public in the period 2012–2016, i.e. “do you associate shale gas with earthquakes?”. We asked our participants to provide the reasoning for the answer they gave. By examining the rationale provided for their answers, we find that an apparent polarisation of views amongst experts was actually the result of different interpretations of the language used to describe seismicity. Responses are confounded by the ambiguity of the language around earthquake risk, magnitude, and scale. We find that different terms are used in the survey responses to describe earthquakes, often in an attempt to express the risk (magnitude, shaking, and potential for adverse impact) presented by the earthquake, but that these terms are poorly defined and ambiguous and do not translate into everyday language usage. Such “bad language” around fracking has led to challenges in understanding, perceiving, and communicating risks around hydraulic-fracturing-induced seismicity. We call for multi-method approaches to understand the perceived risks around geoenergy resources and suggest that developing and adopting a shared language framework to describe earthquakes would alleviate miscommunication and misperceptions. Our findings are relevant to any applications that present – or are perceived to present – the risk of induced seismicity. More broadly, our work is relevant to any topics of public interest where language ambiguities muddle risk communication.

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The structure of attitudes towards shale gas extraction in the United Kingdom

Cited by 12 publications

References 20 publications

Intelligent Evaluation Method of Pressure Relief Gas Drainage in Goaf Based on IoT Perception

Intelligent Evaluation Method of Pressure Relief Gas Drainage in Goaf Based on IoT Perception

The Role of Institutional Trust in Industry, Government, and Regulators in Shaping Perceptions of Risk Associated with Hydraulic Fracturing in the United Kingdom

Fracking bad language – hydraulic fracturing and earthquake risks

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