A systematic quality assessment of Environmental Impact Statements in the oil and gas industry

The global economy relies heavily on oil and gas resources. However, hydrocarbon exploitation projects can cause significant impacts on the environment. But despite the production of numerous Environmental Impact Statements (EISs) to identify/mitigate such impacts, no study has specifically assessed the quality of EISs for both onshore and offshore oil and gas projects, with tested hypotheses. To address this research gap, our paper, for the first time, develops a modified Lee and Colley evaluation model to assess the quality of 19 sampled oil and gas project EISs produced from 1998 to 2008 in Nigeria. Our findings show that Project Description and Communication of Results are the main areas of strength. However, Environmental Impact Prediction, and Project Decommissioning, were among the key areas requiring attention. A key finding, though, is that Mann-Whitney tests suggest that there is no evidence that the quality of EISs for the latter period (2004–2008) is higher than that of the earlier period (1998–2004). We suggest that periodic systematic review of the quality of submitted/approved EISs (c. every 3–5 years) should be established to monitor trends in EIS quality and identify strong and weak areas. This would help to drive continual improvement in both the EIA processes and the resultant EISs of technical engineering projects. Such reviews have the potential to illuminate some of the underlying problems of, and solutions to, oil and gas exploration, production and transportation, and their related environmental impacts. This suggested change would also be useful internationally, including for the burgeoning exploration and production of unconventional hydrocarbon resources.Publisher Statement: NOTICE: this is the author’s version of a work that was accepted for publication in Science of The Total Environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Science of The Total Environment, [572, (2016)] DOI: 10.1016/j.scitotenv.2016.07.083© 2016, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0

Appraisal of local authority development plans

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A distributed model for dynamic optimisation of networks

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Natural Language Processing through the Subtractive Mountain Clustering Algorithm - A Medication Intake Chatbot

In this work, the subtractive mountain clustering algorithm has been adapted to the problem of natural languages processing in view to construct a chatbot that answers questions posed by the user. The implemented algorithm version allosws for the association of a set of words into clusters. After finding the centre of every cluster — the most relevant word, all the others are aggregated according to a defined metric adapted to the language processing realm. All the relevant stored information (necessary to answer the questions) is processed, as well as the questions, by the algorithm. The correct processing of the text enables the chatbot to produce answers that relate to the posed queries. Since we have in view a chatbot to help elder people with medication, to validate the method, we use the package insert of a drug as the available information and formulate associated questions. Errors in medication intake among elderly people are very common. One of the main causes for this is their loss of ability to retain information. The high amount of medicine intake required by the advanced age is another limiting factor. Thence, the design of an interactive aid system, preferably using natural language, to help the older population with medication is in demand. A chatbot based on a subtractive cluster algorithm is the chosen solution.</jats:p