Decision support for environmental impact assessment for Malaysian bauxite mining industry using analytic network process /
The mining industry plays a very important and necessary role in the development of our country. However, uncontrolled mining activities caused detrimental environmental impacts. In recent case of bauxite mining in Kuantan, Pahang, fifteen kilometres of Pahang's coastline were stained red with...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
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Kuala Lumpur :
Kulliyyah of Economics and Management Sciences, International Islamic University Malaysia,
2020
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| Subjects: | |
| Online Access: | http://studentrepo.iium.edu.my/handle/123456789/10422 |
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| Summary: | The mining industry plays a very important and necessary role in the development of our country. However, uncontrolled mining activities caused detrimental environmental impacts. In recent case of bauxite mining in Kuantan, Pahang, fifteen kilometres of Pahang's coastline were stained red with arsenic and heavy metal pollution washed from open-pit bauxite mines into the sea. There are potential catastrophic damages to the ecosystem off the coast of Pahang. This triggered Government of Malaysia to issue temporary ban on bauxite mining while the state government is performing an expensive clean-up works. Environmental and socio-economic protection from mining operations entirely depends on Environmental Impact Assessment (EIA) and its enforcement. However, EIA is an intrinsically complex multi-dimensional procedure. There are many dependences among environmental factors. EIA also has subjectivity issues in the decision making process. Many decisions still depend on expert opinion and justification. Due to its complexity, the implementation of EIA is often not entirely satisfactory. A systematic multiple criteria decision making (MCDM) tools is required to assist EIA panel to study interaction among decision variables and convert subjective elements objectively. This research focuses on developing a decision support framework for EIA, specific for bauxite mining operations. Through various literature surveys, this study introduces Analytic network process (ANP), the latest quantitative methodology in MCDM, into the EIA decision support framework. ANP is much more flexible in handling the MCDM problems in which the criteria are interdependent, it has attracted many scholars' attention and has been applied into many different areas. In the first stage of the research, an exploratory study through literature review and semi structured interviews with relevant subject experts (i.e., two experts – EIA Consultants and one Regulator – DOE Officer) was conducted to understand the criteria, element definition, and the influence network. Ten criteria were selected and grouped into clusters according to their common property/attribute. A decision support framework by consists air, water, soil, noise, waste, terrestrial, aquatic, economics, society, and culture were selected and grouped into three main clusters according to their common property/attribute. The second stage involved development and use of questionnaires to determine the pairwise comparisons. Survey questionnaires were pre-tested to determine content validity and the pilot test was conducted using the different group of subject experts (i.e., two experts – EIA Consultants and one Regulator – DOE Officer). The questionnaires findings were obtained from 22 respondents belonging to the six different categories (EIA consultants specialised in the general environment, EIA consultants specialised in socio-economic, EIA consultants specialised in ecology, DOE enforcement officers, academician, and public residing close to the bauxite mining sites. As ANP methodology, 22 sample size was sufficient to gather the required information accurately. The ANP was used to determine the overall weightage and rank of each criterion. During the third stage, the collected data were synthesised using the ANP-SuperDecision software. The ANP analysis ranked air pollution as the first priority at 16.6% followed by water pollution at 15.5%, soil pollution at 14.0%, economic impact at 12.0%, waste generation at 11.6%, terrestrial impact at 8.8%, cultural impact at 7.7%, aquatic impact at 7.2%, society at 3.9 %, and finally noise at 2.8%. Significant environmental impacts produced by bauxite mining operation is identified and synthesised results from ANP-SuperDecision software was used to develop the decision support framework. Further categorical analysis was conducted among six different group of respondents. Some subjectivity issues were detected during the ranking process for individual categories. Respondents from environmental background rated environmental component higher, while those with socio-economic background prioritised economic impact and those with an ecological background focused on the ecological cluster. Nevertheless, selecting representative samples from all categories provided a good model for decision making pertaining to bauxite mining. Finally, the model was tested by three relevant subject experts from first stage of research. The subject experts fill up the environmental pollution/ impact assessment form based on 2015 Bauxite mining condition and data. Results showed that the overall bauxite mining project at Kuantan scored 21.48 point out of 50, hence, representing an overall project score of 41.92%. Based on this result, the decision on bauxite mining in Kuantan should be rejected by DOE. Study concluded that an ANP network model gives a much more realistic view of complex bauxite mining issues. The study contributes to the application of MCDA tools in EIA specific for bauxite mining operations. Further recommendations to reduce the identified significant environmental impact of the bauxite mining activities have been provided. |
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| Item Description: | Abstracts in English and Arabic. "A thesis submitted in fulfilment of the requirement for the degree of Doctor of Philosophy in Business Administration." --On title page. |
| Physical Description: | xx, 216 leaves : colour illustrations ; 30cm. |
| Bibliography: | Includes bibliographical references (leaves 179-193). |