Adoption of autonomous mining system in Pakistan – Policy, skillset, awareness and preparedness of stakeholders

• Published in: Resources policy Vol. 68; p. 101796
• Main Authors: Ali, Danish, Ur Rehman, Atta
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.10.2020; Elsevier Science Ltd
• Subjects: Adoption of innovations; Artificial intelligence; Automation; Autonomous mining systems; Curricula; Developed countries; Developing countries; Enforcement; Engineers; Government agencies; Government industry relations; Interest groups; LDCs; Machine learning; Mining; Mining industry; Occupational safety; Pakistan; Process automation; Productivity; Quantitative analysis; Respondents; Skills; Stakeholders; Statistical analysis; Technological change; Technology; Technology adoption; Technology utilization; Unemployment; Workplaces; Pakistan; Process automation; Autonomous mining systems; Pakistan; Artificial intelligence; Unemployment; Machine learning
• ISSN: 0301-4207; 1873-7641
• DOI: 10.1016/j.resourpol.2020.101796

Autonomous mining systems comprise of the latest, innovative, and automated technology designed to improve mine productivity, efficiency, and workplace safety. These automated systems have already been adopted in developed countries like Australia and the United States. In recent years research has emerged to address the awareness and preparedness of stakeholders towards the adaption of Autonomous Mining Systems dedicated towards African countries. The present study is a novel effort in evaluating the current scale of knowledge and skills of mining engineers working in Pakistan's mining industry, government sector, and academia. The study focuses on exploring the awareness about the understanding of the autonomous mining systems, and the level of preparedness of all the stakeholders towards the adoption of this modern technology, along with the resulting social impacts, in a developing country like Pakistan. This paper employed closed and open-ended questionnaires, distributed in electronic formats, to achieve the aforementioned objective. The results were analyzed qualitatively using thematic analysis and quantitatively using results synthesis employing statistical analysis in Python. Substantial evidence has been provided indicating a huge knowledge gap and severe lack of planning and policy enforcement towards the adoption of autonomous mining systems in Pakistan. More than 74% of the respondents, with the majority of them belonging to industry and academia, appeared willing to accept the adoption of autonomous mining systems in Pakistan, due to the resulting improvements in safety and productivity. However, government agencies had varied opinions, with the knowledge gap and the fear of increased unemployment being the major reasons for their resistance to technology adoption. The majority of the graduate mining engineers (60.35%) mentioned the inadequacy of the undergraduate and graduate coursework for providing skills needed to work with autonomous systems. Therefore, it is recommended that the mining curriculum be revised to contain more courses that are relevant to gaining skills in autonomous mining. A government-industry-academia collaboration is also recommended to fund and develop facilities that can help bridge this existing knowledge gap, thus facilitating an early adoption of modern technology in Pakistan's mining sector. •Complete policy revamp required from Pakistan government to facilitate AMS adoption.•Mining engineering curriculum has to be revised to include courses on key subjects.•Existing knowledge gap has to be filled through proper training of professionals.•Restructuring of educational institutes is required to improve learning quality.•Assistance program should be initiated to support potential unemployed workforce.