Strategic diagnosis of China’s modern coal-to-chemical industry using an integrated SWOT-MCDM framework
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In this study, a novel framework was proposed by incorporating fuzzy MCDM (multi-criteria decision-making) methods into the SWOT (strength–weakness–opportunity–threat) analysis for diagnosing China’s modern CTC (coal-to-chemical) industry. In the framework, the SWOT analysis was employed to systematically identify the critical factors and then formulate the strategies for promoting the development of this industry. Subsequently, the weights of the factors were accurately determined by using the fuzzy DANP method (decision-making trial and evaluation laboratory-based analytic network process), which tackles the uncertainty in the subjective judgments and the interrelationships among the affecting factors, while the sequence of the strategies was rigorously determined by developing a fusion approach, which reconciles the conflicting rankings derived from four fuzzy MCDM methods for offering a compromised decision. The obtained results were confirmed by performing the sensitivity analysis, which provides two key explanations for the China’s CTC industry. First, two factors from the opportunity perspective, i.e., “clean utilization of coal” and “energy transformation,” and a threat factor of “policy uncertainty” were identified as the most critical factors among the twelve candidates, demonstrating that the external factors (opportunities and threats) play more important roles than the internal drivers (strengths and weaknesses) in affecting the current status of China’s CTC industry. Second, two competitive strategies including drafting national development plan and preferential development of demonstration projects are more favored than the other six strategies, implying that the measures by taking advantage of the strengths to avoid the threats could be effective to promote the development of CTC in China.
KeywordsChina’s coal-to-chemical industry SWOT analysis Fuzzy multi-criteria decision making Factors prioritization Strategic recommendation
This work is supported by the National Science Foundation of China (21776025).
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