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Space-Time Effect Modelling with Machine Learning: A Scalable Approach for Assessing Efficiency of Coal Resources

Resource shortage and environmental degradation have long been the major problems that the current generation is facing, and these problems are even more severe in developing countries, such as China. To address these challenges, being able to utilize existing resources more efficiently is a key. The first step towards efficient use of resources is to design and develop an effective and scalable approach to measure and evaluate resource efficiency. This has been a challenging problem due to a lack of widely accepted standards, as well as the complexity of the data collected by different communities. In this paper, we address this problem using an improved data envelopment analysis (DEA) model of Bootstrap. We leverage machine learning techniques to enhance the scalability, and explore the potentials of predicting future efficiency trend using historical data. Using this approach, we measure the coal resource efficiency of 30 provinces in China from 2000 to 2015. Our results show that the proposed approach can significantly improve the accuracy of the measurement of a traditional Data Envelopment Analysis(DEA) model. In addition, when applying machine learning techniques to the data, the proposed model demonstrates better potentials for scalable and accurate prediction on the coal resource efficiency. Our approach can be easily adopted by various organizations to evaluate the resource efficiency over time, and develop more efficient strategies for resource use based on the measurement data.