Abstract
Carbon emissions exceed standards and similar air pollutants is a common and serious environmental problem resulting from global urbanization. One of the more critical steps in the use of fossil fuels is efficiency measurement,and big data resource management is increasingly applied to improve the capability of production optimization methods. Our research constructs a set of data envelopment analysis models for assessment potential environmental risks by considering daily monitoring data about air quality. A numerical example with five decision making units illustrates that the new model is more effective at warning about potential environmental risks considering multiple objectives. Our model is applied to evaluate the environmental risk of 316 Chinese cities during 2020 and 2021, using the daily emission data of Air Quality Index (AQI). The results provide environmental risk indexes for each city for all 731 days by dividing them into four groups to eliminate temporal heterogeneity. Days in which environmental risks occurred in China’s urban areas were more frequent in 2021 than in 2020, possibly reflecting the growth of production scale and resumption of regular operations after COVID-19’s initial impact waned.
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This work was supported by The National Natural Science Foundation of China (Grant Nos. 72071066, 72188101).
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Pan, Y., Zhou, Z. & Wu, J. A multi-objective model for the assessment of potential environmental risk using big data covering air quality. Ann Oper Res (2024). https://doi.org/10.1007/s10479-024-05954-1
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DOI: https://doi.org/10.1007/s10479-024-05954-1