Monitoring and Comparing Air and Green House Gases Emissions of Various Countries

Shafqat, Ambreen; Sabir, Qurat ul An; Yang, Su‐Fen; Aslam, Muhammad; Albassam, Mohammed; Abbas, Khawar

doi:10.1007/s13253-023-00560-3

Cited by 3 publications

(1 citation statement)

References 36 publications

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“…So far, few papers have discussed developing or utilizing control charts to offer sustainability solutions. For example, Anderson et al [5] applied multivariate control charts to monitor ecological and environmental measurement indices; Morrison [6] used control charts to interpret and monitor environmental data; Gove et al [7] adopted control charts to catch water supply in south-west Western Australia; Oliveira da Silva et al [8] constructed control charts to help in stability and reliability of water quality; Shafqat et al [9] provided triple EWMA mean control chart to monitor and compare Air and Green House Gases Emissions of various countries and identified the critical countries. Control charts serve as effective tools in process control, aiming to enhance the quality and yield of products/parts while reducing scrap/waste of raw materials, minimizing carbon and water footprints, and increasing profits/eco-efficiency and energy efficiency of products.…”

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confidence: 99%

A New EWMA Control Chart for Monitoring Multinomial Proportions

Gan,

Yang,

Chen

2023

Sustainability

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Control charts have been widely used for monitoring process quality in manufacturing and have played an important role in triggering a signal in time when detecting a change in process quality. Many control charts in literature assume that the in-control distribution of the univariate or multivariate process data is continuous. This research develops two exponentially weighted moving average (EWMA) proportion control charts to monitor a process with multinomial proportions under large and small sample sizes, respectively. For a large sample size, the charting statistic depends on the well-known Pearson’s chi-square statistic, and the control limit of the EWMA proportion chart is determined by an asymptotical chi-square distribution. For a small sample size, we derive the exact mean and variance of the Pearson’s chi-square statistic. Hence, the exact EWMA proportion chart is determined. The proportion chart can also be applied to monitor the distribution-free continuous multivariate process as long as each categorical proportion associated with specification limits of each quality variable is known or estimated. Lastly, we examine simulation studies and real data analysis to conduct the detection performance of the proposed EWMA proportion chart.

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