A benchmark-based low carbon performance evaluation and improvement approach for beverage production

•This paper develops the process map based estimation method of the carbon emissions for beverage production.•The overall low carbon performance evaluation model of beverage production lines finds the non-benchmark production lines.•The evaluation and improvement models of the carbon emissions detect the hot spots of high carbon emissions.•The decreasing quantity of the carbon footprint of each non-benchmark machine is given. Year by year the concentration of greenhouse gases keeps going up to facilitate the global warming problem. To mitigate environmental impacts, low carbon manufacturing has attracted much attention worldwide. Hence, this study applies the process map and data envelopment analysis to develop a benchmark-based low carbon performance evaluation and improvement approach for beverage production. This research firstly uses the process map to illustrate the detail process and activities of beverage production. Next, this study analyzes the electricity consumption of each machine to estimate the carbon emissions. Subsequently, from the macro-level perspective, the overall low carbon performance evaluation model of beverage production lines is proposed to evaluate the low carbon performance values of beverage production lines so as to find the non-benchmark low carbon beverage production lines. To improve the low carbon performance of non-benchmark beverage production lines, this research develops the evaluation and improvement models of the carbon emissions of the workstations for beverage production lines to appraise the low carbon performance of each workstation for non-benchmark beverage production lines and then to find the non-benchmark low carbon workstations and further analyze the decreasing quantity of the carbon footprint of each machine for these non-benchmark workstations. Finally, this research uses the beverage production in Taiwan as an example to explain and verify the significant practical values and contributions of the proposed methodology.