A decision-support system for recycling of residents' waste plastics in China based on material flow analysis and life cycle assessment.

Recycling waste plastics is one of the important ways to save petroleum resources and reduce carbon emissions. However, the current recycling rate of waste plastics is still low. Material flow analysis can help determine the flow of waste plastics, and life cycle assessment (LCA) can be used to quantify environmental impacts. The present study integrates these two methods into the model construction of the residents' waste plastics recycling decision-support system. This model construction is followed by sensitivity analysis of the relevant parameters affecting the performance of the waste plastics recycling system. Finally, the present study forecasts the recycling system's performance and environmental impacts by setting four optimization scenarios based on sensitivity analysis. The results show that in 2019, a total of 8.39 million tons of high-end applications were recovered, carbon emissions during the recycling process were 34.9 million tons, and dioxin emissions were 316.11 g TEQ, with a total emission reduction of 24.47 million tons of CO2 compared to the original production. Sensitivity analysis shows that the selection rate of waste plastic recycling, the re-sorting rate of waste plastic recycling plant, and the classification recovery rate of mixed waste had relatively high effects on the recovery performance and environmental benefits of the recycling system. In the scenario of comprehensive improvement, in 2035, the recycling volume of high-end applications will rise to 33.96 million tons, the carbon emissions will rise to 64.73 million tons, the dioxin emissions will drop to 165.98 g TEQ, and the carbon emission reduction will rise to 99.06 million tons. This study has a certain guiding role for policy-makers to formulate industry norms and related policies for waste plastic recycling.

Carbon emission structure decomposition analysis of manufacturing industry from the perspective of input-output subsystem: a case study of China.

In China, manufacturing is the industry that consumes the most energy and emits the most carbon, and the effect of emission reduction on the process of reaching carbon peaking and carbon neutrality is decisive. The existing research on the driving factors of manufacturing carbon emissions has not analyzed the specific structural characteristics of manufacturing carbon emissions from the perspective of industrial relevance, and little attention has been paid to the discussion of carbon emission reduction paths of different manufacturing sectors from the perspective of final demand. This study examines the direct carbon emissions and carbon emissions from final demand in China's manufacturing sector, and decomposes the carbon emissions from final demand into six distinct components using input-output analysis. In addition, this study examines the carbon emission path in manufacturing production activities, as well as the carbon emission reduction potential and scenario prediction of the factors influencing manufacturing carbon emissions. In 2018, the direct carbon emissions and carbon emissions from final demand were approximately 4.61 billion tons and 3.50 billion tons, respectively. Meanwhile, direct and indirect spillovers accounted for 62.1% and 23.1% of carbon emissions from final demand, respectively. Using the carbon emission transfer route map of the manufacturing industry, the direction and amount of carbon emission transfer from various energy sources can be accurately determined. The CR scenario predicts that the manufacturing industry will reach its carbon peak between 2025 and 2030, with a corresponding peak between 4.02 and 4.06 billion tons, and that carbon emissions in 2060 will be 40% lower than in 2018.