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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Oil is an important energy resource. Fluctuation in international crude oil prices affects all aspects of the economy. The exchange rate is one of the important channels for the international crude oil price shock to pass to the real economy and financial markets. The impact of international crude oil price fluctuation on the exchange rate of oil-importing countries has attracted more and more attention. The US dollar is the main invoice and settlement currency of the international oil market. The change of the US dollar exchange rate will inevitably affect the international crude oil price. By combing the relevant classic literature and recent literature, the relationship between international crude oil prices and the exchange rate of oil-importing countries is discussed. Due to the different methods of use, the relationship between the two is not conclusive. This paper probes into the impact paths of national crude oil price fluctuation on the exchange rate of oil-importing countries, but the influence level depends on the relative degree of each country affected by oil prices, which cannot be generalized. The article summarizes the methods of studying the relationship between oil prices and exchange rates, most of which are studying the causal relationship between the two and their mutual influence, nonlinear structure, and volatility spillover effects. Through reviewing and summarizing the relevant literatures, this paper argues that exploring the inherent laws of oil price volatility buffer under different exchange rate regimes and studying the transmission mode and intensity of oil price fluctuations on exchange rate effects can be the focus of future research. [ABSTRACT FROM AUTHOR]

We analyze and identify the factors that influence coal price fluctuations and construct a system dynamics model of these factors. The simulation results show that the trend in China's future coal prices first declines and then rises, that the trends of coal supply and consumption in China are quite similar, both exhibiting a rising tendency, and that the gap between supply and demand is small and that the market is essentially in equilibrium. However, the increase in the coal industry profit margins also entails an increase in coal prices, and the magnitude of coal price increases also gradually grows. Greater adjustment coefficient factors lead to higher simulated coal price values, and the magnitude of increase also tends to rise. On the one hand, the greater the coal industry policy coefficient is, the closer the simulated coal price is to the value in the real scenario. On the other hand, the smaller the coal industry policy coefficient is, the greater the extent to which the coal price deviates from the real simulated value. [ABSTRACT FROM AUTHOR]