Suchergebnisse

Source at https://doi.org/10.1016/j.jclepro.2017.07.066 . ; With the increase of the concern from the public for environmental pollution and waste of resources, the value recovery through reuse, repair, remanufacturing and recycling from the end-of-use (EOU) and end-of-life (EOL) products have become increasingly important. Reverse logistics is the process for capturing the remaining value from the EOU and EOL products and also for the proper disposal of the non-reusable and non-recyclable parts. A well-designed reverse logistics system will yield both economic and environmental benefits, so the development of an advanced decision-making tool for reverse logistics system design is of significant importance. The paper presents a novel multi-product multi-echelon stochastic programming model with carbon constraint for sustainable reverse logistics design under uncertainty. Compared with the previous stochastic optimization models in reverse logistics system design, which mainly focuses on the expectation of the optimal value, this paper, however, emphasizes on both optimal value expectation and its reliability in decision-making. Due to this reason, a multi-criteria scenario-based risk-averse solution method is developed based on a latest research in order to obtain the optimal solution with high level of confidence. Later in this paper, the application of the model and the augmented solution method is illustrated and the managerial implications are discussed through the numerical experiment and sensitivity analysis. The result of the study shows that the model can be used for providing decision-makers with a deep insight into the relationship between profit and carbon emission requirement, understanding and resolution of the infeasibility caused by capacity limitation, the use of flexible manufacturing system in reverse logistics, and proper use of the government subsidy as a leverage in reverse logistics design.

AbstractIn recent years, the surge in China's CO2emissions has caused increasing international concern. In this paper, we investigate whether and when the turning point in China's CO2emissions would occur. A simple yet powerful neoclassical Green Solow model (GSM) is utilized herein as the main forecasting tool. To verify the capability of this framework to address China's economy, a key prediction of the GSM – the convergence in per capita CO2emissions across Chinese provinces – is empirically verified. By assigning reasonable values to the GSM's key parameters, the trajectories of total CO2emissions are projected for the three regions of China and the whole country. The forecast results show that, under the benchmark scenario, China's total CO2emissions would peak around the year 2047. According to the sensitivity analysis, carbon efficiency is the most important determining factor for whether a turning point in total CO2emissions may occur.

AbstractResearch at the biophysical level constitutes the main approach to study the path through which climate influences infectious diseases, but the influence of socioeconomic factors on climate change and the spread of infectious diseases also cannot be ignored. In the current context, with its emphasis on carbon emission reduction, countries have begun to adopt "total control and trading" methods to reduce greenhouse gas emissions. This paper is based on the monthly relevant data of various provinces and cities from 2015 to 2019, adds the moderating variable of "carbon emission trading volume" based on current research results, establishes a static model of unbalanced panel fixed effects and a dynamic panel model, and deeply analyzes the impact of climate change on infectious disease popularity and the moderating effect of the carbon trading market. The study found that climate warming, frequent precipitation, and shortened sunshine duration will lead to an increase in the number of infectious disease cases and that the "carbon emission trading volume" variable plays a negative role in the positive correlation between climate change (temperature/precipitation) and the number of infectious disease cases. There are seasonal differences, and the moderating effect in summer/autumn is more obvious than that in winter/spring.