No abstract

This paper employs a three-stage approach to estimate low carbon economy efficiency in the largest twenty CO2 emitting countries from 2000 to 2012. The approach includes the following three stages: (1) use of a data envelopment analysis (DEA) model with undesirable output to estimate the low carbon economy efficiency and calculate the input and output slacks; (2) use of a stochastic frontier approach to eliminate the impacts of external environment variables on these slacks; (3) re-estimation of the efficiency with adjusted inputs and outputs to reflect the capacity of the government to develop a low carbon economy. The results indicate that the low carbon economy efficiency performances in these countries had worsened during the studied period. The performances in the third stage are larger than that in the first stage. Moreover, in general, low carbon economy efficiency in Annex I countries of the United Nations Framework Convention on Climate Change (UNFCCC) is better than that in Non-Annex I countries. However, the gap of the average efficiency score between Annex I and Non-Annex I countries in the first stage is smaller than that in the third stage. It implies that the external environment variables show greater influence on Non-Annex I countries than that on Annex I countries. These external environment variables should be taken into account in the transnational negotiation of the responsibility of promoting CO2 reductions. Most importantly, the developed countries (mostly in Annex I) should help the developing countries (mostly in Non-Annex I) to reduce carbon emission by opening or expanding the trade, such as encouraging the import and export of the energy-saving and sharing emission reduction technology.

Maximizing the residual value of retired products and reducing process consumption and resource waste are vital for Generalized Growth-oriented Remanufacturing Services (GGRMS). Under the GGRMS, the traditional product-oriented remanufacturing methods need to be changed: the products in GGRMS should be divided into multiple parts and different parts are treated in different ways to maximize residual value. However, this significantly increases the number of remanufacturing service activities and the complexity of the service activities network. Because a service activity may correspond to multiple service resources, the difficulty of service resources allocating significantly increase as the number of service activities under GGRMS increases. To improve the efficiency of resource matching, we proposed to first merge the redundant service activities in the service activity network, and then allocate the corresponding service resources. Therefore, we first used rough-fuzzy number and structural entropy weighting method to perform a coupling analysis on all service activities in the generalized growth scheme set and to merge redundant service activities. We then considered the interests of both the service providers and integrators and added flexible impact factors to establish a service resource optimization configuration model, and solved it with the Non-Dominated Sorting Genetic Algorithm (NSGA-Ⅱ). Finally, we, taking a retired manual gearbox as an experiment, optimized the service resource allocation for its generalized growth scheme set. The experimental results shown that the overall matching efficiency was increased by 74.56% after merging redundant service activities, showing that the proposed method is effective for the resource allocation of the generalized growth for complex single mechanical products, and can offer guidelines to the development of the RMS.

No abstract

No abstract