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One problem raised by the lack of energy efficiency is the generation of more greenhouse gases (GHGs) that can cause air pollution and climate change. Ecological efficiency (eco-efficiency) means the efficiency of resources used. A poor performance from this efficiency can then be detected for further improvement. In this research, we conduct an assessment on the eco-efficiency for some European countries as they consume a large part of global energy annually. A total of 17 European countries were selected as decision making units (DMUs) and assessed by the Slacks-based measure (SBM) Data Envelopment Analysis (DEA) model. Indices including Catch-Up, Frontier-Shift, and Malmquist Productivity Index (MPI) have been used to evaluate eco-efficiency, as well as efficiency change, technological change, and productivity change, over 2013-2017. In the model, energy consumption and share of renewable energy are used as energy inputs, and labor productivity and gross capital formation are used as economy inputs. On the other hand, GDP is used as a desired output, and CO 2 emissions is used as one undesired output. The experimental results show that the 17 countries as a whole lacked eco-efficiency in 2013-2017, implying more efforts are required to improve their eco-efficiency.Eco-efficiency is measurable based on selected input and output variables. For example, the authors of reference [2] measured the co-efficiency as the ratio between the value of what has been produced (e.g., GDP) and the environment impacts of the product or service (e.g., SO 2 emissions). The purpose of eco-efficiency measurement is to best utilize inputs such as energy, water, soil, and raw materials so as to maximize the benefit to human beings [4] or minimize bad outputs such as pollution and CO 2 , which can damage our environment. For every country in the world, improving eco-efficiency is necessary, as the protection of the environment is everyone's responsibility. Problem DefinitionOne problem raised by the lack of eco-efficiency is the generation of more greenhouse gases (GHGs), which can further lead to the global warming effect. The global warming effect, in turn, can lead to climate change, which might result in disasters and threaten the lives of human beings. International organizations have developed some programs to deal with the global warming effect. For example, the Paris Agreement initiated at the COP21 meeting held in December 2015 has set an objective to keep the rise in average global temperature below 2 • C [5]. The achievement of this difficult objective requires collective efforts from all countries. To achieve this objective, eco-efficiency is considered one of the effective approaches [6,7]. This is why the assessment of eco-efficiency has become the focus of this research. Though every country in the world needs to improve eco-efficiency, in this research, we focused on European countries, as they consume a lot of global energy annually. In fact, energy consumption, especially the use of fossil fuels, is one of the main s...

Abstract:Finding the right strategic alliance partner is a critical success factor for many enterprises. Therefore, the purpose of this study is to propose an effective approach based on grey theory and data envelopment analysis (DEA) for selecting better partners for alliance. This study used grey forecasting to predict future business performances and used DEA for the partner selection of alliances. This research was implemented with realistic public data in four consecutive financial years (2009)(2010)(2011)(2012) of the world's 20 biggest automobile enterprises. Nissan Motor Co., Ltd was set to be the target decision making unit (DMU). The empirical results showed that, among 19 candidate DMUs, Renault (DMU10) and Daimler (DMU11) were the two feasible beneficial alliance partners for Nissan. Although this research is specifically applied to the automobile industry, the proposed method could also be applied to other manufacturing industries.

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With the expansion of its industrial and manufacturing sectors, with the goal of positioning Vietnam as the world’s new production hub, Vietnam is forecast to face a surge in energy demand. Today, the main source of energy of Vietnam is fossil fuels, which are not environmentally friendly and are rapidly depleting. The speed of extraction and consumption of fossil fuels is too fast, causing them to become increasingly scarce and gradually depleted. Renewable energy options, such as solar, wind, hydro electrical, and biomass, can be considered as sustainable alternatives to fossil fuels. However, to ensure the effectiveness of renewable energy development initiatives, technological, economic, and environmental must be taken in consideration when choosing a suitable renewable energy resource. In this research, the authors present a multi-criteria decision-making model (MCDM) implementing the grey analytic hierarchy process (G-AHP) method and the weighted aggregates sum product assessment (WASPAS) method for the selection of optimal renewable energy sources for the energy sector of Vietnam. The results of the proposed model have determined that solar energy is the optimal source of renewable energy with a performance score of 0.8822, followed by wind (0.8766), biomass (0.8488), and solid waste energy (0.8135) based on the calculations of the aforementioned methods.