Celebrating Faculty Achievement 2015

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A novel inverse DEA model with application to allocate the CO2 emissions quota to different regions in Chinese manufacturing industries

This paper aims to address the problem of allocating the CO2 emissions quota set by government goal in Chinese manufacturing industries to different Chinese regions. The CO2 emission reduction is conducted in a three-stage phases. The first stage is to obtain the total amount CO2 emission reduction from the Chinese government goal as our total CO2 emission quota to reduce. The second stage is to allocate the reduction quota to different two-digit level manufacturing industries in China. The third stage is to further allocate the reduction quota for each industry into different provinces. A new inverse data envelopment analysis (InvDEA) model is developed to achieve our goal to allocate CO2 emission quota under several assumptions. At last we obtain the empirical results based on the real data from Chinese manufacturing industries

Carbon efficiency evaluation:an analytical framework using fuzzy DEA

Data Envelopment Analysis (DEA) is a powerful analytical technique for measuring the relative efficiency of alternatives based on their inputs and outputs. The alternatives can be in the form of countries who attempt to enhance their productivity and environmental efficiencies concurrently. However, when desirable outputs such as productivity increases, undesirable outputs increase as well (e.g. carbon emissions), thus making the performance evaluation questionable. In addition, traditional environmental efficiency has been typically measured by crisp input and output (desirable and undesirable). However, the input and output data, such as CO2 emissions, in real-world evaluation problems are often imprecise or ambiguous. This paper proposes a DEA-based framework where the input and output data are characterized by symmetrical and asymmetrical fuzzy numbers. The proposed method allows the environmental evaluation to be assessed at different levels of certainty. The validity of the proposed model has been tested and its usefulness is illustrated using two numerical examples. An application of energy efficiency among 23 European Union (EU) member countries is further presented to show the applicability and efficacy of the proposed approach under asymmetric fuzzy numbers

A game theoretic approach to modeling undesirable outputs and efficiency decomposition in data envelopment analysis

The changing economic conditions have challenged many organizations to search for more effective performance measurement methods. Data envelopment analysis (DEA) is a widely used mathematical programming approach for comparing the inputs and outputs of a set of homogeneous decision making units (DMUs) by evaluating their relative efficiency. Performance measurement in the conventional DEA is based on the assumptions that inputs should be minimized and outputs should be maximized. However, there are circumstances in real-world problems where some output variables should be minimized. We consider the concepts of technical efficiency (the ratio of the desirable outputs to inputs) and ecological efficiency (the ratio of the desirable outputs to undesirable outputs) in DEA. We then introduce a new measure called process environmental quality efficiency (the ratio of the inputs to the undesirable outputs) and use game theory to integrate these three different efficiency scores into one overall efficiency score. The cooperative and non-cooperative game theory concepts are used to integrate different efficiency ratios into a linear model. We also present a case study to exhibit the efficacy of the procedures and to demonstrate the applicability of the proposed models

Otimização robusta de portfólios: Avaliação da eficiência sob condições de risco e incerteza na abordagem de estado de baixa do mercado.

O objetivo desta tese é apresentar uma nova proposta para formação de portfólios robustos a partir da análise estocástica de eficiência de ações de empresas negociadas na Bolsa de Valores, Mercadorias e Futuros de São Paulo (BM&FBovespa). Para isto, informações dos ativos em períodos de baixa do mercado (worst state) foram agrupados por meio do agrupamento hierárquico (hierarchical clustering), e então submetidos a uma análise estocástica de eficiência por meio do modelo Chance Constrained Data Envelopment Analysis. Por fim, para se obter a ideal participação de cada ativos, estes foram submetidos a um modelo clássico da alocação de capital. Os portfólios formados com o método proposto foram analisados e comparados a outros formados por diferentes modelos. A utilização em conjunto de tais abordagens abastecidas de informações de pior estado do mercado permitiu a formação de portfólios robustos que apresentaram um maior retorno acumulado no período de validação, resultaram em portfólios com menores valores beta, e ainda permitiram a inserção de variáveis fundamentalistas na formação dos portfólios