How to control industrial air pollution is an urgent policy problem be solved. Through efficiency evaluation, we can test the implementation effect of policy, provide decision support for development. China's provincial system consists production stage and emission reduction stage. Taking number waste gas treatment facilities as a carry-over variable (i.e., connecting multiple periods), this st...

A range of slack vegetation in southern New Zealand was described by detailed sampling of four dune slacks, contrasting in topographic situation and in vegetation. Comparison is made with a slack previously sampled on Stewart Island. The five slacks differed markedly in the plant communities present. One slack, where there was considerable peat accumulation, was dominated by the megaherb Phormi...

This study analyzes the impact of international trade on environmental efficiency. Using a data envelopment analysis (DEA) model that allows us to treat undesirable outputs and super-efficiency beyond unity, we measure the environmental efficiency of four typical air pollutants—SO2, NOx, particulate matter 10 μm or less in diameter, and CO2—for 98 countries for the period 1970–2008. The resulti...

Abstract We introduce a new method for the estimation of production technologies in multi-input multi-output context, based on OneClass Support Vector Machines with piecewise linear transformation mapping. compare via finite-sample simulation study technique Data Envelopment Analysis (DEA) to estimate technical efficiency. The criteria adopted measuring performance estimators are bias and mean ...

The current paper proposes a slack-based version of the Super SBM, which is an alternative superefficiency model for the SBM proposed by Tone. Our two-stage approach provides the same superefficiency score as that obtained by the Super SBM model when the evaluated DMU is efficient and yields the same efficiency score as that obtained by the SBM model when the evaluated DMU is inefficient. The p...

This paper is concerned with Data Envelopment Analysis of systems with Natural Negative Inputs, and Natural Negative Outputs, i.e. inputs and outputs that, by their nature less than zero. Examples of situations with only positive inputs and positive and natural negative outputs are given and of situations in which both natural negative inputs and natural negative outputs occur. More attention h...

Data envelopment analysis (DEA) is non-parametric linear programming (LP) based technique for estimating the relative efficiency of different decision making units (DMUs) assessing the homogeneous type of multiple-inputs and multiple-outputs. The procedure does not require a priori knowledge of weights, while the main concern of this non-parametric technique is to estimate the optimal weights o...

In the majority of production processes, noticeable amounts of bad byproducts or bad outputs are produced. The negative effects of the bad outputs on efficiency cannot be handled by the standard Malmquist index to measure productivity change over time. Toward this end, the Malmquist–Luenberger index (MLI) has been introduced, when undesirable outputs are present. In this paper, we introduce a D...

Efficiency reflects a scenario of higher quality product with fewer resource inputs and less pollution discharge. In terms of a manufacturing firm, it means a win-win strategy in both economic and environmental categories. The paper extends the Electric Arc Furnace (EAF) activity management by taking pollution discharge as undesirable outputs. After reviewing relative undesirable outputs DEA li...

Problem statement: Data Envelopment Analysis (DEA) is a non-parametric technique for measuring the relative efficiency of a set of production systems or Decision Making Units (DMU) that have multiple inputs and outputs. Sometimes, DMUs have a parallel structure, in which systems composed of parallel units work individually; the sum of their own inputs/outputs is the input/output of the system. ...