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

Greenhouse Gas Emission Efficiencies of World Countries

Greenhouse gas emissions have increased rapidly since the industrial revolution. This has led to an unnatural increase in the global surface temperature, and to other changes in our environment. Acknowledging this observation, the United Nations Framework Convention on Climate Change started an international environmental treaty. This treaty was extended by Kyoto protocol, which was adopted on 11 December 1997. Using the stochastic frontier analysis, we analyze the efficiencies of countries in terms of achieving the lowest greenhouse gas emission levels per GDP output in the years between 1990–2015. We find that the average greenhouse gas emission efficiencies of world countries for the time periods 1990–1997, 1998–2007, 2008–2012, and 2013–2015 are 82.40%, 90.37%, 89.54%, and 84.81%, respectively. Moreover, compared to the 1990–1997 period, 92.50%, 79.51%, and 59.84% of the countries improved their greenhouse gas emission efficiencies in the 1998–2007, 2008–2012, and 2013–2015 periods, respectively. Hence, the Kyoto protocol helped in increasing greenhouse emission efficiency. However, this efficiency-boosting effect faded away over time

Greenhouse Gas Emission Inefficiency Spillover Effects in European Countries

In our study, we examine whether spatial spillover effects exist for greenhouse gas emission efficiency for 38 European countries between 2005 and 2014. We find that inefficiencies of other countries would lead to lower efficiency levels for a country. This negative inefficiency spillover effect goes down till 2008 then goes up till 2011, then stays relatively stable after 2011. Any strategy to reduce inefficiencies of other countries could potentially improve the efficiency levels. We find that human development index shows significant positive impact on greenhouse gas emission efficiency levels. In particular, one standard deviation increase in human development index would lead to a 11.12 percentage points increase in the greenhouse gas emission efficiencies on average. Different countries show different efficiency levels and efficiency growth patterns over time. However, the pattern of spatial spillover is quite similar among all countries over time

A model for sector restructuring through genetic algorithm and inverse DEA

[EN] The aim of this study is to devise a sector restructuring model in which all the decision making units (DMUs) satisfy a predefined global efficiency level. The proposal makes several realistic assumptions regarding the merging of DMUs under specific circumstances. The model computes the global efficiency target by giving preference to merging DMUs over saving inputs, hence considering that the affected stakeholders may be resistant to restructuring, and this resistance may have overall negative effects on the image and reputation of the companies and organizations. In addition, the number of constituents in the new entities can be limited by the decision maker after the restructuring process, so that the model also considers a constraint on cardinality. The proposal combines the inverse data envelopment analysis (InvDEA), which computes the merger's input savings, and the genetic algorithm (GA), which solves the combinatorial problem of identifying the merging units. 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