Improvement restriction data envelopment analysis for new energy in Japan

Japan is faced with "the Fukushima' problem," in which a single nuclear accident has led to drastic electrical power shortages. Owing to the strong backlash of public opinion, almost all of Japan's 54 nuclear plants suspended operations. An intensive search has started for alternative forms of energy, ranging from fossil fuels to new energy, such as solar, wind, geothermal, small-scale hydroelectric and biomass energy. There is no clear-cut direction for energy policy, as each option involves costs and CO2 consequences and Japan has even withdrawn from the Kyoto protocol. A policy that balances energy and the environment is difficult to achieve in the short term; therefore, there is an urgent need for a comprehensive efficiency analysis of new energy in Japan. A popular tool for judging the efficiency of a Decision Making Unit (DMU) is Data Envelopment Analysis (DEA). The development of multiple efficiency improvement solutions based on DEA has progressed in recent years. An example is the Distance Friction Minimisation (DFM) method, based on a generalised distance function, which serves to improve a DMU's performance by tracing the most appropriate movement towards the efficiency frontier. To produce a more realistic improvement plan for low efficiency DMUs, we proposed a Target-Oriented (TO) DFM model that allows reference points that remain below the efficiency frontier. TO-DFM model specifies a Target-Efficiency Score (TES) for inefficient DMUs. This model is able to compute an improvement projection that an input reduction value and an output increase value in order to achieve a TES, even though in reality these values may have an infeasible case, for example Net-Working Rate may be required more than 100% in improvement projection, but it exceed a physical limit. This paper aims to present a newly developed adjusted DEA model, emerging from a blend of the TO-DFM and the Improvement Restriction (IR) approach, for generating an appropriate efficiency-improving projection model. The IR approach specifies a restriction input/output items based on absence or presence of the DMU's improvement limit. This approach can compute an input reduction value and an output increase value in order to achieve a TES that maintains an improvement restriction. The above-mentioned Improvement Restriction TO-DFM model will be applied to an efficiency analysis and will produce a realistic efficiency-improvement projection for new energies in Japan

A Stepwise Efficiency Improvement DEA Model for Airport Operations with Fixed Production Factors

In the spirit of the deregulation movement, Japan is also faced with an ÃgAsia Open SkyÃh agreement which favours aviation liberalization in international services. This means an end to Japan's aviation policy of isolation. In association with this policy change, also environmental concerns grew increasingly severe for small and local regional airports. Consequently, there is a need for an objective analysis of the efficiency of airport operations in Japan. A standard tool to judge the efficiency of such activities is Data Envelopment Analysis (DEA). In the past years, much progress has been made to extend this approach in various directions. Interesting examples are the Distance Friction Minimization (DFM) model and the Context-Dependent (CD) model. The DFM model is based on a generalized distance friction function and serves to improve the performance of a Decision Making Unit (DMU) by identifying the most appropriate movement towards the efficiency frontier surface. Standard DEA models use a uniform input reduction in the improvement projections, but the DFM approach aims to enhance efficiency strategies by introducing a weighted projection function. This approach may address both input reduction and output increase as a strategy of a DMU. Likewise, the CD model yields efficient frontiers at different levels, while it is based on a level-by-level improvement projection. The Stepwise DFM model is an integration of the DFM and the CD model in order to design a stepwise efficiency-improving projection model for a conventional DEA. In general, a DEA model – and neither the mix of the DFM-CD model – doesnÃft take into account a fixed factor. Such a non-controllable of fixed factor may refer to a production factor that cannot be flexibly adjusted in the short run. In our study the newly integrated Stepwise DFM-CD model will be extended with a fixed factor model in order to adapt the DEA model to realistic circumstances in an efficiency improvement projection. The above-mentioned stepwise fixed factor projection model is illustrated on the basis of an application to the efficiency analysis of airport operations in Japan in light of the above mentioned contextual changes in aviation policy.

A Stepwise Efficiency Improvement DEA Model for Airport Operations with Fixed Production Factors

In the spirit of the deregulation movement, Japan is also faced with an gAsia Open Skyh agreement which favours aviation liberalization in international services. This means an end to Japan's aviation policy of isolation. In association with this policy change, also environmental concerns grew increasingly severe for small and local regional airports. Consequently, there is a need for an objective analysis of the efficiency of airport operations in Japan. A standard tool to judge the efficiency of such activities is Data Envelopment Analysis (DEA). In the past years, much progress has been made to extend this approach in various directions. Interesting examples are the Distance Friction Minimization (DFM) model and the Context-Dependent (CD) model. The DFM model is based on a generalized distance friction function and serves to improve the performance of a Decision Making Unit (DMU) by identifying the most appropriate movement towards the efficiency frontier surface. Standard DEA models use a uniform input reduction in the improvement projections, but the DFM approach aims to enhance efficiency strategies by introducing a weighted projection function. This approach may address both input reduction and output increase as a strategy of a DMU. Likewise, the CD model yields efficient frontiers at different levels, while it is based on a level-by-level improvement projection. The Stepwise DFM model is an integration of the DFM and the CD model in order to design a stepwise efficiency-improving projection model for a conventional DEA. In general, a DEA model - and neither the mix of the DFM-CD model - doesnft take into account a fixed factor. Such a non-controllable of fixed factor may refer to a production factor that cannot be flexibly adjusted in the short run. In our study the newly integrated Stepwise DFM-CD model will be extended with a fixed factor model in order to adapt the DEA model to realistic circumstances in an efficiency improvement projection. The above-mentioned stepwise fixed factor projection model is illustrated on the basis of an application to the efficiency analysis of airport operations in Japan in light of the above mentioned contextual changes in aviation policy

An evaluation of energy-environment-economic efficiency for EU, APEC and ASEAN countries

This paper aims to offer an advanced assessment methodology for sustainable national energy-environment-economic efficiency strategies, based on an extended Data Envelopment Analysis (DEA) in which distinct countries are regarded as Decision Making Units (DMUs). The aim is to show how much various countries can improve their combined efficiency profile. Standard DEA models use a uniform input reduction or a uniform output increase in their improvement projections. The development of novel efficiency-improvement solutions based on DEA has greatly progressed in recent years. A recent example is the Distance Friction Minimisation (DFM) method, which aims to generate an original contribution to efficiency-enhancement strategies by deploying a weighted projection function, while it may address both input reduction and output increase as a strategy of a DMU. To design a feasible improvement strategy for low-efficiency DMUs, we develop a Target-Oriented (TO) DFM model that allows for less ambitious reference points that remain below the efficiency frontier. The TO-DFM model calculates then a Target-Efficiency Score (TES) for inefficient DMUs. This model is able to compute an input reduction value and an output increase value in order to achieve this TES. However, in many real-world cases the input factor may not be immediately flexible or adjustable, due to indivisibility (or lumpiness) of the input factor. Usually, a DEA model does not include such a non-controllable or a fixed factor. In this study, we aim to integrate the TO-DFM model with a fixed factor (FF) model in order to cope with realistic circumstances in our search for an efficiency improvement projection in combined energy-environment-economic strategies of individual nations. The present paper aims to offer an original contribution to efficiency enhancement in national sustainability strategies by means of the above described DEA approach. After the description of the methodology, a complementary Super-efficiency (SE) approach to DEA is used in our comparative study on the efficiency assessment of energy-environment-economic targets for the EU, APEC and ASEAN (A&A) countries, using appropriate data sets ranging from the years 2003 to 2012. In the present study, we consider two inputs (primary energy consumption and population) and two outputs (CO2 and GDP), including a fixed input factor, namely the ?population? production factor that cannot be flexibly adjusted. On the basis of our DEA analysis results, it appears that EU countries exhibit generally a higher efficiency than A&A countries. In particular, it turns out that Cyprus, Luxembourg and Ireland may be seen as super-efficient countries in the EU, and Brunei as a high performance country in A&A. The above-mentioned TO-DFM-FF projection model is used to address realistic circumstances and requirements in an operational sustainability strategy for efficiency improvement in inefficient countries in the A&A region

A Generalized Goals-achievement Model in Data Envelopment Analysis

Data Envelopment Analysis (DEA) has become an established tool in comparative analyses of efficiency strategies in both the public and the private sector. The aim of this paper is to present and apply a newly developed, adjusted DEA model – emerging from a blend of a Distance Friction Minimization (DFM) and a Goals Achievement (GA) approach on the basis of the Charnes-Cooper-Rhodes (CCR) method – in order to generate a more satisfactory efficiency-improving projection model in conventional DEA. Our DFM model is based on a generalized Euclidean distance minimization and serves to assist a Decision Making Unit (DMU) in improving its performance by the most appropriate movement towards the efficiency frontier surface. Our DFM approach aims to generate a new contribution to efficiency enhancement strategies by deploying a weighted projection function. In addition, it may address both input reduction and output increase as a strategy of a DMU. The GA model can compute the input reduction value or the output increase value in order to achieve a pre-specified goal value for the efficiency improvement in an optimal way. The above-mentioned DFM-GA model is illustrated empirically by using a data set of efficiency indicators for cities in Hokkaido prefecture in Japan, where the aim is to increase the efficiency of local government finance mechanisms in these cities, based on various input and output performance characteristics

A preference allocation-DFM model in Data Envelopment Analysis -An application to Energy-Environment-Economic efficiency in Japan-

Japan is faced with a gFukushimaf problem, meaning a nuclear accident leading to electrical power shortage. This problem relates to a non-balanced gEnergy-Environment-Economich policy which does not, but should incorporate gelectrical power savingh, glow carbon emissionh, and geconomic growthh. Although it is difficult at this stage, it is necessary to make an effort to achieve more balanced and more efficient gEnergy-Environment-Economich policy in Japan, even if Japan decides to withdraw from the COP (Conference of Parties of United Nations Conventions) 17. A standard tool to judge the efficiency of actors (decision making units) is Data Envelopment Analysis (DEA). The existence of many possible efficiency improvement solutions has in recent years prompted a -rich variety of literature on the methodological integration of the MOLP (Multiple Objective Linear Programming) and the DEA models. In the past years, much progress has been made to extend this approach in several directions. An example is the Distance Friction Minimization (DFM) method. The DFM model is based on a generalized distance friction function and serves to improve the performance of a Decision Making Unit (DMU) by identifying the most appropriate movement towards the efficiency frontier surface. Standard DEA models use a uniform proportional input reduction (or a uniform proportional output increase) in the improvement projections, but the DFM approach aims to enhance efficiency strategies by introducing a weighted projection function. This approach may address both input reduction and output increase as a strategy of a DMU. An advantage of this model is that there is no need to incorporate the value judgment of a decision maker. Nevertheless, in order to achieve efficiency improvement in Japanfs gEnergy-Environment-Economich policy at a regional level, it might be necessary to incorporate a value judgment of a policy maker on political priorities. In our study, we present a newly developed Preference Allocation model in DFM, which is suitable to incorporate a decision makerfs value judgment for the allocation of an input reduction and an output augmentation in an efficiency improvement projection. The above-mentioned Preference Allocation model is illustrated on the basis of an application to the efficiency analysis of gEnergy-Environment-Economich for each prefecture in Japan

Effective Clusters as Territorial Performance Engines in a Regional Development Strategy - A Triple-Layer DEA Assessment of the Aviation Valley in Poland

Regional development policy aims to cope with the challenge of spatial disparities. It is based on a smart combination of various critical capital assets in a region which functionally and spatially interact and which yield synergetic economic opportunities and promising challenges for innovation and progress. The present study regards sustainable territorial performance – as a manifestation of regional development – as the overarching principle for competitive advantages and economic growth in a system of regions, which is particularly induced by territorial capital, comprising human capital, infrastructural capital and social capital. In the long-standing tradition of regional development policy a wide variety of effective facilitators or drivers of accelerated spatial growth has been distinguished, for instance, industrial districts, growth poles, growth centers, industrial complexes, special economic zones, communication axes, and so forth. In the past decades, a new concept has been introduced, viz. economic-technological clusters. An avalanche of literature has been published on the conceptual, operational and policy foundation and relevance of this concept, especially in relation to previously developed regional growth concepts. In this paper, clusters will be regarded as the spatial foci of sustainable territorial performance strategies and synergetic actions by both public and private actors. The present paper aims to address the relevance of cluster concepts for an effective regional development policy, based on the above notion of territorial capital. It does so by introducing a new concept, viz. effective cluster, in which spatial-economic synergy, local/regional concentration of industry, and the supporting role of territorial capital are regarded as the main determinants of a highly performing cluster in a given territory. The effective cluster concept will be tested on the basis of a field study on the aviation and aerospace cluster ‘Dolina Lotnicza’ in the Podkarpackie region in South-East Poland. This is one of the most vibrant high-tech clusters in the country. A new approach based on a triple-layer architecture will be adopted here, viz.: a quantitative comparative analysis of the 16 Polish ‘voivodships’ (main administrative regions in the country, at a NUTS-2 level), a benchmark analysis of the 25 counties (‘powiats’) within the Podkarpackie voivodship (at a NUTS-4 level), and an effective industrial cluster analysis on the basis of the individual aviation firms located in the Podkarpackie region. In each step an extended Data Envelopment Analysis (DEA), characterised by a merger of a Slack-Based Measure (SMB) and a super-efficiency (SE) DEA, will be used in order to achieve an unambiguous ranking of the various regions or Decision Making Units (DMUs). The study will employ an extensive database on individual actors in the cluster, in combination with a broadly composed territorial-capital database for the areas under study. The paper will be concluded with some strategic policy lessons

Comparitive Performance Analysis of European Airports by Means of Extended Data Envelopment Analysis

Data Envelopment Analysis (DEA) has become an established approach for analyzing and comparing efficiency results of corporate organizations or economic agents. It has also found wide application in comparative studies on airport efficiency. The standard DEA approach to comparative airport efficiency analysis has two feeble elements, viz. a methodological and a substantive weakness. The methodological weakness originates from the choice of uniform efficiency improvement assessment, while the substantive weakness in airport efficiency analysis concerns the insufficient attention for short-term and long-term adjustment possibilities in the production inputs determining airport efficiency. The present paper aims to address both flaws by: (i) designing a data-instigated Distance Friction Minimization (DFM) model as a generalization of the standard Banker-Charnes-Cooper (BCC) model with a view to the development of a more appropriate efficiency improvement projection model in the BCC version of DEA; (ii) including as factor inputs also lumpy or rigid factors that are characterized by short-term indivisibility or inertia (and hence not suitable for short-run flexible adjustment in new efficiency stages), as is the case for runways of airports. This so-called fixed factor (FF) case will be included in the DFM submodel of DEA. This extended DEA – with a DFM and an FF component – will be applied to a comparative performance analysis of several major airports in Europe. Finally, our comparative study on airport efficiency analysis will be extended by incorporating also the added value of the presence of shopping facilities at airports for their relative economic performance