Range-adjusted Measure Research Articles

Financial Performance Assessment of Construction Firms by Means of RAM-Based Composite Indicators

This paper aims to provide a novel construct that is based on data envelopment analysis (DEA) range adjusted measure (RAM) of efficiency and demonstrate its practical implementation by evaluating the financial performance of a sample of three upper-class contracting license (Classes 5–7) Greek construction firms. In a two-step framework, firm efficiency (i.e., composite indicators (CIs)) is produced firstly by means of RAM using single financial ratios, which are selected by grey relational analysis (GRA), and then Tobit regression is employed to model the CIs. In light of the results, only 4% of the sampled firms are efficient, and the firm ranking is consistent with the ranking of Grey Relational Grande (GRG) values produced by GRA. Moreover, the firms with a contracting license of the highest level (Class 7) appear not to be superior in efficiency to their counterparts that belong to Classes 5–6.

Airline energy efficiency measures using a network range-adjusted measure with unified natural and managerial disposability

In recent years, the issue of aviation carbon emissions has caused wide public concern. How to measure airline performance under the premise of considering aviation carbon emissions has become a hot issue. In this paper, airline energy efficiency is divided into three stages as follows: operations, services, and sales. A network range-adjusted measure with unified natural and managerial disposability model is proposed to calculate the environmental efficiencies of 29 airlines from 2008 to 2015. We get some interesting results. (1) Scandinavian has the highest overall efficiency among these 29 airlines during 2008–2015. (2) For the other 28 airlines, the improvement of the undesirable outputs is not the most urgent work to improve overall efficiency. (3) The overall efficiency has no obvious fluctuation in this period.

Inclusive and sustainable industrial development in China: An efficiency-based analysis for current status and improving potentials

Inclusive and sustainable industrial development (ISID) is a call for action to promote industrialization by minimizing the environmental footprint and enhancing social inclusion. This study proposes a new framework of an evaluation system to assess the ISID for 30 provinces in China (2011–2016). As there does not exist a generally accepted evaluation system of ISID, this study proposes a dual-formulation of ISID to rationalize the trade-offs among externalities brought by industrialization: (i) ISID represents the aspiration of countries to promote industrialization and consequently sustain economic growth by reducing the adverse environmental and social effects which manifest in the overall economy (ii) ISIDsdg9 considers the same aspects of ISID but limits the externalities within the industrial sector. This study applied two newly developed efficiency evaluation approaches, Multi-Directional Efficiency Analysis (MEA) and Range-Adjusted Measure (RAM), to evaluate and analyze the ISID for China. This study finds that: Among the 30 provinces, (i) Beijing and Tianjin are the benchmark provinces for promoting ISID and ISIDsdg9 regimes, respectively. (ii) Throughout the 2011–2016, the overall performance of China on ISID and ISIDsdg9 regimes has improved modestly. (iii) Based on the analysis of the barrel effect, SO2 emission is the key binding constraint to achieving the frontier status of ISID and ISIDsd9 regimes (v) Under the evaluation of ISID and ISIDsdg9 regimes, the average annual growth potentials of industrial value added (IVA) is 280.52 billion CNY and 927.54 billion CNY, respectively. This present study recommends local government in China to advance their ISID performance by exploiting the policies targeting emission mitigation concerning fossil fuels while considering Beijing and Tianjin as the benchmark provinces.

Congestion assessment for the Belt and Road countries considering carbon emission reduction

Abstract In the context of resources saving and climate change, this study extends the Cooper-family model by using a range adjusted measure (RAM) approach to incorporate carbon emissions under the data envelopment analysis (DEA) framework. Comparing congestion effects under three orientations (operational orientation, environmental orientation, and sustainable orientation) of 46 primary countries along the Belt and Road (B&R countries) from 2003 to 2016, this study has a sight for the impact mechanism of development orientation on congestion effects and put forward policy suggestions on development orientation for B&R countries. The results show that: 1) Congestion effects have existed in some B&R countries during the sample period. Labor congestion mainly occurs under environmental orientation, capital congestion mainly appears when operational performance or sustainable development is the priorities orientation, and energy congestion under operational orientation is more severe than the other orientations. 2) Countries rich in a production factor tend to have congestion of such production factor. Specifically, high population density countries, developed countries, and countries rich in energy resources tend to have labor congestion, capital congestion, and energy congestion, respectively. 3) Sustainable orientation will help to alleviate congestion effects. Serious capital and energy congestion have occurred under operational orientation, whereas they are basically eliminated when under sustainable orientation. The findings are helpful to improve resource utilization efficiency and provide foreign investors with investment reference information.

Efficiency measure on the truck restriction policy in China: A non-radial data envelopment model

The paper reports results from a use of non-radial DEA i.e., range adjusted measure (RAM) which belongs to data envelopment analysis to evaluate the performances of the truck restriction policy in China. We try to explore what is inside the “black box” on the restriction policy by empirical study. Using the non-radial DEA model, we work with an approach that allows a flexible designation of three input variables and two output variables based on varying perspectives in China. The multipurpose approach reveals whether one policy is truly beneficial instead of officials’ arguments, captured in the form of DEA scores. The modified model can better reflect macro-transportation process while earlier relevant models ignored truckers. This paper contributes to increasing traffic volumes and decreasing traffic accidents from the point of technical efficiency. The results of our paper reveal that: (a) China's truck restriction policies have achieved a significant “level effect” on efficiency improvement. (b) There are remarkable provincial differences in the effectiveness of the restriction policy. (c) Slack decomposition shows that excessive investment is the main source of inefficiency. Moreover, we delivery policy implications that: (a) the restriction policies greatly contribute to the efficiencies, and stringent policies are still needed in the following days; (b) the policy-making should suit local conditions, according to our classification of 30 provinces; (c) based on the worst performance of capital stock variable, the authorities should focus more on regulation of investment in transportation sector at a latter stage.

Atmospheric environmental productivity across the provinces of China: Joint decomposition of range adjusted measure and Luenberger productivity indicator

This paper adopts data envelopment analysis, namely the range adjusted measure (RAM) which is based on the additive structure, to measure the technical efficiency and productivity change. Note that we distinguish among energy and non-energy inputs in the analysis. The Luenberger productivity indicator is decomposed for the provinces of China for the period of 2006–2015. Specifically, we seek to identify the key directions for energy conservation and emission reduction by analyzing the environmental productivity change decomposition. The results show that sulfur dioxide and carbon dioxide emissions from primary energy consumption along with primary energy consumption are important variables contributing to atmospheric environmental inefficiency in Chinese provinces. Furthermore, static efficiency scores associated with energy consumption and atmospheric emission (sulfur dioxide, nitrogen oxides and carbon dioxide emissions) are lower for Hebei, Shandong, Henan and Shanxi in North China, whereas Beijing, Tianjin, Shanghai, Guangdong and Fujian show higher values of this kind of efficiency. Atmospheric emissions contribute to the growth in the atmospheric environmental productivity to a higher extent if compared to energy consumption. Therefore, Chinese energy policy should address the environmental regulation of energy consumption. • Joint decomposition of RAM and Luenberger productivity indicator. • Identify the key directions for energy conservation and emission reduction. • Atmospheric emissions contribute to the growth in the productivity. • Government can opt for source control or end-of-pipe control.

Green Transportation and Logistics Performance: An Improved Composite Index

This article constructs an environmental logistics performance index (ELPI) for assessing the overall performance in green transportation and logistics practices of 112 countries. The index is measured by logistics performance index (LPI), CO2 emissions and oil consumption from the transport sector, using a range-adjusted measure (RAM) of the data envelopment analysis (DEA). ELPI effectively reflects the tradeoff between logistics efficiency and environmental protection in transportation. This article analyzes the impact of income and region on ELPI scores and discusses those countries’ reduction potential in oil consumption intensity and carbon intensity. The main finding of the research work is that ELPI is strongly correlated with LPI, and countries with high performance in LPI generally perform well in ELPI. Similar to the characteristics of LPI, ELPI is also closely related to income and region. During our study period, high income countries performed best, while Sub-Saharan Africa countries performed worst. However, some exceptions such as Venezuela, RB and Benin, indicate that the level of development of logistics performance and green transportation can outperform or lag behind their income or region group peers.

Efficiency assessment of switchgrass cultivation areas using sustainable indicators under epistemic uncertainty

This paper proposes a unified fuzzy data envelopment analysis model to address the optimization of switchgrass cultivation locations based on sustainable development indicators under epistemic uncertainty. In this study, each potential switchgrass cultivation location is considered as a decision making unit and a range-adjusted measure is utilized as a data envelopment analysis model for calculating the unified efficiency of each potential location for switchgrass cultivation and determining the most appropriate ones. By reason of the epistemic uncertainty associated with sustainable indicators, fuzzy set theory is incorporated in the proposed model. Inasmuch as the values of a number of indicators used in the model are in the form of possibility distributions, a new possibilistic programming approach is used for transforming the unified fuzzy data envelopment analysis model into an equivalent crisp one. The proposed model is verified and validated by comparing its results with those of the equivalent crisp one through performing a real case study in Iran at different levels of uncertainty. The computational results demonstrate the applicability of the proposed model in determining the appropriate locations for switchgrass cultivation based on sustainable development and under uncertainty.

Low-carbon economy efficiency analysis of China's provinces based on a range-adjusted measure and data envelopment analysis model

To guide the policy adjustment in the new normal economic mode, this study uses a range-adjusted measure and data envelopment analysis model to evaluate the provincial comprehensive efficiencies and explore the factors causing poor performance in China during 2001–2014. Empirical analysis draws the following conclusions. 1) The comprehensive efficiencies of most provinces present upward trends, which imply the positively contribution of previous efficiency-related policies. 2) Contrary to economic levels, the comprehensive efficiency of the central region is lower than that in the east but higher than that in the west. This result confirms the existence of extensive growth and implies that developed regions should be largely responsible for the low comprehensive efficiency of the country. 3) Except for a few outliers, the comprehensive efficiency level of a province is highly correlated to its climatic characteristics. This phenomenon offers new ideas for the central government to create differential efficiency-related policies. 4) For east provinces, labor input, energy input, gross domestic product (GDP) output, and CO2 emissions are all important in improving the comprehensive efficiency. Efficiency-related policies should focus on technical progress. For west and central provinces, labor input and GDP output are the main contributors to inefficiency. The key policy direction of these provinces should be the improvement of human resource efficiency.

Modelling undesirable outputs in multiple objective data envelopment analysis

Recent empirical and conceptual work in data envelopment analysis (DEA) have emphasised its potential importance in highlighting the environmental performance of economic entities. Initial work in this emerging research area has focused on the separation of output factors into desirable and undesirable ones. In this paper, we describe recent developments in the modelling undesirable outputs. In particular, the modelling of undesirable outputs in the range adjusted measure (RAM) is investigated. We discuss some of the difficulties of RAM in assessing the environmental efficiency of decision-making units (DMUs) and develop a multiple objective DEA model to overcome these difficulties. The proposed multiple objective model is solved as a linear programming and its applicability as a mechanism for assessing environmental efficiency is demonstrated by evaluating the technical, ecological and process environmental quality efficiency scores of China’s provinces.

A combination of range-adjusted measure, cross-efficiency and assurance region for assessing sustainability of suppliers in the presence of undesirable factors

Assessing sustainability of suppliers and selecting the most sustainable supplier are the most important activities in sustainable supply chain management (SSCM). Data envelopment analysis (DEA) is frequently used to select suppliers. We extend range adjusted measure (RAM) model as one of the DEA models. We mix different models of RAM as well as cross-efficiency and assurance region methods when undesirable factors exist. Our model does not give zero results. This helps to incorporate all inputs and outputs in evaluating sustainability of suppliers. A real case study is provided.

A combination of range-adjusted measure, cross-efficiency and assurance region for assessing sustainability of suppliers in the presence of undesirable factors

Assessing sustainability of suppliers and selecting the most sustainable supplier are the most important activities in sustainable supply chain management (SSCM). Data envelopment analysis (DEA) is frequently used to select suppliers. We extend range adjusted measure (RAM) model as one of the DEA models. We mix different models of RAM as well as cross-efficiency and assurance region methods when undesirable factors exist. Our model does not give zero results. This helps to incorporate all inputs and outputs in evaluating sustainability of suppliers. A real case study is provided.

Sustainability Assessment of Supply Chains by Inverse Network Dynamic Data Envelopment Analysis

This paper focuses on assessing sustainability of supply chains. In this paper, at first, we propose network dynamic range adjusted measure (RAM) model. Then, inverse version of network dynamic RAM model is proposed. Our inverse network dynamic data envelopment analysis (DEA) model changes both inputs and outputs of decision making units (DMUs) so that current efficiency scores of DMUs remain unchanged. We change inputs and outputs without any change in efficiency score of DMU under evaluation while inputs and outputs may have large ranges. A case study shows efficacy of our proposed model.

Airline environmental efficiency measures considering materials balance principles: an application of a network range-adjusted measure with weak-G disposability

In this paper, we focus on assessing airline environmental efficiency with network structure and build a three-stage efficiency production process. Then, we propose a new model, network range-adjusted measure with weak-G disposability, to measure the environmental efficiency of 29 global airlines based on the data from 2008 to 2015. Finally, a second-stage regression analysis is done to explore the important influencing factors. The main findings are as follows: (1) Eva Air is the airline with the highest efficiency among these 29 airlines; (2) Delta is at the bottom of the efficiency ranking among the 29 airlines and this result is in correlation with its old aircraft fleet; (3) the average efficiency change index in 2014 is the highest in the period 2009–2015; (4) only average fleet age has a slightly significant impact on the overall efficiency and Services efficiency.

Analysis of Low-Carbon Economy Efficiency of Chinese Industrial Sectors Based on a RAM Model with Undesirable Outputs

Industrial energy and environment efficiency evaluation become especially crucial as industrial sectors play a key role in CO2 emission reduction and energy consumption. This study adopts the additive range-adjusted measure data envelope analysis (RAM-DEA) model to estimate the low-carbon economy efficiency of Chinese industrial sectors in 2001–2013. In addition, the CO2 emission intensity mitigation target for each industrial sector is assigned. Results show that, first, most sectors are not completely efficient, but they have experienced and have improved greatly during the period. These sectors can be divided into four categories, namely, mining, light, heavy, and electricity, gas, and water supply industries. The efficiency is diverse among the four industrial categories. The average efficiency of the light industry is the highest among the industries, followed by those of the mining and the electricity, gas, and water supply industries, and that of the heavy industry is the lowest. Second, the electricity, gas, and water supply industry shows the biggest potential for CO2 emission reduction, thus containing most of the sectors with large CO2 emission intensity mitigation targets (more than 45%), followed by the mining and the light industries. Therefore, the Chinese government should formulate diverse and flexible policy implementations according to the actual situation of the different sectors. Specifically, the sectors with low efficiency should be provided with additional policy support (such as technology and finance aids) to improve their industrial efficiency, whereas the electricity, gas, and water supply industry should maximize CO2 emission reduction.

Estimation of innovation’s green performance: A range-adjusted measure approach to assess the unified efficiency of China’s manufacturing industry

A DEA-RAM model is adopted to study the unified efficiency of innovation’s green performance in 29 sectors of China’s manufacturing industry over the period of 2005–2012 in this paper. This study provides evidences that innovation in China’s manufacturing industry shows a shift to green innovation and contributes increasingly to green growth. However, the lag in the environmental efficiency remains a major constrain for improving innovation’s green performance of China. A significant regional disparity exists in the unified efficiency between western and eastern areas of China. The gap in the unified efficiency tends to increase across regions.

A new data envelopment analysis model for evaluating the performance of expert systems in supply chain management

In supply chain management (SCM), the company that evaluates expert systems (ES) has competitive advantage over competitors. Performance evaluation of the ES in SCM is a significant yet complex task which requires careful consideration of various performance criteria. Therefore, evaluation of the performance of ES in SCM is an important issue and it has a strategic significance for every company. One of the techniques that can be used for evaluation of the performance of ES is data envelopment analysis (DEA). In the current study, we introduce a novel model for evaluating the performance of ES in SCM using new range adjusted measure (RAM) model in the existence of dual-role factor and stochastic data. We present a case study in SCM of a drink industry in Iran.

A new data envelopment analysis model for evaluating the performance of expert systems in supply chain management

In supply chain management (SCM), the company that evaluates expert systems (ES) has competitive advantage over competitors. Performance evaluation of the ES in SCM is a significant yet complex task which requires careful consideration of various performance criteria. Therefore, evaluation of the performance of ES in SCM is an important issue and it has a strategic significance for every company. One of the techniques that can be used for evaluation of the performance of ES is data envelopment analysis (DEA). In the current study, we introduce a novel model for evaluating the performance of ES in SCM using new range adjusted measure (RAM) model in the existence of dual-role factor and stochastic data. We present a case study in SCM of a drink industry in Iran.

CO2 emissions reduction of Chinese light manufacturing industries: A novel RAM-based global Malmquist–Luenberger productivity index

Climate change has become one of the most challenging issues facing the world. Chinese government has realized the importance of energy conservation and prevention of the climate changes for sustainable development of China's economy and set targets for CO2 emissions reduction in China. In China industry contributes 84.2% of the total CO2 emissions, especially manufacturing industries. Data envelopment analysis (DEA) and Malmquist productivity (MP) index are the widely used mathematical techniques to address the relative efficiency and productivity of a group of homogenous decision making units, e.g. industries or countries. However, in many real applications, especially those related to energy efficiency, there are often undesirable outputs, e.g. the pollutions, waste and CO2 emissions, which are produced inevitably with desirable outputs in the production. This paper introduces a novel Malmquist–Luenberger productivity (MLP) index based on directional distance function (DDF) to address the issue of productivity evolution of DMUs in the presence of undesirable outputs. The new RAM (Range-adjusted measure)-based global MLP index has been applied to evaluate CO2 emissions reduction in Chinese light manufacturing industries. Recommendations for policy makers have been discussed.

Performance assessment of airlines using range-adjusted measure, strong complementary slackness condition, and discriminant analysis

This study integrates RAM (range-adjusted measure), SCSC (strong complementary slackness condition), and DEA–DA (data envelopment analysis–discriminant analysis) to rank airlines. As conventional DEA models do not fully use all inputs and outputs, they result zero in many multipliers. These sorts of DEA models may yield many efficient decision-making units (DMUs). This decreases the discrimination power of DEA. To overcome this limitation, this study proposes a novel application of RAM–DEA/SCSC along with DA. A case study demonstrates the applicability of our proposed approach.