Environmental efficiency under weak disposability: an improved super efficiency data envelopment analysis model with application for assessment of port operations considering NetZero

[1]  Usama Awan,et al.  Enabling Progress in Developing Economies: A Novel Hybrid Decision-Making Model for Green Technology Planning , 2021, Sustainability.

[2]  Usama Awan,et al.  Achieving green product and process innovation through green leadership and creative engagement in manufacturing , 2021, Journal of Manufacturing Technology Management.

[3]  M. M. Q. Abro,et al.  Assessing Hybrid Solar-Wind Potential for Industrial Decarbonization Strategies: Global Shift to Green Development , 2021, Energies.

[4]  Usama Awan,et al.  Does green transformational leadership lead to green innovation? The role of green thinking and creative process engagement , 2021, Business Strategy and the Environment.

[5]  Ali Emrouznejad,et al.  A robust credibility DEA model with fuzzy perturbation degree: An application to hospitals performance , 2021, Expert Syst. Appl..

[6]  A. Wall,et al.  The external costs of port activity for port cities: An environmental efficiency analysis of Spanish ports , 2021, International Journal of Sustainable Transportation.

[7]  Yuan-tao Zhang,et al.  Analysis of carbon emission performance and regional differences in China’s eight economic regions: Based on the super-efficiency SBM model and the Theil index , 2021, PloS one.

[8]  Razamin Ramli,et al.  An integrated bi-objective data envelopment analysis model for measuring returns to scale , 2021, Eur. J. Oper. Res..

[9]  Ali Emrouznejad,et al.  Overall efficiency of operational process with undesirable outputs containing both series and parallel processes: A SBM network DEA model , 2021, Expert Syst. Appl..

[10]  Feng He,et al.  Does haze pollution affect public health in China from the perspective of environmental efficiency? , 2021, Environment, Development and Sustainability.

[11]  C. Serrano-Cinca,et al.  Managing for Stakeholders Using Multiple-Criteria Decision-Making Techniques , 2021, Social Indicators Research.

[12]  Cosimo Magazzino,et al.  A machine learning approach on the relationship among solar and wind energy production, coal consumption, GDP, and CO2 emissions , 2020 .

[13]  Rosalia Castellano,et al.  Evaluating the economic and environmental efficiency of ports: Evidence from Italy , 2020 .

[14]  Yang Wang,et al.  Optimizing wind/solar combinations at finer scales to mitigate renewable energy variability in China , 2020 .

[15]  Huaping Sun,et al.  Energy insecurity, pollution mitigation, and renewable energy integration: prospective of wind energy in Ghana , 2020, Environmental Science and Pollution Research.

[16]  Guo Wei,et al.  Efficiency Evaluation and PM Emission Reallocation of China Ports Based on Improved DEA Models , 2020, Economic Impacts and Emergency Management of Disasters in China.

[17]  A. Khattak,et al.  Buyer-Driven Knowledge Transfer Activities to Enhance Organizational Sustainability of Suppliers , 2020, Sustainability.

[18]  Edmundas Kazimieras Zavadskas,et al.  Multi-Criteria Decision-Making (MCDM) for the Assessment of Renewable Energy Technologies in a Household: A Review , 2020 .

[19]  M. M. Kasim,et al.  Efficiency of banks in Malaysia: A super efficiency approach , 2019, Engineering Economics.

[20]  A. Wall,et al.  Environmental efficiency for a cross-section of Spanish port authorities , 2019, Transportation Research Part D: Transport and Environment.

[21]  Ali Emrouznejad,et al.  Comprehensive performance evaluation of banking branches: A three-stage slacks-based measure (SBM) data envelopment analysis , 2019, International Review of Economics & Finance.

[22]  Yuvraj Gajpal,et al.  Evaluating the Environmental Performance and Operational Efficiency of Container Ports: An Application to the Maritime Silk Road , 2019, International journal of environmental research and public health.

[23]  Ruzelan Khalid,et al.  Measuring the Efficiency of Community Colleges Using Super Efficiency Approach for the Case of Non-discretionary Factors in Data Envelopment Analysis with Sensitivity Analysis , 2019, International Journal of Process Management and Benchmarking.

[24]  Karambu Kiende Gatimbu,et al.  Environmental efficiency of small-scale tea processors in Kenya: an inverse data envelopment analysis (DEA) approach , 2019, Environment, Development and Sustainability.

[25]  Razamin Ramli,et al.  Estimating the return to scale of an integer-valued data envelopment analysis model: efficiency assessment of a higher education institution , 2019 .

[26]  Ali Emrouznejad,et al.  A novel inverse DEA model with application to allocate the CO2 emissions quota to different regions in Chinese manufacturing industries , 2018, J. Oper. Res. Soc..

[27]  Razamin Ramli,et al.  Developing a two-stage approach of super efficiency slack-based measure in the presence of non-discretionary factors and mixed integer-valued data envelopment analysis , 2018, Expert Syst. Appl..

[28]  Adnan Kasman,et al.  Environmental technical efficiency in EU member and candidate countries: A parametric hyperbolic distance function approach , 2018 .

[29]  Ali Emrouznejad,et al.  A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016 , 2018 .

[30]  Yongjun Li,et al.  Environmental performance evaluation of Chinese industrial systems: a network SBM approach , 2018, J. Oper. Res. Soc..

[31]  Jie Wu,et al.  Performance evaluation of Chinese port enterprises under significant environmental concerns: An extended DEA-based analysis , 2017 .

[32]  Qiang Cui,et al.  Environmental efficiency measures for ports: an application of RAM-Tobit-RAM with undesirable outputs , 2017 .

[33]  Paul Rouse,et al.  Estimating the hyperbolic distance function: A directional distance function approach , 2016, Eur. J. Oper. Res..

[34]  Razamin Ramli,et al.  A HYBRID INTEGER DATA ENVELOPMENT ANALYSIS BASED ON AN ALTERNATIVE APPROACH OF SUPER SLACK BASED MEASURE FOR MEASURING SUPER EFFICIENCY AND INEFFICIENCY OF DECISION MAKING UNITS , 2016 .

[35]  K. Zaman,et al.  Dynamic linkages among energy consumption, air pollution, greenhouse gas emissions and agricultural production in Pakistan: sustainable agriculture key to policy success , 2016, Natural Hazards.

[36]  Reinhilde Veugelers,et al.  Organisational change and the productivity effects of green technology adoption , 2016 .

[37]  Muhammad Mushtaq Khan,et al.  Triangular relationship among energy consumption, air pollution and water resources in Pakistan , 2016 .

[38]  Razamin Ramli,et al.  Investigating data envelopment analysis model with potential improvement for integer output values , 2015 .

[39]  Reza Farzipoor Saen,et al.  Using data envelopment analysis for estimating energy saving and undesirable output abatement: a case study in the Organization for Economic Co-Operation and Development (OECD) countries , 2015 .

[40]  Gi-Tae Yeo,et al.  Environmental efficiency analysis of port cities: Slacks-based measure data envelopment analysis approach , 2014 .

[41]  Hong Li,et al.  Energy efficiency analysis on Chinese industrial sectors: an improved Super-SBM model with undesirable outputs , 2014 .

[42]  Somchai Pathomsiri,et al.  Limitations of DEA-based approach and alternative methods in the measurement and comparison of social efficiency across firms in different transport modes: An empirical study in Japan , 2013 .

[43]  Wei Yang,et al.  Regional environmental efficiency evaluation in China: Analysis based on the Super-SBM model with undesirable outputs , 2013, Math. Comput. Model..

[44]  Young-Tae Chang,et al.  Environmental efficiency of ports: a Data Envelopment Analysis approach , 2013 .

[45]  Hsuan-Shih Lee,et al.  A slacks-based measure of super-efficiency in data envelopment analysis: An alternative approach , 2013 .

[46]  Chien-Ming Chen,et al.  Super efficiencies or super inefficiencies? Insights from a joint computation model for slacks-based measures in DEA , 2013, Eur. J. Oper. Res..

[47]  Jiazhen Huo,et al.  Undesirable factors in integer-valued DEA: Evaluating the operational efficiencies of city bus systems considering safety records , 2012, Decis. Support Syst..

[48]  P. Zhou,et al.  Efficiency and abatement costs of energy-related CO2 emissions in China: A slacks-based efficiency measure , 2012 .

[49]  T. Agasisti,et al.  Comparing German and Italian Public Universities: Convergence or Divergence in the Higher Education Landscape?: COMPARING GERMAN AND ITALIAN PUBLIC UNIVERSITIES , 2012 .

[50]  Joe Zhu,et al.  Super-efficiency infeasibility and zero data in DEA , 2012, Eur. J. Oper. Res..

[51]  Adel Hatami-Marbini,et al.  A data envelopment analysis model with discretionary and non-discretionary factors in fuzzy environments , 2011 .

[52]  Jin-Xiao Chen,et al.  A modified super-efficiency measure based on simultaneous input-output projection in data envelopment analysis , 2011, Comput. Oper. Res..

[53]  Vladimir Dobes,et al.  New tool for promotion of energy management and cleaner production on no-cure no-pay , 2010 .

[54]  Joe Zhu,et al.  A slacks-based measure of super-efficiency in data envelopment analysis: A comment , 2010, Eur. J. Oper. Res..

[55]  Wen-Min Lu,et al.  An integrated performance evaluation of financial holding companies in Taiwan , 2009, Eur. J. Oper. Res..

[56]  Hongliang Yang,et al.  Incorporating Both Undesirable Outputs and Uncontrollable Variables into Dea: the Performance of Chinese Coal-fired Power Plants Incorporating Both Undesirable Outputs and Uncontrollable Variables into Dea: the Performance of Chinese Coal-fired Power Plants , 2007 .

[57]  M. Esmaeili,et al.  A slacks-based measure of efficiency for the case of exogenously fixed factors , 2009, Expert Syst. Appl..

[58]  Mirjana Golušin,et al.  Definition, characteristics and state of the indicators of sustainable development in countries of Southeastern Europe. , 2009 .

[59]  Jill Johnes,et al.  Measuring the research performance of Chinese higher education institutions using data envelopment analysis , 2008 .

[60]  B. W. Ang,et al.  Linear programming models for measuring economy-wide energy efficiency performance , 2008 .

[61]  Zhongsheng Hua,et al.  Eco-efficiency analysis of paper mills along the Huai River: An extended DEA approach , 2007 .

[62]  Qinghua Zhu,et al.  The moderating effects of institutional pressures on emergent green supply chain practices and performance , 2007 .

[63]  Leonardo Ramos Rios,et al.  Analysing the Relative Efficiency of Container Terminals of Mercosur using DEA , 2006 .

[64]  B. W. Ang,et al.  Slacks-based efficiency measures for modeling environmental performance , 2006 .

[65]  John Ruggiero,et al.  Evaluating alternative DEA models used to control for non-discretionary inputs , 2006, Comput. Oper. Res..

[66]  K. Cullinane,et al.  The efficiency of European container ports: A cross-sectional data envelopment analysis , 2006 .

[67]  Reza Farzipoor Saen,et al.  Developing a nondiscretionary model of slacks-based measure in data envelopment analysis , 2005, Appl. Math. Comput..

[68]  F. Hosseinzadeh Lotfi,et al.  Undesirable inputs and outputs in DEA models , 2005, Appl. Math. Comput..

[69]  Abdollah Hadi-Vencheh,et al.  Undesirable factors in efficiency measurement , 2005, Appl. Math. Comput..

[70]  Mikko J. Syrjänen,et al.  Non-discretionary and discretionary factors and scale in data envelopment analysis , 2004, Eur. J. Oper. Res..

[71]  Hirotaka Nakayama,et al.  A generalized model for data envelopment analysis , 2004, Eur. J. Oper. Res..

[72]  Kaoru Tone,et al.  Dealing with Undesirable Outputs in DEA: A Slacks-based Measure (SBM) Approach , 2003 .

[73]  William L. Weber,et al.  Characteristics of a Polluting Technology: Theory and Practice , 2002 .

[74]  Lawrence M. Seiford,et al.  Modeling undesirable factors in efficiency evaluation , 2002, Eur. J. Oper. Res..

[75]  Marcos Pereira Estellita Lins,et al.  Review of Methods for Increasing Discrimination in Data Envelopment Analysis , 2002, Ann. Oper. Res..

[76]  Zilla Sinuany-Stern,et al.  Review of ranking methods in the data envelopment analysis context , 2002, Eur. J. Oper. Res..

[77]  Joe Zhu,et al.  Super-efficiency and DEA sensitivity analysis , 2001, Eur. J. Oper. Res..

[78]  Fuqiang Zhang,et al.  Inter-regional comparisons of energy-related CO2 emissions using the decomposition technique , 1999 .

[79]  A. Worthington.  Performance Indicators and Efficiency Measurement in Public Libraries , 1999 .

[80]  John Ruggiero,et al.  Non-discretionary inputs in data envelopment analysis , 1998, Eur. J. Oper. Res..

[81]  Kaoru Tone,et al.  A slacks-based measure of efficiency in data envelopment analysis , 1997, Eur. J. Oper. Res..

[82]  R. Färe,et al.  An activity analysis model of the environmental performance of firms—application to fossil-fuel-fired electric utilities , 1996 .

[83]  John Ruggiero,et al.  On the measurement of technical efficiency in the public sector , 1996 .

[84]  Suthathip Yaisawarng,et al.  The Effects of Sulfur Dioxide Controls on Productivity Change in the U.S. Electric Power Industry , 1994 .

[85]  P. Andersen,et al.  A procedure for ranking efficient units in data envelopment analysis , 1993 .

[86]  A. Charnes,et al.  Polyhedral Cone-Ratio DEA Models with an illustrative application to large commercial banks , 1990 .

[87]  Barton A. Smith,et al.  Comparative Site Evaluations for Locating a High-Energy Physics Lab in Texas , 1986 .

[88]  Rajiv D. Banker,et al.  Efficiency Analysis for Exogenously Fixed Inputs and Outputs , 1986, Oper. Res..

[89]  Boaz Golany,et al.  Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions , 1985 .

[90]  Abraham Charnes,et al.  Preface to topics in data envelopment analysis , 1984, Ann. Oper. Res..

[91]  A. Charnes,et al.  Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis , 1984 .

[92]  Russell Pittman,et al.  Multilateral Productivity Comparisons with Undesirable Outputs , 1983 .

[93]  L. R. Christensen,et al.  MULTILATERAL COMPARISONS OF OUTPUT, INPUT, AND PRODUCTIVITY USING SUPERLATIVE INDEX NUMBERS* , 1982 .

[94]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[95]  D. Aigner,et al.  P. Schmidt, 1977,?Formulation and estimation of stochastic frontier production function models,? , 1977 .

[96]  A. Charnes,et al.  Programming with linear fractional functionals , 1962 .

[97]  Paolo Maria Congedo,et al.  A novel energy-economic-environmental multi-criteria decision-making in the optimization of a hybrid renewable system , 2020 .

[98]  R. Saen,et al.  Developing a new chance-constrained DEA model for suppliers selection in the presence of undesirable outputs , 2012 .

[99]  S. Managi,et al.  The technical efficiency of the Japanese banks : non-radial directional performance measurement with undesirable output , 2012 .

[100]  Sebastián Lozano,et al.  Slacks-based measure of efficiency of airports with airplanes delays as undesirable outputs , 2011, Comput. Oper. Res..

[101]  W. B. Liu,et al.  DEA models with undesirable inputs and outputs , 2010, Ann. Oper. Res..

[102]  Lawrence M. Seiford,et al.  Data envelopment analysis (DEA) - Thirty years on , 2009, Eur. J. Oper. Res..

[103]  Y. Ozcan.  Health Care Benchmarking and Performance Evaluation , 2008 .

[104]  K. Tone.  Continuous Optimization A slacks-based measure of super-efficiency in data envelopment analysis , 2002 .

[105]  Wenbin Liu,et al.  DEA Models via Goal Programming , 1999 .

[106]  Georg Westermann,et al.  Data Envelopment Analysis in the Service Sector , 1999 .

[107]  Lawrence M. Seiford,et al.  INFEASIBILITY OF SUPER EFFICIENCY DATA ENVELOPMENT ANALYSIS MODELS , 1999 .

[108]  Henry Tulkens,et al.  On FDH efficiency analysis: Some methodological issues and applications to retail banking, courts, and urban transit , 1993 .