Two-stage dynamic StoNED model for efficiency evaluation of green technology innovation: evidence from a cross-country study
Authors:
- 31 July 2026
- journal article
- research article
- Published by Springer Nature in Scientometrics
- Vol. 131 (9) , 6123-6156
Abstract
No abstract availableKeywords
Funding Information
- Humanities and Social Science Fund of Ministry of Education of China (24YJC630187)
- National Social Science Fund of China (24BGL155)
- Soft Science Research Project of Shanghai (24692108000)
- Shanghai Municipal Philosophy and Social Science Planning Project (2025EXW008)
- General Project of the National Language Commission of China's 14th Five-Year Research Plan (YB145-124)
This publication has 56 references indexed in Scilit:
- Measuring the efficiency of university departments: an empirical study using data envelopment analysis and cluster analysisScientometrics, 2021
- Innovation efficiency and the impact of the institutional quality: a cross-country analysis using the two-stage meta-frontier dynamic network DEA modelScientometrics, 2021
- Investigating the efficiency of foreign exchange reserves using stochastic frontier analysis: Evidence across the globeInternational Journal of Finance & Economics, 2021
- Shared and unsplittable performance links in network DEAAnnals of Operations Research, 2020
- Stochastic non-smooth envelopment of data for multi-dimensional outputJournal of Productivity Analysis, 2018
- The StoNED age: the departure into a new era of efficiency analysis? A monte carlo comparison of StoNED and the “oldies” (SFA and DEA)Journal of Productivity Analysis, 2013
- Stochastic non-smooth envelopment of data: semi-parametric frontier estimation subject to shape constraintsJournal of Productivity Analysis, 2010
- DEA models for two‐stage processes: Game approach and efficiency decompositionNaval Research Logistics (NRL), 2008
- Quantitative Models for Performance Evaluation and Benchmarking2003
- A model for technical inefficiency effects in a stochastic frontier production function for panel dataEmpirical Economics, 1995