Abstract
Productivity analysis has been an important avenue for economic research. Therefore, medleys of quantitative techniques have been proposed to operationalize productivity analysis. In this article, an extended by‐production model is discussed and applied to ensure a link between the production and the pollution‐generating subtechnologies. The corresponding dual formulations are provided to interpret the economic role of pollution‐generating inputs in the subtechnologies. Finally, we integrate the proposed model with the environmental Luenberger-Hicks-Moorsteen productivity indicator based upon input and output directional distance functions. The proposed model is applied to measure the green economic growth of agricultural sectors of the selected European countries.
Balezentis, T.; Blancard, S.; Shen, Z.; Streimikiene, D. 2021. Analysis of Environmental Total Factor Productivity Evolution in European Agricultural Sector. Decision Sciences. Vol. 52. Number 2, 483–511; Online ISSN:1540-5915; https://doi.org/10.1111/deci.12421; [ABI/INFORM Collection (ProQuest); Academic ASAP (GALE Cengage); Advanced Technologies & Aerospace Database (ProQuest); Business Abstracts (EBSCO Publishing); Business ASAP (GALE Cengage); Business Premium Collection (ProQuest); Current Contents: Social & Behavioral Sciences (Clarivate Analytics); Current Index to Statistics (ASA/IMS); Emerald Management Reviews (Emerald); Expanded Academic ASAP (GALE Cengage); IAOR: International Abstracts in Operations Research (Palgrave Macmillan); InfoTrac (GALE Cengage); Journal Citation Reports/Social Science Edition (Clarivate Analytics); Materials Science & Engineering Database (ProQuest); OmniFile Full Text Mega Edition (HW Wilson); Proquest Business Collection (ProQuest); ProQuest Central (ProQuest); ProQuest Central K-135; ProQuest Politics Collection (ProQuest); ProQuest Sociology Collection (ProQuest); SciTech Premium Collection (ProQuest); SCOPUS (Elsevier); Social Science Premium Collection (ProQuest); Social Sciences Citation Index (Clarivate Analytics); STEM Database (ProQuest); Technology Collection (ProQuest); The DBLP Computer Science Bibliography (University of Trier)].