Paulo Yun Cha
Doctorate – An integrated quantitative framework propose for sustainability assessment in geographical indication production systems
Advisor: Profª. Drª. Adriana Marotti de Mello
Comission: Profs. Drs. Cássia Maria Lie Ugaya, Daielly Melina Nassif Mantovani Ribeiro and Biagio Fernando Giannetti
Link youtube: https://youtu.be/tOpqw0idDYk
This research aims to propose a comprehensive sustainability quantitative evaluation method capable of dealing within a vast range of dimensions at the same time in geographical indication(GI) production systems. While past studies taken account sociological or environmental aspects, setting aside economic dimension, the present study takes account production efficiency and profitability, an elementary condition for regional development. The previous corpus of work struggled even, by turning such evaluations into actionable information in a micro and macro context of production units, and to tackle the difficult tasks of disseminating findings outside the scientific community. In order to contribute to the resolution of these problems, four papers were written aimed at bringing different viewpoints into the discipline. The first study categorized emergy as a solid conceptual framework for sustainability assessment and because of its technical robustness, it developed studies for determining sustainability in several sectors abroad. Some scholars, however, point out that it is difficult to disseminate findings outside the scientific community and convert such analyses into actionable information from the micro and macro viewpoint of the producers and decision makers. The second study major finding was that technology adoption is an important driving factor to elevate GI producers competitiveness. The best ranked producers among the studied set were surprisingly the highest technified and the lowest technified producer, where the the second one is highly leveraged out in intangible assets such as public awareness of his roots, tradition and society contribution. Everything materialized in large revenue by per kilo. However, when taking off social variables of the assessment model, this producer ranks in the lowest rank by considering only economic and environmental efficiency and not taking account social variable such as tradition and owner's age, crucial for a long term sustainability development of Gi's. The investigation of the third study has shown that the clustering process indicated a high homogeneity of the farms within the three clusters. The main differences between the clusters were the tradition, whey produced, total cheese output, gross revenue and paid tax. These variables have a strong impact in the design of farm level strategies, affecting directly the choice of the farms inputs in terms of an integrated and broad concept of long term sustainability. The investigation of the third study has shown a macro perspective of sustainability assessment through clustering process of the production units. The results indicated a high homogeneity of the farms within the three clusters. Different farm types in each formed cluster can be benefited by adequate strategies of development and assessment. Lastly, the fourth study through multi-objective genetic algorithm optimization expose a high potential and feasible interval for improvement. In general, the average output of environment discharge is kept lower than the average of the region. Information asymmetry reduction through digital channels communication, production efficiency increment and addition of value by service disposition are emerged general strategies provided by the genetic algorithm model. Finally, the study certainly adds to our understanding of the sustainability assessment in GI's by providing a robust quantitative framework for assessment, classification and communication of the results much abroad to the academy space, leading to easy communication for managers to achieve better sustainability by changing impactful leverages such as technification, maintenance of clusters proportions or following competitive strategies for each farm.
*Abstract provided by the author