Bootstrap Method of Eco-Efficiency in the Brazilian Agricultural Industry
André Luiz Marques Serrano,
Gabriela Mayumi Saiki,
Carlos Rosano-Penã,
Gabriel Arquelau Pimenta Rodrigues,
Robson de Oliveira Albuquerque,
Luis Javier García Villalba
Affiliations
André Luiz Marques Serrano
Professional Post-Graduate Program in Electrical Engineering (PPEE), Department of Electrical Engineering (ENE), Technology Faculty, University of Brasilia (UnB), Brasilia 70910-900, Brazil
Gabriela Mayumi Saiki
Professional Post-Graduate Program in Electrical Engineering (PPEE), Department of Electrical Engineering (ENE), Technology Faculty, University of Brasilia (UnB), Brasilia 70910-900, Brazil
Carlos Rosano-Penã
Faculty of Economics, Administration and Accounting (FACE), Department Administration (ADM), University of Brasilia (UnB), Brasilia 70910-900, Brazil
Gabriel Arquelau Pimenta Rodrigues
Professional Post-Graduate Program in Electrical Engineering (PPEE), Department of Electrical Engineering (ENE), Technology Faculty, University of Brasilia (UnB), Brasilia 70910-900, Brazil
Robson de Oliveira Albuquerque
Professional Post-Graduate Program in Electrical Engineering (PPEE), Department of Electrical Engineering (ENE), Technology Faculty, University of Brasilia (UnB), Brasilia 70910-900, Brazil
Luis Javier García Villalba
Group of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Computer Science and Engineering, Office 431, Universidad Complutense de Madrid (UCM), Calle Profesor José García Santesmases 9, Ciudad Universitaria, 28040 Madrid, Spain
With the economic growth of the Brazilian agroindustry, it is necessary to evaluate the efficiency of this activity in relation to environmental demands for the country’s economic, social, and sustainable development. Within this perspective, the present research aims to examine the eco-efficiency of agricultural production in Brazilian regions, covering 5563 municipalities in the north, northeast, center-west, southeast, and south regions, using data from 2016–2017. In this sense, this study uses the DEA methods (classical and stochastic) and the computational bootstrap method to remove outliers and measure eco-efficiency. The findings lead to two fundamental conclusions: first, by emulating the benchmarks, it is feasible to increase annual revenue and preserved areas to an aggregated regional level by 20.84% while maintaining the same inputs. Given that no municipality has reached an eco-efficiency value equal to 1, there is room for optimization and improvement of production and greater sustainable development of the municipalities. Secondly, climatic factors notably influence eco-efficiency scores, suggesting that increasing temperatures and decreasing precipitation can positively impact eco-efficiency in the region. These conclusions, dependent on regional characteristics, offer valuable information for policymakers to design strategies that balance economic growth and environmental preservation. Furthermore, adaptive policies and measures can be implemented to increase the resilience of local producers and reduce vulnerability to changing climate conditions.
