Partially Non-discretionary Measures for Green Transportation Corridors Performance Index: A DEA Approach

Isotilia Costa Melo (), Paulo Nocera Alves Junior (), Thiago Guilherme Péra (), Daisy Aparecida Do Nascimento Rebelatto () and José Vicente Caixeta-Filho ()
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Isotilia Costa Melo: São Carlos Engineering School (EESC), University of São Paulo (USP)
Paulo Nocera Alves Junior: Luiz de Queiroz College of Agriculture (ESALQ), University of São Paulo (USP)
Thiago Guilherme Péra: Luiz de Queiroz College of Agriculture (ESALQ), University of São Paulo (USP)
Daisy Aparecida Do Nascimento Rebelatto: São Carlos Engineering School (EESC), University of São Paulo (USP)
José Vicente Caixeta-Filho: Luiz de Queiroz College of Agriculture (ESALQ), University of São Paulo (USP)

A chapter in New Perspectives in Operations Research and Management Science, 2022, pp 89-111 from Springer

Abstract: Abstract Freight transportation is vital to a nation’s long-term development and its performance needs to be carefully evaluated to ensure the efficiency of haulage infrastructure decisions. Frequently, real-world physical barriers pose transportation constraints that are impossible to be completely overpassed or ignored. Previous studies on benchmarking Green Transport Corridors (GTCs) through routes efficiency have not considered the possibility of partially non-discretionary (pND) measures (only a certain percentage of the measure is controllable). The present paper creates a long-distance cargo haulage performance index that will be deemed as Logistic Composite Index (LCI) integrating pND measures using a Data Envelopment Analysis (DEA) methodology. Since infrastructure aspects can be assumed to be a Variable Returns to Scale (VRS), huge investments may be necessary for the possibility of just partially reducing the length of a route in a certain percentage by private and public investment strategies. This characteristic was incorporated, for the first time, with pND measures in a Double-Frontier of a Slack-Based Measure (SBM), and under VRS assumptions (pND-DF-SBM-VRS). Therefore, the present chapter integrates a novelty in DEA literature with practical implications for public investments. The method is applied to the context of soybean transportation, one of the relevant Brazilian exporting products, during the harvest of 2018/2019, from the main mid-sized producing regions to the key exporting ports. The proposed approach and findings provide insights into the public and private long-term investment strategies and infrastructure policies, especially in Brazil and developing countries.

Keywords: Data envelopment analysis (DEA); Partially non-discretionary slack-based measure (pND-SBM); Construction of composite index; Freight transportation; Brazilian soybean (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-030-91851-4_4

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DOI: 10.1007/978-3-030-91851-4_4

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