The Infrastructure Sector Sustainability: Using of the Deterministic Frontier Analysis for Performance-Accounting Measurement

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Abstract

The capacity of the infrastructure sector to generate value for the territory in terms of social inclusion, economic growth, and employment may be determined as of today (2023) on the basis of empirical evidence. With the technological advancements and the development of other modes of transportation over time, the system of roads and highways in particular has come to play a crucial role in the growth of a territory. It is also necessary for the infrastructure system, particularly the transportation sector, to adapt to international norms for sustainable growth. Operationally, this means that projects must be planned and carried out that have the potential to influence the territory's strategic progress from three different angles: economic, social, and environmental. The goal of the current study is to provide a quantitative methodology to aid in the analysis of infrastructure sector performance, particularly that of the transportation one. The framework suggested directs evaluation of the infrastructure sector in terms of sustainability and takes into consideration potential performance indicators in order to develop accounting procedures for the sustainability performance. The formalization of the analytical tool based on the proposed framework adheres to the logical rules of Deterministic Frontier Analysis (DFA). The transport infrastructure sector is evaluated by the DFA in relation to the achievement of the sustainability goals set at the European Union level. In the conclusions, it is discussed how the use of the proposed framework may affect the allocation of initiatives related to the sector being tested on the territory’s sustainable development.

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APA

Sica, R. M., Sica, F., Sessa, M. R., & Sica, N. (2023). The Infrastructure Sector Sustainability: Using of the Deterministic Frontier Analysis for Performance-Accounting Measurement. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 14107 LNCS, pp. 31–41). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-37114-1_3

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