The current combination of sustainable social awareness and the improved decision support systems, including multiple criteria decision models for sustainable development, creates the need for more efficient and accurate public policy decisions based on available technology. The continuous growth of urban public road transport in large cities, and therefore the worsening of air quality, along with recent economic crisis derived from the COVID-19 pandemic, is forcing public administrations to analyze the viability of current models, taking into consideration sustainable alternative energies. This study proposes a novel and consistent analytic hierarchy process (AHP) multicriteria decision-making (MCDM) model that combines both economic and environmental criteria, to evaluate public road transportation vehicles according to their alternative engine technologies and combustion characteristics. The proposed model has been applied to evaluate Madrid’s urban public road transport, based on 2020 data published by the Madrid City Council, compiled by authors, and assessed by a panel of 20 experts to identify criteria and factors included in the AHP-MCDM model. The findings illustrate the economic and environmental impact of alternative vehicles, show that the most sustainable alternative is the plug-in electric vehicle in economic and environmental terms, and assist policymakers and firms in future strategic decisions regarding sustainable urban transport policies.

Urban public transport systems must be economically efficient and additionally environmentally sustainable. Available decision support systems, including multiple criteria decision models, allow identifying which urban public transport vehicles are acceptable and those that should no longer be used in efficient and environmentally friendly cities. Previous research has ranked urban public transport vehicles by applying analytic hierarchy process multi-criteria decision-making models, from economic and non-polluting perspectives. However, until now, the types of vehicles acceptable for fleet renewal have not been identified. This study proposes a consistent combination of the ELECTRE TRI multiple criteria decision sorting method and the DELPHI procedure, the objective of which is to identify which urban public transport vehicles are acceptable, taking into consideration a suggested sustainable threshold, which includes economic and environmental strict requirements. The proposed model is based on 2020 Madrid urban public road transport data, published by Madrid City Council, which were compiled by the authors, and assessed by a panel of 20 experts to identify criteria and factors included in the model. Findings help local administrations to identify which urban public transport vehicles should be progressively replaced by those classified as economically efficient and additionally environmentally sustainable.

The aim of this research is to help public transport managers to make decisions on the type of buses that should compose their public transport fleet, taking into account economic, environmental and social criteria from the point of view of sustainability. This paper fills a knowledge gap by including the social dimension of sustainability in addition to the economic and environmental dimensions. The original nature of this study lies in analyzing complementarities in the structuring of an efficiency and multicriteria problem. Our research analyzes Madrid public bus system data; the problem is structured in a comparative way between two analytical methods, a Data Envelopment Analysis (DEA) and an ELimination Et Choice Translating REality (ELECTRE) III. Our research results show that two main groups of vehicles could play a part in part the theoretical solution. The main conclusions of this research are that (a) plug-in and induction electric vehicles are not comparable to GNC and diesel–hybrid vehicles in terms of cost, pollution and service; and (b) the ELECTRE III model provides more information in solving this problem than the DEA model.

We study financial management performance during 2008–2013 for the Spanish aerospace manufacturing value chain and the links with managerial decisions. Data from company financial statements is analysed with Principal Component Analysis, Data Envelopment Analysis and an Artificial Neural Network. Top financial performers focus on liquidity management rather than on returns: both in the short term, by increasing levels of current assets and funding them with short-term liabilities, as well as increasing asset turnover; and in the long term, by aligning equity to non-current assets, while reducing asset and debt intensity levels. Only the manufacturing value chain is analysed, showing the potential for future research in related fields (e.g. Value chain, country). Benchmarking and forecasting financial performance yields information and enables agility and accuracy in the strategy setting process. This study makes a unique contribution because it applies the scientific method where no previous related studies have done. It offers the novelty of using a single metric while Ratio Analysis requires multiple unweighted measures. We contribute by: (a) providing a method based on publicly information to benchmark and predict financial performance, thus offering benefits for aerospace stakeholders and academia; and (b) employing a big data sample that closely represents the population.

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