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The tourism industry's deep interindustry linkages makes it a significant engine of growth for an economy. When aligned with specific sustainability goals, it becomes an effective means of inclusive prosperity and environmental protection. Coming fresh from the lockdown, investors and stakeholders are more conscious of their investing and spending decisions preferring more and more businesses and industries with sustainable business practices (Leal Filho et al., 2022). Specifically, tourism's generation of business and customer values must also extend to generation of value to the environment, society, and even on governance (GIZ, 2020). This chapter, using a system's view approach, demonstrates how activities in the tourism value chain – besides being an engine of growth – can also be the vehicle to attaining environmental, social, and governance (ESG) aspirations.

This paper examines the dynamics of structural transformation in Morocco since 1970 by analyzing input-output tables expressed in terms of employment and output levels across 24 sectors.

This study employs a twofold methodological approach. Firstly, it examines the evolution of sectoral employment shares over time using World Bank data. Secondly, it utilizes Input-Output analysis to examine structural shifts in Morocco's economy, focusing on sector-specific output and employment data. The primary data source is the Eora Global Supply Chain Database, covering the years 1970, 1980, 1990, 2000, and 2015. Additionally, to transition from production-based to employment-based input-output tables, the study leverages employment and output data from the Penn World Tables to calculate the diagonal labor coefficient matrix.

First, our analysis reveals that Morocco's economic transformation has been slower compared to high-income countries. Structural changes, as evidenced by the evolution of employment shares by sector, show a gradual decline in agricultural employment share over the period 1991-2019, accompanied by a shift towards the services sector. This shift, driven by favorable conditions in the services sector and increased capital use in agriculture, has resulted in premature deindustrialization. The industrial sector's employment share has remained stable due to its capital-intensive nature. Second, Input-Output analysis reveals a pronounced premature tertiarization of the Moroccan economy. Between 1990 and 2000, the tertiary sector saw a dramatic rise in both backward (167%) and forward (68%) linkages, while the primary sector's backward linkages fell by 33% during the same period. Although the primary sector’s linkages increased by 10% from 2000 to 2015, the secondary sector experienced a consistent decline in backward linkages, dropping 12% from 1990 to 2000 and an additional 10% from 2000 to 2015. Employment linkage analysis further underscores this shift, with a 12% increase in the tertiary sector’s backward linkages from 1990 to 2000, contrasted by significant declines in the primary (51%) and secondary (7%) sectors. These trends highlight an unsustainable move towards services without concurrent industrial development, challenging balanced economic development.

As it is unanimous, the structural transformation of Morocco remains relatively slow and characterized by a shift of the labor factor from the primary sector to the tertiary sector, with a limited job creation by the secondary sector considered as the pillar of any structural transformation. This paper advances the field of research on structural transformation by elucidating the premature tertiarization of the Moroccan economy and the slowness pace at which the transformation of its economic fabric is occurring, thereby filling the empirical gap.

The paper is devoted to modeling a pricing policy of competitive firms in a “closed” economy framework.

The proposed model can be regarded as an analog to CGE model and is based on the intersectoral balance methodology incorporating linear demand functions for goods and services.

By performing different model experiments, we show that a certain degree of competition can bring more profit to all competing firms, than in case of complete absence of such competition, what is also supported by empirical investigation. This finding implies that monopolies may perform worse than competitive firms, what contradicts with the modern provisions of economic theory, stating that monopoly is the most lucrative type of market structure for a producer. The discovered effect occurs due to the aggressive pricing policy, adopted by monopolies, spurring up the inflation spiral, which is most obvious if monopolies are strongly interdependent in terms of production matrix. This inflation spiral drives prices too high, what negatively reflects on firms’ costs and, consequently, results in monopolies receiving less profit.

The proposed model can also be useful for understanding and assessing various economic consequences after different external or internal shocks, what is especially crucial when conducting monetary or fiscal policy.

This work aims to analyse the relationship between public health efficiency and well-being considering a panel of 102 Italian provinces from 2000 to 2016 and evaluates if there are omitted variable biases and endogeneity biases and also evaluates if there are heterogeneous effects among provinces with different income levels.

We use a multi-input and output bootstrap data envelopment analysis to assess public health efficiency. Then, we measure well-being indices using the min-max linear scaling transformation technique. A two-stage least squares model is used to identify the causal effect of improving public health efficiency on well-being to account for time-invariant heterogeneity, omitted variable bias and endogeneity bias.

After controlling for important economic factors, the results show a significant effect of an accountable and efficient public health system on well-being. Those effects are concentrated in the North, the most economically, geographically and environmentally advantageous areas.

The use of the sample mean, probably the oldest and most used method for aggregating the indicators, could be affected by variable compensation, with consequent misleading results in the process of constructing the well-being index. Another limitation is the use of lagged values of the main predictor as an instrument in the instrumental variables setting because it could lead to information loss. Finally, the availability of data over a long period of time.

The findings could help policymakers adopt measures to strengthen the public health system, encourage private providers and inspire countries worldwide.

These results draw the attention of local authorities, who play an important role in designing and implementing policies to stimulate local public health efficiency, which puts individuals in the conditions of achieving overall well-being in their communities.

For the first time in Italy, a panel of well-being indices was constructed by developing new methodologies based on microeconomic theory. Furthermore, for the first time, the assessment of the relationship between public health efficiency and well-being is carried out using a panel of 102 Italian provinces.

Whether environmental, social and governance (ESG) factors are a curse or a blessing in the run for performance is still a burning issue. This is all the more true for banks, as their call for action in ESG dimensions clashes with evidence of scandals. As a more aligned to reality view, we propose to regard the mistreatment of ESG issues, both theoretically and empirically, as an undesirable output of banks' everyday activity. Empirically, we question whether 128 leading banks worldwide neglected the minimisation of ESG controversies (ESGC) in pursuing traditional productive efficiency, over the timespan 2011–2021.

To our end, we use oriented distance functions according to the nonparametric efficiency approach of data envelopment analysis (DEA). This framework accounts for undesirable outputs.

Being inefficient in the ESGC domain is not a necessary evil to achieve productive efficiency. Instead, incurring in higher ESGC negatively affects productive efficiency, by causing future decrease of reputation and performance.

We propose a new paradigm of banks’ activity and related efficiency evaluation. In so doing, we favour a real dimension of banks’ engagement in ESG concerns.

The purpose of this paper is to approach predictive maintenance (PdM) of brushless direct current (BLDC) motors using audio signal processing and extracting statistical and spectral features to train classical machine learning (ML) models.

The proposed methodology relies on classification predictive model that shows the motors prone to failure. To verify this, the model was implemented and tested with audio data. The trained models are then deployed to an Industrial Internet of Things (IIoT) application built using Django.

The implementation of the methodology allows for achieving performance as high as 92% accuracy, proving that spectral features should be considered when training ML models for PdM.

The proposed model is an effective decision-making tool that provides an ideal solution for preventive maintenance scheduling problems for BLDC motors.

Project portfolio benefit (PPB) evaluation is crucial for project portfolio management decisions. However, PPB is complex in composition and affected by synergy and ambidexterity. Ignoring these characteristics can result in inaccurate assessments, impeding the management and optimization of benefit. Considering the above complexity of PPB evaluation, this study aims to propose a refined PPB evaluation model to provide decision support for organizations.

A back propagation neural network optimized via genetic algorithm and pruning algorithm (P-GA-BPNN) is constructed for PPB evaluation. First, the benefit evaluation criteria are established. Second, the inputs and expected outputs for model training and testing are determined. Then, based on the optimization of BPNN via genetic algorithm and pruning algorithm, a PPB evaluation model is constructed considering the impacts of ambidexterity and synergy on PPB. Finally, a numerical example was applied to validate the model.

The results indicate that the proposed model can be used for effective PPB evaluation. Moreover, it shows superiority in terms of MSE and fitting effect through extensive comparative experiments with BPNN, GA-BPNN, and SVM models. The robustness of the model is also demonstrated via data random disturbance experiment and 10-cross-validation. Therefore, the proposed model could serve as a valuable decision-making tool for PPB management.

This study extends prior research by integrating the impacts of synergy and ambidexterity on PPB when conducting PPB evaluation, which facilitates to manage and enhance PPB. Besides, the structural redundancy of existing assessment methods is solved through the dynamic optimization of the network structure via the pruning algorithm, enhancing the effectiveness of PPB decision-making tools.

This paper aims to explore recent advances in drift compensation algorithms for Electronic Nose (E-nose) technology and addresses sensor drift challenges through offline, online and neural network-based strategies. It offers a comprehensive review and covers causes of drift, compensation methods and future directions. This synthesis provides insights for enhancing the reliability and effectiveness of E-nose systems in drift issues.

The article adopts a comprehensive approach and systematically explores the causes of sensor drift in E-nose systems and proposes various compensation strategies. It covers both offline and online compensation methods, as well as neural network-based approaches, and provides a holistic view of the available techniques.

The article provides a comprehensive overview of drift compensation algorithms for E-nose technology and consolidates recent research insights. It addresses challenges like sensor calibration and algorithm complexity, while discussing future directions. Readers gain an understanding of the current state-of-the-art and emerging trends in electronic olfaction.

This article presents a comprehensive review of the latest advancements in drift compensation algorithms for electronic nose technology and covers the causes of drift, offline drift compensation algorithms, online drift compensation algorithms and neural network drift compensation algorithms. The article also summarizes and discusses the current challenges and future directions of drift compensation algorithms in electronic nose systems.

This paper aims to measure and unveil the relationship between customer satisfaction and efficiency levels in the most relevant Brazilian airports.

The authors utilize a two-stage network DEA (data envelopment analysis) and AHP (analytic hierarchy process) model as the cornerstones of the study. The first stage of the network productive structure focuses on examining the infrastructure efficiency of the selected airports, while the second stage assesses their business efficiency.

Although the results indicate that infrastructure and business efficiency levels are heterogeneous and widely dispersed across airports, controlling the regression results with different contextual variables suggests that the impact of efficiency levels on customer satisfaction is mediated by a set of socio-economic and demographic (endogenous) and regulatory (exogenous) variables. Furthermore, encouraging investment in airports is necessary to achieve higher infrastructural efficiency and scale efficiency, thereby improving customer satisfaction.

There is a scarcity of studies examining the relationships among customer satisfaction, privatization and airport efficiency, particularly in developing countries like Brazil.