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This paper examines the impact of residents’ human capital investment inequality on the urban–rural income gap, using China’s provincial panel data from 1997 to 2013. The results show that, at the national level as well as at the regional level, residents’ overall human capital investment inequality has a positive significant impact on the urban–rural income gap. In addition, the impact of overall human capital investment inequality increased monotonically from the eastern region inward to the western region. In terms of the relative impact of each component of human capital investment inequality on the urban–rural income gap, migration investment inequality appears to have the greatest impact at the national level, whereas health investment inequality has the greatest impact on the urban–rural income gap in the eastern region, and education investment inequality exhibits the greatest impact in the central and western regions. We also investigate the impact of human capital investment inequality on the urban–rural income gap over different periods. The results show that residents’ overall human capital investment inequality had a positive impact on the urban–rural income gap in the period 1997–2008, but the impact rapidly shrunk in 2009–2013. Furthermore, the impact of residents’ health investment inequality on the urban–rural income gap shows a downward trend, and the impact of residents’ education investment inequality trended slightly upward from 1997 to 2008, and then rapidly shrunk in 2009–2013. Finally, the impact of residents’ migration investment inequality was only significant in 1997–2002.

This chapter aims to provide a comprehensive overview of virtual tourism and its potential contribution to sustainable development in the tourism industry.

In this chapter, a qualitative approach is used to analyse relevant documents and resources to explore the relationship between virtual tourism and sustainability.

The findings of this study indicate that virtual tourism has numerous applications in the tourism industry, with evident potential for the future. Furthermore, this research identifies virtual tourism as a promising alternative for sustainable tourism, offering the potential to address key sustainability issues in the field.

This chapter adds to the existing literature by examining the link between virtual tourism and sustainability, highlighting the potential of virtual tourism as an alternative to traditional sustainable tourism practices. The insights generated from this study can inform the practices of both academics and practitioners in the tourism industry, promoting more sustainable and responsible tourism practices.

The purpose of this chapter is to determine the optimal path of development of social entrepreneurship in Russia and Central Asian countries by performing a scenario analysis of the development of social entrepreneurship and analyzing the opinions of interested parties regarding the outcome of social entrepreneurship in Russia and Central Asian states. The authors’ recommendations for the practical implementation of the optimal path of social entrepreneurship development are described.

The scenario analysis of statistical data showed that the existing practice of standardization of social companies’ activities hinders their executing their functions on raising the quality of life. With the expansion of freedom of social entrepreneurship, the population’s quality of life grows; on the contrary, the increase of state regulation leads to its reduction. The findings show that social entrepreneurship in Russia and Central Asian countries is interested in private investments and social companies’ employees. That is why the representatives of these categories of concerned parties support de-regulation. Consumers of social goods and services are interested in obtaining them freely and thus prefer standardization.

This chapter presents a new direction of social entrepreneurship analysis – by determining the correlation between economic freedom and quality of life. This opens an opportunity for thorough social entrepreneurship research based on authentic and objective quantitative (statistical) data. The obtained conclusions and offered recommendations allow using the mechanism of public–private entrepreneurship for turning social entrepreneurship into something more valuable and useful – a tool for increasing the quality of the population’s life.

The purpose of this chapter is to solve multi-objective formulation for traveling salesman and transportation problems. Computations are based on real data for the road freight transportation of a Spanish company. The company was selected because of its importance in Spanish economy and market. This company is important in the whole country; however, it has its higher importance in the northern part of Spain. The requirements for these models are the minimization of total distance and the CO₂ emissions. To achieve this, it is required to know and carry out the minimization of the total distance traveled by the trucks during the deliveries. The deliveries are going to be executed between the different locations, nodes, in the region, and Elorrio, where the depot is situated. The data have been used to decide the best route in order to obtain a minimization of cost for the company. As it was mentioned earlier, the problems are focused on the reduction of the amount of CO₂ emissions and minimization of total distance; by studying different parameters, the best solutions of route transportation have been obtained. The software used to solve these models is CPLEX solver with AMPL programming language.

The purpose of this chapter is to optimize multi-criteria formulation for green vehicle routing problems by mixed integer programming. This research is about the road freight transportation of a Spanish company of groceries. This company has more power in the north of Spain and hence it was founded there. The data used for the computational experiments are focused in the northern region of Spain. The data have been used to decide the best route in order to obtain a minimization of costs for the company. The problem focused on the distance traveled and the altitude difference; by studying these parameters, the best solution of route transportation has been made. The software used to solve this model is CPLEX solver with AMPL programming language. This has been helpful to obtain the results for the research and some conclusions have been obtained from them.

Purpose – This chapter gives an overview of methods for defining and analysing crash severity.

Methodology – Commonly used methods for defining crash severity are surveyed and reviewed. Factors commonly found to be associated with crash severity are discussed. Approaches for formulating and estimating models for predicting crash severity are presented and critiqued. Two examples of crash severity modelling exercises are presented and findings are discussed. Suggestions are offered for future research in crash severity modelling.

Findings – Crash severity is usually defined according to the outcomes for the persons involved. The definition of severity levels used by law enforcement or crash investigation professionals is less detailed and consistent than what is used by medical professionals. Defining crash severity by vehicle damage can be more consistent, as vehicle response to crash forces is more consistent than that of humans. Factors associated with crash severity fall into three categories – human, vehicle/equipment and environmental/road – and can apply before, during or after the crash event. Crash severity can be modelled using ordered, nominal or several different types of mixed models designed to overcome limitations of the ordered and nominal approaches. Two mixed modelling examples demonstrate better prediction accuracy than ordered or nominal modelling.

Research Implications – Linkage of crash, roadway and healthcare data sets could create a more accurate picture of crash severity. Emerging statistical analysis methods could address remaining limitations of the current best methods for crash severity modelling.

Practical Implications – Medical definitions of injury severity require observation by trained medical professionals and access to private medical records, limiting their use in routine crash data collection. Crash severity is more sensitive to human and vehicle factors than environmental or road factors. Unfortunately, human and vehicle factor data are generally not available for aggregate forecasting.

A destination's ability to attract tourists is associated with the visitor experience and, in recent years, visitors have increasingly used virtual environments and digital innovation, such as social media platforms, to communicate their experience of tourist destinations. A positive well-communicated tourist experience improves the reputation of the destination and has relevant consequences for both the destination's attractiveness and its competitive advantage. On the contrary, when the destination's reputation is negatively affected by visitors' experiences, comments and reviews, such destination might compromise its ability to attract new visitors. Studies in this field agree alike that the tourist experience is negatively affected by overcrowding and overflows phenomena occurring around the visited city attractions. The present research, merging the aforementioned observations, investigates whether visitor density affects the online reputation of the Uffizi Gallery, estimated by extracting visitors' opinions and feedbacks on the city's main attractions from TripAdvisor ratings and from Twitter posts, by applying sentiment analysis to evaluate whether the text is positive, negative, or neutral. The city of Florence is an ideal case study, as the city records almost 16 million tourist overnight stays per year hence highly exposed to the risk of tourist overcrowding and overflows. The research findings reveal that Uffizi Gallery experiences and mood are influenced by the number of visitors insisting and if tourists live a negative experience, this is further exacerbated by the growing density of visitors themselves. We find that, if tourists have a negative experience, this is exacerbated by the density of visitors to the Uffizi Gallery. The results reveal also that tourists' experiences are even more influenced by any general dissatisfaction they experience in the city of Florence in a broader way. Practical implications and theoretical contributions are discussed.