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Tourism in Coimbra today is influenced by the fact that the Univer(s)city was distinguished as a World Heritage Site in 2013. The number of visits has grown very significantly in recent years, but the diversification of the tourist offer is still weak and unable to take advantage of existing resources. This paper aims to present genealogy tourism as an alternative urban cultural tourism in Coimbra.

Methodology involved mapping the Jewish culture elements in the city of Coimbra, and a route was outlined and proposed.

Genealogy tourism resources are identified in the historic centre of the city. These alternative spaces need urban rehabilitation and (re)functionalisation, which allowed the authors to rethink tourism in Coimbra. They are the motivation to visit for all urban cultural tourists, especially Israelis/Jews, and provide contact with places where the experiences of ancestors combine with the history and memory of places, with recent discoveries and the elements of Jewish culture in the city.

It is concluded that the quantity, diversity, authenticity and singularity of the heritage resources that bear witness to the Jewish presence in Coimbra are sufficient assets to create a route, to enrich the tourist experience in the city and to include the destination in the Sephardic routes.

Striving to achieve the goal of carbon neutrality before 2060 indicates that China, as the most extensive power system in the world and a country based on coal power, is imperative to improve the technical level of electric power utilization. This paper aims to explore the nonlinear evolution mechanism of power technology progress under the constraints of net-zero carbon dioxide emissions in China.

This paper, first, based on China’s provincial panel data from 2000 to 2019, uses global direction distance function to measure power technological progress. Second, the threshold regression model is used to explore the nonlinear relationship between carbon emission reduction constraints on electric power technological progress.

There is a significant inverted U-shaped relationship between China’s provincial carbon emission reduction constraints and electric power technological progress. Meanwhile, the scale of regional economic development has a significant moderating effect on the relationship between carbon emission reduction constraints and power technological progress.

This paper puts forward targeted suggestions for perfecting regional carbon emission reduction policy and improving electric power technological progress.

Based on the global directional distance function, this paper extracts power as a production factor in total factor productivity and calculates the total factor electric power technological progress. This paper objectively reveals the influence mechanism of carbon emission reduction constraints on electric power technology progress based on the threshold regression model.

This paper aims to investigate the determinants of international bilateral tourism demand in countries of Southern Common Market (specifically, Argentina, Brazil and Uruguay) and Chile.

In this study, an augmented gravity model is used to investigate the determinants of international bilateral tourism demand in countries of Southern Common Market. The novel aspect of the analysis is that three models of tourism are defined, depending on the spatial distribution of tourist arrivals and departures. An intra-regional model, an extra-regional model and a general model are estimated using a dynamic panel data model.

The results indicate that traditional gravity variables are significant in explaining bilateral inbound arrivals, but the characteristics and the behavior of the demand of tourism vary on whether the country belongs to the sub-regional bloc.

The differences found in this paper might have some impacts on the desired design and direction of the touristic policies of each country.

This study analyzes the determinants of international tourism demand through different bilateral relationships, differentiating between intra- and extra-block tourisms.

The study's objective is to estimate the association of specific perceived employer-provided benefits on employees' intention to leave in different age cohorts during coronavirus disease 2019 (COVID-19). Informed by the psychological theories of ageing, the authors propose three age-cohort-specific hypotheses in three motivational domains: security and health benefits, flexible work arrangement and education-related benefits.

The authors use a large survey of employees in Estonia (n = 7,209) conducted in 2020 and test the association of specific benefits and their interactions with age on employees' intention to leave.

The results show that older cohorts are generally less prone to leave their jobs. Benefits that employers could use during the COVID-19 crisis generally had negative associations with the intention to leave, but age-specific differences were negligible; only the perceived provision of flexible work arrangements reduced the younger cohort's intention to leave relatively more.

This study is one of the few that allows us to make inferences regarding the benefits preferences amongst the working population during an unprecedented health crisis.

The purpose of the paper is to explore how the effects of the COVID-19 pandemic will influence the development of revenue management practice in the visitor attractions sector.

This viewpoint paper builds on the argument that tracking previous patterns of behaviour and trends can be used to predict future actions and developments.

The paper identifies how historically the development of revenue management practice has been driven by major external trigger points often linked to sudden increases in competitive pressures, such as the deregulation of the airline industry, and expands on this to argue that the pandemic is one such trigger point that has fundamentally changed the approach to revenue management through a refocusing on key principles to manage demand and that this could potentially accelerate its development within the visitor attraction sector.

Pre-COVID, the practice of revenue management in the visitor attraction sector was underdeveloped, and the opportunities to develop revenue management had not been discussed in the academic literature. This paper suggests that the challenges of the pandemic that forced visitor attractions to focus on visitor demand management can now begin to be extended to incorporate the management of revenue and will consequently be of value for academics and practitioners.

The purpose of this paper is to study the optimization of the geographical location of a network of points of sale, so that each retailer can have access to a potential geographic market. In addition, the authors study the importance of the distance variable in the commercial viability of a point of sale and a network of points of sale, analysing if the best location for each point (local optimum) is always the best location for the whole (global optimum).

Location-allocation models are applied using p-median algorithms and spatial competition maximization to analyse the actual journeys of 64,740 car buyers in 1240 postal codes using a geographic information system (GIS) and geomarketing techniques.

The models show that the pursuit of individual objectives by each concessionaire over the collective provides poorer results for the whole network of points of sale when compared to coordinated competition. The solutions provided by the models considering geographic and marketing criteria permit a reduction in the length of journeys made by the buyers. GIS allows the optimal control of market demand coverage through the collaborative strategies of the supplying retailers, in this case, car dealerships.

The paper contributes to the joint research of geography and marketing from a theoretical and practical point of view. The main contribution is the use of information on actual buyer journeys for the optimal location of a network of points of sale. This research also contributes to the analysis of the correlation between the optimum local and optimum global locations of a commercial network and is a pioneering work in the application of these models to the automotive sector in the territorial area of the study.

This paper aims to identify and determine regional tourism axes in Hormozgan Province, Iran, as a region with significant potential

The research method is quantitative and uses the fuzzy accreditation tool and TOPSIS model; the identification, determination and ranking of regional tourism axes have been performed by analyzing the spatial distribution of tourism attractions in the GIS environment.

The results show that given the capacities of Hormozgan Province, at least 15 axes are recognizable. This paper highlights regional tourism planning as a tool for urban and rural socio-economic development in potential provinces such Hormozgan.

This study provides a number of practical implications for regional tourism development as follows: it identifies some of the most important potential axes in Hormozgan Province, which can be considered as investment areas in the national and regional tourism development strategy. The spatial results of this study could be embedded in all urban and rural developmental plans in the province. Tourism investment should shift its spatial concentration from the spot approach, especially islands and cities, to the axis approach while equipping those axes as comprehensive spatial strategic regional tourism plans. Sectoral tourism in each sector including sports, economy and nature could be planned as if sectoral institutions and organizations are going to develop their own tourism goals.

This work proposes a methodology to project future freight demand for all commodity types that begin and end in each geographical region and the amount of freight that moves between all origin and destination pairs. Following the traditional four-step demand forecasting framework, the procedure corresponds to trip generation and trip distribution analysis for interregional freight demand. Using future economic growth factors from macroeconomic and input-output models, the amounts of freight production and attraction in each analysis zone are forecasted and taken as given. Subsequently, an iterative matrix balancing method is applied to determine the estimated freight shipment demand for all origin and destination zone pairs. The proposed algorithm is applied to generate predicted future freight demand within the United States from 2010-2050 in five-year increments based on the national freight demand data from 2007. Four different scenarios are proposed that consider variations in both global economic growth and environmental regulation. This study will assist transportation planners and decision makers in public and private sectors to assess how future freight delivery demand on the national scale considering various future global economic growth and environmental policy scenarios will affect various issues such as air quality and human health problems.