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This paper aims to examine the impact of coastline on the rental value of residential property in proximity to the coastline, using the hedonic pricing model from two perspectives. First, Model 1A–C accounted for estimating the influence of coastal amenities while controlling for other housing attributes influencing rent. Second, Model 2A–C accounted for the interaction between coastal amenities/disamenities and other housing attributes influencing rent.

A survey approach was adopted for the data collection process. For both models, property values were measured in proximity to coastline using 0–250 m, 251–500 m and 0–500 m.

Findings revealed that property rental value increases as we move away from the coastline when disamenities are not controlled. The results suggested that for a mean-priced home (N2,941,029 or $8,170) at the mean distance from the coastline (301.83 m), a 1% increase in distance from the coastline would result in a 0.001% or N9.77 ($0.03) increase in rental value.

The implication to real estate valuers is that varying premiums should be considered when valuing a property depending on the distance to the coastline while considering other housing attributes.

This research introduces a novel approach to the hedonic model for determining property values in proximity to coastal environment by estimating the influence of coastal amenities while controlling for other housing attributes influencing rent, on the one hand, and accounting for the interaction between coastal amenities/disamenities and other housing attributes influencing rent, on the other.

This paper aims to examine the unique Chinese context by analyzing the city labels (e.g. smart city and eco city) used by Chinese local governments at or above the provincial capital level to represent themselves (adopted city labels) and the developmental pathways they actually pursued (adopted developmental pathways).

The authors compared the city brand choices to those anticipated based on their geographic and economic contexts (predicted city labels and developmental pathways) as well as the directives outlined in national planning documents (imposed city labels and developmental pathways). The authors identified ten main categories of city labels used to designate themselves and establish the frequency of their use based on municipal plan documents, economic and geographic data and national plan documents and policy reports, respectively.

The authors discovered that both local economic development and geographic factors, as well as top-down administrative influences, significantly impact city branding strategies in the 38 Chinese cities studied. When these models fall short in predicting adopted city labels and pathways, it is often because cities favor a service-oriented reputation over a manufacturing-focused one, and they prefer diverse, multifaceted industrial images to uniform ones.

The originality and value of this paper lie in its contribution to the academic literature on city branding by developing a predictive model for brand development at the municipal level, with explicit attention to the national-local nexus. The paper’s approach differs from existing research in the first cluster of city branding by not addressing issues of stakeholder involvement or adoption and implementation processes. Additionally, the paper’s focus on the political power dynamics at the national level and urban governance details at the municipal level provides a unique perspective on the topic. Overall, this paper provides a valuable contribution to the field of city branding by expanding the understanding of brand development and its impact on the socioeconomic environment.

The purpose of this study is to analyse Benidorm, San Sebastián, Gijón, Málaga, Tenerife Island and Santander smart tourist destinations (STDs) as a touristic model and example to follow by other destinations in Spain and all over the world.

To fulfil the stated objective, this study follows several phases that introduce and classify a set of measures implemented by the six Spanish smart destinations to be designed as a STD.

Findings suggest that being a STD requires a high cost, and this is only accessible to big destinations with enough resources. Of the 50 Spanish provinces, eight are STDs, and these are localised in coastal areas. Obviously, this challenge is not within the reach of any Spanish city. Moreover, findings of the current study prove that the six Spanish smart destinations have a good air accessibility through their six airports, but the accessibility in a STD is not just physical; this must provide digital accessibility to tourists through destination marketing organisation’s website and app that will supply them with information on a wide range of services, including accommodations, tourist attractions, restaurants, public transport, museums and monuments’ locations, amongst many others.

From a resident point of view, a STD cannot be only focused on a technological and tourism context; a STD also requires knowing and meeting the needs of local residents and having a voice in decision-making processes. Hence, this study shows a new perspective on STDs that will benefit the literature on STDs.

The geographic information systems (GIS) sector is witnessing significant growth in recent times at the global level and in the Kingdom of Saudi Arabia at the same pace. In this research, the authors aim to measure the GIS market employability in the Kingdom of Saudi Arabia, the required skills of those seeking GIS employees, existing knowledge and abilities of graduates of relevant academic programs and gaps, if any, between the graduates and the labor market requirements in terms of GIS. Research on GIS education and GIS employability from an information system perspective is scarce; thus, this study is needed.

In this study, a questionnaire was distributed manually and electronically to a specific group of GIS students, teachers and employees. The questionnaire was prepared in Arabic and English language. It was distributed manually to the attendees of the 12th GIS Symposium, organized by Imam Abdulrahman Bin Faisal University at Dammam city, Saudi Arabia in April of 2018. The event is a yearly one that gathers local GIS community and outside speakers and guests. An electronic version of the questionnaire was also available and distributed via personal contacts. Responses were received until the end of 2019.

The results of 107 respondents indicates good GIS foundation (for example in topology, geospatial data processing and cartography); however, programming and business skills (namely cost benefit analysis, organizational perspective and business understanding) are lacking from Saudi GIS graduates. About 67% of respondents acknowledge directly that it is difficult to find locally qualified GIS graduates. Moreover, 55% of respondents classify GIS job candidates as underqualified. This research identifies under qualification in business competencies and computer programming skills for GIS students and workers in Saudi Arabia. This research did not find a statistically significant different response between GIS teachers and GIS employees/employers; however, students’ response was significantly different from these two groups.

Research on GIS education and GIS employability from an Information System perspective is scarce; thus, this study is needed. Moreover, research about this topic in Saudi Arabia and the Arabian Gulf is almost nonexistent.

From a technological determinist perspective, machine learning (ML) may significantly contribute towards sustainable development. The purpose of this study is to synthesize prior literature on the role of ML in promoting sustainability and to encourage future inquiries.

This study conducts a systematic review of 110 papers that demonstrate the utilization of ML in the context of sustainable development.

ML techniques may play a vital role in enabling sustainable development by leveraging data to uncover patterns and facilitate the prediction of various variables, thereby aiding in decision-making processes. Through the synthesis of findings from prior research, it is evident that ML may help in achieving many of the United Nations’ sustainable development goals.

This study represents one of the initial investigations that conducted a comprehensive examination of the literature concerning ML’s contribution to sustainability. The analysis revealed that the research domain is still in its early stages, indicating a need for further exploration.

This study aims to investigate the various systems in logistics industry of Pakistan through the lens of the World Bank's logistics performance indicators (LPI) and understand their impact on the China–Pakistan economic corridor (CPEC) that is a vital part of China's belt and road initiative (BRI).

In this study thematic analysis was performed on twenty-three semi-structured interviews with experts in Pakistan's logistics and supply chain sector to gain an in-depth insight into the logistics performance relative to CPEC.

A performance gap exists in the logistics systems in Pakistan, both for hard and soft infrastructure. The significant challenges are the inefficiencies of the government, minimal use of information and computing technology (ICT), and an incapable workforce. It is essential to be cognizant of the ground realities and amendments required in the existing policies and practices in light of the challenges faced and best practices adopted by developed and developing countries with good standing in logistics performance. This study will guide policymakers and practitioners for hard and soft logistics infrastructure improvement, which may benefit economic corridors in general and CPEC in particular.

This study contributes to the existing literature by highlighting the role of ICT in improving both soft and hard logistics infrastructure, which can lead to significant development of economic corridors. The study makes use of a case study of the CPEC to demonstrate the lack of ICT can hamper the growth of an economic corridor despite billions of dollars of investment in the hard infrastructure development projects.

The rapid development of urban areas in Sleman District, Indonesia, has created new challenges for firefighting response services. One of the primary challenges is to identify the optimal locations for new fire stations, to improve service quality and maximize service coverage within the specified time.

This paper proposes a method for precisely calculating travel time that integrates delay time caused by traffic lights, intersections and congestion. The study highlights the importance of precise calculation of travel time in order to provide a more accurate understanding of the service area covered by the fire stations. The proposed method utilizes network analysis in ArcGIS, the analytical hierarchy process (AHP) and simple additive weighting (SAW) to accurately calculate travel time and to identify the best locations for new fire stations. The identification of new site was based on service safety, service quality, service costs and demographic factors and applied to the Sleman district in Indonesia.

The results showed that the total area covered by old and new fire stations decreased from 61% to 31.8% of the study area when the adjusted default speed scenario was implemented.

The results indicated that the default speed scenario could provide misleading information about the service area, while the adjusted default speed scenario improved service quality and maximized service coverage.

The proposed method provides decision-makers with an effective tool to make informed decisions on optimal locations for new fire stations and thus enhance emergency response and public safety.