Search Results (71)

Artificial Intelligence (AI) has emerged as a transformative force in the field of architectural design. This study aims to systematically analyze the influence mechanisms of Artificial Intelligence-based Architectural Design (AIAD) by constructing a comprehensive hybrid model that integrates the Analytic Hierarchy Process (AHP), Decision-Making Trial and Evaluation Laboratory (DEMATEL), Interpretive Structural Modeling (ISM), and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC). Based on the previous quantitative literature review, 6 primary categories and 18 secondary influencing factors were identified. Data were collected from a panel of fifteen experts representing the architecture industry, academia, and computer science. Through weighting analysis, causal mapping, hierarchical structuring, and driving–dependence classification, the study clarifies the complex interrelationships among influencing factors and reveals the underlying drivers that accelerate or constrain AI adoption in architectural design. By quantifying the hierarchical and causal influence of factors, this research provides theoretical findings and practical insights for design firms undergoing digital transformation. The results extend previous meta-analytical studies, offering a decision-support system that bridges academic research and real-world applications, thereby guiding stakeholders toward informed adoption of artificial intelligence for future cultural tourism development and regional spatial innovation. Full article

Assisted reproduction is a rapidly expanding pillar of medical tourism. Greece combines a liberal legal framework, internationally accredited clinics, and comparatively competitive costs, attracting cross-border patients seeking ART services. Following the 2022 amendment (Law 4958/2022) which amends the original law n.3305/2005, treatment is permitted up to age 54 under specific authorization, while court-approved surrogacy, anonymous gamete donation, and the adoption of decision-support technologies (e.g., AI-assisted embryo assessment, PGT-A) underpin the sector’s growth. This review synthesizes legal, medical, economic, and social dimensions, drawing on Q1 literature and official datasets (WHO, OECD, ESHRE/ICMART), and compares Greece with Spain, the USA, the Czech Republic, and Ukraine. Quantitative indicators include age-stratified success rates and indicative treatment costs. We discuss benefits and risks for patients and the health system, highlighting policy options for sustainable, ethically robust reproductive tourism in Greece. Full article

This study introduces a novel informatics framework for assessing regional sustainability by integrating Twin Mean-Variance Two-Stage Data Envelopment Analysis (TMV-TSDEA) with a desirability-based decision analytics system. The model evaluates both the efficiency and stability of economic and environmental performance across regions, supporting evidence-based policymaking and strategic planning. Applied to 16 Thai provinces, the framework incorporates a wide range of indicators—such as investment, population, tourism, industrial output, electricity use, forest coverage, and air quality. The twin mean-variance approach captures not only average efficiency but also the consistency of performance over time or under varying scenarios. A two-stage DEA structure models the transformation from economic inputs to environmental outcomes. To ensure comparability, all variables are normalized using desirability functions based on standardized statistical coding. The TMV-TSDEA framework generates composite performance scores that reveal clear disparities among regions. Provinces like Bangkok and Ayutthaya demonstrate a consistent high performance, while others show underperformance or variability requiring targeted policy action. Designed for integration with smart governance platforms, the framework provides a scalable and reproducible tool for regional benchmarking, resource allocation, and sustainability monitoring. By combining informatics principles with advanced analytics, TMV-TSDEA enhances transparency, supports decision-making, and offers a holistic foundation for sustainable regional development. Full article

This study addresses the critical need for accurate tourism demand (TD) forecasting in Bulgaria using economic indicators, developing robust predictive models to navigate post-pandemic market volatility. The COVID-19 pandemic exposed tourism’s vulnerability to systemic shocks, highlighting deficiencies in traditional forecasting approaches. Bulgaria’s tourism industry, characterized by strong seasonal variations and economic sensitivity, requires enhanced methodologies for strategic planning in uncertain environments. The research employs comprehensive comparative analysis of machine learning (ML) and deep machine learning (DML) methodologies. Monthly overnight stay data from Bulgaria’s National Statistical Institute (2005–2024) were integrated with COVID-19 case data, Consumer Price Index (CPI) and Bulgarian Gross Domestic Product (GDP) variables for the same period. Multiple approaches were implemented including Prophet with external regressors, Ridge regression, LightGBM, and gradient boosting models using inverse MAE weighting optimization, alongside deep learning architectures such as Bidirectional LSTM with attention mechanisms and XGBoost configurations, as each model statistical significance was estimated. Contrary to prevailing assumptions about deep learning superiority, traditional machine learning ensemble approaches demonstrated superior performance. The ensemble model combining Prophet, LightGBM, and Ridge regression achieved optimal results with MAE of 156,847 and MAPE of 14.23%, outperforming individual models by 10.2%. Deep learning alternatives, particularly Bi-LSTM architectures, exhibited significant deficiencies with negative R² scores, indicating fundamental limitations in capturing seasonal tourism patterns, probable data dependence and overfitting. The findings, provide tourism stakeholders and policymakers with empirically validated forecasting tools for enhanced decision-making. The ensemble approach combined with statistical significance testing offers improved accuracy for investment planning, marketing budget allocation, and operational capacity management during economic volatility. Economic indicator integration enables proactive responses to market disruptions, supporting resilient tourism planning strategies and crisis management protocols. Full article

Ecotourism, as a resilient and sustainable form of tourism, plays an increasingly vital role in regional economic growth and ecological conservation, particularly in the face of challenges such as climate change and rapid urbanization. This study employs spatial-temporal analysis tools including GIS, Fragstats, and GeoDa to examine the dynamic evolution of ecotourism suitability levels (ESL) and landscape patterns (LP) in the Yangtze River Delta (YRD) from 2002 to 2022. By incorporating spatial autocorrelation analysis, the relationship between ESL and LP is investigated to assess the adaptive capacity of the regional ecotourism system. The results reveal the following: (1) Overall Trends: ESL in the YRD has generally increased over the past two decades, with expansions observed in both high and very low suitability areas, while areas of low suitability have contracted. (2) Spatial Patterns: Core cities such as Shanghai, Hangzhou, Nanjing, and Hefei exhibit high ESL; however, these areas also face intensified landscape fragmentation and decreased ecological connectivity. (3) Landscape Patterns: The region has experienced increasing landscape fragmentation and diversity, particularly in economically advanced zones, posing significant challenges to ecological resilience. (4) Spatial Clustering: Notable spatial clustering of ESL and LP indices is identified in highly urbanized areas, underscoring the necessity for adaptive landscape planning and flexible policy frameworks. This study provides empirical evidence and strategic recommendations to enhance the resilience and sustainability of ecotourism in rapidly urbanizing regions, supporting adaptive responses to crises and informed long-term decision-making. Full article

Artificial intelligence (AI) is playing an increasingly important role in driving sustainable change across coastal and marine environments. Artificial intelligence offers strong support for environmental decision-making by helping to process complex data, anticipate outcomes, and fine-tune day-to-day operations. In busy coastal zones such as the Mediterranean where tourism and boating place significant strain on marine ecosystems, AI can be an effective means for marinas to reduce their ecological impact without sacrificing economic viability. This research examines the contribution of artificial intelligence toward the development of environmental sustainability in marina management. It investigates how AI can potentially reconcile economic imperatives with ecological conservation, especially in high-traffic coastal areas. Through a focus on the impact of social and technological context, this study emphasizes the way in which local conditions constrain the design, deployment, and reach of AI systems. The marinas of Ibiza and Monaco are used as a comparative backdrop to depict these dynamics. In Monaco, efforts like the SEA Index^® and predictive maintenance for superyachts contributed to a 28% drop in CO₂ emissions between 2020 and 2025. In contrast, Ibiza focused on circular economy practices, reaching an 85% landfill diversion rate using solar power, AI-assisted waste systems, and targeted biodiversity conservation initiatives. This research organizes AI tools into three main categories: supervised learning, anomaly detection, and rule-based systems. Their effectiveness is assessed using statistical techniques, including t-test results contextualized with Cohen’s d to convey practical effect sizes. Regression R² values are interpreted in light of real-world policy relevance, such as thresholds for energy audits or emissions certification. In addition to measuring technical outcomes, this study considers the ethical concerns, the role of local communities, and comparisons to global best practices. The findings highlight how artificial intelligence can meaningfully contribute to environmental conservation while also supporting sustainable economic development in maritime contexts. However, the analysis also reveals ongoing difficulties, particularly in areas such as ethical oversight, regulatory coherence, and the practical replication of successful initiatives across diverse regions. In response, this study outlines several practical steps forward: promoting AI-as-a-Service models to lower adoption barriers, piloting regulatory sandboxes within the EU to test innovative solutions safely, improving access to open-source platforms, and working toward common standards for the stewardship of marine environmental data. Full article

Small Island Developing States (SIDS) face energy security challenges due to reliance on imported fossil fuels and limited land for renewable energy. This study evaluates the techno-economic feasibility of integrating Ocean Thermal Energy Conversion (OTEC) and Seawater Air Conditioning (SWAC) systems as a sustainable solution. The research focuses on (1) developing a scalable onshore OTEC-SWAC system and assessing feasibility across 32 SIDS using 20 years of oceanic and atmospheric data, (2) analyzing key system parameters such as pipeline length, pump sizing, and cooling requirements and their effect on capital cost, and (3) developing a scalable cost estimation model for Levelized Cost of Energy (LCOE) predictions. The techno-economic analysis reveals that 30 of the 32 SIDS are technically feasible for OTEC power generation with a temperature gradient of 20 °C. The proposed system is economically feasible in 23 of the SIDS with a calculated average LCOE of 0.16 USD/kWh, which is 67% lower than the diesel LCOE, which is on average 0.46 USD/kWh, making it a cost-competitive alternative. The developed reduced form of the model enables scalable LCOE calculations based on pipeline length and ocean temperature differentials, aiding policymakers in decision-making. By reducing fossil fuel dependency and supporting green tourism, this study provides actionable insights for sustainable energy adoption in SIDS. Full article

A series of policy measures have been implemented to support the integration of agriculture and cultural tourism (ACT), positioning it as a pivotal component of the modern rural industrial system. Additionally, the rapid growth of e-commerce live streaming has surpassed the limitations of traditional promotional methods in terms of geographic and media reach. The enterprise can attract more consumer groups through live streaming. This article analyzes the signal game between the ACT enterprise and consumers and discusses how consumer types affect enterprise decision-making in both static and dynamic contexts. We have come to the following conclusion: (1) The severity of punishment for companies that transmit incorrect signals is the main influencing factor for the balance to be established. (2) The more types of consumers an enterprise attracts, the lower its price. (3) The comparison of profits in static and dynamic contexts depends on changes in consumer types and the impact of the first-stage strategy on the second stage. Full article

This research aims to analyze the spatial and temporal distribution of residents and tourists in an urban context, assessing the risk of overtourism. To achieve this, a tourist city is conceptualized as a complex system and examined through an agent-based model (ABM), which simulates the interactions between heterogeneous agents and their environment. This computational approach enables the exploration of emergent spatial-temporal patterns and facilitates the interpretation of overtourism as a real-world experiment. The case study focuses on Santa Marta (Colombia), a well-established coastal destination currently facing potential entry into a phase of tourism decline if management remains reactive. Simulation results reveal a high risk of overtourism and illustrate the differentiated effects of two plausible management strategies at distinct spatial scales. Additionally, this study proposes a tourism intensity indicator, addressing the problem of overestimating tourism pressure in existing metrics. The proposed model offers a valuable decision-support tool for assessing impacts and designing proactive management measures in destinations experiencing rapid tourist growth across multiple spatial and temporal dimensions. Full article

Urban mass tourism and rural depopulation increasingly threaten heritage sites worldwide, leading to socio-economic and environmental challenges. This study adopts a holistic approach to sustainable tourism planning by examining 84 cultural and natural heritage sites in and around Safranbolu and Amasra, two cities in Türkiye that are listed on the UNESCO World Heritage List and the Tentative List. Inspired by historical travelers’ itineraries, it proposes an eco-cultural tourism route to create a resilient heritage network. A participatory methodology integrates charettes within Erasmus+ workshops, crowdsourcing, various analysis methods while engaging stakeholders, and AI-powered clustering for route determination. The study follows a four-stage framework: (1) data collection via collaborative GIS, (2) eco-cultural route development, (3) stakeholder participation for inclusivity and viability, and (4) assessments and recommendations. Results highlight the strong potential of heritage assets for sustainable tourism while identifying key conservation risks. Interviews and site analysis underscore critical challenges, including the absence of integrated site management strategies, insufficient capacity-building initiatives, and ineffective participatory mechanisms. Moreover, integrating GIS-based crowdsourcing, machine learning clustering, and multi-criteria decision-making can be an effective planning support system. In conclusion, this study enhances the sustainability of heritage and tourism by strengthening participatory eco-cultural development and mitigating mass tourism and abandonment’s negative impacts on the heritage sites. Full article

Thermal cities represent a valuable example of cultural heritage as an expression of territorial relationships, reflecting the interplay between the physical characteristics of the landscape and human creativity. Their cultural value was recognized with the inscription of 11 spa towns in the UNESCO World Heritage List in 2021. However, since the late 20th century, shifting economic and social conditions have led to a widespread crisis in thermal tourism, resulting in abandonment and degradation. So far, this issue has been primarily addressed through tourism and economic models, largely neglecting the landscape perspective. This article, instead, argues that a landscape-based approach is essential for understanding the complexity of the problem and for providing sustainable solutions. The paper seeks to answer two research questions: (i) the first concerns the role of landscape design within the conservation framework of thermal heritage; (ii) the second addresses the creation of new values and opportunities, investigating how landscape design can support a sustainable and context-sensitive transformation of thermal cities. The study adopts the Research-through-Design (RTD) methodology and takes advantage of the landscape design proposal developed for Montecatini Terme, in Italy, as an opportunity to explore the broader issue of rethinking traditional spa towns in crisis. As a result of this design and research experience, it is argued that landscape design plays a crucial role in establishing an integrated system capable of supporting the sustainable development of spa towns and recommendations for decision-makers are provided. Full article

The rich history of the olive tree is deeply connected to the heritage of the Mediterranean region. There are olive trees that are still productive and their age has been calculated by the use of methods of increment core sampling, radiocarbon dating (C14) and luminescence dating (OSL) to be over two thousand years old. However, the age of these trees is not usually known and it is not easy to calculate. As a result, deciding whether an olive tree is monumental is a rather complicated task. The goal of this paper is to present the design and implementation of an intelligent system that uses multi-criteria decision-making to evaluate olive trees and make the decision of whether they are monumental. This information is further used by a system to decide whether an olive grove is monumental or not. The methodology is implemented in a cross-platform application called “Olea App”. The system evaluates different olive trees and evaluates trees and olive groves to select the one that is considered the best to be promoted. The system uses and combines three different multi-criteria decision-making theories, namely, analytical hierarchy process (AHP), simple additive weighting (SAW), and multicriteria optimization and compromise solution (VIKOR) and evaluates olive trees based on tangible and intangible criteria. The method proposed was used to evaluate trees in the Ionian Islands and has proven very effective. The cross-platform application could be used by other researchers to evaluate their olive trees and groves if they cannot apply methods for the estimation of the tree’s age such as the methods of OSL. This work introduces a novel, technology-driven solution for the identification, evaluation, and preservation of monumental olive trees. By integrating scientific, cultural, and technological perspectives, the study provides a sustainable and accessible methodology to ensure these ancient natural landmarks are protected for future generations. The Olea app represents a significant advancement in heritage tree conservation, offering a structured, transparent, and scalable approach to preserving olive tree ecosystems while supporting sustainable tourism and economic incentives for their protection. Full article

As a subset of the human–environment system, the tourism ecosystem focuses on the complex dynamics and interactions between tourism activities and the natural environment. Among these, tourism ecological security (TES) is one of the core issues in the study of tourism ecosystems, aiming to balance economic development and ecological environment protection. Currently, the risk early warning of TES has not received widespread attention, and there is an urgent need for a tourism ecological safety risk early warning system to achieve TES monitoring, risk assessment, and decision support. Therefore, this study established a comprehensive TES evaluation system, systematically analyzed the evolution of TES in Sichuan Province from 2010 to 2022, and used the geographical detector to reveal the influencing factors and driving mechanisms of TES. Based on these achievements, an early risk warning system for TES was established based on the Bayesian network model, simulating the response of TES under single-variable and multi-variable scenarios. The research results reveal that TES changes with environmental changes, resource utilization and consumption, and the development of the tourism industry, and there are differences in the driving factors of TES under different conditions. There is a synergistic effect between the influencing factors of TES, and there is a threshold effect in the regulation of tourism ecological safety, revealing the efficiency and limitations of different regulatory strategies. The early risk warning model for TES based on the Bayesian network has high prediction accuracy and can provide effective support for the management and regulatory policies of TES. Full article

Since tourism involves the movement of people, mobility and tourism are deeply interconnected, with mutual growth dynamics but also shared negative effects, such as greenhouse gas emissions. Among the wide spectrum of policies and strategies for making tourism sustainable, soft mobility has gained increasing importance, becoming more and more a widespread form of active tourism, especially after the COVID-19 pandemic. A sustainable approach, aimed at maintaining a balance between economic development and environmental protection, requires greater promotion of soft mobility and integration with rail transport. To make active tourism truly sustainable, the support of the railway network, a low-emission collective transport system, is needed, allowing people to reach destinations and move between destinations, taking advantage of intermodality. This needs the improvement of the connection between the two transport systems, the creation of services for visitors, and the organization of an appropriate railway service. Within this framework, the present work defines a methodology that, starting from the analysis of the relationships between railway stations, the soft mobility network, and attractions in the surrounding territory, allows us to identify stations that could serve as intermodal hubs for connecting local resources through soft mobility, according to a priority scale defined by the Soft Mobility HUB (SMH) Index. The methodology, applied to Italian railway stations, is based on the use of GIS (Geographic Information System) and AHP (Analytic Hierarchy Process), allowing a rigorous, transparent, and participatory approach. The proposed model, which is replicable in other contexts, has been effective in identifying, in different scenarios, the stations most suitable for becoming hubs for soft mobility. It can serve as a support tool for decision-makers to maximize investments by focusing on stations capable to provide the greatest contribution to the development of active and diversified tourism. Full article

Tourism is a dynamic industry that significantly contributes to the global economy, driven by the increasingly diverse preferences of tourists. Addressing these preferences requires sophisticated decision-making models capable of handling the uncertainty and subjectivity of human judgments. This study proposes sustainable models for effectively capturing and evaluating individual tourist preferences using the Analytic Hierarchy Process (AHP) and the Fuzzy Analytic Hierarchy Process (Fuzzy AHP). These models leverage the strengths of the AHP to construct a flexible decision-making framework that adapts to diverse tourist preferences, offering personalized recommendations. In this study, three main criteria are considered: types of tourism, tourism facilities, and tourism areas. Tourists are encouraged to provide their preferences for these criteria and sub-criteria, enabling the AHP and Fuzzy AHP to recommend suitable destinations. An analysis was conducted with 30 respondents providing pairwise comparisons of the tourism criteria, which were then used to generate tourist attraction recommendations using both the AHP and Fuzzy AHP. The study assessed respondents’ satisfaction with the recommendations, finding that both methods were effective, with a slight preference for the Fuzzy AHP due to its ability to better capture individual preferences. The results underscore the potential of these models in sustainably enhancing decision support systems in the tourism industry, offering tailored recommendations that align more closely with tourist expectations. Full article