The pandemic crisis and the resulting global uncertainties have obviously had a severe impact on the healthcare supply chain (HSC), leading scholars, healthcare executives, and policymakers to focus on the sustainability of the HSC. Technologies have emerged and developed rapidly in recent years, especially in the healthcare industry, for coping with the pandemic crisis and supporting the “new normal” for humankind. Within this context, various new technologies have been implemented to maximize the supply chain process, ensure patient and healthcare worker safety, and improve the quality of care. Hence, the integration of a technological dimension with the traditional three pillars of sustainability may aid in attempts to define the potential attributes of these dimensions of sustainability. Therefore, this study aimed to identify the key attributes of a sustainable healthcare supply chain (SHSC), and this paper presents a new, four-dimensional model for SHSCs, consisting of social, environmental, economic, and technological dimensions. A systematic literature review was conducted, resulting in the identification of 35 potential SHSC attributes. The Fuzzy Delphi Method (FDM) was then applied to determine the appropriateness of these potential attributes according to the consensus of 13 experts, including healthcare workers in a variety of medical specialties, who profoundly understand HSC sustainability. The results yielded 22 appropriate attributes, which were then categorized across the four dimensions. Consequently, a new model of an SHSC, which prioritizes patient safety, was constructed and is proposed here. This SHSC model can be applied strategically to the healthcare industry to enhance the safety of both medical personnel and patients in a sustainable manner.

An integration between the principles of supply chain management in sport tourism and a sustainability-balanced scorecard leads to the development of guidance and assessment criteria for a city to be a sustainable sport tourism destination. This paper aims to present a sport tourism sustainability management model (STSM) for the development of assessment indices when selecting a city to be developed as a sport tourism city. The model consists of five perspectives, namely the financial perspective, customer perspective, internal process perspective, learning and growth perspective, and sustainability perspective. By using the Delphi technique to obtain a consensus from experts, university lecturers, independent organizations/associations, and business entities in relation to sport tourism management, the researchers define elements to assess complex perspectives for the sustainable development of sport tourism. The results show that there are five perspectives and eighteen elements in relation to the sustainable development of sport tourism. All perspectives and elements have a high consensus, as measured by Kendall’s Coefficient of Concordance (W), of 0.488. To validate the model, the researchers examined the correlation among the five perspectives using a structural equation model and found that both the absolute fit and the incremental fit are satisfactory. As the model aligns and explains empirical data, it can support decision making for management teams, be used to effectively and efficiently draft a strategy for sustainable development in sport tourism, and improve the livelihoods of residents.

The current studies on carbon dioxide (CO₂) emissions and the impacts on public health focus mainly on evaluating CO₂ emissions from two types of emission sources. The first is a fixed source type from industrial plants, which can be controlled or easily evaluated. The second is a mobile source type from the transport sector, especially from medium- and heavy-duty vehicles (MHDVs), which produce high emissions. However, the common methods of evaluation of the average value of CO₂ emissions per kilometer of a vehicle use a general Intergovernmental Panel on Climate Change (IPCC) model that does not consider the topography or road conditions. This affects the accuracy of CO₂ emission assessments and, in turn, affects the accuracy of any analysis needed to establish health policies and the management of public health within the affected area. In this paper, therefore, we present the development of emission coefficient calculations with varying topography conditions for MHDVs with consideration of the health effects on the populace. The study area was the Asian highway network in Thailand that passes through all regions and is geographically diverse. Data were collected from the Department of Highway’s records, global positioning system (GPS) and electronic control unit (ECU) with data consisting of road data, slope, distance, traffic level and vehicle weight, as well as fuel consumption along the transportation route. To analyze and map out the correlation of the CO₂ emission coefficients for each road slope, we determined the coefficient of the CO₂ emissions using multiple linear regression analysis and validated this using Huber–White robust standard errors for heteroscedasticity. This method was more cost-efficient and time-efficient compared to the conventional approaches. We also created CO₂ emission maps with risk points for health effects, and we propose policy designs and plans to manage the traffic level in each of these areas prone to higher levels of such emissions.

Since the coronavirus (COVID-19) pandemic, it has been clear that the health dimension (HEDm) has a severe impact on sustainability, which was originally considered from the pillars of society, environment and economy. Hence, the integration of the health dimension into the other three pillars is plausible to define guidelines and criteria for progress monitoring and policy assessment towards a health-sustainable city. The objective of this study aims to present The Health Sustainability Model (HSM), a four-dimensional model for health sustainability (health, economy, environment, and society), using the Del-phi method to determine potential indicators agreed by eighteen experts, including physicians who deeply understand issues on health sustainability, and assess complex dimensions of health in the context of sustainability. The researchers have found that 45 indicators, later grouped into 15 elements and 4 dimensions, have a high level of agreement with Kendall’s W (KW) at 0.36. The HSM was then examined by the structural equation model (SEM) with reliability and validity shown as follows: the absolute fit with CMIN/DF = 1.44, RMSEA = 0.033, GFI = 0.96, AGFI = 0.94, RMR = 0.025, and the incremental fit with NFI = 0.94, CFI = 0.98, TLI = 0.97, and IFI = 0.98. Based on the results, the model is valid, in line with the empirical data. For further application, the HSM is expected to support city planners and decision makers by identifying room for improvement in each dimension through the indicators employed in the model. In contrast to existing studies that mainly use qualitative data, by conducting quantitative assessment, the model enables policy makers to objectively evaluate conditions and appropriately design policies to improve residents’ well-being.

The coronavirus pandemic causing millions of deaths around the world has raised awareness of the importance of healthcare in a city, especially its efficiency in the city’s system. Although the health dimension is found to have critical effects on sustainable development in addition to the existing three dimensions, the majority of sustainability assessments that are developed based on the UN’s Sustainable Development Goals (SDGs) do not define standards for each indicator. On the contrary, an effective assessment model should be dynamic and suitable to the context of each city by referring to standardized criteria, such as those in a health indicator standard (HIS), instead of comparing with other cities. Hence, this research presents a new sustainability assessment model that integrates the health dimension (HEDm) with the other three dimensions generally used in studies of sustainable development (STD) and develops an assessment method to evaluate real data with references to the HIS from reliable organizations, such as the World Health Organization (WHO) and the United Nations (UN), for the SDGs. By using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) with the framework obtained from literature reviews, ISO Standards (ISO37120, ISO37122, ISO37123) and U4SSC, the researchers formed indicators in four dimensions with a total of 15 elements and 45 indicators. Afterwards, the weight of each indicator was determined with the Analytic Hierarchy Process (AHP) conducted in each dimension and element, resulting in a four-dimensional sustainability assessment model based on HIS value that limits the need to compare with other cities. The results from Khon Kaen province have shown the weaknesses and strengths of the target city that policymakers can reference to formulate a policy and strategy to improve HIS values in each dimension to meet standards, elevate city capacity and raise quality of life for the people living in the area.

The Smart Cities (SCs) models currently widely employed are identical and inconsiderate of Economics Driven (ED), Local Context (LC), and Sustainability (St) factors. These are key factors to driving, constructing, and developing smart cities. This paper presents a process wherein “the Local Smart Sustain Cities Model (LSSCsM)” is combined and modeled with Exploratory Factor Analysis technique (EFA) to design a smart city that fits the local features of a given area. This particular process creates a Smart Cities Model (SCsM) that has unique sustainability and local context factors. This paper also presents the smart cities Priority Action Ranking (PAR) process using Fuzzy Logic Decision Making (FLDM) to evaluate the strengths and weaknesses of each smart city economics driver and characteristic and prioritize the direction planning of each factor and characteristic. The resulting smart cities model can then be used as the foundation of sustainable smart cities that avoid the pitfall of using incompatible smart cities models as the base and consequently failing, thus avoiding the extravagant costs associated with an unsuccessful project of such scale.