Are we really addressing the roadblocks to adoption of renewable and sustainable energy technologies? Total interpretive structural modeling approach.

Urban areas serve as a vital contribution to the global structural change towards renewable and sustainable energy technologies which also influence climate change. The aim of this paper is to identify the adoption roadblocks to renewable and sustainable urban energy technologies. This research has three parts: a mini-systematic literature study was conducted to identify the most prevalent roadblocks. Using total interpretive structural modeling (ISM), the relationships between the roadblocks and the source of causation were then examined. The roadblocks are classified based on their dependence and driving powers using MICMAC analysis in the third part of this research. The principal results and major conclusions demonstrate that all roadblocks are necessary for renewable and sustainable urban energy technologies. The roadblocks at level I are insufficient infrastructure, lack of coordination among authorities, lack of quality and reliable data and information, and competition with non-renewable technologies; roadblocks in level II are lack of skilled and trained personnel, limited public participation, awareness, and consumer interest, and lack of standardized technology; roadblock in level III is high initial investment cost; and lastly, roadblocks in level IV are lack of subsidies and financial support programs and absence of coherent related policies. Furthermore, as a result of the MICMAC analysis, none of the aforementioned roadblocks are classified as autonomous variables, implying that they are all required. The dependent roadblocks to renewable and sustainable energy technologies are defined as lack of coordination among authorities, lack of information, and competition with non-renewable technologies. Moreover, linkage roadblocks have high dependence and driving powers which are insufficient infrastructure, limited awareness and consumer interest, and lack of standardized technology. Lastly, high initial investment costs, lack of subsidies and financial support programs, absence of coherent related policies, and lack of skilled and trained personnel are the driving roadblocks with high driving power however not dependent.

Drivers for circular economy development: making businesses more environmentally friendly.

Stakeholders have been pressuring companies to develop more environmentally friendly strategic and operational solutions. In this sense, companies are seeking alternatives that reduce the negative impacts of organizational activities, Circular Economy (CE) is one of the solutions with the greatest potential for success. Thus, the goal of this paper is to provide drivers for organizations' transition from a linear to a CE. For this reason, content analysis was used as the scientific method, for being appropriate for the interpretation of qualitative data and the identification, clustering, and systematization of themes in a given field of knowledge. In the case of this work, a set of 30 articles with information related to the implementation and development of CE were analyzed, allowing the identification of 19 key elements of CE. These key elements were then grouped and systematized into four drivers: decision-making; capacity and training; sustainable practices; and green supply chain. Scientifically, this work contributes to the improvement and increase of the block of knowledge about the CE, because the drivers can be used to advance the state of the art and as a starting point for the development of new research. In an applied way, the drivers proposed in this article provide a range of actions for managers to make their companies greener and improve their organizational performance, thus contributing environmentally and socially to the planet.

Using emerging technologies to improve the sustainability and resilience of supply chains in a fuzzy environment in the context of COVID-19.

In rapidly changing business conditions, it has become extremely important to ensure the sustainability of supply chains and further improve the resiliency to those events, such as COVID-19, that can cause unexpected disruptions in the value supply chain. Although globalized supply chains have already been criticized for lack of control over sustainability and resilience of supply chain operations, these issues have become more prevalent in the uncertain environment driven by COVID-19. The use of emerging technologies such as blockchain, Industry 4.0 analytics model and artificial intelligence driven methods are aimed at increasing the sustainability and resilience of supply chains, especially in an uncertain environment. In this context, this research aims to identify the problematic areas encountered in building a resilient and sustainable supply chain in the pre-COVID-19 era and during COVID-19, and to offer solutions to those problematic areas tackled by an appropriate emerging technology. This research has been contextualized in the automotive industry; this industry has a complex supply chain structure and is one of the sectors most affected by COVID-19. Based on the findings, the most important problematic areas encountered in SSCM pre-COVID-19 are determined as supply chain traceability, demand planning and production management as well as purchasing process planning based on cause and effect groups. The most important issues to be addressed during COVID-19 are top management support, purchasing process planning and supply chain traceability, respectively.

Impact of epidemic outbreaks (COVID-19) on global supply chains: A case of trade between Turkey and China.

COVID-19 has negative impacts on supply chain operations between countries. The novelty of the study is to evaluate the sectoral effects of COVID-19 on global supply chains in the example of Turkey and China, considering detailed parameters, thanks to the developed System Dynamics (SD) model. During COVID-19 spread, most of the countries decided long period of lockdowns which impacted the production and supply chains. This had also caused decrease in capacity utilizations and industrial productions in many countries which resulted with imbalance of maritime trade between countries that increased the freight costs. In this study, cause and effect relations of trade parameters, supply chain parameters, demographic data and logistics data on disruptions of global supply chains have been depicted for specifically Turkey and China since China is the biggest importer of Turkey. Due to this disruption, mainly exports from Turkey to China has been impacted in food, chemical and mining sectors. This study is helpful to plan in which sectors; the actions should be taken by the government bodies or managers. Based on findings of this study, new policies such as onshore activities should consider to overcome the logistics and supply chain disruptions in global supply chains. This study has been presented beneficial implications for the government, policymakers and academia.

Big data analytics and the effects of government restrictions and prohibitions in the COVID-19 pandemic on emergency department sustainable operations.

Grounded in dynamic capabilities, this study mainly aims to model emergency departments' (EDs) sustainable operations in the current situation caused by the COVID-19 pandemic by using emerging big data analytics (BDA) technologies. Since government may impose some restrictions and prohibitions in coping with emergencies to protect the functioning of EDs, it also aims to investigate how such policies affect ED operations. The proposed model is designed by collecting big data from multiple sources and implementing BDA to transform it into action for providing efficient responses to emergencies. The model is validated in modeling the daily number of patients, the average daily length of stay (LOS), and daily numbers of laboratory tests and radiologic imaging tests ordered. It is applied in a case study representing a large-scale ED. The data set covers a seven-month period which collectively means the periods before COVID-19 and during COVID-19, and includes data from 238,152 patients. Comparing statistics on daily patient volumes, average LOS, and resource usage, both before and during the COVID-19 pandemic, we found that patient characteristics and demographics changed in COVID-19. While 18.92% and 27.22% of the patients required laboratory and radiologic imaging tests before-COVID-19 study period, these percentages were increased to 31.52% and 39.46% during-COVID-19 study period. By analyzing the effects of policy-based variables in the model, we concluded that policies might cause sharp decreases in patient volumes. While the total number of patients arriving before-COVID-19 was 158,347, it decreased to 79,805 during-COVID-19. On the other hand, while the average daily LOS was 117.53 min before-COVID-19, this value was calculated to be 165,03 min during-COVID-19 study period. We finally showed that the model had a prediction accuracy of between 80 to 95%. While proposing an efficient model for sustainable operations management in EDs for dynamically changing environments caused by emergencies, it empirically investigates the impact of different policies on ED operations.

Resilience and complexity measurement for energy efficient global supply chains in disruptive events.

The whole world is faced with the COVID-19 epidemic that causes major disruptions in global supply chains. The aim of study is to evaluate the effects of COVID-19 on energy efficient global supply chains (SCs) and to model the global supply chain resilience and energy management affected during the COVID-19 considering trade between Turkey and China, and Turkey and the EU. In this study, firstly using System Dynamics (SD) model, the behavior of countries against COVID-19 for a certain period of time is observed, subsequently the increase in complexity is analyzed with entropy measurement to determine whether the systems are resilient or not and to mark the differences arising from reporting in the first and second wave of the pandemic in the developed model. It is determined that the second wave reporting differences is less than first wave reporting differences except Turkey. From the learning effect perspective, it has been seen that the effect on the economy and foreign trade are less than first wave of pandemic even though the number of patients originating in the second wave are higher. It means that countries responded to the second wave of COVID-19 in a more resilient way. It is found that as a major finding of this study, perceived complexity of the system decreases in the second wave because of the resilience of supply chain considering learning effect and centralized decision making ensure increasing resilience and resilience measure in global supply chains. The study is highly helpful for governments, decision makers and managers to understand and manage the impacts of COVID-19 on global supply chains being resilient and energy efficient.

The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods.

In recent years, machine learning models based on big data have been introduced into marketing in order to transform customer data into meaningful insights and to make strategic decisions by making more accurate predictions. Although there is a large amount of literature on demand forecasting, there is a lack of research about how marketing strategies such as advertising and other promotional activities affect demand. Therefore, an accurate demand-forecasting model can make significant academic and practical contributions for business sustainability. The purpose of this article is to evaluate machine learning methods to provide accuracy in forecasting demand based on advertising expenses. The study focuses on a prediction mechanism based on several Machine Learning techniques-Support Vector Regression (SVR), Random Forest Regression (RFR) and Decision Tree Regressor (DTR) and deep learning techniques-Artificial Neural Network (ANN), Long Short Term Memory (LSTM),-to deal with demand forecasting based on advertising expenses. Deep learning is a powerful technique that can solve marketing problems based on both classification and regression algorithms. Accordingly, a television manufacturer's real market dataset consisting of advertising expenditures, sales and demand forecasting via chosen machine learning methods was analyzed and compared in terms of the accuracy of demand forecasting. As a result, Long Short Term Memory has been found to be superior to other models in providing highly accurate prediction results for demand forecasting based on advertising expenses.

Circular dairy supply chain management through Internet of Things-enabled technologies.

Internet of Things-enabled technologies help to collect data and make it understandable, especially in supply chain processes, thus minimizing the problems that may arise in supply chains. It is extremely important to support this process with Internet of Things-enabled technologies, especially in supply chains that are vulnerable to disruptions such as the dairy supply chain. Moreover, dairy supply chains are the type of supply chains where the most waste is generated; evaluating this waste is very beneficial to the circular economy. Therefore, monitoring data in dairy supply chains and using Internet of Things-enabled technologies prevent losses; it is critical to have Internet of Things-enabled circular dairy supply chains in operation. The aim of this study is to determine the success factors of Internet of Things-enabled circular dairy supply chains based on the various stages of these chains; we hope to match each dairy supply chain stage with a success factor of Internet of Things-enabled technology and determine a ranking for these factors. Hence, six success factors of Internet of Things-enabled circular supply chains are weighted for each stage of the chain; Internet of Things-enabled digital technologies are then matched with each stage of the chain, and the success factor is determined. The ranking of factors can then be drawn up through the integration of Step Wise Weight Assessment Ratio Analysis (SWARA) and Technique for Order Preference Similar to Ideal Solution (TOPSIS). The outcome of this study will provide managers and policy makers with insights into Internet of Things-enabled circular dairy supply chains.

Big Data-Enabled Solutions Framework to Overcoming the Barriers to Circular Economy Initiatives in Healthcare Sector.

Ever-changing conditions and emerging new challenges affect the ability of the healthcare sector to survive with the current system, and to maintain its processes effectively. In the healthcare sector, the conservation of the natural resources is being obstructed by insufficient infrastructure for managing residual waste resulting from single-use medical materials, increased energy use, and its environmental burden. In this context, circularity and sustainability concepts have become essential in healthcare to meliorate the sector's negative impacts on the environment. The main aim of this study is to identify the barriers related to circular economy (CE) in the healthcare sector, apply big data analytics in healthcare, and provide solutions to these barriers. The contribution of this research is the detailed examination of the current healthcare literature about CE adaptation, and a proposal for a big data-enabled solutions framework to barriers to circularity, using fuzzy best-worst Method (BWM) and fuzzy VIKOR. Based on the findings, managerial, policy, and theoretical implementations are recommended to support sustainable development initiatives in the healthcare sector.

Integrating Social Dimensions into Future Sustainable Energy Supply Networks.

Environmental protection and sustainable development have become an inevitable trend in many areas, including the energy industry. The development of energy supply networks is strongly correlated with the economics of energy sources as well as ecological and socio-political issues. However, the energy supply network is often distant from the social perspective. This paper therefore combines examination of perceptions and awareness of general public (web-based questionnaire) and top energy experts (a Delphi survey) on the energy supply network and identifies their potential integration in energy supply decision making processes. The results showed that public should be better informed as well as integrated into designing energy supply network as the prosumers gain power and the energy suppliers will no longer dominate the market. Public actors are ready to shape sustainable energy supply and also willing to pay 5.8% more for a sustainable energy supply. The majority are prepared to invest in renewable energy supply network close to their place of residence. Another result is that the public is calling for a shift in priority towards more sustainable and socially friendlier energy supply rather than focusing mainly on the economic and technical perspectives.

Using system dynamics to assess the environmental management of cement industry in streaming data context.

The cement industry can be regarded as one of the major sources of anthropogenic air pollution. It uses a significant amount of energy while creating substantial amount of potentially health-threatening carbon monoxide (CO), sulfur dioxide (SO2), nitrogen oxides (NOx) and dust particles. Hence, the cement industry can be regarded as a primary area for study in the development of green manufacturing. In this study, an urban cement factory is analyzed. The major contribution of the article is the development of a holistic approach to identify the variables impacting cement production and environmental factors creating air pollution in the area, a system dynamics model has been developed incorporating streaming data. To understand the effect of a cement factory on an urban area, some strategic level decisions are also analyzed with the study in order to reveal their impact on environment. The impact of cement production on air pollution cannot be evaluated separately from other air quality factors; therefore, the contribution of each factor has to be identified in order to understand the specific issues affecting a region. With the use of the model, future implications of various air quality factors on environmental sustainability can be assessed. According to the results, PM10 value, which is currently above the World Health Organization (WHO) air pollution critical level of 50 µg/m3 for 30% of the days in a year, will climb to more than 50% in 2023. Moreover, governments can also recognize the severe impacts of location selection for cement industries, unplanned and excessive building licensing, and uncontrolled immigration on environment of an urban living. Therefore, output of the study is potentially beneficial in guiding governmental decisions to ensure the sustainability of air quality.