A State-of-the-Art Review of Sharing Economy Business Models and a Forecast of Future Research Directions for Sustainable Development: A Bibliometric Analysis Approach

The area of sharing economy business models (SEBMs) is expanding worldwide. To date, a few qualitative literature reviews concentrating on specific business models have been undertaken, while several have focused on the general concept of the sharing economy. Meanwhile, there is a lack of quantitative reviews in this area. Therefore, a retrospective review of the evolution of the SEBM area and prospective forecasts based on quantified data are urgently needed. In order to fill the gaps and critically evaluate the extant literature on the SEBM area and its scientometrics-related topics, this paper combines the Scopus and Web of Science databases to establish a dataset for a thorough bibliometric analysis. With 951 studies from 552 sources identified, this research provides comprehensive and nuanced information covering the most influential authors and their contributions to the subject, impactful articles with their citation details, ranked sources with their h_, g_ and m-index as well as collaboration maps for authors, affiliations and countries. Graphical representation of knowledge mapping depicts the evolution of publications over time and the emerging trends of current interests and potential directions for future research for sustainable development. This study revealed that Sustainability is the most relevant and second most impactful journal in SEBM research. More importantly, this research deployed keyword dynamic and thematic evolution to detect the current and future trending topics, providing seven future research directions: (1) drivers-, location- and competition-related topics;(2) SEBMs in emerging economies;(3) country-, region- and culture-oriented SEBMs;(4) the link between e-commerce and social media frameworks and SEBMs;(5) sustainability and SEBMs;(6) new technologies and SEBMs and (7) COVID-19 effects on SEBMs. Overall, the results of this study theoretically enrich the sharing economy business model literature and have substantial implications for policymakers and practitioners. © 2023 by the author.

Data-driven predictive control for smart HVAC system in IoT-integrated buildings with time-series forecasting and reinforcement learning

Optimising HVAC operations towards human wellness and energy efficiency is a major challenge for smart facilities management, especially amid COVID situations. Although IoT sensors and deep learning were applied to support HVAC operations, the loss of forecasting accuracy in recursive prediction largely hinders their applications. This study presents a data-driven predictive control method with time-series forecasting (TSF) and reinforcement learning (RL), to examine various sensor metadata for HVAC system optimisation. This involves the development and validation of 16 Long Short-Term Memory (LSTM) based architectures with bi-directional processing, convolution, and attention mechanisms. The TSF models are comprehensively evaluated under independent, short-term recursive, and long-term recursive prediction scenarios. The optimal TSF models are integrated with a Soft Actor-Critic RL agent to analyse sensor metadata and optimise HVAC operations, achieving 17.4% energy savings and 16.9% thermal comfort improvement in the surrogate environment. The results show that recursive prediction leads to a significant reduction in model accuracy, and the effect is more pronounced in the temperature-humidity prediction model. The attention mechanism significantly improves prediction performance in both recursive and independent prediction scenarios. This study contributes new data-driven methods for smart HVAC operations in IoT-enabled intelligent buildings towards a human-centric built environment. © 2023 The Authors

Methodological framework for a deeper understanding of airline profit cycles in the context of disruptive exogenous impacts

This paper combines the k-means clustering method in combination with PCA and the system dynamic modeling approach to derive a better insight into the behavior of airline profitability during the time span of 1995 until 2020. The model includes various explanatory variables that capture different aspects of airline economic and operational metrics, whose fluctuations may affect the airline profitability. By forecasting these exogenous variables, the system dynamic model is used to predict airline profitability through 2025 and answer the question of whether the US airline industry will return to its pre-COVID 19 pandemic state. The latter research question can be agreed with, as the effect of introducing a fourth dimension derived from Principal Component Analysis (PCA) to sufficiently cover the variation within the dataset during the years of COVID-19 pandemic diminishes towards the end of the forecast period. Furthermore, the key measures from PCA imply that under the assumption of continuous growth and a non-exogenous shock, future years will not cluster in past years. The six different clusters from 2019 to 2025 showed how the system stays in a certain state for a few years and then drifts further to a new state. There are only a few variables that change to transfer from one cluster to the next. © 2022 The Authors

Economic forecasting with an agent-based model

We develop the first agent-based model (ABM) that can compete with benchmark VAR and DSGE models in out-of-sample forecasting of macro variables. Our ABM for a small open economy uses micro and macro data from national accounts, sector accounts, input–output tables, government statistics, and census and business demography data. The model incorporates all economic activities as classified by the European System of Accounts (ESA 2010) and includes all economic sectors populated with millions of heterogeneous agents. In addition to being a competitive model framework for forecasts of aggregate variables, the detailed structure of the ABM allows for a breakdown into sector-level forecasts. Using this detailed structure, we demonstrate the ABM by forecasting the medium-run macroeconomic effects of lockdown measures taken in Austria to combat the COVID-19 pandemic. Potential applications of the model include stress-testing and predicting the effects of monetary or fiscal macroeconomic policies. © 2022 The Author(s)

Medicine of the future: How and who is going to treat us?

Medicine's ability to quickly respond to challenges raises questions from researchers, practitioners, and society as a whole. Our task in this study was to identify key and atypical current factors influencing the development of medicine and to predict the development of medicine in the short, medium, and long term. To implement our study, we selected 22 medical experts and applied the three-level Delphi method. The current trends caused by COVID-19 have a short-term impact, but they will launch other drivers that will transform the healthcare industry. Well-being technologies, data-informed personalization, and climate change will become key drivers for the development of medicine over the period of 1–50 years. Expert opinion is divided about the future of mass availability of advanced medical treatment and sustainable development of healthcare. © 2023 The Authors

How Internet of Things can influence the sustainability performance of logistics industries – A Chinese case study

The introduction of new information technologies has started reshaping global industrial sectors and supply chains. Due to the introduction of the Internet of Things (IoT) real-time management strategies have been adopted by the global logistics industry, turning the branch into an intelligent service supplier. This paper assesses the influence of IoT on the Chinese logistics sector and related environmental performance between 2011 and 2018. This paper establishes an evaluation framework for the logistics performance under social, economic and environmental dimensions by using time entropy weighting. Using a grey correlation approach, we identify a strong positive correlation between the logistics performance and the IoT market scale. We further find a significant and increasing correlation trend for an expansion of the IoT market and the reduction of carbon and PM 2.5 intensity. The environmental regulation though positively correlated with logistics sustainability, shows less potential to directly improve economic and social performance. It also indirectly promotes sustainability performance of the logistics industry through support for technological innovation. High investment in IoT is estimated to limit the potential of small and medium-sized enterprises to increase their labor compensation and expand the scale of employment. Finally, we project China's IoT market developments for 2021–2025 using a grey forecasting model considering the influence of investment confidence and COVID-19. The results indicate that China's share of the global IoT market will likely rise from 18% to 30% by 2025.

COVID-19 epidemic forecasting based on a comprehensive ensemble method

Since December 2019, COVID-19 epidemic is continuing to spread globally. It not only jeopardizing the lives and health of people around the world seriously and putting a severe test on the public medical and health system, but also causes a huge impact on economic and trade activities and has a deep influence on the international community. In order to help researchers and policy makers understand the mechanism of virus transmission and adopt reasonable anti-epidemic policies to inhibit the further spread of the virus, some studies have adopted mathematical prediction models to simulate the spread of the virus and the development of the epidemic. However, the existing research has certain limitations, such as single method selection, excessive reliance on model parameters selection, and virus transmission and policy adjustments caused time variability of data. To solve the above problems, this paper proposes a comprehensive ensemble forecasting framework, which bases on six single prediction models, including time-varying Jackknife model averaging (TVJMA), time-varying parameters (TVP), time-varying parameter SIR (vSIR), logistic regression (LR), polynomial regression (PNR), autoregressive moving average (ARMA). The proposed method is used to predict the cumulative number of confirmed cases in the 6 most severely affected countries in different regions. Empirical results show that for a single prediction method, the TVJMA method outperforms the other five methods;the comprehensive ensemble forecasting method is significantly better than any single method in most cases, especially, the multi-model combined forecasting method based on error correction weights improves the prediction accuracy significantly. For different prediction steps, the comprehensive ensemble forecasting method is robust. © 2022, Editorial Board of Journal of Systems Engineering Society of China. All right reserved.

Exact Forecasting for COVID-19 Data: Case Study for Turkey

The novel coronavirus COVID-19 (SARS-CoV-2) with the first clinical case emerged in the city of Wuhan in China in December 2019. Then it has spread to the entire world in very short time and turned into a global problem, namely, it has rapidly become a pandemic. Within this context, many studies have attempted to predict the consequences of the pandemic in certain countries. Nevertheless, these studies have focused on some parameters such as reproductive number, recovery rate and mortality rate when performing forecasting. This study aims to forecast COVID-19 data in Turkey with use of a new technique which is a combination of classical exponential smoothing and moving average. There is no need for reproductive number, recovery rate and mortality rate computation in this proposed technique. Simulations are carried out for the number of daily cases, active cases (those are cases with no symptoms), daily tests, recovering patients, patients in the intensive care unit, daily intubated patients, and deaths forecasting and results are tested on Mean Absolute Percentage Error (MAPE) criterion. It is shown that this technique captured the system dynamic behavior in Turkey and made exact predictions with the use of real time dataset.

The effect of population and tourism factors on Covid-19 cases in Italy: Visual data analysis and forecasting approach

At the beginning of 2020, the new coronavirus disease (Covid-19), a deadly viral illness, is declared as a public health emergency situation by WHO. Consequently, it is accepted as pandemic that affected millions of people worldwide. Italy is one of the most affected countries by Covid-19 disease among the world. In this article, our main goal is to investigate the effect of intensity of Covid-19 cases based on the population size and tourism factors in certain regions of Italy by visual data analysis. The regions of Lombardia, Veneto, Campania, Emilia-Romagna, Piemonte are the top five regions covering 58.50% of the total Covid-19 cases diagnosed in Italy. It has been shown by visual data analysis that population and tourism factors play an important role in the spread of Covid-19 cases in these five regions. In addition, a prediction model was created using Bi-LSTM and ARIMA algorithms to forecast the number of Covid-19 cases occurring in these five regions in order to take early action. We can conclude that these northern regions have been affected mostly by Covid-19 and the distribution of the resident population and tourist flow factors affected the number of Covid-19 cases in Italy.