The goal of this study was to examine the interlinkage of renewable energy, technology innovation, human capital, and governance on environment quality by using a panel quantile regression in Asian emerging economies over the period of 1990–2019. The results indicated that higher economic growth, population density, technological innovation in renewable energy, and exploitation of natural resources have significantly raised CO₂ emissions in emerging Asia. Furthermore, larger capital, more use of renewable energy, green technology, and human capital development can improve environmental sustainability in Asia. As for governances, proxied by corruption rates, no evidence indicated that it has resulted in more damage, unlike earlier studies have suggested. The findings indicated that the three channels exposed in the Kuznets hypothesis can serve as a reference for proposals for environmental policies (scale of consumption, energy composition, and choice of technologies). There are opportunities to reduce CO₂ emissions through investments in human development, investing in new technologies to increase efficiency in energy (generation and consumption), increasing working capital (GCF), and migrating to more environmentally friendly energy. The negative link between carbon dioxide emissions and economic growth, increases in population density, and exploitation of natural resources can compromise the achievement of sustainable environmental goals.

Conventional electronic voting systems use a centralized scheme. A central administration of these systems manages the entire voting process and has partial or total control over the database and the system itself. This creates some problems, accidental or intentional, such as possible manipulation of the database and double voting. Many of these problems have been solved thanks to permissionless blockchain technologies in new voting systems; however, the classic consensus method of such blockchains requires specific computing power during each voting operation. This has a significant impact on power consumption, compromises the efficiency and increases the system latency. However, using a permissioned blockchain improves efficiency and reduces system energy consumption, mainly due to the elimination of the typical consensus protocols used by public blockchains. The use of smart contracts provides a secure mechanism to guarantee the accuracy of the voting result and make the counting procedure public and protected against fraudulent actions, and contributes to preserving the anonymity of the votes. Its adoption in electronic voting systems can help mitigate part of these problems. Therefore, this paper proposes a system that ensures high reliability by applying enterprise blockchain technology to electronic voting, securing the secret ballot. In addition, a flexible network configuration is presented, discussing how the solution addresses some of the security and reliability issues commonly faced by electronic voting system solutions.

We present a model of traffic flow on generic urban road networks based on cellular automata. We apply this model to an existing road network in the Australian city of Melbourne, using empirical data as input. For comparison, we also apply this model to a square-grid network using hypothetical input data. On both networks we compare the effects of non-adaptive vs adaptive traffic lights, in which instantaneous traffic state information feeds back into the traffic signal schedule. We observe that not only do adaptive traffic lights result in better averages of network observables, they also lead to significantly smaller fluctuations in these observables. We furthermore compare two different systems of adaptive traffic signals, one which is informed by the traffic state on both upstream and downstream links, and one which is informed by upstream links only. We find that, in general, both the mean and the fluctuation of the travel time are smallest when using the joint upstream-downstream control strategy.

Oil and gold are used as investment assets and so they are closely related to the evolution of stock market indices, given that any influence on decisions about investment portfolios can affect stock market returns. Consequently, it is important for investors to analyze the direction of influence between crude oil, gold and stock markets when designing and implementing their investment strategies.Thus, this paper studies the direction of the causality among the three markets for the US case. In doing so, we apply linear and non-linear Granger causality tests for daily data from the Great Moderation onwards. Evidence is provided as to the importance of considering the possibility of nonlinear relationships between the three markets, a feature which cannot be revealed using conventional linear causality tests which would therefore lead to an information loss about the true link.The results for the full sample indicate that the causality goes in all directions, which implies that changes in the stock market returns may be monitored by observing changes in the returns of the two commodity markets considered (and vice versa). This may help to design substitution investment strategies. The results also indicate that the direction of influence between markets does not exhibit material differences between various subsamples, with the exception of the causality relationship between the two commodity markets. This may in part help explain the contradictory results and mixed conclusions found in previous related literature.

We analyzed the relationship between tourism destination competitiveness (TDC) and economic performance using the Travel and Tourism Development Index (TTDI) and various tourism metrics. Employing a Structural Equation Model (SEM) and the latest available 2021 data, we focused on factors such as connectivity, infrastructure, demand drivers, sustainability, and the enabling environment. The findings indicated a positive correlation between these factors and the pillars of destination competitiveness. Key elements such as connectivity, infrastructure, demand drivers, sustainability, and enabling environment significantly influence TDC in Asia. Our research demonstrates that these indicators strongly affect various aspects of performance in Asian countries, including tourism arrivals, revenue, and job creation. We found a negative correlation between certain aspects of destination performance and tourism growth, suggesting that highly competitive destinations may experience lower growth rates than less competitive ones, thereby affecting the overall development of the sector. This highlights the necessity of specific strategies to leverage the positive influence of competitive tourist destinations on economic dynamics and job creation within a destination. This study highlights the crucial role of core resources and infrastructure in enhancing tourism performance and competitiveness, and advocates targeted efforts for sustainable and resilient sector growth. These findings offer valuable insights into the relationship between competitiveness and performance in the Asian tourism industry.