The global financial crisis has affected all countries during the period 2007-2014. Tourist companies, especially in Italy, play a significant role in the economy of a country. The performance of tourism companies, and in particular hotels and travel agencies and tour operators, will be analysed in this paper, relative to the period of the crisis. The aim of this paper is to analyze profitability dynamics of the aforementioned tourist companies, to check whether they have suffered the effects of the global crisis. To this end, data from AIDA database relating to Italian companies in the sector were used. The profitability of these companies has been analyzed using the main profitability ratios, ROI and ROS ratios. An analysis of the trends and ANOVA of these ratios was carried out for the period 2007-2015. In this way, it has been verified whether the global crisis has affected profitability dynamics of hotels and travel agencies and tour operators in Italy. There is a parallelism in the trends of the main operators' indices in the three Italian macro-regions. The best performance is in the South, in a less developed economic context. The crisis has reduced the income indices in the first three years, even if the figure remains positive. Since 2012 there is a clear recovery, especially for hotels; intermediaries, on the other hand, suffer from competition from websites. However, the indices confirm the close relationship between travel agencies, tour operators and hotels. ROI mainly rewards first, while ROS intermediaries.

It's critical for environmental governance to understand the effectiveness of policy interventions as well as their working mechanisms. This paper focuses on the vehicle license plate-based management, a sort of policy intervention understudied in environmental governance literature. Specifically, we study the automobile purchase restriction (APR), a major method of license management to address urban air pollution and traffic congestion recently launched by the authorities of China. However, limited studies have quantified the environmental impact of such purchase restriction. Based on the panel data of PM2.5 concentration in 330 cities in China during 1998-2016, this study aims to explore the causal impact of Beijing automobile purchase restriction on air quality, using the regression control method and high-dimensional data regression of machine learning. The average PM2.5 concentration did not decrease but increased because of the purchase restriction. Over time, the deterioration effect on urban air quality will not alleviate. Besides, the restriction cannot enhance urban air quality by decreasing the increase of PM2.5. Despite excluding the impact of driving restriction, the purchase restriction markedly aggravate urban air quality. This paper has some policy implications for policymakers on the impact of urban vehicle license management with caution. Hence, it is essential to promote urban economy, production, and lifestyle to attain the win-win goal of air pollution control and urban development.

The high price of energy due to green energy policy will cause adjustments across the US economy predicted in the present general equilibrium model that includes energy Btu input with capital and labor to produce manufactures and services. This same model in trade theory examines the effects of a tariff on an imported factor of production such as a natural resource or capital.  Corresponding error correction estimates of the reduced form equations of the model in annual 1970-2018 data prove robust and suggest model modifications. A parametric approach to noncompetitive pricing based on unit cost diminishing with output brings the model much closer to the estimates. Manufactures are revealed to have a higher degree of noncompetitive pricing than services. Assuming constant elasticity of substitution production, a weak degree is revealed to provide the best fit to the error correction estimates. The high price of green energy will cause an inelastic decrease in energy input resulting in increased energy revenue. Outputs of both sectors fall, more in manufactures due to its energy intensity. The capital return and wage also fall given the weak substitution of capital and labor relative to the price of energy. The wage rises slightly in the model but falls considerably in the error correction estimate. The only clear winner is energy input with increased revenue. The government owns a large share of hydrocarbon reserves and will benefit from a higher price of energy.

Stock indexes are used as a barometer of economic health, and fluctuations in these markets can have a substantial influence on the economy. For example, the Covid-19 pandemic caused severe economic disruption, as reflected in stock market indexes. Similarly, Russia's invasion of Ukraine has geopolitical implications that might undermine global trade and economic stability, impacting stock market indexes. Considering these developments, the purpose of this article is to examine the co-movements of the stock markets of Austria (ATX), Poland (WIG), the Czech Republic (PX Prague), Hungary (BUX), Croatia (CROBEX), Serbia (BELEX 15), Romania (BET), and Slovenia (SBI TOP) from February 16, 2018, to February 15, 2023. To achieve the research objectives, the aim is to answer the following research question: i) Have the events of 2020 and 2022 accentuated the co-movements between the stock markets in Central Europe? The results show the presence of 21 shocks between markets (out of a potential 56) during the Tranquil subperiod, with the WIG stock index having a greater predictive influence on the behavior of its peers (4 shocks out of 7 possible). During the Stress subperiod, 45 shocks were confirmed (out of 56 possible). The markets that triggered the most market shocks (7 out of 7 possibilities) were BET, BUX, CROBEX, and SBI TOP. The research question was validated based on the conclusion supplied, as all markets increased their movements, showing a considerable effect of the 2020 and 2022 events on these markets.

The rapid growth of renewable solar and wind energy necessitates extensive, long-term energy storage solutions to stabilize the electric grid and address the intermittent nature of these sources. In the absence of cost-effective long-duration, large-scale (LDLS) energy storage, there is a risk of increased disruptive blackouts and brownouts as intermittent energy sources approach 50% of electricity generation. Our analysis underscores volumetric energy density as a pivotal determinant in LDLS energy storage solutions, which is particularly crucial during periods of diminished renewable resource availability, such as the rainy season. Despite emphasis on conventional electric batteries and mechanical storage technologies such as compressed air and pumped hydro for LDLS, their costs pose a significant obstacle to widespread adoption, especially in emerging economies. In this study, we present a groundbreaking approach to LDLS: on-demand electricity generation from carbon-neutral renewable oil, known as "green oil," leveraging Reliance’s innovative catalytic hydrothermal liquefaction (R-CAT-HTL) technology. Our investigation evaluated two distinct sources of green oil production: organic waste streams (e.g., municipal solid waste, agricultural-waste, etc.) and on purpose production of algal biomass. We demonstrate that burning renewable green oil to produce electricity offers a cost-effective solution for large-scale (GWh) long-duration (many days) energy storage, ensuring the stability, reliability, and resiliency of the electric grid. We also demonstrate that minimum 14 days, ideally 30 days energy storage is needed for continuous uninterrupted service, especially critical for manufacturing facilities. Through a meticulous levelized cost of storage (LCOS) analysis comparing green oil with other LDLS technologies reported in literature, we demonstrate the cost effectiveness of green oil. In this model, green oil serves as stored energy, functions akin to a battery and is efficiently converted into electricity through conventional reciprocating engines or next-generation more efficient turbines. Our findings suggest that green oil derived from diverse organic sources is the most economically viable option for GWh-scale storage, particularly when storage requirements extend beyond a single day. This research thus presents a transformative cost-effective solution to the pressing challenge of LDLS energy storage, accelerating a sustainable and resilient energy future.

Cloud computing is one of the fastest growing domains of the IT industry, however, the growth rates for each of the models on a global and local (Russian) scale have not been generalized, despite the obvious value of such analysis. Modern business and industry in Russia is going through another period of instability and at the same time is forced to follow the trend of digitalization. That is why a balanced scientific analysis and forecast of the development of cloud technologies is needed, as the main factor in improving business efficiency. The purpose of the study: is to review the main models of cloud services for modern business and industry, describing their capabilities separately and the prospects for the market as a whole. The materials for the study were analytical reports of leading consulting companies and materials from specialized web-sites. Result: The article considers cloud technologies as the main global technological trend, describes popular models of cloud services, their opportunities for business and industry. Russian and global leaders in the provision of cloud services are also described, the growth rates of these markets for each of the models are indicated. The most popular and promising cloud service models in 2022 are: UCaaS, CaaS, SaaS, IaaS, RPAaaS, PaaS. The cloud technology market will show strong growth until 2030, generating both hybrid and absolutely new service models. The private business, industrial and public sectors will increasingly move to cloud services. The main trends in the development of cloud technologies until 2030 are: hybrid cloud, artificial intelligence (AI) and cloud computing, deployment of cloud services in the planetary orbit. The results obtained are very valuable for analysts and industry experts. The above trend analysis provides valuable predictive data to research economists, investors, business and industry leaders and other stakeholders on how the cloud services market will grow in the next 5-7 years. The results of the study also have a general theoretical nature, substantiating the importance of further forecasting the development of this market (especially using mathematical methods).