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This paper examines the effect of gasoline price changes on consumer spending behaviour, specifically on food at home (FAH) and food away from home (FAFH) expenditures using monthly data from 1997–2022. We employ the Almost Ideal Demand System (AIDS) to estimate the cross-price elasticity of FAH and FAFH with respect to the price of gasoline. Our results show a negative relationship between gasoline price changes and FAH and FAFH expenditures. The results indicate that when the gasoline price doubles, FAH and FAFH expenditures drop by about 3% and 6%, respectively. This suggests that consumers adjust their food spending patterns in response to changes in gasoline prices, with a more substantial impact on FAFH expenditures. [ABSTRACT FROM AUTHOR]

Price dispersion is a prevalent feature even in markets where, arguably, homogeneous goods are traded. At the heart of the causes of price dispersion lie the firms' strategic interactions with their customers and rivals. Consumers' eagerness to search for lower prices tends to reduce dispersion because it enhances competition. On the other hand, the firm's inability to sustain tacit collusion increases price dispersion. Wholesale cost shock can impact both. We use data on retail gasoline markets from Colombia to assess whether changes in price dispersion following wholesale cost shocks are explained by consumers searching or by firms breaking away from collusive equilibria. We also use a forced divestiture case to explore the role of market structure on the extent of price dispersion. Our findings suggest that changes in price dispersion following wholesale cost shocks are driven by consumers searching more intensely for lower prices. We also find a positive relationship between the number of brands in a market and how dispersed prices are. [ABSTRACT FROM AUTHOR]

Natural gas plays an increasingly important role in the energy landscape, and its price experiences significant volatility and is easily affected by oil due to its substitutability. Therefore, this study investigates the emergence of natural gas bubbles in terms of the relationship between oil prices and natural gas prices (ROPGP) during the sample period from January 1997 to January 2023. Using a theoretical framework covering oil and gas prices that examines price volatility and structural dependencies, this analysis combines subsample rolling window causality tests, Generalized Supremum Augmented Dickey-Fuller (GSADF) bubble detection methods, and logistic regression models. The empirical results indicate that due to Europe's diversified energy policies, the ROPGP has an adverse effect on the emergence of natural gas price bubbles in Europe. In Japan, however, the ROPGP positively influences natural gas bubbles due to the scarcity of indigenous energy resources and the reliance on natural gas contracts linked to imported oil. Conversely, in the US, the ROPGP does not significantly impact natural gas price bubbles, which is attributed to the country's self-sufficiency in natural gas production. These findings highlight the differing impacts of the ROPGP on natural gas markets in various regions, shaped by their unique energy structures, policies, and geopolitical factors. [ABSTRACT FROM AUTHOR]

New York City is the most populous city in North America and the fourth most populous in the world. Due to the high population density and significant commuting population, the city experiences a large number of vehicles operating in a congested environment, leading to a substantial number of traffic accidents. This study examines a dataset compiled by the New York Police Department, which records every major vehicular accident in New York City from 2013 to 2023, exploring aspects such as accident types, severity, causes, and locations. This period includes the COVID-19 pandemic and other external factors like fluctuating gasoline prices, the rise of for-hire vehicle (FHV) services, and vehicles with new safety features. Data from multiple sources are analyzed to understand how these factors impacted accident rates during this timeframe. The analysis shows that the COVID-19 pandemic significantly reduced accidents due to decreased motor vehicle traffic, with post-pandemic accident rates remaining at less than half of pre-pandemic levels. This sustained decline correlates with reduced traffic, increased FHV usage over taxis, and a growing number of new vehicles with advanced safety features. This study uses these datasets to develop a mathematical model to quantify these correlations and to provide insight for urban planners and policymakers seeking to improve road safety and manage traffic flow. [ABSTRACT FROM AUTHOR]

After long-term operation, the performance of components in the GTCC system deteriorates and requires timely maintenance. Due to the inability to directly measure the degree of component malfunction, it is necessary to use advanced exergy analysis diagnosis methods to characterize the components' health condition (degree of malfunction) through operation data of the GTCC system. The dissipative temperature is used to describe the degree of malfunction of different components in the GTCC system, and an advanced exergy analysis diagnostic method is used to establish a database of overall operating condition component malfunctions in the GTCC system. Ebsilon software is used to simulate the critical parameters of the malfunctions of the GTCC system components and to obtain the changes in the dissipative temperature of different components. Meanwhile, the fuel consumption and economic changes of the GTCC system on a characteristic power supply day under health and malfunction conditions are analyzed. Finally, the effects of maintenance costs, electricity, and gas prices on maintenance expenses and profits are analyzed. The results show that the GTCC system maintenance profit is 6.07 $/MWh, while the GTCC system maintenance expense is 5.83 $/MWh. Compared with the planned maintenance mode, the malfunction maintenance mode saves 0.24 $/MWh. Simultaneously, the maintenance coefficient of GTCC should be adjusted under different malfunctions to obtain a more accurate maintenance period. [ABSTRACT FROM AUTHOR]

A Pigouvian tax is a tax that is imposed to correct an externality, which arises when a person engages in behavior that harms others. Pigouvian taxes are popular among academics--with prominent legal scholars and economists arguing for their imposition on myriad harmful goods and activities, like carbon emissions and alcohol. Policymakers have been receptive to at least some of these arguments as evidenced by taxes recently imposed on or proposed for a variety of externality-generating goods, including guns, plastic bags, and sugary drinks. The conventional economic rationale for Pigouvian taxes assumes that they affect behavior by increasing the prices of taxed goods and not by altering people's underlying preferences for them. For example, a carbon tax reduces driving by making gasoline more expensive, but it otherwise leaves people's desire to drive unchanged. In other words, people would resume their previous level of driving if the carbon tax went away. This conclusion follows from the standard assumption in economics that people's tastes and preferences are !xed and determined exogenously to public policy. Challenging that standard analysis, I argue that Pigouvian taxes can in fact shape preferences and that policymakers should consider using them for that purpose. For instance, a carbon tax might cause more people to take the train or ride a bike and, through repeated or habitual behavior over time, to develop a taste for these alternative modes of transportation--a taste that would make driving less attractive, separate from the increase in gas prices. This Article is the !rst to examine in detail the psychological mechanisms through which Pigouvian taxes can alter preferences. I argue that, because preferences are malleable, the harm to individual consumers resulting from Pigouvian taxes will often be smaller than economists claim. Moreover, I show that malleable preferences dramatically expand the scope for and potential bene!ts of Pigouvian taxes. For example, they create the possibility that socially bene!cial behaviors encouraged by the taxes--such as the installation of solar panels by homeowners--will become contagious and spread through the population. This social multiplier effect results from changes in social norms or from psychological processes like the mere exposure effect. I conclude that preference shaping can cause Pigouvian taxes to be much more effective in achieving public policy goals than legal scholars and economists have traditionally assumed--a point that I illustrate in a variety of contexts, including environmental law, gun policy, and public health policy. [ABSTRACT FROM AUTHOR]

According to many studies, the transmission of oil prices to retail fuel prices is asymmetric. Fuel prices react faster if oil prices rise and more slowly if oil prices fall. Different standard econometric procedures lead to different results. The Linex approach, which is based on formulating the non-linear adjustment cost function, reflects the theory. It uses the generalised method of moments to estimate the reaction functions, which demands many observations. The paper investigates the price asymmetry in the Croatian retail fuel market using standard approaches and the Linex approach. The simple and dynamic asymmetry models, error correction models, threshold autoregressive co-integration, and the Linex approach are used to verify the hypothesis of asymmetric reactions of gasoline and diesel prices in Croatia. The results using the standard methods are mixed, while the Linex approach indicates price asymmetry, the size of which is measured with the average price bias. The results correspond to other studies worldwide. The authors' preferred Linex approach detects price asymmetries, even with large data samples with frequent changes in trends and volatilities. According to the approach, the question is not whether prices are formed asymmetrically but the size of the asymmetry. [ABSTRACT FROM AUTHOR]

We estimate a model of incomplete information price competition where consumers endogenously choose whether to use a price search website. We examine how consumer search and prices would change under different transparency regimes. We find that aggregate consumer welfare is maximized when the price search website lists the lowest 20% of prices (i.e., one station in markets with less than five stations, and two stations in markets with more than five stations), with consumer expenditures falling by 1.5% relative to a regime where all are listed. [ABSTRACT FROM AUTHOR]

In this paper, we study the autoregressive (AR) model with normal inverse Gaussian (NIG) innovations. The NIG distribution is semi heavy-tailed and is helpful in capturing the extreme observations present in the data. The expectation-maximization (EM) algorithm is used to estimate the parameters of the considered AR(p) model. The efficacy of the estimation procedure is shown on the simulated data for AR(2) and AR(1) models. A comparative study is presented, where the classical estimation algorithms are also incorporated, namely, Yule-Walker and conditional least squares methods along with EM method for model parameter estimation. In simulation study, the maximum likelihood estimation (MLE) of NIG distribution by EM algorithm and iterative Newton-Raphson method are also compared. The real-life applications of the introduced model are demonstrated on the NASDAQ stock market index data and US gasoline price data. The studies show that AR(1) model with NIG residuals is good fit for financial data with extreme values as well as for gasoline price data. [ABSTRACT FROM AUTHOR]

The global transition towards environmentally friendly energy sources plays a major role in addressing both energy security and climate change. Brazil is at the forefront of this transition due to its rich natural resources and increasing investments in biofuels. Therefore, this investigation examines the consumption patterns and interactions between ethanol, primarily sourced from sugarcane, and gasoline within Brazil's energy framework. Ethanol's renewability, reduced environmental impact, and superior combustion characteristics position it as a feasible substitute for traditional fossil fuels. Nonetheless, obstacles like competition for land use and inadequate distribution infrastructure impede its widespread acceptance. This study explores the economic interaction between ethanol and gasoline, focusing on pricing dynamics and regional influences. Using consumer preferences and the accessibility of ethanol, this research identifies a range of price ratios within which consumer preferences shift from gasoline to ethanol in various Brazilian regions. The study also classifies Brazilian states into three distinct ranges based on the ethanol-to-gasoline price ratio in 2023 for a granular analysis of the economic dynamics influencing fuel choice. The research identifies states with competitive and dominant ethanol markets by examining the interplay between ethanol market share, fuel prices, and the adoption of flex-fuel vehicles (FFVs) in the country. Lastly, the findings support the importance of regional economic conditions and the influence of price ratios on consumer behavior, highlighting that ethanol's market share does not always correlate with favorable pricing. [ABSTRACT FROM AUTHOR]

The article discusses the annual revisions to GDP for the US economy, indicating a higher short-term growth rate and reduced risk of layoffs. The GDP growth forecast for 2025 is above consensus at 2.5%. Factors such as productivity growth and positive employment reports contribute to the strengthening of the forecast. The Federal Reserve is expected to cut interest rates further, with a cumulative decline of 100bps by the end of the year. [Extracted from the article]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We investigate whether the cost pass-through in the European retail gasoline market is the same regardless of whether cost changes are driven by exchange rate fluctuations or driven by fluctuations in the dollar spot price of gasoline. We find that the two cost pass-through rates are not the same: we find that the latter exceeds the former. The effect is quantitatively small, but robust and statistically significant. This pattern is not due to a lower persistence of exchange rate changes, refinery supply contracts, or economic fluctuations. The lower variability of exchange rates relative to that of oil prices explains a portion of the response gap. A possible explanation for the remaining gap is that consumers draw a direct link between the crude oil and retail gasoline prices, which affects their price expectations and their search intensity, and thus the retailers' pass-through. Because pass-through is sometimes used to assess market competitiveness and contributes to the forecast of the Consumer Price Index, it is important to recognize that the source of variation in the underlying costs may have an effect on the assessment of market conduct and inflation. [ABSTRACT FROM AUTHOR]

Study of large rail traffic databases presents formidable challenges for transport system specialists, more particularly while keeping both space and time factors together with the possibility of showing influencing factors related to the users and the transport network environment. To perform such a study, a bibliographic analysis in both statistics and transport revealed that geometrical methods for feature extraction and dimension reduction can be seen as suitable. Since there are several methods/options with, in principle, required input data, this article aims at comparing Principal Component Analysis (PCA) and Correspondence Analysis (CA) for traffic frequency data, both methods being actually used with such data. The procedure stands as follows. First a grand matrix is built where the rows correspond to time windows and the columns to all the possible origin-destination links. Then this large frequency matrix is studied using PCA and CA. The next part of the procedure consists in studying the effects of influencing factors with the possibility of keeping the quantitative scales with PCA or using fuzzy segmentation with CA, the corresponding data being considered as supplementary column points. The procedure is applied on a rail transport network including 10 stations (one corresponding to the airport) and one-hour time windows for 4 months, the available influencing factors being the temperature, rain level and gas price. The comparative analysis shows that CA graphical outputs are more complicated than PCA ones, but reveal more specific results, e.g. the network user behavior related to the airport, while PCA mainly opposes link clusters with low vs. high frequencies. Fuzzy windowing performed using actual and simulated data reduces the loss of information when averaging, e.g. over time, and can show non-linear relational phenomena. The possibility of displaying new traffic data in real time is also considered. [ABSTRACT FROM AUTHOR]

This analysis compares the energy efficiency and operational costs of combustion vehicles (Hyundai Accent 1.6 L and Chevrolet Sail 1.5 L) with the Nissan Leaf, an electric vehicle, under current fuel and electricity pricing in Ecuador. Combustion vehicles, converting gasoline into mechanical energy, demonstrate substantial energy losses, leading to higher operational costs, especially with recent gasoline price hikes to USD 2.722 per gallon. In stark contrast, the Nissan Leaf exhibits significantly greater energy efficiency, consuming only 15–20 kWh per 100 km, which translates to lower running costs (USD 11.20 to fully charge a 40 kWh battery). Despite the clear economic and environmental benefits of electric vehicles, their adoption in Ecuador is hampered by geographical challenges such as diverse terrain that can affect vehicle range and battery longevity. Moreover, the limited and uneven distribution of EV charging stations, mostly concentrated in urban areas, poses significant barriers. For broader implementation, a strategic expansion of the EV infrastructure and careful consideration of the national energy grid's capacity to support increased electric vehicle uptake are essential. Addressing these challenges is crucial for realizing the full potential of electric vehicles in enhancing Ecuador's sustainability and energy independence. [ABSTRACT FROM AUTHOR]

Copyright of Energy Policy Journal / Polityka Energetyczna is the property of Mineral & Energy Economy Research Institute of the Polish Academy of Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article investigates the effect of global oil and wheat prices, and local price shocks on the real price of wheat flour in Lebanon. We estimate a structural vector autoregressive model with exogenous variables (SVARX) using Bayesian methods. We then compute the impulse response functions and find that global commodity price shocks play a trivial role. Meanwhile, local gasoline price and wheat flour price‐specific shocks trigger large increases in the Lebanese wheat flour price on impact. Furthermore, since 2020, local gasoline price and wheat flour price‐specific shocks have contributed the most to the historical variation in the Lebanese wheat flour price. [ABSTRACT FROM AUTHOR]

This article provides a comprehensive analysis of the fragmentation of the globalized economy and the macroeconomic costs stemming from the war in Ukraine. The conflict has had far-reaching consequences, disrupting global supply chains, trade flows, and economic stability. The research delves into the specific impacts on various sectors, including energy, agriculture, and technology, showcasing the intricate interdependencies of the modern global economy and how regional conflicts can reverberate worldwide. Using a blend of quantitative data and qualitative insights, the article details the significant disruptions in energy markets, which have led to soaring oil and gas prices and exacerbated inflationary pressures globally. It also examines the critical blockade of Ukrainian agricultural exports, which has heightened food security concerns, especially in developing countries. Moreover, the article explores the broader implications for international trade and investment. It discusses how the fragmentation of global supply chains has forced businesses to reevaluate their reliance on single-source suppliers, resulting in a reconfiguration of production networks. The war has also highlighted vulnerabilities in global financial systems, with increased market volatility and heightened risk aversion among investors. In addressing these challenges, the article emphasizes the importance of coordinated policy responses and international cooperation. It underscores the need for diplomatic efforts to restore stability and mitigate the adverse economic effects of the conflict. The findings advocate for enhanced economic resilience through collaborative international strategies, recognizing the necessity of a united approach in addressing the complexities of geopolitical conflicts in an interconnected world. [ABSTRACT FROM AUTHOR]

The Government of the People's Republic of Bangladesh and PETROBANGLA have announced the Bangladesh Offshore Bidding Round 2024, inviting International Oil Companies (IOC s) to explore nine Shallow Sea (SS) blocks and fifteen Deep Sea (DS) blocks. This initiative aims to bolster the nation's energy sector by leveraging international expertise and investment. Bidders must demonstrate significant offshore production capabilities and international experience. On the invitation it remarked an information package is available, providing comprehensive geological data and the Bangladesh Offshore Model Production Sharing Contract (PSC)-2023. Contracts will be awarded based on competitive bids, offering favourable terms including full profit repatriation, uncapped attractive gas prices, no signature bonuses, and PETROBANGLA covering Corporate Income Tax. Additional incentives include no import duty for operational equipment, a bank guarantee for the Minimum Exploration Program, and profit-sharing options based on an R-factor. This initiative represents a significant step toward realizing Bangladesh's energy potential, combining high expectations with strategic readiness to meet global standards in offshore exploration. This study aims to analyse the 2024 bidding round for successful offshore resource extraction, developing hypotheses from primary and secondary data, and outlining potential successes and achievements expected from the initiative. [ABSTRACT FROM AUTHOR]

The electric vehicles (EVs) market in Malaysia, though growing remains relatively small. Previous related studies on Malaysia focused mainly on consumer preferences and the country's readiness towards a higher adoption of environmentally friendly alternative vehicles. This study extends previous work to examine consumer preferences towards EVs through a market survey conducted in Klang Valley. Then follows a comparative economic and environmental cost-benefit analysis of EVs relative to conventional vehicles and hybrid vehicles to provide some understanding on the market diffusion of the former. The key findings from the survey suggest that pricing and the maintenance costs of EVs negatively influence the uptake of EVs despite the positive environmental attitudes of consumers. The cost-benefit analysis, in turn, implies that the EV transition will accelerate with rising petrol prices and falling battery costs. The study concludes with some implications for sustainability based on the adoption of EVs. [ABSTRACT FROM AUTHOR]

In this study, the economic analysis of the architectural part of the building, the use of both the solar thermal and solar electric systems in a poultry farm were discussed. In this economic analysis, the implementation of architectural solutions as well as the utilization of the solar thermal system considering the global gas price and the current gas rates in the country and finally the use of solar electric system with different subsidies were analyzed. Economic analysis was conducted using RETSCREEN software. The results showed that the internal rate of return (IRR) for the architectural solutions of using double-glazed windows and wall insulation, considering the global gas price, was estimated as 53.1% and, the investment return, taking into account the inflation and discount rates, was equal to 2.6 years, which is the breakeven point of investing in this solution. Utilizing the solar thermal system and without considering the cost of the underfloor heating system, the IRR value is equal to 34.1%, and considering the cost of the underfloor heating system, the IRR value is equal to 27.9%, which is economic in terms of these solutions. This option proves to be cost-effective. When employing the solar electric system with an IRR of 4.4%, this solution proves uneconomical under all circumstances due to the IRR falling below the bank rate and yielding a negative Net Present Value (NPV). However, when considering a 50% government subsidy and a 1.5-fold increase in the electricity purchase tariff for the solar electric system, this solution approaches the threshold of economic viability. [ABSTRACT FROM AUTHOR]

Summary: We propose a per‐cluster instrumental variable (PCIV) approach for estimating linear correlated random coefficient models in the presence of contemporaneous endogeneity and two‐way fixed effects. This approach estimates heterogeneous effects and aggregates them to population averages. We demonstrate consistency, showing robustness over standard estimators, and provide analytic standard errors for robust inference. In Monte Carlo simulation, PCIV performs relatively well in finite samples in either dimension. We apply PCIV in estimating the price elasticity of gasoline demand using state fuel taxes as instrumental variables. We find significant elasticity heterogeneity and more elastic gasoline demand on average than with standard estimators. [ABSTRACT FROM AUTHOR]

Most of the world's population lives in countries that ban the self‐service sale of gasoline. Causal effects of this regulation can hardly be assessed in these countries due to a lack of policy changes, but a recent quasi‐experiment in the state of Oregon allows us to analyze the impact of the ban. From 1992 to 2017, the state of Oregon was one of two U.S. states that banned self‐service at gasoline stations. Oregon adjusted regulations at the start of 2018 to allow self‐service at gasoline stations in counties with populations below 40,000 individuals. I examine the repeal of this self‐service ban and its effects on gasoline prices. I apply a difference‐in‐differences design using high frequency data of gasoline prices and find that repealing the self‐service ban reduced gasoline prices by 4.4 cents per gallon in affected Oregon counties. This effect represents approximately $90 in expected annual savings for a household with three licensed drivers. [ABSTRACT FROM AUTHOR]

This article estimates a model of optimal search where consumers learn the distribution of gasoline prices during their driving trips. Our model incorporates traffic information and leverages this ordered search environment to recover parameters of the search and learning process using only station‐level price and market share data. We find that learning is a crucial component of search in this market. Consumers' prior beliefs regularly deviate from the true price distribution but are updated quickly following each new price observation. Counterfactuals reveal how these learning dynamics generate asymmetric search patterns commonly associated with asymmetric cost pass‐through. [ABSTRACT FROM AUTHOR]

This study presents an in-depth analysis of gasoline price forecasting using the adaptive network-based fuzzy inference system (ANFIS), with an emphasis on its implications for policy-making and strategic decisions in the energy sector. The model leverages a comprehensive dataset from the U.S. Energy Information Administration, spanning over 30 years of historical price data from 1993 to 2023, along with relevant temporal features. By combining the strengths of fuzzy logic and neural networks, the ANFIS approach can effectively capture the complex, nonlinear relationships present in the data, enabling reliable price predictions. The dataset's preprocessing involved decomposing the date into year, month, and day components to enhance the model's input features. Our methodology entailed a systematic approach to ANFIS regression, including data preparation, model training with the inclusion of the previous week's prices as an additional feature, and rigorous performance evaluation using MSE, RMSE, and correlation coefficients. The results indicate that incorporating previous prices significantly enhances the model's accuracy, as reflected by improved scores and correlation metrics. The findings have significant implications for the energy sector, where stakeholders can leverage the ANFIS model's insights for strategic decision-making. Accurate gasoline price forecasts are instrumental in devising pricing strategies, managing risks associated with price volatility, and guiding policy formulation. The model's predictive capability enables energy companies to optimize resource allocation, plan for future investments, and maintain competitive advantage in a market influenced by fluctuating prices. Moreover, policymakers can utilize these predictions to assess the impact of energy policies on market prices and consumer behavior, ensuring that regulatory measures align with market dynamics and sustainability goals. In addition to the ANFIS model, we also employed Vector Autoregression (VAR) and Autoregressive Integrated Moving Average (ARIMA) models to validate our approach and provide a comprehensive understanding of time series forecasting within the energy sector. Notably, the ANFIS model achieves a score of 0.9970 and a robust correlation of 0.9985, demonstrating its ability to accurately forecast gasoline prices based on historical data and features. The integration of these traditional techniques with advanced ANFIS modeling offers a robust framework for accurate and reliable gasoline price prediction, which is vital for informed policy-making and strategic planning in the energy industry. [ABSTRACT FROM AUTHOR]

Although firms' expectations on future price dynamics are key for monetary policy, we still lack information on the expected effect of the transition to a net-zero economy on future prices. This paper fills this gap in the literature using survey data collected in the first quarter of 2022 on Italian firms' expectations of the impact of the Fit-for-55 - the package established by the EU to achieve its climate targets - on the prices of the goods and services they purchase. First, it documents that almost half of the sample expects that the Fit-for-55 plan will increase their input costs over the short and medium term, while the rest foresee no effect on prices. Then, it shows that firms extrapolate their private experience of high energy prices and shortages in raw materials when forming their expectations. These are salient events that are likely to be recalled relative to the others stored in memory when forming expectatons. In contrast, aggregate information such as energy, oil, and gas prices do not enter the expectations formation process. Finally, a propensity score analysis shows that the expected price impact of the green transition shapes firms' pricing and investment choices such that firms expecting the transition to raise the prices of the inputs they will purchase in the medium term increase (or intend to increase) their selling prices more than the others and are more likely to invest in improving their energy efficiency. [ABSTRACT FROM AUTHOR]

In the technological globe, blockchain technology is expanding tremendously in every field where ledgers and security plays a role. The smart contract, as a significant part of the blockchain that automatically executes whenever the pre-conditions are satisfied for the business to proceed also ensures trust even among unknown parties and completely eradicates the middleman. In order to deploy or interact with a smart contract, the gas cost is to be paid in teams of ether or wei in the Ethereum blockchain network. The gas cost is mainly charged for the effort or computational power required to process the operations present in the transactions of the smart contract. The gas cost becomes overpriced, due to the increasing demand to process a huge number of transactions in the network and the inflation in the price of the cryptocurrency and the normal users of the network does not know how to determine gas price paid to the miners and ended up paying an overprice for processing their transactions quickly. So, there arises a need to optimize the gas cost and find the right strategy balancing both transaction cost and the wait time. In this paper, various approaches such as merge task, Unit256 vs. Uint8, data type preference, unnecessary libraries, dead code elimination, short circuiting and loop operation are proposed and techniques such as Monte-Carlo approach and M/M/1 queuing are experimented to reduce the gas cost in both the phases of contract generation and contract deployment in the Ethereum network. The proposed techniques can be utilized to reduce the overall gas cost also minimize the extortionate smart contract vulnerabilities thus, providing a way to develop optimized smart contracts and provide direction to contribute to future research. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach to identify the presence of a latent factor in the co-movements of gasoline and diesel prices in the three major European Union economies, (France, Germany, and Italy) using daily data from January 3, 2005, to June 28, 2021. More precisely, we advance an artificial neural networks algorithm estimated through a machine learning experiment through the backpropagation system to show that the neural signal is altered by an element that could coincide with a latent factor in the fuel price co-movements. We consider the role of the fuel tax systems and the connection between gasoline and diesel prices in these countries. The estimations indicate the presence of an unobservable component (the latent factor) in the fuel price co-movements, capable of influencing NN. This result validates the previous findings reported in the literature, indicating an excess co-movement in fuel prices. It also has implications in terms of fuel price forecasts in the short run. [ABSTRACT FROM AUTHOR]

This study deals with the evolution of fuels' prices over time in the EU. The central research inquiry revolves around whether there exists any correlation among the trajectories followed by national prices in the gasoline and diesel markets. The EU, and more specifically the Euro-Area, by its construction, is treated as an OCA (Optimum Currency Area). In an OCA, certain conditions are met to ensure the smooth functioning of a common currency. The fuel price synchronization is essential because it contributes to the effective implementation of policies and promotes stability across the entire macroeconomy. The study covers the period of 2017–2022. For each type of fuel and year, we construct an individual network where network nodes represent the EU member states while the edges connecting these nodes represent strong temporal fuel price correlations among the member states. The properties of the resulting networks are analyzed within a Complex Network framework. Our goal is twofold: first, to detect any potential convergence or divergence in the trajectories of the prices, and second, to investigate the impact of tax and duty implementation on the co-movements of the prices. The empirical findings show that diesel markets have a more pronounced pattern of price co-movements compared to the gasoline markets. Moreover, the application of fuel taxation policies seems to adversely affect the co-movements of prices. [ABSTRACT FROM AUTHOR]