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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]

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]

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]

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]

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 article explores the methods and benefits of expanding small-scale solar electricity to reduce reliance on fossil fuels, alleviate financial energy burdens, and enhance community resilience. It discusses the mechanics and costs of small-scale solar installations, the advantages for low-income and weather-vulnerable communities, and the integration of distributed solar into the traditional utility system. The article includes state case studies, such as Hawaii's increase in distributed solar generation and California's efforts towards energy equity. It concludes with best practices for successful expansion, including incentives for property owners, non-property owners, solar installation contractors, and specific communities. The text also discusses the risks and benefits of off-grid PV systems, financing options, and the paradigm shift towards distributed solar generation in the utility industry. It highlights the success of Hawaii and California in promoting on-site solar generation and provides recommendations for the development of laws and policies to support its expansion. The article emphasizes the benefits of on-site solar generation for local communities, particularly low-income and weather-vulnerable communities, and the potential for collaboration between legislatures, utilities, and regulators to facilitate its growth. It also addresses the challenges faced by lower-income communities and suggests strategies to overcome them. The article concludes by emphasizing the importance of expanding distributed solar electricity generation for its cost reduction, improved energy security, and environmental benefits. [Extracted from the article]

Using an experimental approach, we show that inflation expectations respond to gasoline price fluctuations. The effect, however, is small compared to other goods (food, durable goods) and not disproportionately large relative to the expenditure share of gasoline in households' basket. The effect also weakens with the forecast horizons and displays substantial asymmetry: inflation expectations respond significantly more to positive than negative gasoline price shocks. A counterfactual exercise suggests that while the sharp rise in gasoline prices that followed the Russian invasion of Ukraine contributed substantially to short-term inflation expectations dynamics, the subsequent decline in gasoline prices did not. [ABSTRACT FROM AUTHOR]

Vehicle electrification can significantly decarbonize the transportation sector. Widespread adoption of electric vehicles (EVs) depends on their cost relative to conventional alternatives. Here we compare the total cost of ownership (TCO) of gasoline, hybrid, and electric vehicles. First, we review previous TCO studies, showing that the components (e.g., purchase price, financing, taxes, fees, insurance, refueling, maintenance, repair, and home charging equipment for EVs), parameters (e.g., vehicle miles traveled, discount rate, and lifetime), and methods differ greatly. Then, we develop a comprehensive TCO model comparing across five vehicle classes, three powertrains, and three EV ranges. Using 14 cities in the United States and multiple charging scenarios, we investigate TCO variability based on location and use pattern. We include adjustments for local gasoline prices, electricity rate plans, home charging access, and the impact of local temperatures and drive cycles on fuel economy, among other factors. We show that for a 300‐mile range midsize electric SUV, TCO varies by $52,000, or nearly 40%, across locations. Home charging access reduces the lifetime cost by approximately $10,000 on average, and up to $26,000. EVs are more competitive in cities with high gasoline prices, low electricity prices, moderate climates, and direct purchase incentives, and for users with home charging access, time‐of‐use electricity pricing, and high annual mileage. In general, we find that small and low‐range EVs are less expensive than gasoline vehicles. Larger, long‐range EVs are currently more expensive than their gasoline counterparts. And midsize EVs can reach cost parity in some cities if incentives are applied. [ABSTRACT FROM AUTHOR]

Copyright of Comparative Economic Research is the property of Wydawnictwo Uniwersytetu Lodzkiego 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.)

The article titled "Differential Pricing: Violation of Fundamental Principles or Justified Measure?" discusses a case brought by Peru against Ecuador regarding the alleged violation of principles in the Andean Community. Peru claims that Ecuador's differential pricing system for fuel, which charges higher prices for vehicles with foreign license plates, violates principles of national treatment, non-discrimination, equal legal treatment, and free competition. The Court of Justice of the Andean Community rules in favor of Peru, stating that the pricing system violates these principles. The article also discusses the action for non-compliance within the Andean Community legal system, emphasizing the importance of smooth transportation services for economic integration and the welfare of consumers. It concludes that the differential pricing in Ecuador's fuel sale violates Andean Community laws and should be discontinued. [Extracted from the article]

Copyright of Investigación Económica is the property of Universidad Nacional Autonoma de Mexico, Facultad de Economia 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 policy insights paper explores the development of public-private partnerships (PPPs) in the Gulf Cooperation Council (GCC) region over the past three decades and argues that although GCC states did not embark on a full-fledged experience with PPPs, unstable oil and gas prices are changing this approach. PPPs are increasingly gaining strong political support, and GCC states have made numerous reforms to the existing regulatory and institutional systems to enable a more extensive uptake of PPPs that encompasses a broader spectrum of social and economic infrastructure services. This article offers an overview of the historical development of PPPs in the GCC region since the 1990s, illustrating policy discussions and motivations for adopting PPPs, the meaning and types of PPP projects that have been implemented or planned in the region, and the most recent developments in the regulatory and institutional frameworks underpinning PPPs. [ABSTRACT FROM AUTHOR]

Global atmospheric methane concentrations rose by 10 to 15 ppb/y in the 1980s before abruptly slowing to 2 to 8 ppb/y in the early 1990s. This period in the 1990s is known as the "methane slowdown" and has been attributed in part to the collapse of the former Soviet Union (USSR) in December 1991, which may have decreased the methane emissions from oil and gas operations. Here, we develop a methane plume detection system based on probabilistic deep learning and human-labeled training data. We use this method to detect methane plumes from Landsat 5 satellite observations over Turkmenistan from 1986 to 2011. We focus on Turkmenistan because economic data suggest it could account for half of the decline in oil and gas emissions from the former USSR. We find an increase in both the frequency of methane plume detections and the magnitude of methane emissions following the collapse of the USSR. We estimate a national loss rate from oil and gas infrastructure in Turkmenistan of more than 10% at times, which suggests the socioeconomic turmoil led to a lack of oversight and widespread infrastructure failure in the oil and gas sector. Our finding of increased oil and gas methane emissions from Turkmenistan following the USSR's collapse casts doubt on the long-standing hypothesis regarding the methane slowdown, begging the question: "what drove the 1992 methane slowdown?". [ABSTRACT FROM AUTHOR]

This study aims to forecast New York and Los Angeles gasoline spot prices on a daily frequency. The dataset includes gasoline prices and a big set of 128 other relevant variables spanning the period from 17 February 2004 to 26 March 2022. These variables were fed to three tree-based machine learning algorithms: decision trees, random forest, and XGBoost. Furthermore, a variable importance measure (VIM) technique was applied to identify and rank the most important explanatory variables. The optimal model, a trained random forest, achieves a mean absolute percent error (MAPE) in the out-of-sample of 3.23% for the New York and 3.78% for the Los Angeles gasoline spot prices. The first lag, AR (1), of gasoline is the most important variable in both markets; the top five variables are all energy-related. This paper can strengthen the understanding of price determinants and has the potential to inform strategic decisions and policy directions within the energy sector, making it a valuable asset for both industry practitioners and policymakers. [ABSTRACT FROM AUTHOR]

This article provides an update on inflation and relative price changes since the onset of the pandemic. It discusses the relationship between monthly inflation rates and the share of expenditures with relative price increases. The article also examines the distribution of relative price changes during the pandemic compared to the previous four-year period. The data shows that relative price changes have become more dispersed since March 2020, with larger changes occurring after the pandemic began. The article concludes by suggesting that factors driving relative prices may account for the high inflation experienced in recent years. [Extracted from the article]

Ethereum is a major public blockchain. Besides being the second-largest digital currency by market capitalization for its cryptocurrency, the Ether (Ξ), it is also the foundation of Web3 and decentralized applications, or DApps, that are fuelled by Smart Contracts. At the time of this writing, Ethereum still uses Proof of Work (PoW) consensus algorithm to ensure the integrity of the blockchain and to prevent double spend. PoW requires the participation of miners, who are incentivized to assemble blocks of transactions by being rewarded with cryptocurrency paid by transaction originators and by the blockchain network itself via newly minted Ξ. Network fees for transaction submissions are called gas, by analogy to the fuel used by cars, and are negotiable. They are also highly volatile and hence it is critical to predict the direction they are heading into, so that one can time transaction submissions, when feasible. There have been several efforts to predict gas prices, including usage of large Mempools, analysis of committed blocks, and more recent ones using Facebook's Prophet model [Taylor, S. J., & Letham, B. (2017). Forecasting at scale. PeerJ Preprints, 5, e3190v2. ]. In this study, we introduce an innovative approach that employs the DeepAR [Salinas, D., Flunkert, V., Gasthaus, J., & Januschowski, T. (2020). Deepar: Probabilistic forecasting with autoregressive recurrent networks. International Journal of Forecasting, 36(3), 1181–1191. ] model, known for its superior forecasting accuracy over conventional methods by virtue of its ability to learn from multiple related time series. This methodology not only offers immediate advantages but also holds promise for ongoing enhancements. We substantiate our claims through empirical testing, utilizing data extracts from the Ethereum blockchain and cryptocurrency price feeds. This document is an extended version of our ICCS 2022 paper on the same topic. In this paper, we dive deeper into the internals of DeepAR forecasting algorithm [Salinas, D., Flunkert, V., Gasthaus, J., & Januschowski, T. (2020). Deepar: Probabilistic forecasting with autoregressive recurrent networks. International Journal of Forecasting, 36(3), 1181–1191. ], analyse the correlation between the on-chain/off-chain sample data, and describe additional experiments that empirically prove our findings and, finally, perform a comparison of our outputs with those from the Prophet [Taylor, S. J., & Letham, B. (2017). Forecasting at scale. PeerJ Preprints, 5, e3190v2. ] model. [ABSTRACT FROM AUTHOR]

This paper aims to use location-based social media data to infer the impact of the Russia–Ukraine war on human mobility. We examine the impact of the Russia–Ukraine war on changes in human mobility in terms of the spatial range of check-in locations using social media location data. Specifically, we collect users' check-in location data from Twitter and analyze the average gyration of check-ins from a region across the timeline of major events associated with the war. Change-point detection is performed on these time-series check-ins to identify the timeline of abrupt changes, which are shown to be consistent with the timing of a series of sanctions and policies. We find that war-related events may contribute secondary impacts (e.g., the surge in gas prices) to users' travel patterns. The impact of the Russia–Ukraine war on users' travel patterns can differ based on their own scope. Our case study demonstrates that users' gyration in Warsaw, Paris, and Berlin experienced a decrease of over 50% during periods of gas price surges. These changes in users' gyration patterns were particularly noticeable in neighboring countries like Poland compared to the other three countries. The findings of this study can assist policymakers, regulators, and urban planners to evaluate the impact of the war and to be adaptable to city planning after the war. [ABSTRACT FROM AUTHOR]