An Ethanol Blend Wall Shift is Prone to Increase Petroleum Gasoline Demand

The US Environmental Protection Agency announced a waiver allowing an increase in the Fuel-Ethanol blend limit (the “blend wall” ) from 10% (E10) to 15% (E15) on October,2010.Justifications for the waiver are reduced vehicle fuel prices and less consumption of petroleum gasoline, leading to energy security. In this paper, employing Monte Carlo simulations and Savitzky-Golay smoothing filter, an empirical study examines this waiver revealing an anomaly where a relaxation of this blend wall elicits a demand response. Under a wide range of elasticities, this demand response can actually increase the consumption of petroleum gasoline and thus lead to greater energy insecurity. The economics supporting this result and associated policy implications are developed and discussed.Blend wall, Energy security, Ethanol, Resource /Energy Economics and Policy,

Can the U.S. Ethanol Industry Compete in the Alternative Fuels' Market?

The U.S. ethanol fuel industry has experienced preferential treatment from federal and state governments ever since the Energy Tax Act of 1978 exempted 10% ethanol/gasoline blend (gasohol) from the federal excise tax. Combined with a 54¢/gal ethanol import tariff, this exemption was designed to provide incentives for the establishment and development of a U.S. ethanol industry. Despite these tax exemptions, until recently, the U.S. ethanol fuel industry was unable to expand from a limited regional market. Ethanol was dominated in the market by MTBE (methyl-tertiary-butyl ether). Only after MTBE was found to contaminate groundwater and consequently banned in many states did the demand for ethanol expand nationally. Limit pricing on the part of MTBE refiners is one hypothesis that may explain this lack of ethanol entry into the fuel-additives market. As a test of this hypothesis, a structural vector autoregression (SVAR) model of the ethanol fuel market is developed. The results support the hypothesis of limit-pricing behavior on the part of MTBE refiners, and suggest the U.S. corn-based ethanol industry is vulnerable to limit-price competition, which could recur. The dependence of corn-based ethanol price on supply determinants limits U.S. ethanol refiners' ability to price compete with sugar cane-based ethanol refiners. Without federal support, U.S. ethanol refiners may find it difficult to complete with cheaper sugar cane-refined ethanol, chiefly from Brazil.Resource /Energy Economics and Policy,

Estimating China’s Energy and Environmental Productivity Efficiency: A Parametric Hyperbolic Distance Function Approach

Since the beginning of this century, China’s annual GDP growth is over 9%. This growth is fueled by large increases in energy consumption, led by a coal-dominated energy structure, and associated with higher sulfur dioxide emissions and industry dust. In 2008, China accounted for over 17% of the world’s total primary energy consumption and accounts for nearly three-quarters of global energy growth. At an average annual energy growth rate over 12% since 2000, China’s future share of primary energy consumption will continue to increase. A consequence of this growth is China becoming the global leader in sulfur and carbon dioxide emissions. To deal with these energy and environmental challenges, the government set energy saving and pollution reduction target objectives in the 11th Five Year Plan (2006-2010): relative to 2005 by 2010, saving national energy use per unit of GDP by 20% and reducing the country’s primary pollution emissions by 10%. These targets were then disaggregated into energy saving targets for each province. With this disaggregated scheme, similar to country’s target, 20 provinces were assigned a 20% energy saving target, seven provinces were assigned targets below 20%, varying from 12% to 17%, and four provinces were given targets above 20%. These allocation were generally not guided by technical or economic efficiency, and thus may not be optimal from the perspectives of equity and efficiency. Historically less energy efficiency provinces may have more potential to reduce their energy consumption and pollution emissions, while higher efficiency provinces may have less potential. The major objective is to determine the optimal targets for each province required to comply with the national Five Year Plan target. A comparison of the estimated optimal with the current government targets will then reveal the value of incorporating economic theory into the decision calculation of setting disaggregate targets. Determining optimal targets requires consideration of both desirable and undesirable comes from alternative feasible targets. An objective is then to delineate these comes as criterion for selection. The procedure employed is a parametric hyperbolic distance function approach with a translog specification. This procedure provides the flexibility of using energy, labor, and capital stock as inputs to produce the desirable output (GDP) and the undesirable output (sulfur dioxide emissions). The procedure will address the objectives by simultaneously estimating both the desirable and undesirable comes. Specifically, the production frontier and environmental productivity efficiency are estimated for each province. The hyperbolic distance function enables the estimation of efficiency scores by incorporating all types of inputs and outputs, and only requires information on input and outputs quantities but not on prices, making it possible to model the emissions in the production process, given nonmarket characteristics of emissions. Based on these parametric estimations, the optimal targets are determined. The trajectory of obtaining these optimal targets for each province is determined by estimating how each province can improve its productive performance through increasing its desirable output and reducing its undesirable output, while simultaneously saving energy inputs. The results provide an empirical measurement of energy efficiency with maximum potential of energy saving for each province at a given technology considering the diverse economic, industry, and energy consumption patterns in the provinces. With a panel data of 29 provinces in China from 2000-2007, the hyperbolic distance function allows us to measure environmental productivity change over time, and then decompose this environmental productivity change into efficiency change, which is the movement toward the frontier, and technical change, which is the shift of the frontier. These further analyses help us identify potential different contributions of productivity growth for each province in China, and examine how the energy saving program will affect the environmental productivity growth for each province.environmental productivity efficiency, hyperbolic distance function, China's energy policy, Environmental Economics and Policy, Productivity Analysis, Resource /Energy Economics and Policy,

Unity is Strength: Enhancing Precision in Reentrancy Vulnerability Detection of Smart Contract Analysis Tools

Reentrancy is one of the most notorious vulnerabilities in smart contracts, resulting in significant digital asset losses. However, many previous works indicate that current Reentrancy detection tools suffer from high false positive rates. Even worse, recent years have witnessed the emergence of new Reentrancy attack patterns fueled by intricate and diverse vulnerability exploit mechanisms. Unfortunately, current tools face a significant limitation in their capacity to adapt and detect these evolving Reentrancy patterns. Consequently, ensuring precise and highly extensible Reentrancy vulnerability detection remains critical challenges for existing tools. To address this issue, we propose a tool named ReEP, designed to reduce the false positives for Reentrancy vulnerability detection. Additionally, ReEP can integrate multiple tools, expanding its capacity for vulnerability detection. It evaluates results from existing tools to verify vulnerability likelihood and reduce false positives. ReEP also offers excellent extensibility, enabling the integration of different detection tools to enhance precision and cover different vulnerability attack patterns. We perform ReEP to eight existing state-of-the-art Reentrancy detection tools. The average precision of these eight tools increased from the original 0.5% to 73% without sacrificing recall. Furthermore, ReEP exhibits robust extensibility. By integrating multiple tools, the precision further improved to a maximum of 83.6%. These results demonstrate that ReEP effectively unites the strengths of existing works, enhances the precision of Reentrancy vulnerability detection tools

SparseCoder: Identifier-Aware Sparse Transformer for File-Level Code Summarization

Code summarization aims to generate natural language descriptions of source code, facilitating programmers to understand and maintain it rapidly. While previous code summarization efforts have predominantly focused on method-level, this paper studies file-level code summarization, which can assist programmers in understanding and maintaining large source code projects. Unlike method-level code summarization,file-level code summarization typically involves long source code within a single file, which makes it challenging for Transformer-based models to understand the code semantics for the maximum input length of these models is difficult to set to a large number that can handle long code input well, due to the quadratic scaling of computational complexity with the input sequence length. To address this challenge, we propose SparseCoder, an identifier-aware sparse transformer for effectively handling long code sequences. Specifically, the SparseCoder employs a sliding window mechanism for self-attention to model short-term dependencies and leverages the structure message of code to capture long-term dependencies among source code identifiers by introducing two types of sparse attention patterns named global and identifier attention. To evaluate the performance of SparseCoder, we construct a new dataset FILE-CS for file-level code summarization in Python. Experimental results show that our SparseCoder model achieves state-of-the-art performance compared with other pre-trained models, including full self-attention and sparse models. Additionally, our model has low memory overhead and achieves comparable performance with models using full self-attention mechanism.Comment: To appear in SANER'2

Some clarifications about Lema\^itre-Tolman models of the Universe used to deal with the dark energy problem

During the past fifteen years, inhomogeneous cosmological models have been put forward to explain the observed dimming of the SNIa luminosity without resorting to dark energy. The simplest models are the spherically symmetric Lema\^itre-Tolman (LT) solutions with a central observer. Their use must be considered as a mere first step towards more sophisticated models. Spherical symmetry is but a mathematical simplification and one must consider spherical symmetric models as exhibiting an energy density smoothed out over angles around us. However, they have been taken at face value by some authors who tried to use them for either irrelevant purposes or to put them to the test as if they were robust models of our Universe. We wish to clarify how these models must be used in cosmology. We first use the results obtained by Iguchi and collaborators to derive the density profiles of the pure growing and decaying mode LT models. We then discuss the relevance of the different test proposals in the light of the interpretation given above. We show that decaying-mode (parabolic) LT models always exhibit an overdensity near their centre and growing-mode (elliptic or hyperbolic) LT models, a void. This is at variance with some statements in the literature. We dismiss all previous proposals merely designed to test the spherical symmetry of the LT models, and we agree that the value of $H_0$ and the measurement of the redshift drift are valid tests of the models. However, we suspect that this last test, which is the best in principle, will be more complicated to implement than usually claimed.Comment: 18 pages, no figure, section 3 modified, results of section 3.2 changed, sections 4.3 and 4.4 added, other minor changes and references adde

Halo abundances and shear in void models

We study the non-linear gravitational collapse of dark matter into halos through numerical N-body simulations of Lemaitre-Tolman-Bondi void models. We extend the halo mass function formalism to these models in a consistent way. This extension not only compares well with the simulated data at all times and radii, but it also gives interesting clues about the impact of the background shear on the growth of perturbations. Our results give hints about the possibility of constraining the background shear via cluster number counts, which could then give rise to strong constraints on general inhomogeneous models, of any scale.Comment: 5 pages, 3 figures, Accepted in Physics of the Dark Universe, preprint IFT-UAM/CSIC-12-3

Turn the Rudder: A Beacon of Reentrancy Detection for Smart Contracts on Ethereum

Smart contracts are programs deployed on a blockchain and are immutable once deployed. Reentrancy, one of the most important vulnerabilities in smart contracts, has caused millions of dollars in financial loss. Many reentrancy detection approaches have been proposed. It is necessary to investigate the performance of these approaches to provide useful guidelines for their application. In this work, we conduct a large-scale empirical study on the capability of five well-known or recent reentrancy detection tools such as Mythril and Sailfish. We collect 230,548 verified smart contracts from Etherscan and use detection tools to analyze 139,424 contracts after deduplication, which results in 21,212 contracts with reentrancy issues. Then, we manually examine the defective functions located by the tools in the contracts. From the examination results, we obtain 34 true positive contracts with reentrancy and 21,178 false positive contracts without reentrancy. We also analyze the causes of the true and false positives. Finally, we evaluate the tools based on the two kinds of contracts. The results show that more than 99.8% of the reentrant contracts detected by the tools are false positives with eight types of causes, and the tools can only detect the reentrancy issues caused by call.value(), 58.8% of which can be revealed by the Ethereum's official IDE, Remix. Furthermore, we collect real-world reentrancy attacks reported in the past two years and find that the tools fail to find any issues in the corresponding contracts. Based on the findings, existing works on reentrancy detection appear to have very limited capability, and researchers should turn the rudder to discover and detect new reentrancy patterns except those related to call.value().Comment: Accepted by ICSE 2023. Dataset available at https://github.com/InPlusLab/ReentrancyStudy-Dat

iJTyper: An Iterative Type Inference Framework for Java by Integrating Constraint- and Statistically-based Methods

Inferring the types of API elements in incomplete code snippets (e.g., those on Q&A forums) is a prepositive step required to work with the code snippets. Existing type inference methods can be mainly categorized as constraint-based or statistically-based. The former imposes higher requirements on code syntax and often suffers from low recall due to the syntactic limitation of code snippets. The latter relies on the statistical regularities learned from a training corpus and does not take full advantage of the type constraints in code snippets, which may lead to low precision. In this paper, we propose an iterative type inference framework for Java, called iJTyper, by integrating the strengths of both constraint- and statistically-based methods. For a code snippet, iJTyper first applies a constraint-based method and augments the code context with the inferred types of API elements. iJTyper then applies a statistically-based method to the augmented code snippet. The predicted candidate types of API elements are further used to improve the constraint-based method by reducing its pre-built knowledge base. iJTyper iteratively executes both methods and performs code context augmentation and knowledge base reduction until a termination condition is satisfied. Finally, the final inference results are obtained by combining the results of both methods. We evaluated iJTyper on two open-source datasets. Results show that 1) iJTyper achieves high average precision/recall of 97.31% and 92.52% on both datasets; 2) iJTyper significantly improves the recall of two state-of-the-art baselines, SnR and MLMTyper, by at least 7.31% and 27.44%, respectively; and 3) iJTyper improves the average precision/recall of the popular language model, ChatGPT, by 3.25% and 0.51% on both datasets