Bayesian selection of technology assumptions for the transformation from supply-use to input–output tables

ABSTRACT

In the construction of input–output models from supply-use tables, technology assumptions disambiguate how an industry uses inputs in the production recipe of multiple outputs. This paper uses Bayes' theorem to select technology assumptions, taking into account empirical observations. The paper presents a formulation to explore hybrids between product and industry technology assumptions in product-by-product tables. We then present Markov chain Monte-Carlo techniques to implement the Bayesian method for selecting technology assumptions. We apply the method in a case study using Eurostat supply-use tables of 2004 and 2005, exhibiting a volume of secondary products of less than 13%, and 59 products and industries per country. The results show that the choice of technology is not important, given that there is no strong evidence in favour of any of them.     

Pareto efficient allocation of an in-motion wireless charging infrastructure for electric vehicles in a multipath network

Electric vehicles (EV) use an eco-friendly technology that limits the greenhouse gas emissions of the transport sector, but the limited battery capacity and the density of the battery are the major barriers to the widespread adoption of EV. To mitigate this, a good method seems to be the innovative wireless charging technology called ‘On-Line EV (OLEV)’, which is a contactless electric power transfer technology. This EV technology has the potential to charge the vehicle’s battery dynamically while the vehicle is in motion. This system helps to reduce not only the size of the battery but also its cost, and it also contributes to extending the driving range before the EV has to stop. The high cost of this technology requires an optimal location of the infrastructure along the route. For this reason, the objective of this paper is to study the problem of the location of the wireless charging infrastructure in a transport network composed of multiple routes between the origin and the destination. To find a strategic solution to this problem, we first and foremost propose a nonlinear integer programming solution to reach a compromise between the cost of the battery, which is related to its capacity, and the cost of installing the power transmitters, while maintaining the quality of the vehicle’s routing. Second, we adapt the multi-objective particle swarm optimization (MPSO) approach to our problem, as the particles were robust in solving nonlinear optimization problems. Since we have a multi-objective problem with two binary variables, we combine the binary and discrete versions of the particle swarm optimization approach with the multi-objective one. The port of Le Havre is presented as a case study to illustrate the proposed methodology. The results are analyzed and discussed in order to point out the efficiency of our resolution method.     

Fuel consumption for various driving styles in conventional and hybrid electric vehicles: Integrating driving cycle predictions with fuel consumption optimization*

Improving fuel economy and lowering emissions are key societal goals. Standard driving cycles, pre-designed by the US Environmental Protection Agency (EPA), have long been used to estimate vehicle fuel economy in laboratory-controlled conditions. They have also been used to test and tune different energy management strategies for hybrid electric vehicles (HEVs). This paper aims to estimate fuel consumption for a conventional vehicle and a HEV using personalized driving cycles extracted from real-world data to study the effects of different driving styles and vehicle types on fuel consumption when compared to the estimates based on standard driving cycles. To do this, we extracted driving cycles for conventional vehicles and HEVs from a large-scale U.S. survey that contains real-world GPS-based driving records. Next, the driving cycles were assigned to one of three categories: volatile, normal, or calm. Then, the driving cycles were used along with a driver-vehicle simulation that captures driver decisions (vehicle speed during a trip), powertrain, and vehicle dynamics to estimate fuel consumption for conventional vehicles and HEVs with power-split powertrain. To further optimize fuel consumption for HEVs, the Equivalent Consumption Minimization Strategy (ECMS) is applied. The results show that depending on the driving style and the driving scenario, conventional vehicle fuel consumption can vary widely compared with standard EPA driving cycles. Specifically, conventional vehicle fuel consumption was 13% lower in calm urban driving, but almost 34% higher for volatile highway driving compared with standard EPA driving cycles. Interestingly, when a driving cycle is predicted based on the application of case-based reasoning and used to tune the power distribution in a hybrid electric vehicle, its fuel consumption can be reduced by up to 12% in urban driving. Implications and limitations of the findings are discussed.     

混合型基金绩效评估及驱动因素分析——基于DEA-Tobit模型

将随机选出的混合型基金作为研究样本,采用DEA-Tobit二阶段模型进行研究.首先利用数据包络模型对样本进行绩效评估,在此基础上,利用Tobit模型研究相关指标对于基金绩效的影响机制.结果表明,基金本身所面临的风险收益因素以及基金经理团队稳定性对于基金绩效均具显著影响.最后结合实证结果就提升中国基金绩效提出相关的建议.     

基于效应的随机灰规划模型和应用

为了解决同时含有随机因素和灰色因素的不确定规划问题,通过结合区间灰数所属区间两个端点的随机性,给出随机区间灰数和随机区间灰函数的定义,提出了随机灰规划模型。通过综合效应函数理论用随机变量期望值和方差综合量化表示灰数所属区间的两个端点值。应用该理论对综合量化后的两个端点值继续进行综合量化,从而将随机灰规划转化为确定型规划问题。应用遗传算法进行求解。通过综合效应函数的理念,综合随机变量的期望和方差,同时综合区间灰数的区间因素,将随机灰规划数学模型转化为确定型规划模型即基于效应的随机灰规划模型。通过选取不同的综合效应函数,得到了关于不同决策意识下的随机灰规划的最优解。这个方法可为决策者进行不确定决策提供参考。     

A simple characterisation for sustained growth

This article considers an inter-temporal optimisation problem in a general form and gives conditions ensuring the convergence to infinity of the economy. These conditions can be easily verified and applied for a large class of problems in the literature. Some applications for different economies are given as illustrative examples.     

Internal habits formation and optimality

In a very influential model with internal habits, Carroll et al., (2017, 2000), establish that an increase in economic growth may cause a positive change in savings. The optimality of this result, and of many other contributions using a similar framework, has been questioned by some authors who have observed that the parametrization used in these models always implies a utility function not jointly concave in consumption and habits. In this paper, we revisit the optimality issue and, using advanced techniques in Dynamic Programming, we answer the following long-standing open questions: (i) Is the solution found in Carroll et al., (2017, 2000) optimal? (ii) Is it also unique or do other optimal solutions exist?     

基于两阶段法的多车型卷烟配送路径优化研究

在烟草商业企业中,物流是其重要的环节,是连接客户与烟草企业间的纽带,其卷烟配送线路设计的合理与否直接影响到卷烟的配送效率和配送成本,就如何合理的规划线路成为卷烟配送的关键问题。文章以Q烟草公司为例,运用先分组后路线的两阶段法对问题进行求解,先利用K-means聚类算法对区域进行划分,再考虑工作量均衡的条件下引入遗传算法对区域进一步调整,最后利用混合遗传算法对各配送区域进行线路优化,通过实例从线路数、车辆数、空载率、配送里程及总成本等指标上可以看出两阶段法的配送效果更优。     

基于粒子群算法下的交叉口信号配时优化

为了合理地优化交叉口信号配时,通过分析交叉口的评价指标,建立了以车辆的平均延误、停车次数最小、交叉口整体通行能力最大、各相位有效绿灯时间和交叉口周期时长作为约束条件的数学模型。并通过改进前人研究基础上的动态加权系数,将多目标的非线性优化问题转化为单一目标的非线性规划问题,为了得出更科学稳定的解,提出了改善粒子群算法系统稳定性的2种方法,并将其与粒子群算法结合起来。然后以Matlab为求解工具,结合临邑市某一交叉口实例进行求解分析。最后的结果表明,在使用改进后的粒子群算法进行优化后交叉口通行能力较之现状提升了9%,延误下降了28%,停车次数下降了9%,且各项优化结果均优于Webster,改进后的算法在程序中运行300代,到216代才开始收敛,而未改进的算法稳定性较差,优化结果和收敛曲线则随着实验次数的变化而变化,最后的结论证明了该算法和模型的可靠性。     

Economic forecasting with evolved confidence indicators

We present a machine-learning method for sentiment indicators construction that allows an automated variable selection procedure. By means of genetic programming, we generate country-specific business and consumer confidence indicators for thirteen European economies. The algorithm finds non-linear combinations of qualitative survey expectations that yield estimates of the expected rate of economic growth. Firms’ production expectations and consumers’ expectations to spend on home improvements are the most frequently selected variables – both lagged and contemporaneous. To assess the performance of the proposed approach, we have designed an out-of-sample iterative predictive experiment. We found that forecasts generated with the evolved indicators outperform those obtained with time series models. These results show the potential of the methodology as a predictive tool. Furthermore, the proposed indicators are easy to implement and help to monitor the evolution of the economy, both from demand and supply sides.