我国秸秆综合利用面临形势与对策研究

[目的]为加快推动秸秆资源化利用,促进农业绿色发展。[方法]文章在系统梳理我国秸秆综合利用政策文件、相关文献的基础上,全面分析了我国农作物秸秆禁烧和综合利用的发展阶段,提出了秸秆利用面临的形势和存在的主要问题。[结果]我国秸秆综合利用从时间序列上,可以划分为起步阶段、强力推进阶段和攻坚阶段。城镇化的快速推进、农村能源结构的调整、农业供给侧结构性改革的实施、农作物季节矛盾突出等,成为秸秆综合利用面临的新形势、新挑战。当前推进秸秆综合利用存在四大方面问题:秸秆还田成本较高,区域技术规范和技术适宜性缺乏;受"成本地板"和"价格天花板"双重挤压,秸秆产业化利用发展困难;秸秆收储运成本高,技术装备水平低,用地、运输问题尚未解决,收储运体系建设不健全;关键性政策工具尚未破题,缺乏普惠性、针对性的资金扶持。[结论]针对形势与问题,提出了推进秸秆资源化利用的4条对策建议:开展县域秸秆全量化利用、分区施策确定秸秆利用方向、加强政策工具集成创设、扩大试点示范引导。     

我国大麦价格波动特征及其影响因素分析

[目的]大麦价格剧烈波动会直接影响大麦种植户的生产积极性和大麦产业的平稳发展,研究大麦价格波动特征及其影响因素,有助于提升大麦产业链相关主体识别和应对市场风险的能力,促进大麦产业的健康发展。[方法]文章先采用HP滤波法和ARCH类模型分析了2011年4月至2017年2月我国大麦价格波动特征,然后采用脉冲响应函数分析了我国大麦价格波动影响因素。[结果]我国大麦价格波动存在明显的季节性和周期性,样本期内总体上呈现逐渐下降趋势;我国大麦价格具有显著的波动集聚性,我国大麦价格具有显著的不对称性;在该文选择的影响因素中,大麦进口量和国际大麦价格是影响我国大麦价格波动的主要因素。[结论]该文提出必须保障并提高国内大麦合理产能、完善大麦价格监测预警体系、加强国内大麦进口企业整合和推动大麦进口来源多元化的政策建议。     

基于农业发展方式转变的江苏省农业产业升级发展研究

[目的]从定量方面对江苏省农业发展方式转变对农业产业升级贡献结果进行研究,以准确把握江苏省农业发展方式转变进程。[方法]构建基于农业发展方式转变的江苏省农业产业升级评价指标体系,以2007—2016年江苏省10年的年度数据为研究对象,采用熵值法计算各年度每个指标权重以及各准则层和农业发展方式转变综合得分值。[结果](1) 2007—2016年江苏省农业产业方式转变取得了一定成效,处于良好的发展势头;(2) 2012和2014年江苏省基于农业发展方式转变的农业产业升级增长最快,2016年出现大幅下跌,导致该下跌的原因是同时期农业集约型效益的大幅下降;(3)集约型升级和高效型升级对基于农业发展方式转变的江苏省农业产业升级带动作用最大;结构合理型升级发展滞后。[结论]江苏省基于农业发展方式转变的农业产业升级还处于不断探索阶段,加速农业产业结构升级对于江苏省加快转变农业发展方式尤为重要。     

Predicting credit card delinquencies: An application of deep neural networks

The objective of this paper is twofold. First, it develops a prediction system to help the credit card issuer model the credit card delinquency risk. Second, it seeks to explore the potential of deep learning (also called a deep neural network), an emerging artificial intelligence technology, in the credit risk domain. With real-life credit card data linked to 711,397 credit card holders from a large bank in Brazil, this study develops a deep neural network to evaluate the risk of credit card delinquency based on the client's personal characteristics and the spending behaviours. Compared with machine-learning algorithms of logistic regression, naive Bayes, traditional artificial neural networks, and decision trees, deep neural networks have a better overall predictive performance with the highest F scores and area under the receiver operating characteristic curve. The successful application of deep learning implies that artificial intelligence has great potential to support and automate credit risk assessment for financial institutions and credit bureaus.     

Move fast,travel slow: the influence of high-speed rail on tourism in Taiwan

High-speed rail (HSR) and tourism are closely related economic activities because improved mobility is perceived to facilitate tourist behavioral changes. This study examines the influence of HSR on the travel patterns of individual tourists in Taiwan in relation to time, space and carbon emissions. A framework is first provided to discuss how changes in the speed of intercity transportation will affect visitors’ choice of the journey, behavior at destinations and trip quality. In addition, HSR is expected to influence five general aspects of travel decisions relating to mobility and trip emissions, including mode selection, travel distance, length of stay per trip, annual travel frequency and total travel days. In the example of Taiwan, information by onsite sampling of 400 domestic travelers found that HSR had a weak influence on travel distance and length of stay per trip, but was observed to facilitate extended time at each stop, a deeper engagement with the locality, and an approximate 10% reduction in transport carbon emissions through intermodal substitution. These phenomena are in line with the slow travel concept of sustainable tourism consumption.     

Dress for success: the effect of exhibitors’ dress conformity and self-construal on attendees’ approach behavior

ABSTRACT

The purpose of the current study is to investigate how attendees’ approach behavior is influenced by exhibitors’ dress conformity. A 2 × 2 between-subjects quasi-experimental design was utilized for this study. Our results showed that conforming dress for exhibitors can lead to a higher level of approach behavior of trade show attendees as compared with nonconforming dress, and the effect of exhibitors’ dress styles on approach behavior is moderated by attendees’ self-construal. Findings of this study provide significant insights for trade show exhibitors and attendees.     

Emotional intelligence and service quality: a meta-analysis with initial evidence on cross-cultural factors and future research directions

Marketing Letters - In an increasingly competitive market economy, retailers are seeking ways to manage customer perceptions of their service quality. Selecting employees who are high on emotional...     

Modeling the impulse response complex network for studying the fluctuation transmission of price indices

Journal of Economic Interaction and Coordination - We provide a method for analyzing the transmission of fluctuation among price indices, which combines the complex network method and the impulse...     

产业互联下基于双循环的供应链合作稳定性研究

以一条由制造商和零售商组成的二级供应链为研究对象,通过测算产业互联度与双循环程度,研究对供应链合作稳定性的影响.研究发现:在国内环境中,产业互联度与内循环程度呈正比关系,产业互联度越高,内循环程度越高;内循环度程度提高,内需扩大,供应链上的企业选择合作时的整体收益要大于不合作时的收益,因此达到供应链长期稳定合作.通过从国内角度寻找出的规律应用于国际供应链中,为企业参与全球经济提供启示.     

Evaluation of the green technology innovation efficiency of China's manufacturing industries: DEA window analysis with ideal window width

Data Envelopment Analysis (DEA) window analysis with ideal window width is applied to evaluate the green technology innovation efficiency of 28 manufacturing industries in China during 2006–2014. The obtained results are compared with those calculated using the traditional DEA model, and convergence analysis of the efficiency is conducted. Five years is the obtained ideal window width and DEA window analysis with ideal window produces results closer to reality for China's manufacturing industry. The overall efficiency of the green technology innovation in the manufacturing sector is low following a wave-shaped curve – first decreasing, then increasing and decreasing again, with large inter-industrial differences. There are 8 high-, 14 medium- and 6 low-efficiency industries. A convergence trend in the green technology innovation efficiency within the 28 manufacturing industries exists, implying a catch-up effect between them.