Study of haze emission efficiency based on new co-opetition data envelopment analysis

As haze intensifies in China, controlling haze emission has become the country's top priority for environmental protection. Because haze moves across different regions, it is necessary to develop a data envelopment analysis (DEA) model underpinned by both competition and cooperation to evaluate the haze emission efficiency in different provinces. This study innovatively adopts the spatial econometrics to construct the co-opetition matrices of Chinese provinces, then builds the co-opetition DEA model to evaluate the haze emission efficiency of them, and finally uses the haze data of 2015 as an example to assess the applicability of the model. The results of the study include the following: First, compared with the traditional CCR (A. Charnes & W. W. Cooper & E. Rhodes) model, this study constructs the co-opetition DEA cross-efficiency model that integrates haze's feature of cross-border moving; thus, it is more in line with the reality of haze emission and movement. Second, compared with the efficiency value gained from the CCR model, the haze emission efficiency values for Tianjin and Guangdong, two decision-making units, register greater variance when using the DEA model. The reason might lie in that they have a different spatial transportation relationship with their surrounding provinces. Third, the haze emission efficiency of provinces, according to the evaluation based on the co-opetition DEA method, varies greatly: Those with high efficiency are mostly inland provinces with slow economic growth and adverse climatic conditions, whereas many of the provinces with low efficiency are located in the relatively prosperous East China. The specific co-opetition DEA model constructed in this study enriches the research on the DEA model, which can be applied to the emission efficiency evaluation of similar pollutants around the world and can contribute empirical support to the haze reducing efforts of the government with its empirical results.     

DEA评价方法在超稠油生产经营中的应用探讨

超稠油的生产工艺非常复杂，影响油井效益的因素相对较多，因此对油井的评价就要评价各类有限资源投入的利用效果，查明积极因素和消极因素及其对经济指标的影响程度，促进经营管理的改善，寻求有效利用有限人力、物力、财力等资源的途径，实现资源的优化配置。本文通过使用DEA模型可对单井效益进行评价，对单井效益进行排队，对规模效益进行分类，对生产各投入因素进行投影分析，研究弱DEA、非DEA有效的原因，寻求对油井优化成本、提高效益的改进方案。     

数据包络分析模型及其应用研究

通过对传统的C^2R模型进行研究并加以改进，建立了C2RHZ模型,利用有色金属矿采选业1993-2001年科技创新的相关数据进行了计算分析。说明了C^2RHZ模型的应用价值．     

全面建设小康社会进程中黄河流域水资源利用效率时空演变分析

提升水资源利用效率,减轻水资源短缺对经济社会发展产生的约束,是实现水资源与经济社会和谐发展的有力支撑。尝试将DEA视窗分析方法应用于水资源利用效率研究,结合超效率DEA模型,探究黄河流域九省区在全面小康社会建设进程中水资源利用效率时空演变特征,在此基础上,通过Malmquist指数分解效率变动的内在原因,并引用空间匹配度计算方法,研究水资源利用效率与全面小康相对水平匹配程度。研究发现:九省区2012—2018年水资源利用效率总体呈增加趋势,水资源利用水平具有显著空间差异性;九省区水资源利用效率的提升主要得益于技术进步TC,制约水资源利用效率提升的主要因素是综合技术效率EC;水资源利用效率与全面小康相对水平的匹配程度呈现出较强的地区差异,部分省区两者动态协同性较差,其中,宁夏需要同时提升水资源利用水平和经济社会发展水平,陕西需要提升水资源利用水平,内蒙古需要提升经济社会发展水平。     

VCSTalk：基于DEA的开源软件开发人员效率评价工具*

介绍了VCSTalk的具体设计和实现架构,并展示了它用于评价若干开源软件项目中的开发人员效率的一个具体实例.     

基于模糊期望值的模糊DEA 交叉效率评价方法

基于模糊DEA的交叉效率评价方法研究是一个崭新的研究课题,有着广阔的应用前景。结合基于模糊期望值的模糊 DEA 模型和交叉效率原理,提出一种新的交叉效率的评价方法。该方法首先求出基于模糊期望值的最优效率值权重,然后由这组模糊最优权重求解他评效率并构造交叉效率矩阵,最后根据求出的模糊期望交叉效率值对各DMU进行排序。     

我国区域固定资产投资效率的DEA分析

利用DEA分析的C2R模型和C2GS2模型对全国31个省市区固定资产投资的技术有效性和规模有效性进行了实证测度,根据测度结果把全国31个省市区划分为不同类型,指出了各种类型固定资产投资的特点,提出了提高其投资效率的对策措施。     

Effective prediction of DEA model by neural network

In this paper,a fast neural network model for the forecasting of effective points by DEA model is proposed,which is based on the SPDS training algorithm.The SPDS training algorithm overcomes the drawbacks of slow convergent speed and partially minimum result for BP algorithm.Its training speed is much faster and its forecasting precision is much better than those of BP algorithm.By numeric examples,it is showed that adopting the neural network model in the forecasting of effective points by DEA model is valid.     

基于复合DEA的分地区高校科研管理效率评价

应用复合DEA方法全面评价了我国分地区高校科研资源的配置现状,分析了2001-2005 5年间影响区域科研资源优化配置的原因,得出目前制约我国高校科研效率的主要因素包括科研经费的投入及利用问题以及高水平师资匮乏两个方面,并提出了相应的改进对策.     

基于DEA模型的上市公司市值管理绩效评价

市值管理是股权分置改革后我国上市公司出现的一个管理新动向,对市值管理绩效进行科学评价对于上市公司改进市值管理,更好地为股东价值最大化服务具有重要意义。在国内首次用数据包络分析（data evelopement analysis,DEA）方法建立了一个评价上市公司市值管理绩效的DEA模型,并用该模型对包括不同行业、不同规模和不同所有制的25家上市公司2008年度的市值管理绩效进行了分析,总结出了5种类型绩效,并具体分析了每类上市公司改进市值管理绩效的路径,对我国上市公司有针对性地改进市值管理有着重要参考作用。