辽河流域生态价值评估中WTP与WTA差异性的实证分析

本文以辽河流域作为研究对象,通过实地走访和调研辽河流域的源头一福德店与入海口一盘锦市,采用条件价值法分别对辽河流域居民的生态受偿意愿(WTA)及生态支付意愿(WTP)进行测算,着重探讨WTP与WTA的差异性,分析引起这种差异的社会经济影响因素。本文采用斯皮尔曼法进行相关性分析,发现WTP与WTA的差异性存在着如下特征:一是从WTP与WTA的概率分布来看,二者存在着较大差异;二是从WTP与WTA各自的影响因素来看,WTP受收入的影响而WTA不受收入的影响,收入效应很好地解释了WTA超过WTP的原因;三是WTA与WTP的比值在经验范围以内为2.357;四是引起WTP与WTA差异的社会经济因素有性别、职业和流域生态补偿政策对受访者的影响程度。而对上述特征进行分析后发现惩罚效应和模糊性也是导致辽河流域居民支付意愿和受偿意愿差异的原因。     

基于Group Lasso的个人信用评价分析

个人信用评价问题研究中,需要建立较多的虚拟变量作为解释变量.Group Lasso可以将相关的虚拟变量作为组进行整体剔除或保留在模型中.结合具体的个人信贷数据,应用Group Lasso方法进行变量选择建立Logistic模型,并与全模型、向前选择和向后选择建立的Logistic模型进行比较,发现Group Lasso方法建立的模型,在变量解释和预测正确率上,都是最优的.     

多因子分层模糊评价法的算法设计探讨——模糊综合评价方法在旅游资源评价中的应用

以模糊分析技术为平台,综合模糊分析、因子分析和层次分析的基本思想,建立了多因素、分层次的资源综合评价模型.试图通过构建旅游资源评价指标体系,将旅游资源评价模糊因素数量化,建立起旅游资源评价的模糊综合评价模型,并在旅游资源价值评价领域加以运用.     

网购平台在线声誉机制作用的实证研究方法评析

在线声誉机制是网购平台重要的信号机制,是平台可持续成长的基础.从理论基础、研究假设、数据来源、变量选择、模型应用等方面,全面梳理了在线声誉机制作用的实证研究方法,包括Hedonic回归、Logit、Probit、Poisson、Tobit、截取正态回归、样本选择回归、固定效应、广义最小二乘法等模型的应用.最后展望未来相关研究及方法,并构建在线声誉机制作用的实证研究综合框架.     

利用计数统计模型方法评价大连星海公园旅游的经济价值

在非市场条件下如何评价旅游资源的经济价值是自然旅游公园管理工作中所面临的一个重要问题.正确地估计这类公园的旅游需求函数则是对其有效地进行经济价值评价的关键.由于旅游次数数据具有非负整数和截断的性质,使用传统的多元线性回归模型来估计旅游需求函数则会产生较大的偏差.因此。本文采用非线性最小二乘、截断泊松分布和截断负二项分布等计量经济学模型估计大连星海公园的旅游需求函数,以此来计算公园旅游的经济价值。     

互联网结伴自助旅游者旅游动机研究

以互联网结伴自助旅游的参与者为研究对象进行半结构访谈与问卷调查,利用探索性因子分析和验证性因子分析方法,分别构建了包含"情感信任、社交归属、安全与经济"3个潜在变量与13个观测变量的结伴动机测量模型,包含"自我价值提升、放松与求新"2个潜在变量与10个观测变量的自助旅游动机测量模型,并运用皮尔逊相关分析法对结伴动机与自助旅游动机两个维度之间的关系进行探讨.研究结果表明:情感信任是互联网结伴自助旅游者最重要的结伴动机,其次是社交归属、安全与经济动机;自助旅游动机方面,自我价值提升比放松与求新动机更为显著;促进结伴旅游行为形成的指标能够提高自助旅游动机,自助旅游是使结伴需求得到满足的一种表现形式.本研究有益于推动互联网结伴自助旅游研究的进一步深入,也有助于旅游虚拟社区管理者及相关旅游企业了解互联网结伴自助旅游者的内在心理及行为,并提供针对性的服务或产品.     

具有多类评价信息的旅游景区游客满意度评价方法

针对多类型评价信息的旅游景区游客满意度评价问题,提出一种基于相对贴近度的评价方法。首先,建立旅游景区旅客满意度评价指标体系;其次,采用实数、区间数、三角模糊数、直觉模糊语言变量和直觉模糊数5种不同类型表示旅游进去的评价信息,提出各个旅游景区关于正理想解的相对贴近度计算方法;据此确定旅游景区满意度排序,并具体给出其评价步骤;最后,通过实例说明该方法的有效性和合理性。     

一类基于属性变量的DEA模型

系统评价不仅应重视系统的经济特征,还必须重视系统的非经济特征.在评价系统的非经济特征时必须考虑属性变量,即不能以货币衡量的反映系统非经济特征的定量变量.在过去关于DEA的研究中并未涉及到这种变量.本文对属性变量进行了明确的定义,并提出一种基于属性变量的DEA效率评价模型,最后通过实例演示了模型的合理性.     

基于因子分析的旅游产业集群评价体系研究

设计旅游产业集群评价体系有助于分析旅游产业集群质量、诊断存在的问题、制定旅游发展战略。构建旅游产业集群初步评价指标体系,采用专家评分法采集数据,为评价指标赋值,运用因子分析法分析法对指标进行定量分析,构建旅游产业集群综合评价模型。该模型有助于探寻旅游产业集群发展的战略思路。     

江西省旅游资源环境承载力评价的科学计算

科学地评价江西省各地区旅游资源环境承载力是制定旅游可持续发展战略的基础.基于评价指标体系的构建原则,建立了江西省旅游资源环境承载力的评价指标体系.结合实例,运用主成分分析法对江西省旅游资源环境承载力进行综合评价.通过Spss和Matlab软件的计算.说明了该评价模型的合理性、有效性及实用性,研究为旅游资源的合理开发提供了一种新的评价方法.     

基于特征价格模型的滨海公园门票价格影响因素分析——以大连滨海公园为例

本文选取大连市2010~2014年29个滨海公园的季度面板数据,运用特征价格法,实证检验了公园自然属性及园区游憩设施属性与公园门票价格之间的关系。研究发现:在自然属性方面,除温度、降水量和沙滩属性外,其他滨海公园的自然属性均对门票价格具有显著的正向影响;而温度和降水量对游客对公园门票价格的边际意愿支付具有显著的负向影响。比较而言,由于很多滨海旅游景点门票价格实行的一票制,这可能是导致园区内各种游憩设施对门票价格产生较小影响的原因。而一些重要的园区游憩设施仍然会对门票价格产生重要的影响,比如水族馆,海洋动物表演,海上观光游船和潜水等都对门票产生显著的影响;相比之下,那些与滨海资源无关的园区游憩设施属性对门票价格没有影响,甚至产生负向影响。在园区游客拥挤度和景区A级排名方面,二者均会对公园门票价格产生显著影响。基于上述研究结果,本文对滨海公园承载力管理、环境保护、活动设施改善、以及门票价格调整等提出了相关政策建议。     

基于蒙特卡洛模拟的滨海旅游环境承载力影响因素重要性排序研究——以大连滨海景区为例

由于层次分析法(AHP)针对指标重要性的测度存在一定的缺陷:其一是主观性对数据收集的干扰;其二是相近指标权重的精确识别容易出现偏差;因此,本文联合引入三角分布函数及蒙特卡洛模拟(MCS),以期对滨海旅游环境承载力所涉评价指标的重要性测度方法进行合理改进。相比之下,研究表明基于蒙特卡洛模拟修正后的层次分析法(MCAHP)可以有效缓解问卷提取评分数据所隐含的主观偏误,最终有效提升评价结论的真实性和可靠性。据此,本文对大连滨海景区环境承载力指标重要性的实证分析结果进行修正,以期得到更为精确与科学的相关结论,同时为大连滨海旅游环境承载力的治理与旅游业的可持续发展提出相应的政策建议。     

三江平原湿地非使用价值支付意愿的影响因素

为了更好地保护三江平原湿地生态系统,应用条件价值评估法(CVM)这一当前国际上流行的生态系统经济价值评价方法,对三江平原湿地的非使用价值进行评价.研究中采用实地调研的方式,进行支付意愿问卷调查,共发放支付卡式CVM问卷552份,回收有效问卷513份.得出三江平原湿地2007年人均支付意愿为71.66元/年.通过对问卷数据的描述性统计,得到支付意愿的分布情况和在各单一因素影响下的变化规律.用主成分分析简化影响因素,运用有序多分类Logit模型,得到在所有影响因素中,个人平均年收入、个人受教育程度以及个人环保态度是影响受访者对三江平原湿地非使用价值支付意愿的主要因素.研究结论使得三江平原湿地开发和保护价值比较成为可能,为政府制定相关政策提供理论基础与科学依据.     

The non-Markovian nature of nested logit choice

Discrete choice models are widely used for understanding how customers choose between a variety of substitutable goods. We investigate the relationship between two well studied choice models, the Nested Logit (NL) model and the Markov choice model. Both models generalize the classic Multinomial Logit model and admit tractable algorithms for assortment optimization. Previous evidence indicates that the NL model may be well approximated by, or be a special case of, the Markov model. We establish that the Nested Logit model, in general, cannot be represented by a Markov model. Further, we show that there exists a family of instances of the NL model where the choice probabilities cannot be approximated to within a constant error by any Markov choice model.     

Traffic assignment model with fuzzy level of travel demand: An efficient algorithm based on quasi-Logit formulas

The place of fuzzy concepts in traffic assignment (TA) models has been studied in recent literature. Keeping fuzzy level of travel demand in mind, we propose a new TA model in which the travel costs of links are depended on their congestion. From the results of such fuzzy TA model, network planners are able to estimate the number of travelers on network links. By using zero–one variables, the proposed model is transformed into a crisp mixed-integer problem with respect to path-flow variables. In order to produce the Logit flows from this problem, Damberg et al. algorithm is modified. Then, the level of certainty is maximized and perceived travel delays are minimized. For a fixed certainty degree, the obtained solution, which is named the fuzzy equilibrium flow, satisfies a quasi-Logit formula similar to ordinary expression of the Logit route choice model. Eventually, we examine the quality of different path enumeration techniques in the proposed model.     

Logit tree models for discrete choice data with application to advice-seeking preferences among Chinese Christians

Logit models are popular tools for analyzing discrete choice and ranking data. The models assume that judges rate each item with a measurable utility, and the ordering of a judge’s utilities determines the outcome. Logit models have been proven to be powerful tools, but they become difficult to interpret if the models contain nonlinear and interaction terms. We extended the logit models by adding a decision tree structure to overcome this difficulty. We introduced a new method of tree splitting variable selection that distinguishes the nonlinear and linear effects, and the variable with the strongest nonlinear effect will be selected in the view that linear effect is best modeled using the logit model. Decision trees built in this fashion were shown to have smaller sizes than those using loglikelihood-based splitting criteria. In addition, the proposed splitting methods could save computational time and avoid bias in choosing the optimal splitting variable. Issues on variable selection in logit models are also investigated, and forward selection criterion was shown to work well with logit tree models. Focused on ranking data, simulations are carried out and the results showed that our proposed splitting methods are unbiased. Finally, to demonstrate the feasibility of the logit tree models, they were applied to analyze two datasets, one with binary outcome and the other with ranking outcome.     

A theoretical analysis of the cross-nested logit model

The emergence of Intelligent Transportation Systems and the associated technologies has increased the need for complex models and algorithms. Namely, real-time information systems, directly influencing transportation demand, must be supported by detailed behavioral models capturing travel and driving decisions. Discrete choice models methodology provide an appropriate framework to capture such behavior. Recently, the Cross-Nested Logit (CNL) model has received quite a bit of attention in the literature to capture decisions such as mode choice, departure time choice and route choice. %The CNL model is an extension of the Nested Logit model, providing %more flexibility at the cost of some complexity in the model formulation. In this paper, we develop on the general formulation of the Cross Nested Logit model proposed by Ben-Akiva and Bierlaire (1999) and based on the Generalized Extreme Value (GEV) model. We show that it is equivalent to the formulations byby Papola (2004) and Wen and Koppelman (2001). We also show that the formulations by Small(1987) and Vovsha(1997) are special cases of this formulation. We formally prove that the Cross-Nested Logit model is indeed a member of the GEV models family. In doing so, we clearly distinguish between conditions that are necessary to prove consistency with the GEV theory, from normalization conditions. Finally, we propose to estimate the model with non-linear programming algorithms, instead of heuristics proposed in the literature. In order to make it operational, we provide the first derivatives of the log-likelihood function, which are necessary to such optimization procedures.     

哈尔滨市冬季PM_(2.5)与影响要素的相关分析

运用相关性分析方法,研究哈尔滨市PM_(2.5)质量浓度与主要空气污染物及气象因素之间的相关关系.建立PM_(2.5)与影响其质量浓度变化的因素的单因变量的偏最小二乘回归分析(PLS1)模型,模型拟合良好,由模型知CO是导致PM_(2.5)质量浓度升高的主要因素.运用通径分析方法,研究解释变量对因变量的直接影响、通过其他解释变量对因变量的间接影响以及各解释变量的对因变量的协同作用.结果表明,各解释变量对PM_(2.5)质量浓度变化的总作用从大到小依次为:CO、PM_(10)、NO_2、风速、湿度、SO_2.     

CONTINGENT VALUATION OF MARINE PROTECTED AREAS: SOUTHERN CALIFORNIA ROCKY INTERTIDAL ECOSYSTEMS

ABSTRACT. Designation as Marine Protected Areas (MPAs) can protect coastal ecosystems, but apparently has not effectively protected the rocky intertidal zone in urban Southern California. Here, illegal collecting and habitat disturbance harm coastal marine life. We surveyed day visitors to sandy beaches or adjacent rocky habitats in Orange County. Using the close‐ended, double‐bounded contingent valuation method, we estimate the benefit of more effective enforcement and management of MPAs designed to avoid coastal ecosystem decay. We solve the problem of sample selection bias, introduced by the likelihood that the sample disproportionately includes respondents who visit more frequently and who may have a higher willingness to pay (WTP). We estimate the WTP for enacting policies to reduce illegal collecting and on‐site habitat disturbance to average $6 per family‐visit. Our estimate is consistent with other studies that estimate consumer surplus at $15 per person‐trip for nearby, lower quality beaches, and extrapolates from other studies to $3.6 $4.8 million per mile of coastline.