中国农村社会养老保险制度变迁与政策评价

“十二五”规划纲要中明确提出要健全覆盖城乡居民的社会保障体系,着实实现新型农村社会养老保险制度全覆盖也随即提上了发展El程。本文通过对我国农村社会养老保险制度的发展脉络进行解读,从制度变迁的视角对这一政策进行评价,分析政策遭遇困境的原因,重点分析新型农村社会养老保险制度存在的缺陷。     

基于灰色综合评价法的住宅开发项目研究

随着我国经济的快速发展,房地产市场由卖方市场向买方市场转变,住宅开发项目也面临着越来越多的挑战.为了让房地产开发企业在这种经济环境下能够健康持续发展,所开发的住宅项目满足市场需求不造成资源浪费,企业必须了解客户需求.文章利用灰色综合评价法,从顾客的视角出发,找出最受顾客欢迎的住宅项目,对其加以分析.旨在让开发商在开发过程中可以充分考虑顾客的需要,重视顾客最关注的因素,借此提升开发企业的竞争力并塑造良好的品牌形象.     

建设项目环境绩效评价指标体系的构建

环境绩效评价的引入和推广使建筑业越来越关注其自身活动对环境的影响。迄今,学术界对环境绩效评价的研究成果显著,不仅基于自然环境、社会环境、经济环境的三角关系形成可持续性评价的研究领域,还基于项目全生命周期管理创立了全生命周期评价的理论体系。基于文献综述和问卷调查,设计出一套适用于建设项目的环境绩效指标,并分析各指标的数据可获得性、重要性及应受关注度,为国内建设项目更加科学地进行环境绩效评价提供了理论支持。     

农村零售区域评价、商店态度与消费者购物地选择

以山东省农村居民为调查对象,探讨了零售区域评价、商店态度与消费者购物地选择之间的关系。通过问卷调查,利用因子分析及多元线性回归分析验证了研究假设。实证分析结果表明,零售区域评价对商店态度产生显著影响,而商店态度是影响农村居民购物地选择决策的关键因素。     

基于熵权—模糊综合评判的产业集群自主创新能力评价方法研究

在对产业集群自主创新能力的内涵及其构成进行分析的基础上,得到了产业集群自主创新能力的评价指标体系。将模糊综合评判中的主因素突出型、加权平均型和全面制约型三类隶属度结合起来,构造了符合客观实际的隶属函数,从而得到了产业集群自主创新能力综合评价模型。同时,在确定权重过程中,利用反映指标真实情况的指标熵,结合专家调查,得到兼顾主客观状况的评价指标权重,通过实证检验证明了此评价方法在产业集群自主创新能力评价中的有效性。     

我国生物质发电的发展环境分析

论述了国内生物质发电项目的发展情况,根据多项生物质发电工程前期工作研究和评审经验,在国家有关主管机构要求完成相关课题研究和调研的基础上,对生物质发电项目对生态环境的影响进行了分析,对目前亟待重视的问题和政策进行了讨论研究,以期为我国生物质发电产业的健康快速发展提供借鉴。     

电网工程建设预算编制与计算标准的跟踪分析系统开发应用

《电网工程建设预算编制与计算标准》（简称《预规》）是电网工程技经工作的基础标准,是在一定技术水平、经济水平下测定的,具有时间局限性,需要根据变化适时修订。由于修订工作量大,修订周期过长,在工程实际中暴露出标准滞后的问题。针对上述问题,基于大量实际工程决（结）算数据,研发了该标准的跟踪分析软件系统,选取了《预规》＂其他费用＂中的3类典型项目费用作为例证,判断＂其他费用＂中对应项目标准的适用性,旨在为适时提出修订《预规》的标准提供参考建议。     

电池储能技术在电力系统中的应用评价及发展建议

随着智能电网时代的到来,世界各国电力系统正在从传统型向现代型转变,而储能技术的应用将成为这场变革的重要环节。对新型储能电池的技术经济特性和发展应用现状进行了介绍,构建了电池储能技术综合评价指标体系,针对储能技术在不同领域的具体应用进行了综合评价,分析了新型储能技术的发展前景并提出了相关建议,以期为政府主管部门、能源电力企业、产业界及行业专家提供参考。     

Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction

This paper reports the results of the NN3 competition, which is a replication of the M3 competition with an extension of the competition towards neural network (NN) and computational intelligence (CI) methods, in order to assess what progress has been made in the 10 years since the M3 competition. Two masked subsets of the M3 monthly industry data, containing 111 and 11 empirical time series respectively, were chosen, controlling for multiple data conditions of time series length (short/long), data patterns (seasonal/non-seasonal) and forecasting horizons (short/medium/long). The relative forecasting accuracy was assessed using the metrics from the M3, together with later extensions of scaled measures, and non-parametric statistical tests. The NN3 competition attracted 59 submissions from NN, CI and statistics, making it the largest CI competition on time series data. Its main findings include: (a) only one NN outperformed the damped trend using the sMAPE, but more contenders outperformed the AutomatANN of the M3; (b) ensembles of CI approaches performed very well, better than combinations of statistical methods; (c) a novel, complex statistical method outperformed all statistical and CI benchmarks; and (d) for the most difficult subset of short and seasonal series, a methodology employing echo state neural networks outperformed all others. The NN3 results highlight the ability of NN to handle complex data, including short and seasonal time series, beyond prior expectations, and thus identify multiple avenues for future research.     

Scoring rules and survey density forecasts

This article provides a practical evaluation of some leading density forecast scoring rules in the context of forecast surveys. We analyse the density forecasts of UK inflation obtained from the Bank of England’s Survey of External Forecasters, considering both the survey average forecasts published in the Bank’s quarterly Inflation Report, and the individual survey responses recently made available to researchers by the Bank. The density forecasts are collected in histogram format, and the ranked probability score (RPS) is shown to have clear advantages over other scoring rules. Missing observations are a feature of forecast surveys, and we introduce an adjustment to the RPS, based on the Yates decomposition, to improve its comparative measurement of forecaster performance in the face of differential non-response. The new measure, denoted RPS*, is recommended to analysts of forecast surveys.