政府动态奖惩机制下绿色建筑供给侧演化博弈研究

从绿色建筑供给侧角度出发，构建消费者效用函数、开发商利润函数和政府与开发商的演化博弈模型，分析消费者绿色偏好、建筑绿色度、成本等因素对建筑价格、需求和利润的影响以及政府动态奖惩政策下系统的演化稳定策略。结果表明：消费者绿色偏好支付系数和建筑绿色度的增大有利于绿色建筑价格、需求和利润的提高，但高成本却使需求和利润降低；政府采用静态补贴与静态税收政策时，博弈系统不存在演化稳定策略，采用动态补贴与静态税收、静态补贴与动态税收、动态补贴与动态税收三种政策组合时，系统存在演化稳定策略；动态补贴与静态税收的政策组合在推动绿色建筑发展方面优于其他政策；开发商开发绿色建筑的概率与补贴上限值呈负相关，与税收上限值呈正相关。     

基于TOPSIS熵权法的城市韧性评价及韧性建设路径研究——以安徽省为例

城市需要足够的韧性以应对复杂多变的外部环境.高质量发展背景下,统筹规划城市韧性建设对城市可持续健康发展具有重要意义.采用TOPSIS熵权法模型测度2011-2018年期间安徽省各地级市韧性评价值,构建韧性城市评价指标体系,分析安徽各城市韧性演化情况.安徽16个地级市的平均韧性水平随时间呈上升趋势,暂不存在区域间城市韧性发展不平衡问题.结合韧性城市建设的总体思路和要求,安徽各城市应不断优化城市的空间结构,增强城市在经济、社会和基础设施等领域的韧性水平.     

基于空间思维能力培养的工程图学教学改革研究

工程图学课程对培养学生的制图能力有着极其重要的作用.目前,一些高校的工程图学课程并未足够重视培养学生的空间思维能力,这就使一些空间思维能力较差的学生对工程图学课程的学习产生心理障碍,进而慢慢丧失兴趣.因此,高校应培养和提升学生的空间思维能力,提高学生的创新能力,增强学生的工程图学空间思维,提高学生的想象能力和推理能力,使学生具有更加全面的制图能力.     

大豆生产者补贴政策改革促进农户种植结构调整了吗?——基于全国446个县的准自然实验

以大豆生产者补贴政策改革作为切入点,将政策实施地区和非实施地区视为一次部分省份优化大豆、玉米种植结构的准自然实验,通过构建PSM-DID模型,考察大豆生产者补贴政策改革对农户种植结构调整的影响及其作用机制.结果表明:(1)大豆生产者补贴政策改革对农户种植结构调整具有显著的时间异质性,政策改革初年(2017年)大豆与玉米的面积之比显著提高6.5％,虽然2018年大豆与玉米的相对补贴额度出现大幅度增长,但大豆与玉米的面积之比仅显著提高3.4％;(2)预期相对收益的提升是2017年农户调整种植结构的重要原因,然而,经过对新制度一年的适应后,农户对种植玉米的路径依赖、补贴错位、地租的不合理增长以及大豆、玉米相对价格不理想等问题逐渐凸显,2018年农户重新调整种植决策,补贴的激励作用被迫减弱.基于此,研究结论为统筹大豆、玉米补贴机制可以促进农户种植结构调整提供经验性证据,也为在农业供给侧结构性改革和大豆振兴压力下如何持续改善种植结构提供政策启示.     

Semiparametric Bayesian analysis for longitudinal mixed effects models with non-normal AR(1) errors

Statistics and Computing - This paper studies Bayesian inference on longitudinal mixed effects models with non-normal AR(1) errors. We model the nonparametric zero-mean noise in the autoregression...     

大都市疏散政策变迁中的工具转向 ——基于北京市政策的文本分析

2014年以来,京津冀协同发展已上升为国家重大战略,战略中的一个重要环节便是通过各种举措疏解核心区的人口和产业.为了剖析疏散政策的发展脉络、工具偏好和时空趋势,运用文本分析方法和政策二维分析框架,借助ROST-CM软件,对北京市及各区县与疏散密切相关的政策73份、共计119万字文件进行文本分析,重点探索疏散政策的工具偏好,以及政策框架、对象和工具的时空变迁.研究指出:北京市的政策调控由单纯的人口迁移政策转向以产业和功能带动人口的模式;在政策工具选择上,涉及人口、产业、空间和设施疏解的供给型和环境型政策工具种类丰富多样,而需求型政策工具种类相对较少;政策文本中高频关键词的阶段性更替也表明政府各个阶段的政策关注点发生了显著变化.     

面向新时代的国民经济核算研究议题及相关问题

本文结合国际国内研究动态分层次归纳、阐释当前国民经济核算的研究议题。具体包括:以国民账户体系2008版(SNA-2008)为起点的前沿研究,SNA中心框架的灵活运用研究,接驳中国国民经济核算体系(CSNA)及其实务的优化和创新研究,国民经济核算数据的综合应用研究。最后针对中国当前国民经济核算研究方法中存在的一些倾向性问题做讨论,比如是否过分关注与GDP的关联, 如何充分发挥卫星账户的作用等。     

Lower bound of average centered L2-discrepancy for U-type designs

Uniform designs are widely used in various scientific investigations and industrial applications. By considering all possible level permutation of the factors, a connection between average centered L₂-discrepancy and generalized wordlength pattern for asymmetrical fractional factorial designs is derived. Moreover, we present new lower bounds to the average centered L₂-discrepancy for symmetrical and asymmetrical U-type designs. For illustration of the theoretical results, the lower bounds for symmetrical and asymmetrical U-type designs are tabulated, and numerical results indicate that our lower bounds behave well and can be recommended for use in practice.     

An additive–multiplicative mean model for panel count data with dependent observation and dropout processes

This paper discusses regression analysis of panel count data with dependent observation and dropout processes. For the problem, a general mean model is presented that can allow both additive and multiplicative effects of covariates on the underlying point process. In addition, the proportional rates model and the accelerated failure time model are employed to describe possible covariate effects on the observation process and the dropout or follow‐up process, respectively. For estimation of regression parameters, some estimating equation‐based procedures are developed and the asymptotic properties of the proposed estimators are established. In addition, a resampling approach is proposed for estimating a covariance matrix of the proposed estimator and a model checking procedure is also provided. Results from an extensive simulation study indicate that the proposed methodology works well for practical situations, and it is applied to a motivating set of real data.     

Fast and approximate exhaustive variable selection for generalised linear models with APES

We present APproximated Exhaustive Search (APES), which enables fast and approximated exhaustive variable selection in Generalised Linear Models (GLMs). While exhaustive variable selection remains as the gold standard in many model selection contexts, traditional exhaustive variable selection suffers from computational feasibility issues. More precisely, there is often a high cost associated with computing maximum likelihood estimates (MLE) for all subsets of GLMs. Efficient algorithms for exhaustive searches exist for linear models, most notably the leaps‐and‐bound algorithm and, more recently, the mixed integer optimisation (MIO) algorithm. The APES method learns from observational weights in a generalised linear regression super‐model and reformulates the GLM problem as a linear regression problem. In this way, APES can approximate a true exhaustive search in the original GLM space. Where exhaustive variable selection is not computationally feasible, we propose a best‐subset search, which also closely approximates a true exhaustive search. APES is made available in both as a standalone R package as well as part of the already existing mplot package.