人- 组织匹配对创新行为的内在影响机制研究

员工是企业创新活动的主要实施者,其创新行为决定了创新的成败,但现有研究对员工创新行为的内在影响机制仍缺乏清晰刻画。本文基于社会认知理论,从心理因素的视角出发,探索人 - 组织匹配对创新行为的内在影响机制。通过收集数据,应用层次回归分析验证所提出的理论假设。结果表明,人-组织匹配不仅能对创新行为产生直接影响,还能够通过内部动机对创新行为产生间接影响。本文从理论上丰富了创新行为的相关研究,实践上为提升员工创新行为提出了对策建议。     

张家界国家森林公园客源市场的空间特征与潜力研究

旅游客源市场的空间特征是旅游地理学研究的重要内容。从数量上度量旅游需求随客源地的空间分布变化,能指导旅游目的地的旅游规划和经营决策。以张家界国家森林公园为例,运用GIS对其国内客源市场进行了空间分析,利用距离累计曲线分析影响其客源分布的因素;最后在引力模型的基础上建立旅游客流空间潜力模型。研究结果表明,张家界国家森林公园的客源市场分布与我国目前经济发展中珠三角、长三角、环渤海三足鼎立的格局保持一致;在此基础上,提出应在保护资源的前提下实现市场优化以达到可持续发展的对策。     

Quantile regression for the qualifying match of GEFCom2017 probabilistic load forecasting

We present a simple quantile regression-based forecasting method that was applied in the probabilistic load forecasting framework of the Global Energy Forecasting Competition 2017 (GEFCom2017). The hourly load data are log transformed and split into a long-term trend component and a remainder term. The key forecasting element is the quantile regression approach for the remainder term, which takes into account both weekly and annual seasonalities, such as their interactions. Temperature information is used only for stabilizing the forecast of the long-term trend component. Information on public holidays is ignored. However, the forecasting method still placed second in the open data track and fourth in the definite data track, which is remarkable given the simplicity of the model. The method also outperforms the Vanilla benchmark consistently.     

Data preprocessing and quantile regression for probabilistic load forecasting in the GEFCom2017 final match

Team QUINKAN competed in the GEFCom2017 final match of hierarchical probabilistic load forecasting by adopting the quantile regression method using the R package quantreg. The weather stations were clustered into 11 groups, from which an optimal one was chosen for each load meter using the boosting method. The load meter records were cleaned and/or supplemented by various methods in order to secure robust quantile predictions. The variation in the regression formulas was kept as small as possible by introducing measures for suppressing prediction instability, although special formulas were employed for loading meters that were of an industrial nature. Several procedures were applied to help improve the accuracy, such as the smoothing of season transitions, coarse graining of the relative humidity, the use of load-oriented day-type definition, the averaging of weather data, and outlier removal.     

A quantile regression approach for estimating panel data models using instrumental variables

We introduce a quantile regression approach to panel data models with endogenous variables and individual effects correlated with the independent variables. We find newly developed quantile regression methods can be easily adapted to estimate this class of models efficiently.     

金融危机后物流产业绿色化发展战略研究——基于国家竞争优势的视角

以减少物流成本以及增加产业增加值为目标的绿色物流无疑是金融危机后我国物流产业发展的最佳选择之一。运用线性回归方法进行分析可得出物流成本、物流增加值与国家经济发展水平具有相关关系。在基于国家竞争优势与＂物流产业钻石模型＂的基础上,得出物流绿色化发展战略,其中包括从绿色物流需求条件到政府行为等五个方面。     

土地利用变化驱动力多尺度因素的定量影响分析

研究目的:揭示多尺度土地利用变化研究中尺度因素对驱动力的定量影响。研究方法:构建Logistic回归模型和二分类反应变量两水平Logistic回归模型,分别基于面向尺度对比的多尺度回归分析和基于系统观点的多水平模型分析两个角度,探讨多尺度土地利用变化核心驱动力及其定量影响关系,并对两类模型的分析结果进行对比分析。研究结果:面向尺度对比的多尺度回归分析法能够从更为微观的角度揭示各个尺度层级上地类变化的核心驱动因子,而基于系统观点的多水平模型分析方法则能够更好地顾及尺度因素的定量影响。研究结论:多水平模型分析方法揭示的各个地类的核心驱动因子集与多尺度回归分析法推导的对应地类的核心驱动因子集之间呈现子集关系,两者在多尺度土地利用变化驱动力的研究方法上能够形成互补。     

城市化进程中的“资源尾效”和“资源诅咒”——基于中国27个煤炭城市的面板数据分析

传统城市化理论往往强调自然资源对城市发展的正面作用,却对城市化的约束和反向作用问题考虑不够。文章基于"资源尾效"和"资源诅咒"两种假说,利用1997-2012年中国27个典型煤炭城市的数据对其煤炭资源开发与城市化发展的关系进行正负效应检验。结果表明:27个煤炭城市中有16个城市出现"资源尾效",其尾效数值为0.001 755;其余11个城市出现"资源诅咒",其煤炭资源对城市化发展的影响系数为-0.207 2。可见,自然资源的开发强度要处于一个合适的水平,并同时注意资本投入、教育和制度等因素才能最大程度上提高城市化水平。     

宗地规模、城市类型与土地出让溢价——基于70个大中城市住宅用地出让数据的实证分析

研究目的:确定住宅用地出让溢价率水平与宗地规模之间的关系,比较不同规模和区位城市中这一关系的差异。研究方法:理论分析,面板数据分析。研究结果:总体上宗地规模对土地溢价率的影响呈现"倒U"型变化关系,不同规模和区位城市中这一关系有差异。人口介于500万～1 000万之间的特大城市中土地溢价水平受宗地规模的影响较为强烈,东部和中部城市中宗地规模大小对土地溢价水平影响显著,西部城市不显著。研究结论:地方政府在总量既定条件下调整宗地投放规模可在一定程度上调控土地溢价率。     

The impact of climate change on labour demand in the plantation sector: the case of tea production in Sri Lanka

Limited opportunities for crop switching and lengthy preharvesting periods make the plantation sector particularly vulnerable to climate change. Surprisingly, however, the economic consequences of climate change on plantation crops are seldom analysed. Drawing on a unique primary panel data set from a representative cross section of 35 tea estates in Sri Lanka over the period 2002–2014, this study implements a structural model of estate profit maximisation to estimate the elasticity of labour demand with respect to different components of weather. Results indicate a negative relationship between labour demand and rainfall in the south‐west monsoon, the north‐east monsoon and the second inter‐monsoon. A positive relationship is found between labour demand and rainfall in the first inter‐monsoon. Overall, predicted changes in rainfall by 2050 are anticipated to reduce labour demand by approximately 1,175,000 person‐days per year across Sri Lanka's tea plantation sector. This is likely to have considerable social and welfare implications, particularly for the Indian Tamil women who comprise the majority of the sector's workforce.