交通拥堵与雾霾污染的因果关系——基于收敛交叉映射技术的经验研究

交通拥堵与雾霾污染是制约现代城市发展的两大顽疾,准确识别交通拥堵与雾霾污染之间的交互影响,有助于城市管理者重新审视现行治堵与治霾政策的合理性。本文借助大数据平台采集了我国99个城市的高德拥堵延迟指数（CDI）、空气质量指数（AQI）及六种分项空气污染物浓度日报数据,首次采用收敛交叉映射（CCM）方法实证考察了交通拥堵与雾霾污染之间的因果关系。研究发现,CDI与AQI以及CDI与分项污染物组成的动态系统均呈现明显的非线性与弱耦合特征。基于CCM检验结果,大多数城市的CDI与AQI之间不存在显著的因果关系;从分项空气污染物的角度,大多数城市的CDI与主要空气污染物之间不存在显著因果关系,但与次要空气污染物之间却存在显著的单向或双向因果关系。上述结果表明,尽管交通拥堵与雾霾污染之间有一定关联,但在因果关系上现有的经验证据并不支持两者相互影响,治堵和治霾不能“一箭双雕”而必须“双管齐下”。本文的研究在经验上丰富了关于交通拥堵与雾霾污染交互影响的讨论,对城市管理者更加谨慎与合理地制定治堵政策与治霾政策有重要现实意义。     

Data Science: A Powerful Catalyst for Cross-Sector Collaborations to Transform the Future of Global Health—Developing a New Interactive Relational Mapping Tool (Demo)

ABSTRACT

The increasingly complex and rapidly changing global health and socioeconomic landscape requires fundamentally new ways of thinking, acting, and collaborating to solve growing systems challenges. Cross-sectoral collaborations between governments, businesses, international organizations, private investors, academia, and nonprofits are essential for lasting success in achieving the Sustainable Development Goals (SDGs), and securing a prosperous future for the health and well-being of all people (United Nations, n.d United Nations. (n.d.). SDGs: Sustainable development knowledge platform. Sustainable Development United Nations. Retrieved from https://sustainabledevelopment.un.org/ [Google Scholar].). Our aim is to use data science and innovative technologies to map diverse stakeholders and their initiatives around SDGs and specific health targets—with particular focus on SDG 3 (Good Health & Well Being) and SDG 17 (Partnerships for the Goals)—to accelerate cross-sector and multidisciplinary collaborations. Initially, the mapping tool focuses on Geneva, Switzerland as the world center of global health diplomacy with over 80 key stakeholders and influencers present. As we develop the next level pilot, we aim to build on users’ interests, with a potential focus on non-communicable diseases (NCDs) as one of the emerging and most pressing global health issues that requires new collaborative approaches. Building on this pilot, we can later expand beyond only SDG 3 to other SDGs.     

The Role of Interorganizational and Organizational Factors on the Decision Mode for Adoption of Interorganizational Systems*

The dramatic growth of interorganizational systems (IOS) has changed the way organizations conduct their business, and has resulted in significant tangible and intangible benefits being realized by participating firms. However, the implementation of these IOS requires the cooperation and commitment of all the participating members. These members may have complex economic and business relationships among themselves that can result in a number of social, political, and economic factors influencing the adoption and implementation of IOS. This study examines the role of interorganizational and organizational factors on the decision mode for adoption of IOS, in the specific context of electronic data interchange (EDI). Four interorganizational factors, based on the socio-political framework derived from research in marketing, and five organizational factors based on research in IS were used in the study. The data for the study were collected through a large scale field survey. Two respondents, the sales/purchase manager and the IS manager, from 201 firms responded to the survey. The results of discriminant analysis of the data reveal that two interorganizational variables, competitive pressure and exercised power, and two organizational variables, internal need and top management support, are important variables to differentiate firms with proactive decision mode from firms with reactive decision mode. The study also evaluates the differences between proactive and reactive firms on three implementation outcomes. Proactive firms are found to have greater extent of adaptation, more external connectivity with trading partners, and better integration of EDI information in their internal IS applications.     

Public relations and artificial intelligence: It’s not (just) about robots

Organizations of all kinds, as well as their in-house or agency public relations teams, increasingly co-opt Artificial Intelligence (AI) to enhance their capabilities. This paper examines a relatively new topic that has received little scholarly attention: the growing relationship between AI and public relations. It outlines several key roles that AI may play in future, based on trends in other industries, and considers the implications for public relations practitioners, their clients and employers. It therefore launches a dialogue about the diversity and extent of AI’s uses in public relations practice. The paper argues that, to date, commentators have placed too much emphasis on AI’s potential for task automation; AI’s broader technological, economic and societal implications for public relations warrant greater critical attention. This does not imply that practitioners need become expert technologists; rather, they should develop a sufficient understanding of AI’s present and potential uses to be able to offer informed counsel.     

Nominal comparability is not enough: (In-)equivalence of construct validity of cross-national measures of educational attainment in the European Social Survey

Educational attainment is a core social background variable covered in each and every survey of individuals. Since educational institutions and qualifications are difficult to compare across countries, cross-national surveys pose a particular challenge to the measurement of educational attainment. This study performs a comparative construct validation of a number of cross-national measures of education using the European Social Survey. The measures comprise two versions of the International Standard Classification of Education (ISCED), the education scheme developed in the project ‘Comparative Analysis of Social Mobility in Industrial Nations’ (CASMIN) and hypothetical as well as actual years of education. The first ISCED measure corresponds to the well-known main ISCED levels. The second one, the European Survey Version of ISCED (ES-ISCED) developed for this study, represents an effort to reflect different types of education within levels of education by considering ISCED sub-dimensions, most importantly ‘programme orientation’. Using linear regression models, it is shown how much explanatory power educational attainment loses when different cross-national variables are used, as compared to country-specific educational attainment variables (CSEVs), and how these losses vary across measures and countries. The dependent variable used for the construct validation is social status as measured by the International Socio-Economic Index (ISEI). Results suggest that harmonisation always entails some loss of explanatory power for at least a few countries. However, there are clear performance differences between the comparable measures in terms of both the average amount of losses as well as the distribution of losses across countries. The use of actual years of education as well as the levels-only ISCED strongly attenuates the education-social status association on average, but also to very different degrees across countries. CASMIN and ES-ISCED fare considerably better: they show the lowest losses of explanatory power and the lowest variation of losses across countries. Hypothetical years of education lie in between. Some practical implications are then proposed, e.g. on how to implement cross-national measures of educational attainment in international surveys.     

人口迁移与流动对我国各地区经济影响的实证分析

在对近十年来我国人口迁移和流动的时空演变格局进行初步考察的基础上,文章利用面板数据的变系数模型分别实证考察了人口迁移和流动对我国各地区(省域)经济的影响。结果发现:(1)我国各地区间人口迁移相对平稳,而人口流动在2000年以后呈现出持续快速增长的态势,且空间流向极不均衡;(2)人口的迁移和流动对中国整体经济来说是一个帕累托改进;(3)户籍制度对于发达地区起到了人才沙漏的作用;(4)对于中部的绝大部分地区以及西部的一些人口输出大省,伴随人口迁出而引起的人力资本流失对当地经济的负面影响开始凸显出来,人口流出虽然基本出现一个正面影响,但对本地经济发展的贡献并不如我们预想的那么明显。     

财政分权背景下的地方政府预算外收入扩张——基于中国省级面板数据的实证研究

本文对财政分权背景下我国地方政府预算外收入扩张的原因进行了理论分析,研究发现:地方政府预算外收入与辖区经济发展水平存在显著的负相关关系;与行政事业单位人员规模存在显著的正相关关系;与预算内财政支出存在显著的负相关关系;同时,在预算内财政支出既定时,预算外收入与转移支付规模存在显著的负相关关系。说明了分权改革后地方政府扩张预算外收入一方面在于弥补其财政支出的缺口;另一方面,由于预算外资金的收和支没有完全脱钩,行政事业单位改革的不彻底性,地方政府追求可自由裁量财政资源最大化的偏好等原因,进一步加剧了预算外收入的扩张。     

额外资源按需分配方法研究

文献Wei Quanling等^[1]的最后一段提出了这样一类额外资源分配问题：假设有一些额外的投入资源将要分配给全部或者部分决策单元,如果我们希望分配结果对于整个系统（该系统由全部决策单元组成）来说是最有益的,那么应当如何分配这些额外的投入资源?他们同时还指出系统中哪些决策单元能够获得额外的投入资源,这不仅取决于决策单元的效率,还需要考虑规模报酬。针对此类问题,本文提出一种额外资源按需分配方法：首先,综合决策单元的技术效率与其投影点的规模弹性构建发展曲线,并由决策单元的发展曲线获知其投入变动与产出变动的对应关系;然后,把额外资源分成若干等份,依次分配每一个等份的额外资源,每次分配使系统的总产出增加实现最大,直至分配完全部的额外资源,或者分配某一份额外资源不再带来任何的产出增加,分配就会停止;最后,把决策单元在每一次分配过程中获得的额外资源进行累加,得到这个决策单元最终的分配结果。全文的最后分别在"一个投入一个产出"和"两个投入一个产出"情况下给出两个具体的分配算例。分配结果表明,这种按需分配方法不会缩减任意一个决策单元当前占有的投入资源数量,并且还可以有效地在辨识出某种额外资源在规模上和结构上存在的冗余,从而有效地避免不必要的分配。     

A multicriteria sorting approach based on data envelopment analysis for R&D project selection problem

In this paper, multiple criteria sorting methods based on data envelopment analysis (DEA) are developed to evaluate research and development (R&D) projects. The weight intervals of the criteria are obtained from Interval Analytic Hierarchy Process and employed as the assurance region constraints of models. Based on data envelopment analysis, two threshold estimation models, and five assignment models are developed for sorting. In addition to sorting, these models also provide ranking of the projects. The developed approach and the well-known sorting method UTADIS are applied to a real case study to analyze the R&D projects proposed to a grant program executed by a government funding agency in 2009. A five level R&D project selection criteria hierarchy and an assisting point allocation guide are defined to measure and quantify the performance of the projects. In the case study, the developed methods are observed to be more stable than UTADIS.     

Data-driven operations management: organisational implications of the digital transformation in industrial practice

Abstract

The ongoing digital transformation on industry has so far mostly been studied from the perspective of cyber-physical systems solutions as drivers of change. In this paper, we turn the focus to the changes in data management resulting from the introduction of new digital technologies in industry. So far, data processing activities in operations management have usually been organised according to the existing business structures inside and in-between companies. With increasing importance of Big Data in the context of the digital transformation, the opposite will be the case: business structures will evolve based on the potential to develop value streams offered on the basis of new data processing solutions. Based on a review of the extant literature, we identify the general different fields of action for operations management related to data processing. In particular, we explore the impact of Big Data on industrial operations and its organisational implications.