Aviation Safety and Data Mining in Marketing Dimension

The aviation industry is a sector that is developing, changing and growing every day in terms of technological and legal framework. There are generally three factors that enable airlines to hold on to the market. These factors are safety, service quality and price. Airline companies can analyze the customers in the market with a focus on price and quality and develop a business model according to their expectations. For example, business class and economy class passenger expectations are different from each other, so the service and price to be offered to them will be different. However, all customers have one common expectation and that is safety. No matter how high quality the service is or how cheap the price is, no one wants to fly with an airline or plane that is not safe. From an airline company’s point of view, an accident or breakdown of one of the company’s aircraft can cause irreparable image loss and financial damage. If we look at past examples, we see that there are many airline companies or maintenance organizations that could not recover after an accident and went bankrupt. Safety is an indispensable factor. Therefore, there is a unit in the sector called the safety management system (SMS), which collects data by taking a proactive and reactive approach. The way and purpose of the safety management system is to take a proactive approach to recognize and prevent unsafe situations before they cause accidents or breakdowns, or to take a reactive approach to find the causes of accidents and breakdowns that have occurred as a result of certain factors and to take the necessary measures to prevent the same situations from happening again in the sector. The field of data mining, which is necessary to predict the future behavior of customers in the field of marketing, is an area that marketing also values. In this study, data mining studies to ensure safety in the aviation industry and the security of customer information in marketing will be emphasized, firstly, the concept and importance of data mining will be mentioned.

Activity Data and Emission Factor for Forestry and Other Land Use Change Subsector to Enhance Carbon Market Policy and Action in Malawi

Activity data and emission factors are critical for estimating greenhouse gas emissions and devising effective climate change mitigation strategies. This study developed the activity data and emission factor in the Forestry and Other Land Use Change (FOLU) subsector in Malawi. The results indicate that “forestland to cropland,” and “wetland to cropland,” were the major land use changes from the year 2000 to the year 2022. The forestland steadily declined at a rate of 13,591 ha (0.5%) per annum. Similarly, grassland declined at the rate of 1651 ha (0.5%) per annum. On the other hand, cropland, wetland, and settlements steadily increased at the rate of 8228 ha (0.14%);5257 ha (0.17%);and 1941 ha (8.1%) per annum, respectively. Furthermore, the results indicate that the “grassland to forestland” changes were higher than the “forestland to grassland” changes, suggesting that forest regrowth was occurring. On the emission factor, the results interestingly indicate that there was a significant increase in carbon sequestration in the FOLU subsector from the year 2011 to 2022. Carbon sequestration increased annually by 13.66 ± 0.17 tCO2 e/ha/yr (4.6%), with an uncertainty of 2.44%. Therefore, it can be concluded that there is potential for a Carbon market in Malawi.

Multi-Blockchain Based Data Trading Markets With Novel Pricing Mechanisms

In the era of big data,there is an urgent need to establish data trading markets for effectively releasing the tremendous value of the drastically explosive data.Data security and data pricing,however,are still widely regarded as major challenges in this respect,which motivate this research on the novel multi-blockchain based framework for data trading markets and their associated pricing mechanisms.In this context,data recording and trading are conducted separately within two separate blockchains:the data blockchain(DChain) and the value blockchain(VChain).This enables the establishment of two-layer data trading markets to manage initial data trading in the primary market and subsequent data resales in the secondary market.Moreover,pricing mechanisms are then proposed to protect these markets against strategic trading behaviors and balance the payoffs of both suppliers and users.Specifically,in regular data trading on VChain-S2D,two auction models are employed according to the demand scale,for dealing with users’ strategic bidding.The incentive-compatible Vickrey-Clarke-Groves(VCG)model is deployed to the low-demand trading scenario,while the nearly incentive-compatible monopolistic price(MP) model is utilized for the high-demand trading scenario.With temporary data trading on VChain-D2S,a reverse auction mechanism namely two-stage obscure selection(TSOS) is designed to regulate both suppliers’ quoting and users’ valuation strategies.Furthermore,experiments are carried out to demonstrate the strength of this research in enhancing data security and trading efficiency.

Analysis and Visualization of Marketing, Statistical and Macroeconomic Data With GIS

The most common way to analyze economics data is to use statistics software and spreadsheets.The paper presents opportunities of modern Geographical Information System （GIS） for analysis of marketing, statistical, and macroeconomic data. It considers existing tools and models and their applications in various sectors. The advantage is that the statistical data could be combined with geographic views, maps and also additional data derived from the GIS. As a result, a programming system is developed, using GIS for analysis of marketing, statistical, macroeconomic data, and risk assessment in real time and prevention. The system has been successfully implemented as web-based software application designed for use with a variety of hardware platforms （mobile devices, laptops, and desktop computers）. The software is mainly written in the programming language Python, which offers a better structure and supports for the development of large applications. Optimization of the analysis, visualization of macroeconomic, and statistical data by region for different business research are achieved. The system is designed with Geographical Information System for settlements in their respective countries and regions. Information system integration with external software packages for statistical calculations and analysis is implemented in order to share data analyzing, processing, and forecasting. Technologies and processes for loading data from different sources and tools for data analysis are developed. The successfully developed system allows implementation of qualitative data analysis.

Research on the Application of Energy Internet Big Data in Integrated Energy Market

Energy Internet is deeply integrated by Internet concept, information technology and energy industry, and Energy Internet Big Data are one of core technologies that achieve energy-information-economic interconnection and improve the development and evolution of Energy Internet. This paper describes the concept and characteristics of Energy Internet Big Data, and feasibility of applying Energy Internet Big Data to integrated energy market. On this basis, as for integrated energy market and multi-subjects of Energy Internet, typical application and technical system based on Energy Internet Big Data in integrated energy market is put forward, which provides a reference for the analysis and decision of integrated energy market in Energy Internet.

数据“利益束”:数据权益制度新论

创设排他性、利己性的数据权利不利于海量数据的高效流通与多元价值的实现,加剧了数据私权化与效用化之间的内生性冲突。在权益区分保护理论框架下,采取数据利益保护进路更具正当性和相对优势。我国宜将数据利益作为数据权益制度的中心,建构人与人之间动态灵活、有机立体的利他性数据“利益束”范式,以有效协调和整合多元主体之间复杂的数据利益关系,并在数据“利益束”范式下,充分利用Web3.0以用户为中心的开放式价值互联网效能,通过点对点的数据利益流通来构建全网络利益格局。我国还应建立合理的激励与收益实现机制,促进所有数据主体共创和共享数据红利,在数据财产行为主义保护模式基础上,建立智能化治理体系来保障数据安全,最终形成中国自主且更能适应数字经济发展规律的新型数据权益制度体系,以此推进我国数字经济的健康发展。

公共数据进入数据要素市场模式研究

公共数据是数据要素市场的重要组成部分,其如何进入数据要素市场以及其参与数据要素市场的模式分为几类,学界对此少有研究。通过梳理和比较,深入挖掘国内外公共数据参与数据要素市场的模式类型,归纳得出公共数据进入数据要素市场有两大类、五种模式,包括一级市场(授权运营)+二级市场(场内交易)模式,公共数据开发利用+场外交易模式,依托数据平台构建公共数据流通市场模式,借助数据经纪商、数据中介的交易模式,以及通过(公共)数据信托参与数据市场模式等。在此归纳基础上,演绎出公共数据进入数据要素市场的理想模式,这一理想模式分为数据获取、数据产品生产、数据产品交易三个各有其独特特征的环节,可为我国公共数据进入数据要素市场的策略选择提供参考。

我国数据要素市场治理的模式、现状与对策

要推动数据要素市场高质量发展,首先需要构建科学高效的市场治理机制。本文对数据要素市场治理素现状进行了梳理,提出市场化治理、垂直型治理和关系型治理等三种治理模式,并比较其特点,分析其现状与瓶颈。在此基础上,本文指出我国需要同时采用多种治理模式,形成以市场化治理为主、垂直型治理为辅、关系型治理作为重要补充的数据要素市场治理格局,并就三种治理模式的进一步完善提出了对策建议。

数据要素市场体系建构与价值实现路径探索

数据要素市场体系的顶层设计是加快推进数字要素市场化配置、推动我国数字经济高质量发展的前提基础与关键部署。通过剖析当前全国数据要素统一大市场建设面临的瓶颈与挑战,本文分析数据要素市场建设的特殊性,提出全国统一数据要素市场体系“一体两翼”“三基”“七要点”的总体架构思路,以及战略布局、结构布局和空间布局相统一的总体布局方案,并从市场体系建设新特征、内在结构与运行模式解析、数据要素市场与传统要素市场一体化联动机理揭示与场景应用示范等方面探索促进数据要素市场价值升级与高质量发展的实现路径,从而为推动数据资源化、资产化和资本化的可持续运营提供参考借鉴。

用户视角下的教育数据要素交易:安全感知与建议

教育数据要素作为推动教育变革的核心动力,正通过数据要素市场交易推动教育数字化转型纵深发展。然而,目前教育数据要素市场在数据确权、市场监督以及市场交易等制度层面存在不足,阻碍教育数据要素价值的释放。用户作为数据要素市场的核心参与者,其对教育数据要素交易的安全感知与建议是推进数据要素交易有序发展的重要因素。研究面向四类不同用户群体开展了定向调查,发现用户总体对教育数据要素交易持积极态度,且具有较为全面的安全感知,不同用户群体则在教育数据要素基本态度、安全感知与安全建议上存在差异。为加快教育数据要素交易市场建设,可以从加强教育数据要素市场保障,提高高等院校用户市场参与意愿,构建教育数据要素安全体系,满足不同用户群体的安全需求,塑造多元教育数据要素交易市场,推动交易机制向需求侧转型等方面改善。

数据要素市场化推进力评价指标体系构建研究

[目的/意义]构建数据要素市场化推进力评价指标,旨在发现数据要素市场发展的关键因素与策略,推动数据要素理论与实践的发展。[研究设计/方法]通过文本分析、专家咨询等方法,对数据要素市场化推进力评价指标体系予以构建。[结论/发现]形成了以基础环境、保障支撑和执行推进为三大维度的细化至33项四级指标的评估指标体系,并立足我国实际展开应用与验证,随后就指标体系的优化提出展望。[创新/价值]从政府主导的数据要素市场化推进的视角构建评价指标体系,并融入信息资源管理的学科基础理论与方法。

数据要素市场化推进:进展、问题与对策

[目的/意义]系统地总结当前全国各地的数据要素市场化推进的实践进展及经验做法,为政策制定和实践工作提供参考,为相关领域的研究提供实证基础。[研究设计/方法]基于数据要素市场化推进力的评价指标体系,从基础环境、保障支撑、执行推进三个维度,对31个省(自治区、直辖市)的推进情况展开深入调查和综合分析。[结论/发现]我国数据要素市场化推进整体上呈三级梯队分布并呈现出显著的地域特征。各地在产业环境发展、制度保障、技术平台建设、公共数据开放与授权应用、数据流通交易方面取得了积极成效,但也存在着区域发展不平衡、公共数据制度体系不完善、场内数据交易规模不足等问题。基于此从区域间产业协调发展、制度保障体系建设和加强公共数据供给激活数据流通三个方面提出了政策建议。[创新/价值]对全国数据要素市场化推进进行了全景式的调查和研究,提炼共性问题,总结发展经验,为推动数据要素市场化向纵深发展提供借鉴。

数字经济赋能中国式现代化:逻辑机理、现实梗阻与创新路径

合理发挥数字经济优势是全面推进与拓展中国式现代化的必由之路。数字经济可为中国式现代化提供市场体系、基础设施、技术网络、经济支柱支持。研究发现:数据要素融入、数字基建布设、数字技术下沉及数字业态创新为中国式现代化注入动能。尚需注意,数字治理局限性、城乡发展脱轨化、技术应用片面化、数字文化偏向性等诸多梗阻并存,限制数字经济对中国式现代化的赋能作用发挥。为此,应寻求数字组织再嵌入社会基层治理、重塑数字基建组织性网络动态结构、探索数字技术全流程生态治理应用、构建文化产业数字化新型发展业态,以扎实推进中国式现代化。

平台企业数据垄断的影响及规制研究

平台经济的蓬勃发展使数据升级为重要生产要素。囿于平台经济的固有特征、数据垄断的隐蔽性以及相关法律和监管漏洞,数据垄断问题日益凸显。对亚马逊和美团利用数据垄断违规获利的案例进行分析,发现数据垄断对市场会产生阻碍市场竞争、实施自我优待、抑制市场创新的反竞争效应,导致消费者受到价格歧视、隐私泄露的负向影响。在根据案例分析启示和借鉴国内外关于数据垄断问题的治理经验基础上,从政府、平台企业、消费者三个层面提出平台企业数据垄断问题的规制策略。

公共数据商业化利用:市场价值、竞争理路与法治化促进

公共数据兼具社会公共属性和市场商业价值,是优质生产要素,其商业化利用涵盖开放、开发、流通等多个环节。在实现数字权力向数字资产转化的场景中,公共数据商业化利用需统筹安全与发展、归集与共享、管制与流通等多个维度和价值。当前,我国公共数据范围界定未明确,相关主体面临不愿、不会、不敢开放,还存在无序失序利用的问题。基于此,结合公共数据特征,科学界定公共数据范围,借助《反不正当竞争法》第三次修订的契机,扩展“商业数据”条款为“数据商业化利用”条款,以数据行为为基础,将公共数据纳入数据商业化利用范畴,构建以反不正当竞争法为基础的公共数据商业化利用规则体系。在保障高水平安全的前提下,鼓励和支持公共数据商业化流通使用。

数据要素市场化赋能银行数字化转型了吗?——来自中国城市层面的经验证据

深耕数据要素市场化沃土,以释放数据要素价值助力银行数字化转型,是做好数字金融大文章,充分发挥金融供血机能的关键举措。本文基于2010—2021年284个地级市面板数据,深入探讨数据要素市场化对银行数字化转型的影响。研究结果表明,数据要素市场化有助于推动银行数字化转型,同时这种影响在城市能级、金融韧性和商业信用环境方面表现出显著的异质性特征。作用机制分析发现,资源配置效率是数据要素市场化促进银行数字化转型的作用渠道,金融科技和银行业竞争在数据要素市场化赋能银行数字化转型过程中发挥正向调节效应。经济后果检验发现,数据要素市场化对银行数字化转型的促进效果可进一步作用于实体经济的发展。研究结果为银行数字化转型的实现路径提供了理论和实践参考。

数字经济下新商科交叉融合课程探索与实践——以“大数据营销”课程为例

在新文科背景下,文科和理科知识的融合成为数字经济发展中人才培养的重要一环。交叉融合课程作为适应数字经济发展的重要教学内容之一,是文科和理科知识融合的示范课程。新商科是新文科的重要组成部分,学生的数据分析思维是解决商业问题的基础。以往的教学模式已无法满足新商科人才培养的要求,因此,关注交叉学科课程的教学模式改革是培养数字经济人才的关键环节。针对“大数据营销”交叉融合课程教学中存在的问题,以培养学生用数据思维分析和解决实际问题的能力为导向,从教学内容、教学方式、师资等方面进行了探讨,以期为交叉融合课程教学提供参考。

数据要素价值释放研究进展

数字经济时代,数据成为重要生产要素,透过开放共享与市场化配置释放其价值。文章基于文献内容,详细探讨数据作为生产要素的特征,并深入阐述数据要素价值释放理论、路径与问题,分析目前数据要素确权、定价机制及价值释放过程,探讨政府数据、企业数据及个人数据等不同主体数据要素价值释放模式与路径,指出未来数据要素价值释放研究方向。

反垄断视野下数据开放与隐私保护的冲突与协调

数据是数字经济发展的重要驱动力,数字平台之间的竞争大多围绕数据展开。为释放数据要素价值、规范数字市场竞争,数据开放与隐私保护几乎同时进入反垄断法的视野。数据开放试图以恢复性救济化解数据垄断难题,隐私保护作为竞争考量因素也被纳入反垄断法的规制框架,然而二者在反垄断实践中常难两全。作为反垄断救济措施的数据开放可能因权利保障不足、责任缺位等造成隐私泄漏与侵犯;隐私保护也可被头部平台企业作为拒绝数据开放的竞争抗辩理由。这实质上是数据双重属性、数据主体价值取向差异以及数据竞争行为规制局限性等多重因素共同作用的结果。反垄断法的多维调整目标与数据开放隐私保护二者协调的底层逻辑具有共通性,兼顾市场竞争与规模经济的有效竞争理论可作为冲突调和的基本向导。在此基础上,进行适度的数据开放和合理的隐私保护,方能寻求并实现二者之间的最佳平衡。