Complex adaptive system theory,agent-based modeling,and simulation in dominant technology formation

Dominant technology formation is the key for the hightech industry to“cross the chasm”and gain an established foothold in the market(and hence disrupt the regime).Therefore,a stimulus-response model is proposed to investigate the dominant technology by exploring its formation process and mechanism.Specifically,based on complex adaptive system theory and the basic stimulus-response model,we use a combination of agent-based modeling and system dynamics modeling to capture the interactions between dominant technology and the socio-technical landscape.The results indicate the following:(i)The dynamic interaction is“stimulus-reaction-selection”,which promotes the dominant technology’s formation.(ii)The dominant technology’s formation can be described as a dynamic process in which the adaptation intensity of technology standards increases continuously until it becomes the leading technology under the dual action of internal and external mechanisms.(iii)The dominant technology’s formation in the high-tech industry is influenced by learning ability,the number of adopting users and adaptability.Therein,a“critical scale”of learning ability exists to promote the formation of leading technology:a large number of adopting users can promote the dominant technology’s formation by influencing the adaptive response of technology standards to the socio-technical landscape and the choice of technology standards by the socio-technical landscape.There is a minimum threshold and a maximum threshold for the role of adaptability in the dominant technology’s formation.(iv)The socio-technical landscape can promote the leading technology’s shaping in the high-tech industry,and different elements have different effects.This study promotes research on the formation mechanism of dominant technology in the high-tech industry,presents new perspectives and methods for researchers,and provides essential enlightenment for managers to formulate technology strategies.

Exploration of the Integrated Training Model for Information Technology Teachers

With the acceleration of the social information process,information awareness and information skills have become the basic qualities of every citizen.The establishment of the training mechanism for scientific and technological innovation talents from the beginning of higher education is insufficient to meet the needs of the development of the times.It is imperative to improve the training of information technology innovation talents and explore a new training model.This paper describes the general situation of the development of education in the field of information technology from a domestic and international perspective.It then analyzes the existing problems,explores new exploration models and implementation suggestions,and puts forward prospects at the end of the paper.

Comprehensive Evaluation of Talent Training Model for Modern Rehabilitation Therapy Technology:A Case Study of School Y

The purpose of this study is to comprehensively evaluate the modern training model of rehabilitation therapy technology talents.Selecting the third-year students of the rehabilitation therapy technology program in School Y as the research subject,300 questionnaires were collected and the effective response rate was 92%.The strengths and weaknesses of the modern training model were analyzed through a mixed qualitative and quantitative research method.It was found that 68%of the students thought that the modern model had obvious advantages in practical teaching,but 42%of the students thought that it still needed to be improved in personalized teaching.This study provides an empirical basis and specific suggestions for optimizing the cultivation of rehabilitation therapy technology talents.

The Model of“Post,Course,Competition,Certificate”Integrated Education for Electronic Information Engineering Technology Major in Higher Vocational Colleges

The core education function of higher vocational colleges is to train technical talents with high quality,so as to meet the needs of talents in the development stage of our society.Under the guidance of talent training,higher vocational colleges need to pay attention to establishing an all-round and three-dimensional education model,and promote innovation of higher vocational education on the basis of this.It is also a way to promote the innovation of higher vocational education to vigorously promote the construction of“post,course,competition,certificate”mode in the construction of education mode.Through the construction of“post,course,competition,certificate”mode,the education mode of higher vocational colleges is gradually improved,so as to strengthen the effectiveness of talent training in higher vocational colleges.Therefore,in this paper,the author puts forward some suggestions to promote the construction of the integrated education mode of the electronic information engineering technology major in higher vocational colleges,so as to help improve the talent training level of higher vocational colleges.

基于STIRPAT模型湖南省建筑碳排放影响因素研究

以湖南省为对象,利用IPCC法测算了2010年至2021年的建筑碳排放,并采用随机性环境影响评估模型(STIRPAT)分析了人口数量、城镇化率、人均GDP、第三产业占比和碳排放强度等五大因素对建筑碳排放的影响。结果显示影响程度依次为:人口数量(8.351)、城镇化率(0.951)、人均GDP(0.466)、碳排放强度(0.232)、第三产业占比(0.180)。在此基础上,通过情景分析预测了这些因素在基准、低碳和高碳情景下的变化趋势,并提出了建筑减排措施与建议,为制定区域性建筑碳减排政策提供了决策支持。

基于STIRPAT的河北省碳达峰预测研究

我国已宣布力争2030年前二氧化碳排放达到峰值,为确保河北省能够保质保量完成碳达峰目标,采用联合国政府间气候变化专门委员会(Intergovernmental Panel on Climate Change, IPCC)排放因子法测算河北省2005-2021年化石能源消费碳排放量,选取人口、人均GDP、城镇化率、产业结构、能源强度和能源结构6个因素,构建了河北省碳排放人口、财富和技术影响(stochastic impacts by regression on population, affluence, and technology, STIRPAT)预测模型,通过构建河北省碳排放情景,对河北2022-2040年碳排放量进行了预测。结果表明在基准情景和经济发展情景下,河北省碳排放趋势是持续上升的,未出现达峰点;产业转型、绿色发展和目标导向情景下出现了峰值点,其中目标导向情景在2029年达峰,绿色发展情景在2030年达峰,碳达峰量分别为81 626.658万吨二氧化碳和86 018.255万吨二氧化碳,产业转型情景在2035年达峰,碳达峰量为85 214.349万吨二氧化碳。按照目前情景发展下河北省难以在2030年实现碳达峰,为保质保量完成达峰目标,需要以能源绿色低碳发展为关键手段,同时以科技和制度创新为动力,调整优化产业结构和能源结构。

基于STIRPAT模型的山东省居住建筑运行碳排放影响因素研究

为有效实施建筑领域节能减排,实现双碳目标,以2005-2021年山东省居住建筑运行阶段碳排放数据为研究对象,利用STIRPAT模型对人口数量、人均GDP、人均消费支出、人均可支配收入、城镇化水平、居住建筑面积、单位建筑面积能耗、能源消耗强度和能源结构等影响碳排放的因素进行岭回归分析。结果表明:人口数量、人均GDP、人均消费支出、人均可支配收入、城镇化水平、居住建筑面积、单位建筑面积能耗和能源消耗强度等的增加会促使碳排放量的增加,但能源结构的增加会抑制碳排放量的增加,其中人口数量是影响碳排放的关键因素。据此提出山东省居住建筑节能减排的建议和对策。

基于STIRPAT模型的黑龙江省工业碳排放情景分析与峰值预测

在碳达峰、碳中和背景下,进行黑龙江省工业部门碳排放因素分析与情景预测研究,对实现黑龙江省工业绿色低碳发展具有重要意义。首先,根据IPCC准则测算黑龙江省历年工业碳排放量,基于拓展STIRPAT模型,从人口因素、经济发展水平、技术水平3个方面,确定人均生产总值的平方、人口规模、工业总产值、工业能源消耗、能耗效率、能源结构6个自变量,借助岭回归方法消除自变量间的多重共线性,建立黑龙江省工业部门的碳排放影响因素模型;然后,从经济发展、人口规模、能源消耗、能耗效率4个方面分析黑龙江省社会经济实际运行情况,并结合宏观政策,分别对自变量进行变量涨幅确定,对2020—2050年黑龙江省工业部门碳排放量设定基准情景、低碳情景、高能耗情景进行预测分析。研究发现:1)黑龙江省工业低碳发展面临化石能源需求大、能源转化效率不足的问题,在影响工业碳排放的6个变量中,人均生产总值的平方、工业总产值、工业能源消耗、能源结构对黑龙江省工业部门碳排放量起到促进作用,人口规模、能耗效率对黑龙江省工业部门碳排放起到抑制作用,其中工业能源消耗正向促进作用最为显著;2)黑龙江省工业碳排放量在各情景下均呈现先增加后减少的演变趋势,而在峰值出现的时间及高度上有所差异,低碳情景、基准情景、高能耗情景下黑龙江省工业部门碳排放量达峰时间分别为2030年、2035年和2045年,碳排放峰值分别为7135万t、8997万t、12368万t。对此提出加大工业能源结构调整力度,重视能源转化技术升级,完善绿色低碳发展相关政策等建议。

基于STIRPAT模型的广西碳排放影响因素分析

通过在时间尺度上分析了广西碳排放的变化趋势,并与我国不同省域碳排放水平进行比较,总结提炼出广西的碳排放特征;结合广西发展特征以2005—2020年广西经济社会环境发展数据为基础,利用拓展的STIRPAT模型从经济因素、人口因素、结构因素和技术因素4个方面对广西碳排放影响因素进行分析。结果表明:人口规模与人口结构对广西碳排放的促进作用最为显著,人口规模与人口结构每增加1%,碳排放量将增加0.401%和0.199%。人均GDP、第二产业产值、进出口总额的增长均会显著促进碳排放,根据环境库兹涅茨曲线,广西经济发展程度仍处于转折点之前。研究与试验投入、研究与试验人员是对碳排放贡献率最大的因素,贡献率分别为16.81%和14.51%,是减少广西地区碳排放的重要工作方向。

安徽省碳排放时空格局演变与碳达峰路径预测——基于STIRPAT扩展模型和岭回归模型

基于双碳目标和空间耦合视角,对NPP/VIIRS夜间灯光数据进行修正并拟合碳排放模型,补充在2018—2021年中国碳核算数据库县级碳排放数据,通过空间自相关模型分析安徽省县级单元碳排放的时空变化特征。根据“因素筛选—模型构建—耦合计算—交互影响”的思路,基于拓展后的STIRPAT模型和岭回归模型,对不同发展模式下的碳排放进行预测演算,利用地理探测器揭示空间碳排放驱动因素的交互作用,为实现碳达峰提供路径依据。结果表明:(1)1997—2021年安徽省碳排放呈现波动上升再趋于平稳的趋势,在2013年后进入碳达峰前的平台期,自北向南呈由高到低分布,以主要工业城市为主形成了不同规模的高密度碳排放中心;(2)预测得到的碳排放达峰时间区间为2030—2045年,峰值范围为4.472亿t~5.558亿t;(3)基准情景下,安徽将在2040年碳达峰,绿色发展情景最早达峰且峰值最低,是对比后确定的最优碳达峰路径;(4)人口和能源结构的驱动力最强,城镇化水平和人均GDP与各种驱动因素的交互作用增强效果显著。

基于STIRPAT模型的甘肃省碳排放影响因素研究

为探索“双碳”背景下甘肃省碳达峰实现路径,文章基于STIRPAT模型,立足甘肃省情选取六个影响因素,利用1997年-2020年数据构建了甘肃省碳排放岭回归模型,并以此对2020年-2035年3种政策情景下甘肃省的碳排放量进行预测。结果表明,GDP、碳排放强度和能源消耗强度是促进甘肃省碳排放增长的主要因素。在低碳和基准情景下,甘肃省碳排放将于2030年实现碳达峰,而后开始逐年下降。在高碳情景下,甘肃省碳排放量将逐年攀升,在2035年前碳达峰较为困难。最后提出提高经济发展质量,优化第三产业内部结构,推进传统能源绿色清洁高效发展是减少甘肃省碳排放的有效路径。

基于STIRPAT模型的辽宁省能源碳排放影响因素分析

辽宁省是传统的工业和农业大省,在能源结构持续调整和农业机械化水平逐渐提高的背景下,为制定科学合理的减排措施,必须探明其碳排放主要来源。为此,采用排放因子法计算2012—2021年主要能源消耗碳排放量,利用STIRPAT模型构建辽宁省能源消耗碳排放测算模型,分析影响能源消耗碳排放的主导因素。结果发现,煤炭是辽宁省消耗的最主要能源,占总能耗的55%左右;能源消耗碳排放总量整体呈明显增加趋势,而碳排放强度呈下降趋势,10年间降低11.3 kt CO_(2)/亿元;第二产业产值、农业碳排放效率、农业经济发展水平、第一产业产值、人口、人均GDP、城镇化率、农业机械化程度、能源加工转换效率是影响辽宁省碳排放的主要因素;基于STIRPAT模型构建辽宁省碳排放测算模型,通过验证发现平均相对误差为-4.91%,准确度较好;对模型分析发现人口变化对辽宁省能源碳排放呈负向影响,而人均GDP、第二产业产值、农业经济发展水平、农业机械化程度对辽宁省能源碳排放呈正向影响。因此为抑制辽宁省能源碳排放量的快速增加,需要控制人口数量,优化产业结构,保持人均GDP增速,第二产业产值和农业机械化程度在一个合理的水平。

集成清单算法和STIRPAT模型的能源系统碳排放影响因素研究——以四川省为例

立足燃料燃烧和区域电力交易两大单元构建能源系统碳排放核算方法,基于四川省2000—2021年的实际数据,结合拓展后的STIRPAT模型实证分析人口规模、经济发展、技术创新、能源结构和产业结构对碳排放的差异化影响效应。研究结果表明:①化石燃料燃烧是能源系统关键碳排放源,其中工业终端能源消费占据主导地位,交通运输业和居民生活碳足迹也不容忽略;区域电力交易在保障能源供应安全的同时间接推动中国低碳发展进程。②人口规模通过人群和文明效应驱动碳排放,未来影响程度取决于多孩生育政策对人口负增长趋势的扭转成效;经济发展与能源消费有待脱钩,考虑到经济上升期现状和“污染避难所”现象,其仍为碳排放关键驱动因素;经费投入增加推动节能降碳领域科研成果产出及应用,并通过技术驱动和创新补偿降低碳排放水平;能源结构高碳特征对温室效应贡献显著,立足资源禀赋的消费结构转型有助于降低排放强度;第二产业在国民经济中的主体地位带动能源系统碳排放显著增加,近年来以高新技术产业为代表的第三产业占比提升有效控制了高污染、高能耗产业的碳排放。

Soil erosion susceptibility mapping of Hangu Region,Kohat Plateau of Pakistan using GIS and RS-based models

Soil erosion is a crucial geo-environmental hazard worldwide that affects water quality and agriculture,decreases reservoir storage capacity due to sedimentation,and increases the danger of flooding and landslides.Thus,this study uses geospatial modeling to produce soil erosion susceptibility maps(SESM)for the Hangu region,Khyber Pakhtunkhwa(KPK),Pakistan.The Hangu region,located in the Kohat Plateau of KPK,Pakistan,is particularly susceptible to soil erosion due to its unique geomorphological and climatic characteristics.Moreover,the Hangu region is characterized by a combination of steep slopes,variable rainfall patterns,diverse land use,and distinct soil types,all of which contribute to the complexity and severity of soil erosion processes.These factors necessitate a detailed and region-specific study to develop effective soil conservation strategies.In this research,we detected and mapped 1013 soil erosion points and prepared 12 predisposing factors(elevation,aspect,slope,Normalized Differentiate Vegetation Index(NDVI),drainage network,curvature,Land Use Land Cover(LULC),rainfall,lithology,contour,soil texture,and road network)of soil erosion using GIS platform.Additionally,GIS-based statistical models like the weight of evidence(WOE)and frequency ratio(FR)were applied to produce the SESM for the study area.The SESM was reclassified into four classes,i.e.,low,medium,high,and very high zone.The results of WOE for SESM show that 16.39%,33.02%,29.27%,and 21.30%of areas are covered by low,medium,high,and very high zones,respectively.In contrast,the FR results revealed that 16.50%,24.33%,35.55%,and 23.59%of the areas are occupied by low,medium,high,and very high classes.Furthermore,the reliability of applied models was evaluated using the Area Under Curve(AUC)technique.The validation results utilizing the area under curve showed that the success rate curve(SRC)and predicted rate curve(PRC)for WOE are 82%and 86%,respectively,while SRC and PRC for FR are 85%and 96%,respectively.The validation results revealed that the FR model performance is better and more reliable than the WOE.

Study on the Mathematical Model of Technology Diffusion in Technical Economic System

In the technical and economic system,there is a certain interaction between various technologies,mainly in the form of technology diffusion.By constructing the mathematical model of technology diffusion,the trend of technology diffusion can be predicted,the reasonable distribution of science and technology productivity can be realized,and the policy optimization measures can be formulated on the basis of evaluating the existing policies.This paper first points out the basic idea of digital model of technology diffusion,and then focuses on the analysis of various parameters of mathematical model of technology diffusion,and puts forward the application conclusion of mathematical model of technology diffusion,in order to provide reference for relevant industry personnel.

From Digital Human Modeling to Human Digital Twin: Framework and Perspectives in Human Factors

The human digital twin(HDT)emerges as a promising human-centric technology in Industry 5.0,but challenges remain in human modeling and simulation.Digital human modeling(DHM)provides solutions for modeling and simulating human physical and cognitive aspects to support ergonomic analysis.However,it has limitations in real-time data usage,personalized services,and timely interaction.The emerging HDT concept offers new possibilities by integrating multi-source data and artificial intelligence for continuous monitoring and assessment.Hence,this paper reviews the evolution from DHM to HDT and proposes a unified HDT framework from a human factors perspective.The framework comprises the physical twin,the virtual twin,and the linkage between these two.The virtual twin integrates human modeling and AI engines to enable model-data-hybrid-enabled simulation.HDT can potentially upgrade traditional ergonomic methods to intelligent services through real-time analysis,timely feedback,and bidirectional interactions.Finally,the future perspectives of HDT for industrial applications as well as technical and social challenges are discussed.In general,this study outlines a human factors perspective on HDT for the first time,which is useful for cross-disciplinary research and human factors innovation to enhance the development of HDT in industry.

Drivers for Inter-city Innovation Networks Across Chinese Cities:Modelling Physical Versus Intangible Effects

Cross-region innovation is widely recognized as an important source of the long-term regional innovation capacity.In the recent past,a growing number of studies has investigated the network structure and mechanisms of cross-region innovation collaboration in various contexts.However,existing research mainly focuses on physical effects,such as geographical distance and high-speed railway connections.These studies ignore the intangible drivers in a changing environment,the more digitalized economy and the increasingly solidified innovation network structure.Thus,the focus of this study is on estimating determinants of innovation networks,especially on intangible drivers,which have been largely neglected so far.Using city-level data of Chinese patents(excluding Hong Kong,Macao,and Taiwan Province of China),we trace innovation networks across Chinese cities over a long period of time.By integrating a measure on Information and Communications Technology(ICT)development gap and network structural effects into the general proximity framework,this paper explores the changing mechanisms of Chinese innovation networks from a new perspective.The results show that the structure of cross-region innovation networks has changed in China.As mechanisms behind this development,the results confirm the increasingly important role of intangible drivers in Chinese inter-city innovation collaboration when controlling for effects of physical proximity,such as geographical distance.Since digitalization and coordinated development are the mainstream trends in China and other developing countries,these countries'inter-city innovation collaboration patterns will witness dramatic changes under the influence of intangible drivers.

基于LMDI-STIRPAT模型的黄河三角洲“碳达峰”预测

气候变化问题已成为人类可持续发展所面临的长期挑战,尽早实现“双碳”目标,对全球环境变化和促进各国经济的可持续发展都具有重要意义。以黄河三角洲的中心城市东营市为研究区域,通过LMDI模型对东营市的碳排放量影响因素进行了分解,采用STIRPAT模型对东营市的碳排放量进行了预测。研究结果表明:碳排放强度效应在影响东营市碳排放量的特征因子中是主要的抑制因素;经济发展效应和能源强度效应对东营市的碳排放量起促进作用,是主要的促进因素;情景预测结果显示,优化情景和低碳情景下东营市可以实现2030年前“碳达峰”目标,分别在2027年和2029年实现“碳达峰”,CO_(2)排放量为10638.63、10799.35万t。指出了优化能源结构,提高能源利用效率,实施绿色低碳转型高质量发展战略是黄河三角洲在2030年碳排放量达到峰值的重要保障。

How R&D investment promotes green technology innovation in the context of digitalization?-An empirical analysis based on provincial panel data

Green technology innovation is an important driving force and source to promote my country’s high-quality development,and it is the core path to achieve sustainable development.This paper uses my country’s provincial panel data from 2016 to 2019 to study the impact mechanism of R&D investment on green technology innovation,and introduces the level of digitization,using the panel threshold model to discuss its role in the impact mechanism of R&D investment on green technology innovation.The study found that when the level of digitalization in a region is low,increasing R&D investment does not necessarily improve the ability of green technology innovation;when the level of digitalization is relatively high,R&D investment has a positive role in promoting green technology innovation.Therefore,it is necessary to improve policies to encourage enterprises to increase investment in research and development;at the same time,it is necessary to promote the coordinated development of digital foundation,digital investment,digital literacy,digital economy and digital application,and promote the deep integration of digitalization and green technology innovation.

Perspectives and Experiences of Education Stakeholders: A Quantitative Study on the Adoption of Artificial Intelligence in Executive Training Using Structural Equation Modeling

The recent increase in the use of artificial intelligence has led to fundamental changes in the development of training and teaching methods for executive education. However, the success of artificial intelligence in regional centers for teaching and training professions will depend on the acceptance of this technology by young executive trainees. This article discusses the potential benefits of adopting AI in executive training institutions in Morocco, specifically focusing on CRMEF Casablanca Settat. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003), this study proposes a model to identify the factors influencing the acceptance of artificial intelligence in regional centers for teaching professions and training in Morocco. To achieve this, a structural equation modeling approach was used to quantitatively describe the impact of each factor on AI adoption, utilizing data collected from 173 young executive trainees. The results indicate that perceived ease of use, perceived usefulness, trainer influence, and personal innovativeness influence the intention to use artificial intelligence. Our research provides managers of CRMEFs with a set of practical recommendations to enhance the implementation conditions of an artificial intelligence system. It aims to understand which factors should be considered in designing an artificial intelligence system within regional centers for teaching professions and training (CRMEFs).