Spatial Disparity and Efficiency of Science and Technology Resources in China

In the knowledge economy era,science and technology(S&T) resources are getting more and more important in shaping regional competiveness and building regional innovation capacity.As such,the spatial distribution of S&T resources is a key to understanding regional development and disparities.By designing an input-output indicator system,this paper develops an evaluation model to examine the spatial distribution of S&T resources in China and assess their spatial efficiency.Moreover,the paper tries to explain spatial differences in the efficiency of S&T resources in China.Major findings are:1) the input and output of S&T resources in China shows a clear T-shaped spatial structure,i.e.,concentrated mainly in the coastal region and along the Changjiang(Yangtze) River;2) the efficiency of S&T resources in China displays strong spatial disparities,with the level of efficiency descending from the east to the west while high efficiency appearing in only several clusters;3) the utilization rates of S&T resources in most provinces are quite low,resulting in low efficiency of S&T resources allocation.The paper suggests that the utilization rate of S&T resources should be raised and the commercialization of S&T outputs should be enhanced to improve the efficiency of S&T resources in China.

Application of Blockchain Technology in Optimizing Resource Allocation Efficiency

Based on the existing literature,this paper analyzed and explored the factors that affect the efficiency of resource allocation,and evaluated the current situation of resource allocation in various fields.It is found that with the emergence of the new pattern of China’s economic development,the economic growth rate has slowed down,and the method of large-scale production driving economic growth is no longer applicable to the current economic situation.Nowadays,when structural transformation and upgrading have entered a new stage,to achieve the optimization of resource allocation efficiency,it is necessary to start from three aspects:improving information transparency,upgrading industrial structure,and planning government intervention.Through the adaptation analysis of blockchain technology and the existing problems in optimizing resource allocation efficiency,it explored the feasibility of blockchain technology to optimize resource allocation efficiency,and promote economical,efficient and high-quality development.

Resource Allocation of Agricultural Science and Technology R&D

The status quo of resource allocation of agricultural science and technology R&D(research and development)both at home and abroad,including the amount and function of agricultural science and technology research funds,human resources in the resources of agricultural science and technology R&D,the efficiency of resource allocation of agricultural science and technology R&D,the management system of agricultural scientific innovation and the operation status of scientific funds,is analyzed.The problems in the current resource allocation of agricultural science and technology R&D are put forward,including unreasonable resource allocation;low efficiency,and low efficiency of the transformation of agricultural scientific achievements.The highly effective resource allocation of agricultural science and technology R&D is analyzed from the aspects of resource allocation structure,environment,channel,spatial layout and industrial chain.

The measure and characteristics of spatial-temporal evolution of China＇s science and technology resource allocation efficiency

According to the connotation and structure of science and technology resources and some relevant data of more than 286 cities at prefecture level and above during 2001-2010, using modified method--Data Envelopment Analysis （DEA）, science and tech- nology （S＆T） resource allocation efficiency of different cities in different periods has been figured out, which, uncovers the distributional difference and change law of S＆T resource allocation efficiency from the time-space dimension. Based on that, this paper has analyzed and discussed the spatial distribution pattern and evolution trend of S＆T resource allocation efficiency in different cities by virtue of the Exploratory Spatial Data Analysis （ESDA）. It turned out that： （1） the average of S＆T resource allocation efficiency in cities at prefecture level and above has always stayed at low levels, moreover, with repeated fluctuations between high and low, which shows a decreasing trend year by year. Besides, the gap between the East and the West is widening. （2） The asymmetrical distribution of S＆T resource allocation effi- ciency presents a spatial pattern of successively decreasing from Eastern China, Central China to Western China. The cities whose S＆T resource allocation efficiency are at higher level and high level take on a cluster distribution, which fits well with the 23 forming urban agglomerations in China. （3） The coupling degree between S＆T resource allocation efficiency and economic environment assumes a certain positive correlation, but not completely the same. The differentiation of S＆T resource allocation efficiency is common in regional devel- opment, whose existence and evolution are directly or indirectly influenced by and regarded as the reflection of many elements, such as geographical location, the natural endowment and environment of S＆T resources and so on. （4） In the perspective of the evolution of spatial structure, S＆T resource allocation efficiency of the cities at prefecture level and above shows a notable spatial autocorrelation, which in every period presents a positive correlation. The spatial distribution of S＆T resource allocation efficiency in neighboring cities seems to be similar in group, which tends to escalate stepwise. Meanwhile, the whole differentiation of geographical space has a diminishing tendency. （5） Viewed from LISA agglomeration map of S＆T resource allocation efficiency in different periods, four agglomeration types have changed differently in spatial location and the range of spatial agglomeration. And the conti- nuity of S＆T resource allocation efficiency in geographical space is gradually increasing.

OFDM系统中基于能量效率的低复杂度算法的研究与仿真

早先的无线通信传输主要以频谱效率为设计标准,在保证用户服务质量的同时,对有限的资源进行合理的分配从而得到较好的系统性能。关于频谱效率的研究主要是在系统发射功率有限的情况下并考虑用户公平性的同时,最大化系统的容量。而随着各种高速无线通信服务的应用越来越广泛,以及无处不在的无线接入使得能量的消耗越来越多。而对于电池容量有限的移动终端来说是一个较大的瓶颈。与此同时,无线网络的能量消耗的产生了越来越多的温室气体,并加重了运营费用。因此,以能量效率为设计准则的绿色通信越来越得到重视并成为未来无线网络设计的准则。本文提出了一种OFDM系统中基于能量效率的复杂度较低。本文首先提出了OFDM系统中能量效率的优化模型,接着采用经典的注水法,求出了最优的功率分配。通过仿真证明了该算法是最优的。

Can the low-carbon city pilot policy promote the upgrading of high-carbon emitting enterprises? Evidence from China

As a crucial environmental reform system to realize“carbon peaking”and“carbon neutrality”,the pilot policy of low-carbon cities(LCCs)puts pressure and challenges on high-carbon emitting enterprises(HCEEs)while providing opportunities for these firms to take the path of independent transformation.Employing the data of Chinese listed enterprises from 2006 to 2016 and adopting a difference-in-differences(DID)model,we evaluated the impact of LCC construction on the upgrading of HCEEs and its mechanisms.The results indicate that LCC construction enhances the upgrading of HCEEs in the pilot cities.The conclusions remain stable after a series of robustness tests.The mechanism analysis reveals that LCC construction triggers the upgrading of HCEEs by promoting resource allocation efficiency,R&D investment,and green technology innovation.The heterogeneity results indicate that this positive effect is more pronounced for HCEEs in regions with more stringent environmental law enforcement.This study also observes that the upgrading impact is more promi‐nent for state-owned enterprises,enterprises with higher bargaining power,and enterprises whose managers have a long-term vision.The above results provide directions for upgrading HCEEs and replicable evidence for cities in developing economies to fulfill the win-win target of environmental protection and economic transfor‐mation.

A Novel Energy and Communication Aware Scheduling on Green Cloud Computing

The rapid growth of service-oriented and cloud computing has created large-scale data centres worldwide.Modern data centres’operating costs mostly come from back-end cloud infrastructure and energy consumption.In cloud computing,extensive communication resources are required.Moreover,cloud applications require more bandwidth to transfer large amounts of data to satisfy end-user requirements.It is also essential that no communication source can cause congestion or bag loss owing to unnecessary switching buffers.This paper proposes a novel Energy and Communication(EC)aware scheduling(EC-scheduler)algorithm for green cloud computing,which optimizes data centre energy consumption and traffic load.The primary goal of the proposed EC-scheduler is to assign user applications to cloud data centre resources with minimal utilization of data centres.We first introduce a Multi-Objective Leader Salp Swarm(MLSS)algorithm for task sorting,which ensures traffic load balancing,and then an Emotional Artificial Neural Network(EANN)for efficient resource allocation.EC-scheduler schedules cloud user requirements to the cloud server by optimizing both energy and communication delay,which supports the lower emission of carbon dioxide by the cloud server system,enabling a green,unalloyed environment.We tested the proposed plan and existing cloud scheduling methods using the GreenCloud simulator to analyze the efficiency of optimizing data centre energy and other scheduler metrics.The EC-scheduler parameters Power Usage Effectiveness(PUE),Data Centre Energy Productivity(DCEP),Throughput,Average Execution Time(AET),Energy Consumption,and Makespan showed up to 26.738%,37.59%,50%,4.34%,34.2%,and 33.54%higher efficiency,respectively,than existing state of the art schedulers concerning number of user applications and number of user requests.

数字化转型对企业ESG表现的影响研究

现阶段,通过数字化转型提升ESG表现已成为企业提升竞争力、实现可持续发展的重要策略选择。文章基于2011—2021年我国沪深两市A股制造业上市公司数据实证考察企业数字化转型对ESG表现的影响效应与作用机制。研究结果表明:企业数字化转型对ESG表现具有显著正向效应,且这一结论在进行多项内生性处理和稳健性检验后仍然成立;企业数字化转型主要通过提升企业绿色创新绩效、抑制管理者短视行为及提高资源配置效率三条途径提升ESG表现;分析师关注和媒体关注强化了企业数字化转型对ESG表现的促进作用;基于所有权性质、要素密集度及规模等级的不同,企业数字化转型对ESG表现的影响具有显著异质性。研究结论可为企业推进数字化转型、提升ESG表现提供相应对策建议。

数字化转型对企业非效率投资的非线性作用研究

选取2011—2021年A股上市公司数据,探究数字化转型对企业非效率投资的非线性影响。结果表明:数字化转型对企业非效率投资表现出倒“U”型特征,数字化转型初期会通过加大财务困境风险来提高企业非效率投资水平,后期通过优化资源配置和促进技术创新来降低企业非效率投资水平。异质性分析发现,数字化转型对企业非效率投资的倒“U”型作用在国有企业、高信息透明度企业和数字普惠金融发展水平较低城市的企业中更为明显。研究为助力企业突破数字化转型拐点、实现对企业非效率投资的有效抑制提供参考。

公共数据开放能否促进区域绿色技术创新——基于政府数据平台上线的准自然实验

公共数据作为公共服务的重要基础资源,对地方经济政策的成功实施具有重要的推动作用。以中国地方政府推动公共数据开放平台上线作为准自然实验,基于2007—2022年中国地级市面板数据,采用多期双重差分模型进行实证分析,以检验公共数据开放是否有效促进了区域绿色技术创新。研究发现:公共数据的开放显著推动了区域绿色技术创新的发展,并且这一结论在经过一系列稳健性检验后依然成立,进一步在PSM-DID和工具变量回归分析中得到验证。机制检验发现:这一影响主要归因于区域信息壁垒的消除和科技资源配置效率的提升。此外,公共数据开放对区域绿色技术创新的影响在不同地区间呈现出明显的异质性。特别是在中部地区、金融发展水平高以及公众环境关注度较高的地区,公共数据开放对区域绿色技术创新的促进作用更为显著。研究为量化评估数据要素开放共享在绿色技术创新中的经济效应提供了重要参考,有助于激发各地数据要素的开发潜能,并为制定合理的经济政策提供有力支持。

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。

数字化转型推动企业绿色创新——基于信息效应、资源效应与技术效应的分析

基于2007—2022年企业微观层面的面板数据,深入探究数字化转型协同推动企业实质性绿色创新、策略性绿色创新的效果及其影响机制。研究表明:(1)数字化转型能够带动企业绿色创新水平的提高,该影响对于实质性绿色创新更为显著,经稳健性检验结论依然成立;(2)从机制路径来看,信息披露、资源配置与技术整合起到中介作用,企业数字化转型经由信息效应、资源效应、技术效应为绿色创新提供了有力支持;(3)由异质性分析可知,数字化转型对企业绿色创新的促进效果在国有企业、内资企业、非重污染行业和高科技企业中更为明显。提出响应数字化转型战略、畅通数字化发展通道、打造数智绿色创新生态等三方面的政策建议。

金融科技与经济包容性绿色增长:理论分析和经验证据

文章基于2011—2021年中国272个城市的面板数据,考察了金融科技对经济包容性绿色增长的影响、作用机制及空间效应。结果表明:金融科技显著促进经济包容性绿色增长,在推动经济增长和社会公平方面表现尤为突出;技术创新和金融资源配置是金融科技促进经济包容性绿色增长的重要机制;金融科技对经济包容性绿色增长存在空间溢出效应,有助于带动周边城市经济包容性绿色增长;金融科技有助于促进东部地区、核心城市及金融发展水平较高城市的经济包容性绿色增长。因此,营造良好的金融创新环境,深化信息技术与传统金融融合,发挥金融科技的溢出效应,能够赋能经济包容性绿色增长。

绿色金融如何推动经济高质量发展?

基于2012—2021年我国31个省级行政区面板数据,运用GMM模型探究绿色金融推动经济高质量发展的效果和机制。研究发现:(1)绿色金融对经济高质量发展存在着显著的促进作用;(2)机制检验表明,绿色金融可以通过提高绿色消费水平、信贷配置效率和绿色持续创新能力促进经济高质量发展;(3)地区异质性分析表明,绿色金融对东部地区经济高质量发展的促进效应更显著。在此基础上,从提高绿色消费水平、优化信贷资源配置、提高绿色持续创新能力以及制定差异化绿色金融政策等方面提出对策建议。

数据要素的减污降碳双重环境福利效应分析

本文运用2006~2022年我国30个省(区、市)的面板数据,实证考察了数据要素的减污降碳效果。研究发现:数据要素能够实现减污降碳的双重环境福利,且主要通过资源配置效率提升、产业结构优化升级、绿色技术创新和公众环境关注度提升的渠道实现;异质性分析发现,数据要素在促进减污降碳方面存在市场化水平和数字基础设施异质性特征。进一步研究发现,数据要素在实现本地区减污降碳的同时,也会对邻近地区产生空间溢出效应;结构效应回归结果显示,数据要素对减污降碳的影响效果整体呈现地区环境污染和碳排放越高其减污降碳效果越明显的态势。本文研究结论为在双碳目标和新发展理念下如何借力数据要素“东风”实现减污降碳的“双赢”提供了重要启示。

区域市场分割对企业绿色技术创新的影响研究

党的十九大报告提出“构建市场导向的绿色技术创新体系”。本文以基于相对价格法计算的区域市场分割指数与2004~2022年制造业上市公司的绿色技术创新指标为基础,分析市场分割对企业绿色技术创新的影响。结果表明:区域市场分割显著抑制企业绿色技术创新,且抑制过程具有一定的持续性。进一步分析发现:资本品和劳动力市场分割对企业绿色技术创新的抑制作用同样显著;市场分割通过环境规制、创新资源配置、数字普惠金融效应抑制企业绿色技术创新;位于东西部地区、低市场化水平地区、低集中度行业的企业、处于低融资约束水平和低数字化转型程度的企业以及国企的绿色技术创新受到的抑制作用更显著。

创新要素错配对碳排放效率的影响——基于空间溢出效应视角的研究

合理配置创新要素、不断提高碳排放效率是中国在加快实现“双碳”目标过程中亟须解决的现实问题,而相关研究对其可能存在的空间效应的讨论相对不足。从理论层面剖析创新要素错配对碳排放效率的影响机制和空间溢出效应,将要素错配以一种扭曲税的形式表现,从投入产出视角通过超效率SBM模型测度碳排放效率,通过全要素生产率离散度法和数据包络分析法构建创新要素错配和碳排放效率的测度框架,基于2012—2021年中国30个省份的面板数据,利用空间杜宾模型、空间自相关检验方法实证检验创新要素错配对碳排放效率的影响及空间溢出效应,并将30个省份划为五大区域展开异质性分析。结果发现:创新要素错配显著抑制本地区碳排放效率提升并对空间关联地区的碳排放效率产生不利影响;创新要素错配对碳排放效率的影响存在地区异质性,中部和西南地区创新要素错配对本地区碳排放效率起到抑制作用,而东部和东北、西北地区的影响不明显;五大地区创新要素错配对空间关联地区的碳排放效率均起到抑制作用。据此,提出不断优化创新要素市场配置机制、有效鼓励企业开展低碳技术创新和应用、形成区域创新合力等助力中国碳排放效率提升的政策建议。

河南科技资源配置效率评价研究

为进一步提升河南科技资源配置效率,运用DEA-Malmquist指数模型,从静态和动态两个视角对河南17个地级市及济源示范区2012—2021年期间的科技资源配置效率进行测度.研究发现,从静态评价结果来看,河南科技资源配置综合技术效率平均值为0.739,不同地区之间存在差异,综合效率的提升主要依赖纯技术效率;从动态评价结果来看,河南全要素生产率整体呈波动上升态势,年平均增长率为16%,技术进步对其贡献最大.分地区来看,其中综合提高型的城市有10个,技术进步水平提高型的城市有7个,技术效率提高型的有1个.

企业ESG表现与绿色全要素生产率

在着力实现“双碳”目标的背景下,深入考察企业ESG表现对绿色全要素生产率的影响对于推动企业绿色转型升级及可持续发展具有重要意义。本文基于2012—2022年中国A股上市公司数据,实证探究了企业ESG表现对绿色全要素生产率的影响效应及作用机制。研究发现,企业ESG表现显著提升了绿色全要素生产率水平,并主要通过激励企业绿色技术创新、改善资源配置效率、缓解企业融资约束、优化人力资本结构等途径对其产生影响。异质性检验表明,对于国有企业、重污染行业企业、东部地区企业以及ESG评级不确定性较小企业,ESG表现对其绿色全要素生产率的促进效应更为明显。进一步研究发现,企业ESG表现中的社会责任及公司治理因素对绿色全要素生产率产生了显著的正向影响。此外,企业ESG表现在促进绿色技术效率提升的同时,也显著推动了企业绿色技术进步。本文研究揭示了企业ESG表现对绿色全要素生产率具有积极影响,进而为企业更好地履行社会责任、实现绿色低碳转型提供启示。

农业保险是否促进了中国农业绿色发展?

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