Growth in Total Factor Productivity of Shandong Province for Environmental Constraints

This paper analyzed the total factor productivity of Shandong Province and its impact on the local economic environment through Malmquist-Luenberger productivity index. The results of the paper were included as follows. Without consideration of the environmental constraints, Index M of the total factor productivity of Shandong Province from high to low was Heze, Jinan, Qingdao, Weihai, Dongying, Zaozhuang, Jining, Tai'an, Dezhou, Liaocheng, Yantai, Zibo, Weifang, Linyi, Binzhou, Rizhao, Laiwu, among which Index M of Linyi, Binzhou, Rizhao, Laiwuwere were less than 1; taking the environmental constraints into account, Index ML of the total factor productivity of Shandong Province was reduced, from high to low was Jinan, Qingdao, Weihai, Yantai, Heze, Liaocheng, Tai'an, Weifang, Jining, Linyi, Zibo, Dongying, Zaozhuang, Rizhao, Laiwu, Binzhou, Dezhou, among which Index ML of 9 cities behind Jining were less than 1. In terms of the development mode, Jinan, Qingdao, Tai'an, Weihai, Jining and Heze were resource-conserving and environment-friendly cities; Dongying and Zaozhuang were resourceconserving and environment-polluted cities; Liaocheng, Linyi, Weifang and Yantai were resource-wasted and environment-friendly cities; Binzhou, Dezhou, Laiwu, Rizhao and Zibo were resource-wasted and environment-pol uted cities.

Analysis of the Contribution of Total Factor Productivity to Agricultural Yield in Anhui Province

By analyzing data concerning agricultural development from 1981 to 2010 in Anhui Province,the total factor productivity and growth rate of agriculture from 1981 to 2010 are estimated empirically.By dividing years from 1981 to 2010 into several subintervals,this paper evaluates and analyzes initial stage of reform and opening up,soft landing stage,the stage of financial crisis,the stage of subprime mortgage crisis and the impact of all factors on total factor growth rate of agriculture.Then it analyzes the contribution of growth of total factor productivity to total yield of agricultural economy.The research finds that the growth of total factor productivity in Anhui Province is significant to agricultural development.

Total Factor Productivity and Influence Factors of Economic Environment in the Yellow River Delta

The paper analyzed total factor productivity of Binzhou City, and key factors that influence its economic growth and effluent discharge. The results showed that(i) production efficiency of the city during 2005–2013 kept stable, mean of Malmquist-Luenberger index was 1.044,9 without considering effluent discharge, and the average annual increase rate was 4.49%, and that was 1.020,4 considering effluent discharge, the average annual increase rate was 2.04%, and the increase rate reduced by 2.45%.(ii) GDP growth showed significant negative correlation and positive correlation with capital input and energy input, but insignificant negative correlation and positive correlation with labor force input and technological progress.(iii) Effluent discharge showed insignificant negative correlation and positive correlation with capital input and energy input, but significant negative correlation and positive correlation with labor force input and technological progress.

An Empirical Study on the Influence of Agricultural Public Infrastructure on Total Factor Productivity of China’s Grain Production

A critical method of ensuring grain production is to increase the total factor productivity(TFP),and the key measure to increase the TFP of grain production lies in the construction of agricultural public infrastructure.For this topic,existing literature lacks systematic and empirical analysis.Therefore,research on the influence of agricultural public infrastructure on the TFP of China’s grain production has relatively strong policy implications and theoretical value.For this study,we collected panel data for grain inputs and outputs as well as for agricultural public infrastructure in China’s provinces(autonomous regions/municipalities)from 1990 to 2017,and adopted the stochastic frontier function(SFF)approach to measure the TFP of provincial-level grain production.Through this empirical study,we analyzed the influence of agricultural public infrastructures,such as irrigation,roads,and electric power facilities on the TFP of China’s agriculture.We found that such facilities have a positive influence on the TFP of grain production.Specifically,when the input for irrigation facilities is increased by 1 percent,the TFP of grain production will rise by 5.74 percent.Based on this finding,policy recommendations are proposed for enhancing grain TFP through agricultural public infrastructure construction.

The impact of environmental constraint on China's industrial productivity growth:An empirical study based on an directional environmental production model

Using input, output and pollution data of industrial enterprises in 30 Chinese provinces and municipalities from 1998 to 2005, this paper creates an environmental production frontier function model to assess China's industrial growth sources, particularly environmental control and changes in the industrial environmental structure's impact on industrial productivity. It found that (1) at its current stage, China's rapid industrial growth is accompanied by a slow increase in pollution; (2) environmental total factor productivity has become a primary driver of rapid growth with less pollution; (3) environmental control didn't cause any substantial restraint on China's industrial growth; (4) improvement in industrial environmental structure contributes an increasingly large share to economic growth with less pollution. China's industrial productivity is increasing, its growth model is being transformed, and the environment is exerting a diminishing constraint on economic growth.

Productivity Growth in China's Agriculture During 1985-2010

This paper made an empirical analysis of China＇s agricultural growth path and influential factors using the province-level panel data of agricultural inputs and outputs during 1985-2010.The findings indicate that the increase in agricultural inputs and TFP contributed 40.6 and 55.2% to the agricultural output growth,respectively;China＇s agriculture had jumped out of the pattern which output growth was mainly driven by increasing input.Of the total inputs,chemical fertilizer had the most important contribution to the output growth,followed by mechanical inputs.The contribution of land and labor was negative.China＇s agricultural output growth belonged to the type of induced technology innovation.China＇s agricultural TFP growth had characteristics of fluctuations over time and unbalanced between regions,but the gap between the eastern,the middle,and the western regions has been narrowed.

OFDI、环境规制与中国工业绿色全要素生产率

本文选用方向距离函数和ML生产率指数,测算了能源环境约束下2003～2015年我国省际层面工业绿色全要素生产率及其分解项,并借助面板模型分析OFDI、环境规制及两者合力作用对工业绿色全要素生产率的影响及其传导机制。研究发现:绿色全要素生产率增长主要来源于绿色技术进步,环境规制力度较高的地区对应较高的绿色全要素生产率增长率;OFDI和环境规制均有利于绿色全要素生产率的提升,符合"波特假说",同时环境规制能够促使OFDI发挥技术溢出与结构优化效应来改善绿色全要素生产率;区域间OFDI、环境规制及两者合力作用效应存在明显的"两极分化"现象,政府应合理把握环境规制力度并向中等标准组看齐;门槛检验显示,OFDI的作用效应存在显著的环境规制、经济发展、对外开放和人力资本门槛。

Measurement and Analysis of Agricultural Production Efficiency in Taiwan of China Based on Three-stage DEA Model

Agricultural production efficiency in Taiwan of China in 2015 is studied by using three-stage DEA model. The results show that total output value of agriculture,forestry,animal husbandry and fishery is taken as output index,while employees of agriculture,forestry,animal husbandry and fishery,number of tractors,investment in dry land and paddy field are taken as input indexes,which meets " isotropic" condition assumption of model application. Natural disaster,number of workers in agricultural production and marketing class,expenditure on farmland water conservancy are not favorable for the promotion of agricultural production efficiency,while multiple cropping index is favorable for promoting agricultural production efficiency. Three-stage DEA model effectively eliminates the influences of environmental and random factors on agricultural production efficiency. After environmental and random factors are eliminated,comprehensive technical efficiency in Taiwan of China declines because of the decline of pure technical efficiency. Each county and city of Taiwan could be divided into different types according to pure technical efficiency and scale efficiency,and it can have some emphasis in improving agricultural production efficiency according to their own efficiency characteristics.

Contents and spatial distribution patterns of heavy metals in the hinterland of the Tengger Desert,China

The desert in northern China is one of important sources of loess and one significant source of material for sandstorms in Asia.The sand/dust that is transported from desert when sandstorms occur can destroy the growth of crops,cause serious losses and great harm to the economic construction and life safety,and cause natural environment pollution.Hence,it is very important to deepen the research into heavy metals in surface deposits at vulnerable ecological region of arid land of northern China to guide local industrial and agricultural development and improve environmental protection.In this research,10 heavy metal elements(Cr,Mn,Fe,Co,Ni,Cu,Zn,Cd,Pb,and Th)were tested and analyzed in 33 soil sample sites collected from the hinterland of the Tengger Desert,northern China.The results showed that the average abundance of Th exceeded its background soil value of China by more than 5.2 times,which suggests that the Tengger Desert is polluted by Th.In addition,based on principal component analysis,spatial differentiation,and correlation analysis,we identified the source of element with a coefficient of variation in abundance of greater than 0.5 or exceeding the background soil value of China.Principal component analysis and correlation analysis showed that the sources of heavy metals of Cr,Mn,Fe,Co,Ni,Cu,and Cd were similar,while those of Th and Zn were different.Moreover,based on the contents and spatial distribution characteristics of those heavy metal elements,we found that the formation of heavy metal elements enrichment areas is caused by industrial pollution,development of irrigated agricultural,geological,and geomorphic conditions,and the sedimentary environment in the study area.Our result can provide information on the environmental background values of soils in the hinterland of the Tengger Desert.

Do ICTs Boost Agricultural Productivity?

Based on panel data from the Rural Fixed Point Survey of the Ministry of Agriculture over the period 2004-2016 and supplementary survey data on information and communications technology(ICT)applications in the countryside,this paper employs the difference in differences(DID)method to analyze the effects of ICT applications on rural households’agricultural total factor productivity(TFP)with mobile phone signal,internet and 3G mobile network connections as indicators,and decomposes and evaluates the constituent factors.Our findings reveal a positive effect of ICTs on rural households’TFP,which primarily stemmed from rising agricultural technical efficiency.However,ICTs exerted no significant effect on agricultural technical progress during this paper’s data period due to limited rural human capital.These findings are consistent with robustness test results based on counterfactual and matching methods.

数字乡村建设、资源要素错配与农业绿色全要素生产率

数字乡村建设与农业绿色发展深度融合是推动农业高质量发展的新动力引擎。本文选取2011-2021年中国31个省级面板数据,构建数字乡村建设和农业绿色全要素生产率的评价体系,多维度分析数字乡村建设对农业绿色全要素生产率的影响及作用机制。研究发现,数字乡村建设既可以直接促进农业绿色全要素生产率提升,也可以通过带动绿色技术进步而促进农业绿色全要素生产率提升,还可以通过降低土地资源错配、资本资源错配、劳动力资源错配程度而间接促进农业绿色全要素生产率。数字乡村建设对农业绿色全要素生产率的影响异质性明显,东部地区、南方、粮食主产区的数字乡村建设的正向推动作用较强。

数字普惠金融如何赋能乡村产业振兴

厘清数字普惠金融发展赋能乡村产业振兴的作用机制,有助于缓解农村金融资源配置效率不高、金融基础设施不完善和金融服务体系薄弱与日益增长的乡村产业发展需求之间的矛盾。现有文献对数字普惠金融与农村经济现代化做了较为丰富的研究,但是鲜有将其与乡村产业振兴联系起来进行理论与实证的研究。本文从乡村产业生产、乡村产业延伸、乡村产业功能和乡村产业支撑四个维度构建我国乡村产业振兴水平评价指标体系,并多维度实证检验了数字普惠金融对乡村产业振兴的影响。主要有三点发现:一是数字普惠金融发展对乡村产业振兴水平和乡村产业振兴速度均具有显著的正向促进作用;二是数字普惠金融发展覆盖广度、使用深度和数字化程度对乡村产业振兴的边际效应呈现明显差异;三是数字普惠金融不仅可以直接缓解经营主体约束和激活市场活力促进乡村产业振兴,还可以通过农业科技创新、乡村产业融合和农业全要素生产率间接影响乡村产业振兴。

全要素生产率视角下数字经济对农业碳排放影响——来自中国农业的经验证据

数字经济在蓬勃发展的同时表现出碳减排效应。理论上,数字经济可以通过提升农业全要素生产率,从而赋能农业碳减排。实际操作中,利用熵值法测度了2014—2020年中国30个省份农业农村数字经济发展指标,利用中介模型分析农业全要素生产率在数字经济影响农业碳排放的过程中的中介效应。在此基础上进行计量分析结果表明:中国农业数字经济蓬勃发展的同时呈现出区域发展不均衡态势,东部的农村数字经济发展水平高于中西部。数字经济能够显著降低农业碳排放强度,但是这种减排效应具有明显的区域差异,西部地区减排效果明显优于东部和中部地区。数字经济在降低农业碳排放强度的过程中,农业全要素生产率发挥了部分中介效应,中介效应占比为31.1%。这一结论在进行一系列的稳健性检验后仍然成立。

农产品贸易促进了中国农业绿色发展吗?——基于农业绿色全要素生产率视角

农业绿色全要素生产率的提高有助于农业绿色化转型与可持续性发展。基于2006—2020年中国30个省份数据,选择SBM-ML指数法,利用MaxDEA软件测算农业绿色全要素生产率,使用系统GMM模型与动态面板门槛模型实证检验农产品贸易对农业绿色全要素生产率的影响。研究发现:农产品贸易能够有效提升农业绿色全要素生产率并且其影响效应存在区域异质性,在东部地区较为显著;农产品贸易对农业绿色全要素生产率的影响存在地区经济发展水平的门槛效应,地区经济发展水平越高,农产品贸易对农业绿色化发展的正向溢出作用越明显。因此,各省份要推动农产品贸易的持续健康发展,政府应当依照地区贸易发展状况与经济发展水平因地制宜地制定有关农产品贸易政策,充分发挥农产品贸易的正向溢出作用,促进农业绿色发展。

金融科技能提升城市包容性绿色全要素生产率吗?

包容性绿色全要素生产率强调绿色发展、社会公平与经济增长协调并进,是判断城市经济发展质量的重要依据。本文以2011—2019年中国284个地级及以上城市的数据为样本,实证检验了金融科技对城市包容性绿色全要素生产率的影响及作用机制。研究发现,金融科技对城市包容性绿色全要素生产率具有明显的促进作用,主要表现在技术进步指数显著提升,这一结论在经过一系列内生性处理和稳健性检验后依然成立。机制分析显示,金融科技能够通过促进绿色创新、增加环保投资以及激发创业活力提升城市包容性绿色全要素生产率。异质性分析表明,金融科技对包容性绿色全要素生产率的提升作用在人力资本水平、金融发展水平和基础设施水平较高的城市中更为明显。本文的研究为加快实现经济绿色发展和包容性增长的“双重红利”提供了重要的经验证据。

农地流转提升了农业绿色全要素生产率吗?

在2006-2021年中国30个省份的面板数据基础上,本文通过SBM-GML超效率模型测算中国各省份农业绿色全要素生产率。采用双向固定效应模型和工具变量模型等方法,实证分析农地流转和农业绿色TFP之间的内在联系,并分析其影响路径机制。研究发现,中国农业绿色全要素生产率能够持续增长的主要原因在于技术进步。而农地流转能够推动农业绿色全要素生产率增长则是得益于技术效率改进。从异质性分析来看,农地流转对农业绿色全要素生产率增长的促进作用最显著的是东部地区和非粮食主产区,但在西部地区却正好相反。因此,中国应当加快推进农地流转工作进展,促进农业生产和经营管理模式变革,加快研发、推广和应用农业绿色生产技术,构建区域协作互助机制促进地区交流,进而促进农业绿色全要素生产率的提高。

数字普惠金融、企业全要素生产率与绿色创新

绿色创新是企业提升绩效获得持续发展的重要动力,数字普惠金融和企业全要素生产率对企业绿色创新具有重要影响。以2011—2021年A股上市公司为样本,运用面板回归模型检验数字普惠金融对企业绿色创新的影响。研究发现:数字普惠金融对企业绿色创新具有显著促进作用,特别是相对于非国有企业、低污染行业和东部地区企业而言,数字普惠金融对国有企业、高污染行业和中西部地区企业的绿色创新促进力度更大;此外,企业全要素生产率(TFP)是数字普惠金融对企业绿色创新影响的中介,发展数字普惠金融可以提高企业TFP,从而推动企业绿色创新;环境监管在数字普惠金融与企业绿色创新之间发挥着积极的调节作用,提升环境监管水平将进一步增强数字普惠金融对企业绿色创新的促进作用。

环境规制与绿色全要素生产率:促进还是抑制?

在“双碳”背景下通过制定环境规制政策提高绿色全要素生产率,推动经济绿色发展具有现实意义。以2000—2020年我国30个省级行政区的面板数据为基础,构建超效率(SBM—GML)模型阐释环境规制与绿色全要素生产率之间的内在逻辑,在分别控制时间和地区的双固定效应和面板分位数回归模型(PQRM)的基础上得出以下结论:(1)环境规制对绿色全要素生产率的作用效果是先抑制后促进;(2)由于经济发展水平的区域性差异,环境规制对高端分位点省级行政区的绿色全要素生产率的促进程度远小于低端分位点省级行政区;(3)技术进步是我国东、中、西和东北等地区绿色全要素生产率提高的关键因素,全域技术效率尤其是规模效率是阻碍绿色全要素生产率提升的关键因素。因此,地方政府首先要结合当地资源禀赋及自然环境承受能力,完善环境规制政策,缩小区际差异;其次要加强基础性研究,破除技术垄断,优化生产流程,提升规模经济,提高绿色全要素生产率。

生产性服务数智化对农业绿色全要素生产率的影响——兼论政府环境规制的调节作用

为推进农业农村现代化,亟需以生产性服务业数智化为契机,对传统农业生产方式进行全方位改造,提高农业综合生产能力。在理论阐述农业生产性服务数智化影响农业绿色全要素生产率(AGTFP)的内在机制基础上,构建包含金融服务、科技信息服务和商贸流通服务三部分的农业生产性服务数智化综合评价指标体系,并通过回归模型对2014—2020年我国29个省(区、市)的面板数据进行实证检验。结果表明:生产性服务数智化改造对AGTFP水平的提升具有显著的促进作用。具体来讲,金融服务和商贸流通服务的数智化改造对AGTFP的影响呈现当期有效的特征,而科技信息服务数智化改造在较长的时间内仍可以保持对AGTFP的显著促进效应;农业生产性服务数智化促进作用存在明显的区域异质性,从强到弱依次为粮食主产区、粮食产销平衡区、粮食主销区;政府环境治理能够强化农业生产性服务数智化对AGTFP的影响。基于此,应大力推进现代信息技术对传统农业生产方式的改造,构建以科技信息服务为基础,金融服务、商贸流通服务为引领,拉动农业绿色全要素生产率提升的长效增长机制。

数智经济推动共同富裕的机制与效应研究

共同富裕是社会主义的本质要求,数字化、智能化是引领共同富裕的重要驱动力。本文从数字化和智能化两个维度出发构建了数智经济发展水平的指标体系,并利用2012~2022年我国省际面板数据,实证分析了数智经济推动共同富裕的作用路径与影响效应。研究发现:数智经济通过提高农业全要素生产率推动共同富裕。在区域经济发达程度较低时,数智经济的发展可能会妨碍共同富裕目标的实现,但这种抑制效果将进一步削弱,直到区域经济发展达到一定水平,数智经济发展才有助于促进共同富裕。同时,数智经济发展还存在明显的空间溢出效应,可以提高邻近地区的共同富裕程度。因此,各地区应加快数智经济发展进度,使数智经济的建设更有效地服务于共同富裕目标。