Research on the impact of manufacturing servitization on environmental pollution:spatial econometric analysis of sulfur dioxide based on 284 prefecture-level cities

Using China’s regional input–output table,the paper constructs indicators of manufacturing servitization,matches manufacturing servitization at the regional level with city data,and uses spatial econometrics to empirically analyze the impact of manufacturing servitization on urban sulfur dioxide(SO_(2))emissions within the classical Environmental Kuznets Curve(EKC)framework.The results show that manufacturing servitization can reduce SO_(2) emissions.Producer servitization and consumptive services can both significantly reduce industrial SO_(2) emissions.Transportation and warehousing servitization,information servitization,leasing,and commercial servitization,technology research and development servitization significantly reduce SO_(2) emissions;technology research and development servitization,in particular,have the largest influence coefficient,while the reduction effect of servitization in the wholesale and retail and finance sectors is not significant.The study also found that servitization reduced the SO_(2) emissions through technological innovation and industrial structure upgrading.

Measurement Analysis and Evaluation of Twenty-Five Years of Mathematics Education Research in China:Visual Econometric Analysis Based on CiteSpace Knowledge Mapping(1999-2024)

Since the new century,China’s mathematics curriculum reform in basic education has continued to move forward in attempts and explorations,presenting many new changes,trends,movements,and developments.Sorting out,analyzing,and summarizing the achievements,experiences,problems,and challenges in this journey are conducive to providing insights for the reform and development of the Chinese basic education mathematics curriculum in the new era.This paper analyses the research on mathematics education in China(1999-2024)using the visual measurement of CiteSpace knowledge mapping,hoping to provide directions for the future of mathematics education in China.

Econometric Analysis of Factors Affecting Special Purpose Forests in China

The forest estate is one of our most important natural resources. It is also the material foundation for sustainable forestry development in China. It is a timely topic of concern within the forestry community and touches the entire society. From a social economic point of view, the sustainable development has been promoted by the efficient use of the forest resources in a continuing improvement of the environment. The objective of the present analysis is to measure the effect of various factors, such as population, economic development and related policies on the use of the forest resources. The 29 provinces of Mainland China are classified in three regions, based largely on their level of economic development. For each region, the factors which affect the area of special purpose forests (SPF) are analyzed. Some recommendations are made for further improvement.

The Impact of Urbanization and Factor Inputs on China's Economic Growth--A Spatial Econometrics Approach

This paper utilizes a panel data of 31 provinces in China spanning from 2007 to 2014.Spatial econometrics is applied to carry out regression analysis of the impact of urbanization and factor inputs on China's economic growth.By comparing differences among regions,this paper finds that in the regional level,the positive effect of urbanization in the Eastern region and the Western region is significant,and the positive effect of the proportion of input factors in the Central region is also significant but to a lesser extent.In general,there exists spatial spill-over effect between urbanization and factor inputs structure and economic growth,i.e.,both are capable of producing positive effect,but the input role played by the scale factor has diminishing marginal effect.Urbanization is more likely to become the driving force of economic growth and to stimulate economic growth.

Innovative Research on the Blending Learning Mode of Econometrics

Due to the outbreak of the Covid-19 in 2020,online education has become the mainstream.After the epidemic,the blending learning mode has also become a key goal of the teaching reform of colleges and universities,and the blending learning mode of various courses has blossomed everywhere.In this context,this paper used the Econometrics course as the carrier,analyzed the many unreasonable problems in the traditional Econometrics course,and proposed an optimization plan and path for the blending learning mode to address these problems.

An Econometric Model for SINOPEC Stock Price Tendency on Domestic Securities Market

A time series analysis method was used to establish an econometric model for SINOPEC＇S stock price tendency on the domestic securities market under the background of sharp oil price rises in recent years. The model was proven to be a non-stationary time series and unit root process, as tested with the Dickey-Fuller method, and the result of a practical case showed that this model could well reflect SINOPEC stock price tendency on the securities market of China. It would be a guide for research and prediction of stock price tendency.

Causal inference using regression-based statistical control: Confusion in Econometrics

Regression is a widely used econometric tool in research. In observational studies, based on a number of assumptions, regression-based statistical control methods attempt to analyze the causation between treatment and outcome by adding control variables. However, this approach may not produce reliable estimates of causal effects. In addition to the shortcomings of the method, this lack of confidence is mainly related to ambiguous formulations in econometrics, such as the definition of selection bias, selection of core control variables, and method of testing for robustness. Within the framework of the causal models, we clarify the assumption of causal inference using regression-based statistical controls, as described in econometrics, and discuss how to select core control variables to satisfy this assumption and conduct robustness tests for regression estimates.

Econometric Modeling and Model Falsification

A recent literature on qualitative analysis has shown that its successful application in testing the consistency of the sign patterns of a proposed structure and an estimated reduced form was far less restricted than a previous literature had proposed. A frequent example used in this demonstration was the qualitative analysis of Klein’s Model I. For this, the proposed structural sign pattern was falsified by the sign pattern of the estimated reduced form. As a result, the subsequent application two-stage least squares would always find quantifications of the structure that could not possibly have resulted in the sign pattern of the estimated reduced form. We view this result as a diagnostic calling for further analysis. We show that the Klein model fails standard over identification tests. We make modest amendments to the model that resolves this problem but find that the resulting estimated reduced form still falsifies the structure, calling for further developmental effort. Our point is that qualitative falsification should be viewed as a diagnostic in developing a model, rather than a criterion for entirely dismissing the model.

Using Spatial Econometrics to Measure Ozone Pollution Externalities

This paper summarizes my previous work in Lin (2010), in which I use spatial econometrics to analyze air pollution externalities. In Lin (2010), state-by-state source-receptor transfer coefficients that can be used as a basis for a location- differentiated permit system are estimated. Results affirm the importance of regional transport in determining local ozone air quality, although owing to non-monotonicities in ozone production the externality is not always negative. Because the origin of emissions matters, results also reject a non-spatially differentiated NOx cap and trade program as an appropriate mechanism for reducing ozone smog.

Time Series Econometrics: A Critique

This is a critical note regarding the currently established econometrics of time series. The criticism involves commonly practiced mechanistic modeling and testing of relationships, taking econometrics away from economics. Among others, modeling economic trends as simple functions of time is extremely naive and testing for cointegration lacks a proper economic foundation.

Econometric analysis of China’s GDP growth and industrial structure

Since the Reform and Opening-up in 1978,China’s economy,society and other fields have developed very rapidly.Economic growth has achieved leapfrog development,and the three industries have achieved great development,but the overall industrial structure needs to be improved.At present,China’s economy has entered the“New Normal”.In order to achieve“Sound and Rapid”economic development,it is imperative to transform the industrial structure.Using econometric analysis and based on China’s economic data from 1978 to 2020,this paper explores the different impacts of three industrial sectors on economic growth.Thus it analyzes the internal mechanism of China’s economic growth.It is concluded that the contribution of the tertiary industry to the national GDP is the largest,the level of secondary industry is medium,and the primary industry is the smallest.Finally,according to the calculation results,this paper puts forward corresponding suggestions for the optimization of China’s industrial structure,in order to achieve sound and rapid development of the national economy.

The spatial econometric analysis of the impact of internet on provincial innovation in China

Researches in the impact of the information technology on R&D activities have been noticed since this century to provide a foundation for the relationship between information technology and R&D activities.Using methods of spatial econometrics,this paper examines the impact of Internet on innovation and its spatial spillovers between Chinese provinces over 2001-2007.This paper find that Internet plays an important role in innovative activities,and spatial spillovers of the provinces to its neighbors are positive but weak when spatial effect of endogenous spatial lag variable is taken into consideration.

Examine the Reliability of Econometrics Software: An Empirical Comparison of Time Series Modelling

Researchers must understand that naively relying on the reliability of statistical software packages may result in suboptimal, biased, or erroneous results, which affects applied economic theory and the conclusions and policy recommendations drawn from it. To create confidence in a result, several software packages should be applied to the same estimation problem. This study examines the results of three software packages (EViews, R, and Stata) in the analysis of time-series econometric data. The time-series data analysis which presents the determinants of macroeconomic growth of Sri Lanka from 1978 to 2020 has been used. The study focuses on testing for stationarity, cointegration, and significant relationships among the variables. The Augmented Dickey-Fuller and Phillips Perron tests were employed in this study to test for stationarity, while the Johansen cointegration test was utilized to test for cointegration. The study employs the vector error correction model to assess the short-run and long-term dynamics of the variables in an attempt to determine the relationship between them. Finally, the Granger Causality test is employed in order to examine the linear causation between the concerned variables. The study revealed that the results produced by three software packages for the same dataset and the same lag order vary significantly. This implies that time series econometrics results are sensitive to the software that is used by the researchers while providing different policy implications even for the same dataset. The present study highlights the necessity of further analysis to investigate the impact of software packages in time series analysis of economic scenarios.

Review on the Current Stochastic Numerical Methods for Econometric Analysis

The main aim of this paper is to present and emphasize the contribution of stochastic numerical methods as must tools for the modern econometric modelisation. Indeed, the stochastic numerical methods play an important role in mathematical modelling and the econometric analysis because they model uncertainties that govern the real-world data. However these powerful tools are not well-known and understood by many economists and financial econometricians.

Discussion on Collaborative Teaching of Econometrics in the Field of Environmental Economics

To capitalize on the synergies between the Econometrics course and the Environmental Economics major,this paper aims to enhance students’ability to conduct empirical analysis and practical application using econometric models.It also seeks to promote collaborative teaching through case studies and model research.The primary focus is on the hot research issues within the field of environmental economics,utilizing the econometric model as a vehicle for instruction.To achieve this,the paper proposes the development of a comprehensive case library specific to environmental economics.This resource will serve to optimize the case teaching approach,incorporating the use of econometric software,and fostering interactive teaching models between educators and students.By implementing these strategies,the paper outlines a path and mode for collaborative teaching that effectively bridges the gap between econometrics and environmental economics.

Application of Bayesian Econometrics in Policy Evaluation

This study explores the application of Bayesian econometrics in policy evaluation through theoretical analysis. The research first reviews the theoretical foundations of Bayesian methods, including the concepts of Bayesian inference, prior distributions, and posterior distributions. Through systematic analysis, the study constructs a theoretical framework for applying Bayesian methods in policy evaluation. The research finds that Bayesian methods have multiple theoretical advantages in policy evaluation: Based on parameter uncertainty theory, Bayesian methods can better handle uncertainty in model parameters and provide more comprehensive estimates of policy effects;from the perspective of model selection theory, Bayesian model averaging can reduce model selection bias and enhance the robustness of evaluation results;according to causal inference theory, Bayesian causal inference methods provide new approaches for evaluating policy causal effects. The study also points out the complexities of applying Bayesian methods in policy evaluation, such as the selection of prior information and computational complexity. To address these complexities, the study proposes hybrid methods combining frequentist approaches and suggestions for developing computationally efficient algorithms. The research also discusses theoretical comparisons between Bayesian methods and other policy evaluation techniques, providing directions for future research.

Sister-city Ties and Chinese Outward Foreign Direct Investment:A Spatial Econometric Analysis

This paper employs dynamic spatial econometric methods to analyze the impact of the sister-city relationship on Chinese outward foreign direct investment(OFDI)using a linked country-level dataset from 2003 to 2016.The results show strong and robust evidence that the sister-city relationship has been a crucial OFDI location determinant in host countries and their neighbors.Specifically,the sister-city tie between China and the host country has stimulated Chinese OFDI in host countries.Moreover,Chinese OFDI in host countries would be reduced if China concluded sister-city ties with their neighbors to which we refer as the neighboring effect.Further mechanism tests show that sister cities have promoted OFDI in host countries via four channels:reducing political risk,decreasing information asymmetry,narrowing institutional distance,and mitigating cultural differences.This tendency for sister-city links to promote OFDI has varied substantially depending on OFDI entry modes(i.e.,greenfield or cross-border mergers and acquisitions),motivation(i.e.,resource-,market-,technology-,or efficiency-oriented OFDI),and Sino–foreign geographical relationships(i.e.,Belt and Road Initiative countries or other countries).

关于"Econometrics"学术译名的统一问题

本文讨论"Econometrics"的学术译名应该是"经济计量学"还是"计量经济学"的问题。作者从英文翻译习惯、语法结构、学科形成若干条件、与统计学的关系、实际用法、学科名称惯例六个方面阐述了作者主张采用"经济计量学"译名的观点。

The Influencing Factors of sCER Price Dynamics Under the Clean Development Mechanism: Theory and Econometric Analysis

In order to explore the factors and their complex mechanism affecting the price dynamics under the clean development mechanism （CDM）, this article employs the secondary Certified Emission Reduction （sCER） carbon price as the study object, and analyzes its influencing factors from aspects of the international carbon-reduction policies, macroeconomic fluctuations, energy and similar carbon products prices. The innovation of this paper lies in： Introducing necessary factor （the developing countries pricing power） and the application of several international representative indicators to un- derline the ＂world＂ nature of CDM; utilizing different econometric models to obtain noteworthy and more robust results. The authors test the theoretical findings with multiple stationary time series from the launch of CDM to present （2008-2016）. The results reveal that sCER price fluctuation shows the characteristic of asymmetry and substantial persistence. There is a strong statistically significant relationship between macroeconomic conditions, coal and oil prices, with the price of sCER. The authors discover that the pricing power of developing countries indeed has a clear but small impact on the sCER price changes, whereas the price elasticity of supply under CDM is so weak. The interaction between EU emission allowances （EUAs） and sCER presents a shift from dependency to substitution.

Dynamic changes and convergence of China's regional green productivity:A dynamic spatial econometric analysis

Low-carbon economic development is at the heart of the post-pandemic green recovery scheme worldwide.It requires economic recovery without compromising on the environment,implying a critical role that green productivity plays in achieving the carbon neutrality goal.Green productivity measures the quality of economic growth with consideration for energy consumption and environmental pollution.This study employs the slacks-based measure directional distance function(SBM-DDF)approach and the Malmquist-Luenberger(ML)index to calculate green productivity and its components of 30 provinces in China between 2001 and 2018.Using a spatial panel data model,we empirically analyzed the conditionalβ-convergence of China's green productivity.We found that overall,since 2001,China's green productivity has demonstrated a continuous upward trend.When taking into account spatial factors,China's green productivity demonstrates a significant conditionalβ-convergence.In terms of regional effects,the results indicate that the green productivity of the eastern and western regions demonstrates club convergence,implying a more balanced green economic development.Moreover,the convergence rate of China's green productivity increases with the addition of environmental regulation variable,and so the corresponding convergence time decreases.It indicates that environmental regulations help to facilitate the convergence of China's green productivity,narrowing the gap between the regional green economic development.The findings provide guideline for achieving a low-carbon development and carbon neutrality from a regional green productivity perspective.