The scale of e-commerce in China continues to expand, and it has a relatively good development prospect. However, there are many problems in cost control in the development of e-commerce in our country. E-commerce companies have many problems in terms of promotion costs, warehousing costs, and operating costs. Although predecessors have made great achievements in this field and helped e-commerce companies solve problems related to cost control, there are still many problems in e-commerce companies. Question. This article starts with big data and Internet of Things technology, and focuses on storage costs and transportation costs to solve the problems of cost control in e-commerce companies. The problems existing in the development of e-commerce enterprises are: (1) The cost control system is not mature enough, the business scope is not clear, the cost control lacks innovative awareness, the logistics cost remains high, and the logistics management model is unreasonable. In order to solve these problems, we built a transportation cost optimization model and an operation cost optimization model based on big data and Internet of Things technology. Transportation cost and operation cost are two important components of the cost of e-commerce enterprises in my country. The model has been verified to effectively reduce transportation costs and operating costs, which is of great significance to reducing the cost of e-commerce enterprises in my country, can solve the problems of e-commerce enterprise development, and promote the development of e-commerce in my country. Finally, according to the model constructed by the questionnaire survey and the article, we put forward corresponding suggestions (1) optimize customer relationship, improve the enterprise model (2) promote enterprise innovation, and promote the transformation of marketing costs (3) improve the degree of informatization of e-commerce enterprises.
{"title":"Cost control problems and countermeasures of e-commerce enterprises under the background of big data and Internet of Things","authors":"Jing Wang","doi":"10.3233/jcm-226931","DOIUrl":"https://doi.org/10.3233/jcm-226931","url":null,"abstract":"The scale of e-commerce in China continues to expand, and it has a relatively good development prospect. However, there are many problems in cost control in the development of e-commerce in our country. E-commerce companies have many problems in terms of promotion costs, warehousing costs, and operating costs. Although predecessors have made great achievements in this field and helped e-commerce companies solve problems related to cost control, there are still many problems in e-commerce companies. Question. This article starts with big data and Internet of Things technology, and focuses on storage costs and transportation costs to solve the problems of cost control in e-commerce companies. The problems existing in the development of e-commerce enterprises are: (1) The cost control system is not mature enough, the business scope is not clear, the cost control lacks innovative awareness, the logistics cost remains high, and the logistics management model is unreasonable. In order to solve these problems, we built a transportation cost optimization model and an operation cost optimization model based on big data and Internet of Things technology. Transportation cost and operation cost are two important components of the cost of e-commerce enterprises in my country. The model has been verified to effectively reduce transportation costs and operating costs, which is of great significance to reducing the cost of e-commerce enterprises in my country, can solve the problems of e-commerce enterprise development, and promote the development of e-commerce in my country. Finally, according to the model constructed by the questionnaire survey and the article, we put forward corresponding suggestions (1) optimize customer relationship, improve the enterprise model (2) promote enterprise innovation, and promote the transformation of marketing costs (3) improve the degree of informatization of e-commerce enterprises.","PeriodicalId":45004,"journal":{"name":"Journal of Computational Methods in Sciences and Engineering","volume":"40 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138997812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A time series prediction model was developed to predict the number of confirmed cases from October 2022 to November 2022 based on the number of confirmed cases of New Coronary Pneumonia from January 20, 2021 to September 20, 2022. We will analyze the number of confirmed cases in the Philippines from January 1, 2020 to September 20, 2022 to build a prediction model and make predictions. Among the works of other scholars, it can be shown that time series is an excellent forecasting model, particularly around dates. The study in this work begins with the original data for inference, and each phase of inference is based on objective criteria, such as smooth data analysis utilising ADF detection and ACF graph analysis, and so on. When comparing the performance of algorithms with functions for time series models, hundreds of algorithms are evaluated one by one on the basis of the same data source in order to find the best method. Following the acquisition of the methods, ADF detection and ACF graph analysis are undertaken to validate them, resulting in a closed-loop research. Although the dataset in this study was generated from publicly available data from the Philippines (our data world for coronaviruses), the ARIMA model used to predict data beyond September 20, 2022 exhibited unusually high accuracy. This model was used to compare the performance of several algorithms, each evaluated using the same training data. Finally, the best R2 for the ARIMA model was 92.56% or higher, and iterative optimization of the function produced a predictive model with an R2 of 97.6%. This reveals the potential trajectory of coronaviruses in the Philippines. Finally, the model with the greatest performance is chosen as the prediction model. In actual implementations, several subjective and objective elements, such as the government’s epidemic defence measures, the worldwide pandemic condition, and whether the data source distributes the data in a timely way, might restrict the prediction’s accuracy. Such prediction findings can be used as a foundation for data releases by health agencies.
{"title":"Utilizing time series for forecasting the development trend of coronavirus: A validation process","authors":"Xusong Zhang, Feng Wang","doi":"10.3233/jcm226993","DOIUrl":"https://doi.org/10.3233/jcm226993","url":null,"abstract":"A time series prediction model was developed to predict the number of confirmed cases from October 2022 to November 2022 based on the number of confirmed cases of New Coronary Pneumonia from January 20, 2021 to September 20, 2022. We will analyze the number of confirmed cases in the Philippines from January 1, 2020 to September 20, 2022 to build a prediction model and make predictions. Among the works of other scholars, it can be shown that time series is an excellent forecasting model, particularly around dates. The study in this work begins with the original data for inference, and each phase of inference is based on objective criteria, such as smooth data analysis utilising ADF detection and ACF graph analysis, and so on. When comparing the performance of algorithms with functions for time series models, hundreds of algorithms are evaluated one by one on the basis of the same data source in order to find the best method. Following the acquisition of the methods, ADF detection and ACF graph analysis are undertaken to validate them, resulting in a closed-loop research. Although the dataset in this study was generated from publicly available data from the Philippines (our data world for coronaviruses), the ARIMA model used to predict data beyond September 20, 2022 exhibited unusually high accuracy. This model was used to compare the performance of several algorithms, each evaluated using the same training data. Finally, the best R2 for the ARIMA model was 92.56% or higher, and iterative optimization of the function produced a predictive model with an R2 of 97.6%. This reveals the potential trajectory of coronaviruses in the Philippines. Finally, the model with the greatest performance is chosen as the prediction model. In actual implementations, several subjective and objective elements, such as the government’s epidemic defence measures, the worldwide pandemic condition, and whether the data source distributes the data in a timely way, might restrict the prediction’s accuracy. Such prediction findings can be used as a foundation for data releases by health agencies.","PeriodicalId":45004,"journal":{"name":"Journal of Computational Methods in Sciences and Engineering","volume":"44 3","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138999007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Constructing the evaluation system of ideological and political education of new media in colleges is both beneficial to evaluate the established ideological and political education work and an important guide to improve the corresponding work. At present, promoting ideological and political education work with high integration of information technology has become an important way of ideological and political education work in colleges. However, the theoretical circles are still not focused enough on how to evaluate the ideological and political education work in colleges. Based on the characteristics of the information age, this paper establishes the teaching quality evaluation system of ideological and political education courses in colleges, introduces BP neural network evaluation method, and obtains strong empirical support through simulation experiments, so as to build a feasible teaching quality evaluation model of ideological and political courses in colleges. At the same time, the corresponding optimization suggestions are put forward, including improving the relevance of ideological and political education work, dynamically grasping students’ ideological and political information and doing a good job of data processing, and improving the professional information literacy of the ideological and political work team, in order to provide some reference for the efficient development of ideological and political education work in colleges.
{"title":"Teaching quality evaluation of college civic and political science courses based on BP neural algorithm","authors":"Chen Wang","doi":"10.3233/jcm-226935","DOIUrl":"https://doi.org/10.3233/jcm-226935","url":null,"abstract":"Constructing the evaluation system of ideological and political education of new media in colleges is both beneficial to evaluate the established ideological and political education work and an important guide to improve the corresponding work. At present, promoting ideological and political education work with high integration of information technology has become an important way of ideological and political education work in colleges. However, the theoretical circles are still not focused enough on how to evaluate the ideological and political education work in colleges. Based on the characteristics of the information age, this paper establishes the teaching quality evaluation system of ideological and political education courses in colleges, introduces BP neural network evaluation method, and obtains strong empirical support through simulation experiments, so as to build a feasible teaching quality evaluation model of ideological and political courses in colleges. At the same time, the corresponding optimization suggestions are put forward, including improving the relevance of ideological and political education work, dynamically grasping students’ ideological and political information and doing a good job of data processing, and improving the professional information literacy of the ideological and political work team, in order to provide some reference for the efficient development of ideological and political education work in colleges.","PeriodicalId":45004,"journal":{"name":"Journal of Computational Methods in Sciences and Engineering","volume":"58 3","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138999338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The particulate matter samples of gasoline direction injection (GDI) gasoline engine obtained from road experiments are photographed to obtain the emission particulate matter images based on the transmission electron microscope under high altitude environment. The results show that the micro-morphology characteristics of the particulate matter emitted by GDI gasoline engine in plateau area are similar to those in low altitude area, and the main morphology is cloud-like, filamentous, flocculent, chain-like, cluster-like, like altitude contour line, etc. The images are processed by MATLAB and Python software, and the fractal dimension, average gray value and gray histogram are obtained. The fractal dimensions of the emission particulate data obtained are between 1.81 and 1.89, which has some deviation compared with the fractal dimension of the emission particulate data at low altitude of 1.58 and 1.80. With the increase of altitude, its fractal dimension increases more obviously. It can be seem from the characteristics of gray value and gray histogram of particle images that the average gray value of GDI gasoline engine particles is higher. The gray histogram distribution of 5–50 nm images is uniform, and the individual particles in the images are clear. The gray histogram of 100–500 nm images is scattered, so it is easy to extract and separate the image edge contour. 5–50 nm images are used to study the details and characteristics of individual particles, and 100–500 nm transmission electron microscope images are used to study the overall morphology of particles.
{"title":"Microcosmic characteristics of particulate matter emitted by GDI gasoline engine in plateau environment","authors":"Jian Zhang, Hao Zhou, Xiaoying Liu, Chao He","doi":"10.3233/jcm226978","DOIUrl":"https://doi.org/10.3233/jcm226978","url":null,"abstract":"The particulate matter samples of gasoline direction injection (GDI) gasoline engine obtained from road experiments are photographed to obtain the emission particulate matter images based on the transmission electron microscope under high altitude environment. The results show that the micro-morphology characteristics of the particulate matter emitted by GDI gasoline engine in plateau area are similar to those in low altitude area, and the main morphology is cloud-like, filamentous, flocculent, chain-like, cluster-like, like altitude contour line, etc. The images are processed by MATLAB and Python software, and the fractal dimension, average gray value and gray histogram are obtained. The fractal dimensions of the emission particulate data obtained are between 1.81 and 1.89, which has some deviation compared with the fractal dimension of the emission particulate data at low altitude of 1.58 and 1.80. With the increase of altitude, its fractal dimension increases more obviously. It can be seem from the characteristics of gray value and gray histogram of particle images that the average gray value of GDI gasoline engine particles is higher. The gray histogram distribution of 5–50 nm images is uniform, and the individual particles in the images are clear. The gray histogram of 100–500 nm images is scattered, so it is easy to extract and separate the image edge contour. 5–50 nm images are used to study the details and characteristics of individual particles, and 100–500 nm transmission electron microscope images are used to study the overall morphology of particles.","PeriodicalId":45004,"journal":{"name":"Journal of Computational Methods in Sciences and Engineering","volume":"20 4","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138999858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Based on the data of urban residents’ consumption in Jiangsu Province, this paper looks for the Statistical Yearbook of Jiangsu Province from Jiangsu Statistics Bureau, collects the required data of residents’ consumption structure, and defines the components of residents’ consumption structure in Jiangsu Province from eight aspects: clothing, food, housing, transportation and communication, medical care, culture, education, entertainment and other miscellaneous commodities. First, make a systematic theoretical explanation of these eight factors, so as to help us better understand how these eight factors are pieced together into a whole of residents’ consumption structure. Then it analyzes the present situation of consumption structure. Secondly, based on the relevant theories of residents’ consumption structure, in order to know the changes of the components of residents’ consumption structure in Jiangsu Province over the years, this paper analyzes the relevant data of the collected structural factors by using factor analysis and ELES model, based on the data from 2009 to 2018. In the last part, according to the previous analysis results, the characteristics of urban residents’ consumption structure in Jiangsu Province are summarized, and some suggestions and opinions are given. Finally, the future consumption structure is predicted according to the historical data, and some suggestions are given.
{"title":"Diversification of residents’ consumption structure based on ELES model","authors":"Peichun Feng","doi":"10.3233/jcm-226937","DOIUrl":"https://doi.org/10.3233/jcm-226937","url":null,"abstract":"Based on the data of urban residents’ consumption in Jiangsu Province, this paper looks for the Statistical Yearbook of Jiangsu Province from Jiangsu Statistics Bureau, collects the required data of residents’ consumption structure, and defines the components of residents’ consumption structure in Jiangsu Province from eight aspects: clothing, food, housing, transportation and communication, medical care, culture, education, entertainment and other miscellaneous commodities. First, make a systematic theoretical explanation of these eight factors, so as to help us better understand how these eight factors are pieced together into a whole of residents’ consumption structure. Then it analyzes the present situation of consumption structure. Secondly, based on the relevant theories of residents’ consumption structure, in order to know the changes of the components of residents’ consumption structure in Jiangsu Province over the years, this paper analyzes the relevant data of the collected structural factors by using factor analysis and ELES model, based on the data from 2009 to 2018. In the last part, according to the previous analysis results, the characteristics of urban residents’ consumption structure in Jiangsu Province are summarized, and some suggestions and opinions are given. Finally, the future consumption structure is predicted according to the historical data, and some suggestions are given.","PeriodicalId":45004,"journal":{"name":"Journal of Computational Methods in Sciences and Engineering","volume":"42 22","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138995956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Due to limitations in computing power, the passive radar based on GNSS signal may not be able to use all the GNSS signals, requiring to make signal selection. Based on CRLB, a new radiation sources adaptive selection method for passive radar is proposed. With the goal of minimizing the CRLB of GNSS signals and the number of GNSS signals as constraints, an adaptive selection model is established. Simulation experiments were conducted using the selection model and particle filter algorithm. Experimental results show that the calculation accuracy of this algorithm is slightly lower than the tracking results obtained using all six satellites, but the calculation time is greatly reduced, reducing the requirement for radar computing power.
{"title":"Research on adaptive selection method of radiation sources in passive radar based on GNSS signal","authors":"Huatao Tang, Hao Cha, Feng Liu, Bin Tian, Lei Zuo","doi":"10.3233/jcm227005","DOIUrl":"https://doi.org/10.3233/jcm227005","url":null,"abstract":"Due to limitations in computing power, the passive radar based on GNSS signal may not be able to use all the GNSS signals, requiring to make signal selection. Based on CRLB, a new radiation sources adaptive selection method for passive radar is proposed. With the goal of minimizing the CRLB of GNSS signals and the number of GNSS signals as constraints, an adaptive selection model is established. Simulation experiments were conducted using the selection model and particle filter algorithm. Experimental results show that the calculation accuracy of this algorithm is slightly lower than the tracking results obtained using all six satellites, but the calculation time is greatly reduced, reducing the requirement for radar computing power.","PeriodicalId":45004,"journal":{"name":"Journal of Computational Methods in Sciences and Engineering","volume":"29 7","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138996010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Using population geographic data to analyze the dynamics of the central urban to explore the negative space and implement green carbon offset retrofits is beneficial for the Dual Carbon goals. We construct a grading system of urban vitality values with ArcGIS to assess the vitality of city blocks and screen out negative spaces. Taking the central urban of Anda as an example, we found that the vitality values along the main road in Anda were high. In contrast, the vitality values of residential sites in the southern and western parts were generally low. By analyzing the distribution of negative space in Anda, we finally recommend that Anda adopt the strategy of “fertile land creation” for the western part, to practice the planning of urban green space projects with joint investment of factories in the southern part, to optimize the low-carbon urban travel space, to implement the green transformation of small negative urban spaces, and to enhance the ecological construction of existing parks and green spaces. We extracted the negative urban spaces and explored the paths and implementability of the carbon offsetting renewal strategies intending to provide some reference for realizing Dual Carbon goals and transforming negative urban spaces in China.
{"title":"Optimization path of carbon offsetting in negative urban spaces under dual carbon goals","authors":"Hong Jiao, Lei Bao","doi":"10.3233/jcm-226997","DOIUrl":"https://doi.org/10.3233/jcm-226997","url":null,"abstract":"Using population geographic data to analyze the dynamics of the central urban to explore the negative space and implement green carbon offset retrofits is beneficial for the Dual Carbon goals. We construct a grading system of urban vitality values with ArcGIS to assess the vitality of city blocks and screen out negative spaces. Taking the central urban of Anda as an example, we found that the vitality values along the main road in Anda were high. In contrast, the vitality values of residential sites in the southern and western parts were generally low. By analyzing the distribution of negative space in Anda, we finally recommend that Anda adopt the strategy of “fertile land creation” for the western part, to practice the planning of urban green space projects with joint investment of factories in the southern part, to optimize the low-carbon urban travel space, to implement the green transformation of small negative urban spaces, and to enhance the ecological construction of existing parks and green spaces. We extracted the negative urban spaces and explored the paths and implementability of the carbon offsetting renewal strategies intending to provide some reference for realizing Dual Carbon goals and transforming negative urban spaces in China.","PeriodicalId":45004,"journal":{"name":"Journal of Computational Methods in Sciences and Engineering","volume":"344 6","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138996502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yao Liang, Guanghong Du, Anzong Li, Wenhui Chen, Xiaojun Li, Yixiao Guo, Zhiguang Li
For signal transmission of mud pressure waves in logging-while-drilling systems, as more measurement parameters are adopted, the conventional signal transmission rate of approximately 1 bps cannot meet the requirements of parameter uploading. Transmission rates greater than 10 bps are widely used by current Chinese and international enterprises in their continuous wave transmission systems. Due to the increasing transmission rates, a conventional single-channel pressure sensor cannot effectively identify the key features of an original signal with a low signal-to-noise ratio at a high transmission rate. This issue results in a low success rate for pressure wave recognition and decoding. This paper addresses a method for the collection of pressure wave signals using multi-channel pressure sensors through the analysis of the signal transmission changes of mud pressure waves with well depths and echo interference caused by drilling rod reflection. In this research, numerical simulation and experimental verification were used to calculate the cross-correlations of the multi-channel signals to effectively remove interference noises such as reflective waves and improve the signal-to-noise ratio of the original signal. The results showed that this method could effectively improve the SNR after the wave filtering of the original pressure wave. The characteristics of the continuous mud pressure wave signals were analysed using a circulation test. A corresponding band-pass filter was designed to remove pump noises to restore the sinusoidal pressure wave signal required by the original transmission rules.
{"title":"A signal processing method for continuous mud pressure wave signals in logging-while-drilling systems","authors":"Yao Liang, Guanghong Du, Anzong Li, Wenhui Chen, Xiaojun Li, Yixiao Guo, Zhiguang Li","doi":"10.3233/jcm-226986","DOIUrl":"https://doi.org/10.3233/jcm-226986","url":null,"abstract":"For signal transmission of mud pressure waves in logging-while-drilling systems, as more measurement parameters are adopted, the conventional signal transmission rate of approximately 1 bps cannot meet the requirements of parameter uploading. Transmission rates greater than 10 bps are widely used by current Chinese and international enterprises in their continuous wave transmission systems. Due to the increasing transmission rates, a conventional single-channel pressure sensor cannot effectively identify the key features of an original signal with a low signal-to-noise ratio at a high transmission rate. This issue results in a low success rate for pressure wave recognition and decoding. This paper addresses a method for the collection of pressure wave signals using multi-channel pressure sensors through the analysis of the signal transmission changes of mud pressure waves with well depths and echo interference caused by drilling rod reflection. In this research, numerical simulation and experimental verification were used to calculate the cross-correlations of the multi-channel signals to effectively remove interference noises such as reflective waves and improve the signal-to-noise ratio of the original signal. The results showed that this method could effectively improve the SNR after the wave filtering of the original pressure wave. The characteristics of the continuous mud pressure wave signals were analysed using a circulation test. A corresponding band-pass filter was designed to remove pump noises to restore the sinusoidal pressure wave signal required by the original transmission rules.","PeriodicalId":45004,"journal":{"name":"Journal of Computational Methods in Sciences and Engineering","volume":"8 S1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138997598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study investigated factors of the green innovation performance of industrial enterprises in 30 provinces and autonomous regions across China. This study used literature analysis and DEMATEL method to identify the factors of green innovation performance of industrial enterprises. A green innovation performance evaluation index system was then constructed, and a mathematical model was established using factor analysis method to empirically analyze the factors of green innovation performance of industrial enterprises across 30 provinces and autonomous regions in China from 2012 to 2016. The results show that green innovation strength, technological development level, energy consumption, and environmental protection are the three factors of green innovation performance. Firstly, the influence of innovation strength on comprehensive green innovation performance has the largest weight, meaning that it has the largest impact on comprehensive green innovation performance. Secondly, the influence of the level of scientific and technological development on the comprehensive green innovation performance is weaker. Thirdly, the main influencing factor is the level of energy consumption and environmental protection. Finally, based on the empirical analysis, it is suggested that industrial enterprises should increase investment in green innovation, developing science and technology, and improving ecological environment, so as to enhance the green innovation performance of industrial enterprises.
{"title":"Empirical analysis of the factors of the green innovation performance of industrial enterprises based on DEMATEL method and factor analysis method","authors":"Yafeng Li, Yao He, Wei Yang, Xintao Yu","doi":"10.3233/jcm-226924","DOIUrl":"https://doi.org/10.3233/jcm-226924","url":null,"abstract":"This study investigated factors of the green innovation performance of industrial enterprises in 30 provinces and autonomous regions across China. This study used literature analysis and DEMATEL method to identify the factors of green innovation performance of industrial enterprises. A green innovation performance evaluation index system was then constructed, and a mathematical model was established using factor analysis method to empirically analyze the factors of green innovation performance of industrial enterprises across 30 provinces and autonomous regions in China from 2012 to 2016. The results show that green innovation strength, technological development level, energy consumption, and environmental protection are the three factors of green innovation performance. Firstly, the influence of innovation strength on comprehensive green innovation performance has the largest weight, meaning that it has the largest impact on comprehensive green innovation performance. Secondly, the influence of the level of scientific and technological development on the comprehensive green innovation performance is weaker. Thirdly, the main influencing factor is the level of energy consumption and environmental protection. Finally, based on the empirical analysis, it is suggested that industrial enterprises should increase investment in green innovation, developing science and technology, and improving ecological environment, so as to enhance the green innovation performance of industrial enterprises.","PeriodicalId":45004,"journal":{"name":"Journal of Computational Methods in Sciences and Engineering","volume":"82 3","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138999600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bowen Lei, Changlin Ao, Yuehua Wei, Yulin Long, Nan Jiang
Effectively assessing public preferences for air quality improvement policies is extremely important to environmental policy formulation, but developing policies that cater to public tastes is a great challenge. Although the random parameters logit (RPL) model in the choice experiment is widely used in relevant studies, it remains limited in revealing additional preference heterogeneity. Given this, the study applies two extended models in exploring public preference heterogeneity for air quality policies. An RPL model with heterogeneity in means and variances (RPL-HMV) and an RPL model with correlated random parameters (RPL-CRP) are used to provide more beneficial insights for policy analysis. The study shows that better-educated groups are more willing to pay for increasing urban green coverage, and income increases the randomness of such preferences’ distribution among groups. From the perspective of preferences, reducing heavy pollution days is positively associated with decreasing morbidity of respiratory diseases caused by outdoor air pollution and negatively correlated with improving urban green coverage. In addition, compared to the RPL-CRP model, the willingness to pay in the RPL model is overestimated by 14.72%. The study further clarifies public preferences for air quality policies, and the extra information revealed by extended models provides more valuable references for policy-making.
{"title":"What are public preferences for air quality improvement policies? Additional information from extended choice models","authors":"Bowen Lei, Changlin Ao, Yuehua Wei, Yulin Long, Nan Jiang","doi":"10.3233/jcm-226980","DOIUrl":"https://doi.org/10.3233/jcm-226980","url":null,"abstract":"Effectively assessing public preferences for air quality improvement policies is extremely important to environmental policy formulation, but developing policies that cater to public tastes is a great challenge. Although the random parameters logit (RPL) model in the choice experiment is widely used in relevant studies, it remains limited in revealing additional preference heterogeneity. Given this, the study applies two extended models in exploring public preference heterogeneity for air quality policies. An RPL model with heterogeneity in means and variances (RPL-HMV) and an RPL model with correlated random parameters (RPL-CRP) are used to provide more beneficial insights for policy analysis. The study shows that better-educated groups are more willing to pay for increasing urban green coverage, and income increases the randomness of such preferences’ distribution among groups. From the perspective of preferences, reducing heavy pollution days is positively associated with decreasing morbidity of respiratory diseases caused by outdoor air pollution and negatively correlated with improving urban green coverage. In addition, compared to the RPL-CRP model, the willingness to pay in the RPL model is overestimated by 14.72%. The study further clarifies public preferences for air quality policies, and the extra information revealed by extended models provides more valuable references for policy-making.","PeriodicalId":45004,"journal":{"name":"Journal of Computational Methods in Sciences and Engineering","volume":"29 6","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139000711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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