Development Trend Prediction of Chengdu Plain Economic Zone Based on Multiple Linear Regression Grey Correlation Degree

Hu, ShiWei; Wang, DongLiang; Feng, Lei; Lu, YiYi

doi:10.1155/2022/2016441

Cited by 2 publications

(2 citation statements)

References 20 publications

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“…Linear regression is the main empirical tool in economics, and numerous studies have reported the significance of linear regression models (74)(75)(76)(77). Using regression models, this study confirms the connection between low-carbon economic development, health, the growth of the economy, and the emitting of carbon, and sectors.…”

Section: Model Formulationsupporting

confidence: 74%

Under the different sectors: the relationship between low-carbon economic development, health and GDP

Bai

Zhou

Yang

et al. 2023

Front. Public Health

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Developing a modern low-carbon economy while protecting health is not only a current trend but also an urgent problem that needs to be solved. The growth of the national low-carbon economy is closely related to various sectors; however, it remains unclear how the development of low-carbon economies in these sectors impacts the national economy and the health of residents. Using panel data on carbon emissions and resident health in 28 province-level regions in China, this study employs unit root tests, co-integration tests, and regression analysis to empirically examine the relationship between carbon emissions, low-carbon economic development, health, and GDP in industry, construction, and transportation. The results show that: First, China’s carbon emissions can promote economic development. Second, low-carbon economic development can enhance resident health while improving GDP. Third, low-carbon economic development has a significant positive effect on GDP and resident health in the industrial and transportation sector, but not in the construction sector, and the level of industrial development and carbon emission sources are significant factors contributing to the inconsistency. Our findings complement existing insights into the coupling effect of carbon emissions and economic development across sectors. They can assist policymakers in tailoring low-carbon policies to specific sectors, formulating strategies to optimize energy consumption structures, improving green technology levels, and aiding enterprises in gradually reducing carbon emissions without sacrificing economic benefits, thus achieving low-carbon economic development.

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Section: Model Formulationsupporting

confidence: 74%

Under the different sectors: the relationship between low-carbon economic development, health and GDP

Bai

Zhou

Yang

et al. 2023

Front. Public Health

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show abstract

“…The second method uses statistical models to predict PM 2.5 concentration. In 2021, Xu Dong et al [17] used the basic monitoring index data on temperature, inhalable particulate matter PM 10 , and CO in Chengdu as the research object, and built a multivariate linear regression (MLR) model for PM 2.5 concentration. In the same year, Xu Yixin et al [18] combined a wavelet analysis with a regression model, and used the W-MLR model to analyze PM 2.5 .…”

Section: Introductionmentioning

confidence: 99%

Application of a Deep Learning Fusion Model in Fine Particulate Matter Concentration Prediction

Zou

Wang³

2023

Atmosphere

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With the rapid development of urbanization, ambient air pollution is becoming increasingly serious. Out of many pollutants, fine particulate matter (PM2.5) is the pollutant that affects the urban atmospheric environment to the greatest extent. Fine particulate matter (PM2.5) concentration prediction is of great significance to human health and environmental protection. This paper proposes a CNN-SSA-DBiLSTM-attention deep learning fusion model. This paper took the meteorological observation data and pollutant data from eight stations in Bijie from 1 January 2015 to 31 December 2022 as the sample data for training and testing. For the obtained data, the missing values and the data obtained from the correlation analysis performed were first processed. Secondly, a convolutional neural network (CNN) was used for the feature selection. DBILSTM was then used to establish a network model for the relationship between the input and actual output sequences, and an attention mechanism was added to enhance the impact of the relevant information. The number of units in the DBILSTM and the epoch of the whole network were optimized using the sparrow search algorithm (SSA), and the predicted value was the output after optimization. This paper predicts the concentration of PM2.5 in different time spans and seasons, and makes a comparison with the CNN-DBILSTM, BILSTM, and LSTM models. The results show that the CNN-SSA-DBiLSTM-attention model had the best prediction effect, and its accuracy improved with the increasing prediction time span. The coefficient of determination (R2) is stable at about 0.95. The results revealed that the proposed CNN-SSA-DBiLSTM-attention ensemble framework is a reliable and accurate method, and verifies the research results of this paper in regard to the prediction of PM2.5 concentration. This research has important implications for human health and environmental protection. The proposed method could inspire researchers to develop even more effective methods for atmospheric environment pollution modeling.

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Development Trend Prediction of Chengdu Plain Economic Zone Based on Multiple Linear Regression Grey Correlation Degree

Cited by 2 publications

References 20 publications

Under the different sectors: the relationship between low-carbon economic development, health and GDP

Under the different sectors: the relationship between low-carbon economic development, health and GDP

Application of a Deep Learning Fusion Model in Fine Particulate Matter Concentration Prediction

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