Influential Factor Analysis and Projection
of Industrial CO&lt;sub&gt;2&lt;/sub&gt; Emissions in China Based
on Extreme Learning Machine Improved
by Genetic Algorithm

Li, Yanmei; Hu, Hongdan

doi:10.15244/pjoes/110973

Cited by 4 publications

(4 citation statements)

References 22 publications

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“…e most direct way to do so is by constructing a hybrid framework to combine an optimization algorithm and the ELM, thereby optimizing the input weights and the biases of the hidden layer randomly generated by the ELM [42]. Following previous studies [44][45][46], considering the sound performance in global search, GA is utilized to optimize the input weights and the biases of the hidden layer to improve the performance of the ELM in stock prediction. A detailed procedure of the GA-ELM algorithm is given in Algorithm 1.…”

Section: Optimization Of the Elm Model Using The Gamentioning

confidence: 99%

“…To overcome this drawback, an effective strategy is to introduce an intelligent algorithm to optimize the input weights and biases of the hidden layer of ELM [40,42,43]. e genetic algorithm (GA), a classic and excellent intelligent algorithm, has been utilized to optimize machine learning models, i.e., ELM [44][45][46] and BP [47]. In particular, ELM optimized by the GA (GA-ELM) has been successfully applied in various prediction topics, such as CO 2 emission [44], wind power [45], and gas concentration [46].…”

Section: Introductionmentioning

confidence: 99%

“…e genetic algorithm (GA), a classic and excellent intelligent algorithm, has been utilized to optimize machine learning models, i.e., ELM [44][45][46] and BP [47]. In particular, ELM optimized by the GA (GA-ELM) has been successfully applied in various prediction topics, such as CO 2 emission [44], wind power [45], and gas concentration [46]. erefore, this paper introduces the GA-ELM to predict stock prices and then screens stocks with high-return potential based on the prediction results.…”

Section: Introductionmentioning

confidence: 99%

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Socially Responsible Investment Portfolio Construction with a Double-Screening Mechanism considering Machine Learning Prediction

Jun

Chen

2021

Discrete Dynamics in Nature and Society

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Although socially responsible investment (SRI) has developed into an important investment style, only a small number of studies discuss SRI portfolio construction. In view of the overwhelming breakthrough of machine learning in prediction, this paper proposes SRI portfolio construction models by combining a double-screening mechanism considering machine learning prediction and an extended global minimum variance (GMV) model (or extended maximum Sharpe ratio (MSPR) model), which are, respectively, named double-screening socially responsible investment (DSSRI) portfolio models I and II. The proposed models consist of two stages, i.e., stock screening and asset allocation. First, this paper develops a novel double-screening mechanism incorporating environmental, social, and corporate governance (ESG) and return potential criteria to ensure that high-quality stocks with good ESG performance and high-return potential are input into the optimal portfolio. Specifically, to obtain accurate stock return predictions, an extreme learning machine model optimized by the genetic algorithm is employed to predict stock prices. Next, to trade off the financial and ESG objectives of SRI investors, an extended GMV model (or extended MSPR model) considering the ESG factor is introduced to determine the capital allocation proportion of the stocks. We take the A-share market of China as the sample to verify the effectiveness of the proposed models. The empirical results demonstrate that compared with alternative models, the proposed models can yield better annualized return and ESG score performance as well as competitive Sharpe ratio performance.

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Section: Optimization Of the Elm Model Using The Gamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Socially Responsible Investment Portfolio Construction with a Double-Screening Mechanism considering Machine Learning Prediction

Jun

Chen

2021

Discrete Dynamics in Nature and Society

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“…The model achieved higher prediction accuracy for the photovoltaic power output [18]. Moreover, Li and Hu optimized the ELM by GA to predict industrial CO 2 emissions and achieved good prediction efficiency [19]. However, the GA has the weak globe exploration ability, which may make the search process premature convergence.…”

Section: Introductionmentioning

confidence: 99%

A Low Discrepancy Heuristic Evolution ELM and Ground Effect Theory-Based Serial Hybrid Soft Sensor Model of Floating Height in Air Cushion Furnace

et al. 2021

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In air cushion furnace, the floating height is a key process parameter which greatly affects the quality and production efficiency of high quality mental strips. However, the floating height is hard to be collected in the complex and abominable industry environment. Furthermore, due to the flow field characteristics, some important process variables are difficult to accurately calculate by traditional mechanism modeling methods. In order to accurately predict the floating height, firstly, a low discrepancy heuristic evolution ELM and ground effect theory based serial hybrid soft sensor model is proposed, which constituted by a mechanism model and two data driven models. Secondly, based on the force equilibrium equation and ground effect theory, the mechanism model is constructed, which describes the relationship between the floating height and the process variables including the jet impact angle. Thirdly, a low discrepancy heuristic evolution ELM is proposed as the data driven model to predict the jet impinging angle. In the data driven model, the novel dual mutation strategies collaboration differential evolution is proposed to guarantee the low discrepancy and physical applicability of data driven model. The effectiveness of the proposed method was validated on the self-developed air cushion experiment platform and got desirable experimental results. The research lays an important foundation for the successful implementation of monitoring and control of the strip floating process.

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The Effect of China's Pilot Low-Carbon City Initiative on Enterprise Labor Structure

et al. 2022

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Based on the background of China's pilot low-carbon city initiative in 2010, 2012, and 2017, this article captures the exogenous change of enterprise labor structure based on A-share listed companies from 2007 to 2019 in Shenzhen and Shanghai Stock exchanges. With the integration of macro data on the city level and micro data on the enterprise level, adopting the time-varying difference-in-differences (DID) model, we found that 1) China's pilot low-carbon city initiative can significantly promote the upgrading of enterprise labor structure; 2) China's pilot low-carbon city initiative can significantly increase R&D investment of listed companies, suggesting that R&D investment is a channel for the impact of China's pilot low-carbon city initiative on enterprise labor structure in the pilot cities; 3) the heterogeneity analysis shows that the labor structure of the state-owned listed companies has been optimized significantly, while the labor structure of the non–state-owned listed companies is not significant. Meanwhile, the labor structure of the listed companies under high-quality government control has been optimized significantly, while the labor structure of the listed companies under low-quality government control is not significant. Overall, our study shows that the pilot low-carbon city initiative has played a governance role in China and optimized enterprise labor structure.

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Influential Factor Analysis and Projection of Industrial CO<sub>2</sub> Emissions in China Based on Extreme Learning Machine Improved by Genetic Algorithm

Cited by 4 publications

References 22 publications

Socially Responsible Investment Portfolio Construction with a Double-Screening Mechanism considering Machine Learning Prediction

Socially Responsible Investment Portfolio Construction with a Double-Screening Mechanism considering Machine Learning Prediction

A Low Discrepancy Heuristic Evolution ELM and Ground Effect Theory-Based Serial Hybrid Soft Sensor Model of Floating Height in Air Cushion Furnace

The Effect of China's Pilot Low-Carbon City Initiative on Enterprise Labor Structure

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