<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In the context of energy saving and emission reduction to protect the environment has become a social consensus, environmental benefit is a social responsibility that enterprises must actively undertake, and the technological advantages of information technology companies make them more equipped to enhance environmental benefits. Therefore, this paper proposes countermeasures for information technology companies to enhance their social responsibility and environmental benefits. To achieve practical countermeasures for energy savings and emission reduction, this paper proposes a method to improve the LSTM power prediction model using the Sparrow search algorithm for the company’s electricity management. The model optimizes the network structure of traditional LSTM by searching for parameters such as learning rate, iteration number, and the number of neurons in the two hidden layers of LSTM through the sparrow fitness function. The model proposed in this paper has an average prediction error rate of 3.32% in predicting the annual electricity consumption of a county for five years from 2018 to 2022. After an enterprise introduced the social responsibility practice countermeasures proposed in this paper, the company’s net profit margin increased by 12.2% on average, CO<sub>2</sub> emission reduction was 963.2 tons, the average monthly electricity consumption was reduced by 24936.5 kWh, and the environmental performance score increased from 3.2 to 5.4.</p>
</abstract>

In the context of energy saving and emission reduction to protect the environment has become a social consensus, environmental benefit is a social responsibility that enterprises must actively undertake, and the technological advantages of information technology companies make them more equipped to enhance environmental benefits. Therefore, this paper proposes countermeasures for information technology companies to enhance their social responsibility and environmental benefits. To achieve practical countermeasures for energy savings and emission reduction, this paper proposes a method to improve the LSTM power prediction model using the Sparrow search algorithm for the company’s electricity management. The model optimizes the network structure of traditional LSTM by searching for parameters such as learning rate, iteration number, and the number of neurons in the two hidden layers of LSTM through the sparrow fitness function. The model proposed in this paper has an average prediction error rate of 3.32% in predicting the annual electricity consumption of a county for five years from 2018 to 2022. After an enterprise introduced the social responsibility practice countermeasures proposed in this paper, the company’s net profit margin increased by 12.2% on average, CO2 emission reduction was 963.2 tons, the average monthly electricity consumption was reduced by 24936.5 kWh, and the environmental performance score increased from 3.2 to 5.4.

Environmental Benefit Analysis of Social Responsibility Practices in Information Technology Companies

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In the context of energy saving and emission reduction to protect the environment has become a social consensus, environmental benefit is a social...

Environmental Benefit Analysis of Social Responsibility Practices in Information Technology Companies

Online veröffentlicht: 05. Aug. 2024

Seitenbereich: -

Eingereicht: 08. Apr. 2024

Akzeptiert: 27. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-2072

Schlüsselwörter
LSTM, Sparrow search algorithm, Electricity load forecasting, Environmental benefits

© 2024 Yan Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Environmental Benefit Analysis of Social Responsibility Practices in Information Technology Companies

Online veröffentlicht: 05. Aug. 2024

Seitenbereich: -

Eingereicht: 08. Apr. 2024

Akzeptiert: 27. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-2072

SchlüsselwörterLSTM, Sparrow search algorithm, Electricity load forecasting, Environmental benefits

© 2024 Yan Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
LSTM, Sparrow search algorithm, Electricity load forecasting, Environmental benefits