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Green behavior propagation analysis based on statistical theory and intelligent algorithm in data-driven environment.

Linhe Zhu1, Yi Ding1, Shuling Shen2

  • 1School of Mathematical Sciences, Jiangsu University, Zhenjiang, PR China.

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Summary
This summary is machine-generated.

This study models green behavior propagation using a three-layer network, analyzing information diffusion, awareness, and energy efficiency. It employs Microscopic Markov Chain Approach and reaction-diffusion systems to understand and predict clean energy adoption.

Keywords:
Microscopic Markov chain approachMulti-layer networkNeural networkParameter identificationReaction–diffusion systemTuring bifurcation

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Area of Science:

  • Complex Systems Science
  • Computational Social Science
  • Energy Systems Analysis

Background:

  • Growing emphasis on energy efficiency drives interest in individual green behavior.
  • Understanding the dynamics of green behavior spread is crucial for promoting energy efficiency.

Purpose of the Study:

  • To develop a model analyzing information diffusion, awareness, and green behavior spreading.
  • To investigate the spatial-temporal dynamics of green behavior propagation.
  • To predict clean energy generation and validate the model's effectiveness.

Main Methods:

  • A three-layer network model incorporating information diffusion, awareness, and green behavior.
  • Microscopic Markov Chain Approach (MMCA) for state transfer analysis and threshold computation.
  • Reaction-diffusion systems to model spatial-temporal dynamics and identify Turing bifurcation.
  • Optimal control and Convolutional Neural Networks (CNN) for parameter identification.
  • Comparison with Autoregressive Integrated Moving Average (ARIMA) and other neural networks for energy generation prediction.

Main Results:

  • Derived state transfer equations and thresholds using MMCA.
  • Identified equilibrium points and Turing bifurcation criteria for green behavior propagation.
  • Validated the model through numerical simulations and parameter identification.
  • Compared CNN-based and optimal control parameter identification effectiveness.
  • Predicted China's electrical energy generation and fitted clean energy data using the developed models.

Conclusions:

  • The integrated model effectively captures the complex interplay of factors influencing green behavior and energy efficiency.
  • The study provides a robust framework for analyzing and promoting the adoption of green behaviors, such as clean energy generation.
  • Numerical simulations and comparative analyses confirm the model's predictive power and the effectiveness of the applied methods.