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An intelligent low carbon economy management scheme based on the genetic algorithm enabled replacement recommendation model

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  • Liu, Xiaoxi
  • Yuan, Xiaoling
  • Ye, Nan
  • Zhang, Rui

Abstract

Enhancing energy efficiency, employing renewable energy sources, improving the performance of greenhouse gas (GHG) mitigation, creating innovative technologies to absorb greenhouse gases, and eliminating incentives for environmentally damaging operations are all tasks that support low-carbon advancement. Following the trend of the worldwide shift away from natural fuels as major energy providers to other forms of energy, the concept of a low-carbon economy (LCE) is being implemented. An LCE minimizes greenhouse gas emissions by replacing nonrenewable energy sources with renewable and natural energy resources. In recent years, LCEs have been globally implemented based on the impact of greenhouse gases on climatic conditions. This article introduces a genetic algorithm-enabled replacement recommendation model (GA-RRM) for managing LCEs in open environments. The proposed model identifies the links among energy exhaustion, requirements, and generation for emission management. The balancing process involves new weight assignments and recommendations for different resources. The proposed model generates multiple populating weights using the GA to mitigate the effects of certain factors. The proposed model uses current and previous climatic change factors to identify LCE management trends. GA-RRM is used to manage and analyze an LCE, and traditional energy sources are replaced with green gas-emitting and natural energy sources in an open environment. The performance of GA-RRM is evaluated based on emission control and energy usage scenarios, and high precision is observed.

Suggested Citation

  • Liu, Xiaoxi & Yuan, Xiaoling & Ye, Nan & Zhang, Rui, 2023. "An intelligent low carbon economy management scheme based on the genetic algorithm enabled replacement recommendation model," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    • Handle: RePEc:eee:tefoso:v:193:y:2023:i:c:s0040162523002822
    DOI: 10.1016/j.techfore.2023.122597
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    References listed on IDEAS

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    1. Davide Benedetti & Enrico Biffis & Fotis Chatzimichalakis & Luciano Lilloy Fedele & Ian Simm, 2021. "Climate change investment risk: optimal portfolio construction ahead of the transition to a lower-carbon economy," Annals of Operations Research, Springer, vol. 299(1), pages 847-871, April.
    2. Za'er Abo-Hammour & Othman Alsmadi & Shaher Momani & Omar Abu Arqub, 2013. "A Genetic Algorithm Approach for Prediction of Linear Dynamical Systems," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-12, December.
    3. Nguyen, Quyen & Diaz-Rainey, Ivan & Kuruppuarachchi, Duminda, 2021. "Predicting corporate carbon footprints for climate finance risk analyses: A machine learning approach," Energy Economics, Elsevier, vol. 95(C).
    4. Shuangshuang Fan & Shengnan Peng & Xiaoxue Liu & Baogui Xin, 2021. "Can Smart City Policy Facilitate the Low-Carbon Economy in China? A Quasi-Natural Experiment Based on Pilot City," Complexity, Hindawi, vol. 2021, pages 1-15, July.
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