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

Xiaoxi Liu, Xiaoling Yuan, Nan Ye and Rui Zhang

Technological Forecasting and Social Change, 2023, vol. 193, issue C

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.

Keywords: Emission control; Genetic algorithm; Low-carbon economy; Replacement recommendation model (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:193:y:2023:i:c:s0040162523002822

DOI: 10.1016/j.techfore.2023.122597

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