IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v193y2023ics0040162523002822.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162523002822
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2023.122597?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jeremi Assael & Thibaut Heurtebize & Laurent Carlier & François Soupé, 2023. "Greenhouse gases emissions: estimating corporate non-reported emissions using interpretable machine learning," Working Papers hal-03905325, HAL.
    2. Francisco Porles-Ochoa & Ruben Guevara, 2023. "Moderation of Clean Energy Innovation in the Relationship between the Carbon Footprint and Profits in CO₂e-Intensive Firms: A Quantitative Longitudinal Study," Sustainability, MDPI, vol. 15(13), pages 1-19, June.
    3. Popescu, Ioana-Stefania & Gibon, Thomas & Hitaj, Claudia & Rubin, Mirco & Benetto, Enrico, 2023. "Are SRI funds financing carbon emissions? An input-output life cycle assessment of investment funds," Ecological Economics, Elsevier, vol. 212(C).
    4. Provaty, Sagira Sultana & Hasan, Mostafa Monzur & Luo, Le, 2024. "Organization capital and GHG emissions," Energy Economics, Elsevier, vol. 131(C).
    5. Shobande, Olatunji A. & Ogbeifun, Lawrence & Apergis, Nicholas, 2024. "Crafting monetary policy beyond low carbon legacy," International Review of Economics & Finance, Elsevier, vol. 91(C), pages 764-781.
    6. Wang Gao & Jiajia Wei & Shixiong Yang, 2023. "The Asymmetric Effects of Extreme Climate Risk Perception on Coal Futures Return Dynamics: Evidence from Nonparametric Causality-In-Quantiles Tests," Sustainability, MDPI, vol. 15(10), pages 1-19, May.
    7. Khalfaoui, Rabeh & Mefteh-Wali, Salma & Viviani, Jean-Laurent & Ben Jabeur, Sami & Abedin, Mohammad Zoynul & Lucey, Brian M., 2022. "How do climate risk and clean energy spillovers, and uncertainty affect U.S. stock markets?," Technological Forecasting and Social Change, Elsevier, vol. 185(C).
    8. Maximilian Hettler & Lorenz Graf‐Vlachy, 2024. "Corporate scope 3 carbon emission reporting as an enabler of supply chain decarbonization: A systematic review and comprehensive research agenda," Business Strategy and the Environment, Wiley Blackwell, vol. 33(2), pages 263-282, February.
    9. Yichao Xie & Bowen Zhou & Zhenyu Wang & Bo Yang & Liaoyi Ning & Yanhui Zhang, 2023. "Industrial Carbon Footprint (ICF) Calculation Approach Based on Bayesian Cross-Validation Improved Cyclic Stacking," Sustainability, MDPI, vol. 15(19), pages 1-35, September.
    10. El Khoury, Rim & Alshater, Muneer M. & Li, Yanshuang & Xiong, Xiong, 2024. "Quantile time-frequency connectedness among G7 stock markets and clean energy markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 93(C), pages 71-90.
    11. Samuel-Soma M. Ajibade & Muhammed Basheer Jasser & David Olayemi Alebiosu & Ismail Ahmed Al- Qasem Al-Hadi & Ghassan Saleh Al-Dharhani & Farrukh Hassan & Bright Akwasi Gyamfi, 2024. "Uncovering the Dynamics in the Application of Machine learning in Computational Finance: A Bibliometric and Social Network Analysis," International Journal of Economics and Financial Issues, Econjournals, vol. 14(4), pages 299-315, July.
    12. Lee, Gunhee & Bae, Mincheol & Sohn, Joongchan & Han, Chanwoo & Cho, Jinhyung, 2024. "Does voluntary environmental information disclosure prevent stock price crash risk? – Comparative analysis of chaebol and non-chaebol in Korea," Energy Economics, Elsevier, vol. 131(C).
    13. Cepni, Oguzhan & Şensoy, Ahmet & Yılmaz, Muhammed Hasan, 2024. "Climate change exposure and cost of equity," Energy Economics, Elsevier, vol. 130(C).
    14. Armin Varmaz & Christian Fieberg & Thorsten Poddig, 2024. "Portfolio optimization for sustainable investments," Annals of Operations Research, Springer, vol. 341(2), pages 1151-1176, October.
    15. Ren, Xiaohang & Li, Jingyao & He, Feng & Lucey, Brian, 2023. "Impact of climate policy uncertainty on traditional energy and green markets: Evidence from time-varying granger tests," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    16. Jeremi Assael & Thibaut Heurtebize & Laurent Carlier & François Soupé, 2023. "Greenhouse gas emissions: estimating corporate non-reported emissions using interpretable machine learning," Post-Print hal-03905325, HAL.
    17. Jérémi Assael & Thibaut Heurtebize & Laurent Carlier & François Soupé, 2023. "Greenhouse Gases Emissions: Estimating Corporate Non-Reported Emissions Using Interpretable Machine Learning," Sustainability, MDPI, vol. 15(4), pages 1-28, February.
    18. Düsterhöft, Maximilian & Schiemann, Frank & Walther, Thomas, 2023. "Let’s talk about risk! Stock market effects of risk disclosure for European energy utilities," Energy Economics, Elsevier, vol. 125(C).
    19. Zhang, Ziqi & Su, Zhi & Wang, Ke & Zhang, Yongji, 2022. "Corporate environmental information disclosure and stock price crash risk: Evidence from Chinese listed heavily polluting companies," Energy Economics, Elsevier, vol. 112(C).
    20. Nguyen, Quyen & Diaz-Rainey, Ivan & Kuruppuarachchi, Duminda & McCarten, Matthew & Tan, Eric K.M., 2023. "Climate transition risk in U.S. loan portfolios: Are all banks the same?," International Review of Financial Analysis, Elsevier, vol. 85(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:tefoso:v:193:y:2023:i:c:s0040162523002822. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.