IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v475y2023ics0304380022002794.html
   My bibliography  Save this article

Forecasting GHG emissions for environmental protection with energy consumption reduction from renewable sources: A sustainable environmental system

Author

Listed:
  • Huang, Jiaqing
  • Wang, Linlin
  • Siddik, Abu Bakkar
  • Abdul-Samad, Zulkiflee
  • Bhardwaj, Arpit
  • Singh, Bharat

Abstract

The long-term viability of energy resources as a main input is essential to achieve long-term economic growth of a country and the energy efficiency significantly reduces energy consumption and greenhouse gas emissions, supporting environmental sustainability. As a result, a number of governments, led by those in the developed world, are making an effort to enact laws governing energy efficiency. This study suggests cutting-edge methods for forecasting greenhouse gas emissions and reducing energy demand from renewable sources based on a sustainable environment. Utilizing the statistical regression neural network (SRNN), greenhouse gas emissions have been predicted, and the deep neural network's (DNN) energy efficiency has increased. The SRNN_DNN intensity method out predicts evaluated MLR (multiple linear regression) and second- and third-order non-linear MPR (multiple polynomial regression) techniques according to MAPE (mean absolute percentage error) results. Furthermore, presented methods are considered suitable for computing GHG emissions due to the high accuracy of the SRNN DNN model. The anticipated greenhouse gas emissions related to energy were remarkably similar to the actual emissions of EU (European Union) nations.

Suggested Citation

  • Huang, Jiaqing & Wang, Linlin & Siddik, Abu Bakkar & Abdul-Samad, Zulkiflee & Bhardwaj, Arpit & Singh, Bharat, 2023. "Forecasting GHG emissions for environmental protection with energy consumption reduction from renewable sources: A sustainable environmental system," Ecological Modelling, Elsevier, vol. 475(C).
    • Handle: RePEc:eee:ecomod:v:475:y:2023:i:c:s0304380022002794
    DOI: 10.1016/j.ecolmodel.2022.110181
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380022002794
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2022.110181?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. Liyan Sun & Li Yang & Junqi Zhu & M. Irfan Uddin, 2021. "Prediction of Future State Based on Up-To-Date Information of Green Development Using Algorithm of Deep Neural Network," Complexity, Hindawi, vol. 2021, pages 1-10, July.
    2. Menglu Li & Wei Wang & Gejirifu De & Xionghua Ji & Zhongfu Tan, 2018. "Forecasting Carbon Emissions Related to Energy Consumption in Beijing-Tianjin-Hebei Region Based on Grey Prediction Theory and Extreme Learning Machine Optimized by Support Vector Machine Algorithm," Energies, MDPI, vol. 11(9), pages 1-15, September.
    3. Florian Marcel Nuţă & Alina Cristina Nuţă & Cristina Gabriela Zamfir & Stefan-Mihai Petrea & Dan Munteanu & Dragos Sebastian Cristea, 2021. "National Carbon Accounting—Analyzing the Impact of Urbanization and Energy-Related Factors upon CO 2 Emissions in Central–Eastern European Countries by Using Machine Learning Algorithms and Panel Data," Energies, MDPI, vol. 14(10), pages 1-23, May.
    4. Andrew Adewale Alola & Seyi Saint Akadiri & Ojonugwa Usman, 2021. "Domestic material consumption and greenhouse gas emissions in the EU‐28 countries: Implications for environmental sustainability targets," Sustainable Development, John Wiley & Sons, Ltd., vol. 29(2), pages 388-397, March.
    5. Yu, Baojun & Li, Changming & Mirza, Nawazish & Umar, Muhammad, 2022. "Forecasting credit ratings of decarbonized firms: Comparative assessment of machine learning models," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    6. Magazzino, Cosimo & Mele, Marco & Schneider, Nicolas, 2021. "A machine learning approach on the relationship among solar and wind energy production, coal consumption, GDP, and CO2 emissions," Renewable Energy, Elsevier, vol. 167(C), pages 99-115.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Jingshen & Zhou, Xinzhu & Bai, Rong & Dong, Haoyang & Tang, Tingting & Wang, Zeyu & Yang, Ya & Huang, Feng, 2024. "Impact of environmental supervision reform on green innovation in mineral enterprises," Resources Policy, Elsevier, vol. 88(C).
    2. Yuan, Hong & Ma, Xin & Ma, Minda & Ma, Juan, 2024. "Hybrid framework combining grey system model with Gaussian process and STL for CO2 emissions forecasting in developed countries," Applied Energy, Elsevier, vol. 360(C).

    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. Liu, Guangqiang & Zeng, Qing & Lei, Juan, 2022. "Dynamic risks from climate policy uncertainty: A case study for the natural gas market," Resources Policy, Elsevier, vol. 79(C).
    2. Lang, Qiaoqi & Ma, Feng & Mirza, Nawazish & Umar, Muhammad, 2023. "The interaction of climate risk and bank liquidity: An emerging market perspective for transitions to low carbon energy," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    3. Kaewnern, Hathaipat & Wangkumharn, Sirikul & Deeyaonarn, Wongsathon & Yousaf, Abaid Ullah & Kongbuamai, Nattapan, 2023. "Investigating the role of research development and renewable energy on human development: An insight from the top ten human development index countries," Energy, Elsevier, vol. 262(PB).
    4. Pruethsan Sutthichaimethee & Boonton Dockthaisong, 2018. "A Relationship of Causal Factors in the Economic, Social, and Environmental Aspects Affecting the Implementation of Sustainability Policy in Thailand: Enriching the Path Analysis Based on a GMM Model," Resources, MDPI, vol. 7(4), pages 1-26, December.
    5. Wang, Zhongbao & Razzaq, Asif, 2022. "Natural resources, energy efficiency transition and sustainable development: Evidence from BRICS economies," Resources Policy, Elsevier, vol. 79(C).
    6. Qifan Guan, 2023. "Decomposing and Decoupling the Energy-Related Carbon Emissions in the Beijing–Tianjin–Hebei Region Using the Extended LMDI and Tapio Index Model," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    7. Zhang, Lifeng & Chao, Xiangrui & Qian, Qian & Jing, Fuying, 2022. "Credit evaluation solutions for social groups with poor services in financial inclusion: A technical forecasting method," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    8. Ahmad, Tanveer & Madonski, Rafal & Zhang, Dongdong & Huang, Chao & Mujeeb, Asad, 2022. "Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    9. Wang, Xiang & Yin, Jian & Yang, Yao & Muda, Iskandar & Abduvaxitovna, Shamansurova Zilola & AlWadi, Belal Mahmoud & Castillo-Picon, Jorge & Abdul-Samad, Zulkiflee, 2023. "Relationship between the resource curse, Forest management and sustainable development and the importance of R&D Projects," Resources Policy, Elsevier, vol. 85(PA).
    10. Kuşkaya, Sevda & Bilgili, Faik & Muğaloğlu, Erhan & Khan, Kamran & Hoque, Mohammad Enamul & Toguç, Nurhan, 2023. "The role of solar energy usage in environmental sustainability: Fresh evidence through time-frequency analyses," Renewable Energy, Elsevier, vol. 206(C), pages 858-871.
    11. Gu, Shanghao & Xu, Weihan & Xi, Kunling & Luo, Anxin & Fan, Kangqi & Wang, Fei, 2024. "High-performance piezoelectric energy harvesting system with anti-interference capability for smart grid monitoring," Renewable Energy, Elsevier, vol. 221(C).
    12. Li, Shanshan & Long, Fang & Long, Litao, 2022. "Resources curse and sustainable development revisited: Evaluating the role of remittances for China," Resources Policy, Elsevier, vol. 79(C).
    13. Yu, Jinna & Tang, Yuk Ming & Chau, Ka Yin & Nazar, Raima & Ali, Sajid & Iqbal, Wasim, 2022. "Role of solar-based renewable energy in mitigating CO2 emissions: Evidence from quantile-on-quantile estimation," Renewable Energy, Elsevier, vol. 182(C), pages 216-226.
    14. Jiying Wu & Olivier Joseph Abban & Yao Hongxing & Alex Dankyi Boadi & Evans Takyi Ankomah-Asare, 2022. "The nexus amid foreign direct investment, urbanization, and CO2 emissions: Evidence from energy grouping along the ECOWAS community," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(8), pages 10183-10207, August.
    15. Yu, Jinna & Saydaliev, Hayot Berk & Liu, Zhen & Nazar, Raima & Ali, Sajid, 2022. "The asymmetric nexus of solar energy and environmental quality: Evidence from Top-10 solar energy-consuming countries," Energy, Elsevier, vol. 247(C).
    16. Zhu, Bo & Liang, Chao & Mirza, Nawazish & Umar, Muhammad, 2023. "What drives gearing in early-stage firms? Evidence from blue economy startups," Journal of Business Research, Elsevier, vol. 161(C).
    17. Anton Gerunov, 2023. "Modern Approaches To Forecasting Firm Default Rates Over The Short To Medium Term: An Application To A Panel Of Polish Companies," Yearbook of the Faculty of Economics and Business Administration, Sofia University, Faculty of Economics and Business Administration, Sofia University St Kliment Ohridski - Bulgaria, vol. 22(1), pages 5-15, October.
    18. Anastasovski, Aleksandar, 2023. "What is needed for transformation of industrial parks into potential positive energy industrial parks? A review," Energy Policy, Elsevier, vol. 173(C).
    19. Vítor de Castro Paes & Clinton Hudson Moreira Pessoa & Rodrigo Pereira Pagliusi & Carlos Eduardo Barbosa & Matheus Argôlo & Yuri Oliveira de Lima & Herbert Salazar & Alan Lyra & Jano Moreira de Souza, 2023. "Analyzing the Challenges for Future Smart and Sustainable Cities," Sustainability, MDPI, vol. 15(10), pages 1-18, May.
    20. Yang Liu & Fei Huang & Lili Ma & Qingguo Zeng & Jiale Shi, 2024. "Credit scoring prediction leveraging interpretable ensemble learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 286-308, March.

    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:ecomod:v:475:y:2023:i:c:s0304380022002794. 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.journals.elsevier.com/ecological-modelling .

    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.