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Evaluation research on green degree of equipment manufacturing industry based on improved particle swarm optimization algorithm

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  • Wang, Rong
  • Feng, Yue

Abstract

The paper constructs the green degree evaluation system of equipment manufacturing industry, and then the improved particle swarm optimization algorithm is used to measure the green degree of China's equipment manufacturing industry. The results show that the improved particle swarm optimization algorithm is a better method to evaluate the green degree of China's equipment manufacturing industry. The paper measure the green degree of China's equipment manufacturing industry is 0.51, which is at a low level. So it is need to adopt policies to promote green degree to achieve sustainable development goals. In view of this, the paper proposes policy recommendations for the green degree of China's equipment manufacturing industry from the perspectives of improving relevant policies, technological upgrading and environmental protection input, applying legal policy systems, and increasing financial support.

Suggested Citation

  • Wang, Rong & Feng, Yue, 2020. "Evaluation research on green degree of equipment manufacturing industry based on improved particle swarm optimization algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 131(C).
    • Handle: RePEc:eee:chsofr:v:131:y:2020:i:c:s0960077919304540
    DOI: 10.1016/j.chaos.2019.109502
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    References listed on IDEAS

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    1. Kirch, Guilherme & Terra, Paulo Renato Soares, 2012. "Determinants of corporate debt maturity in South America: Do institutional quality and financial development matter?," Journal of Corporate Finance, Elsevier, vol. 18(4), pages 980-993.
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    Cited by:

    1. Qingbo Huang & Xinxin Xia & Xiaohan Zhang & Yan Li, 2024. "Can the extension of the global value chain production length promote carbon emissions reduction in China's equipment manufacturing industry?," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(2), pages 3165-3192, February.
    2. Dejiang Luo & Su He & Hao Wu & Long Cheng & Junbo Li, 2023. "An Integrated Approach to Green Mines Based on Hesitant Fuzzy TOPSIS: Green Degree Analysis and Policy Implications," Sustainability, MDPI, vol. 15(13), pages 1-14, July.
    3. Malik, Muhammad Faizan & Chang, Ching-Lung & Chaudhary, Naveed Ishtiaq & Khan, Zeshan Aslam & Kiani, Adiqa kausar & Shu, Chi-Min & Raja, Muhammad Asif Zahoor, 2023. "Swarming intelligence heuristics for fractional nonlinear autoregressive exogenous noise systems," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    4. Dinesh Seth & Minhaj Ahemad A. Rehman, 2022. "Critical success factors‐based strategy to facilitate green manufacturing for responsible business: An application experience in Indian context," Business Strategy and the Environment, Wiley Blackwell, vol. 31(7), pages 2786-2806, November.
    5. Wang, Rong & Tan, Junlan, 2021. "Exploring the coupling and forecasting of financial development, technological innovation, and economic growth," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    6. Liu, Lianggui & Zhang, Rui & Chen, Qiuxia, 2022. "High-performance global peak tracking technique for PV arrays subject to rapidly changing PSC," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).

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