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Factors Influencing Carbon Emission and Low-Carbon Development Levels in Shandong Province: Method Analysis Based on Improved Random Forest Partial Least Squares Structural Equation Model and Entropy Weight Method

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  • Yingjie Zhu

    (School of Mathematics and Statistics, Changchun University, Changchun 130022, China
    Key Laboratory of Intelligent Rehabilitation and Barrier-Free for the Disabled, Ministry of Education, Changchun University, Changchun 130022, China)

  • Yinghui Guo

    (School of Mathematics and Statistics, Changchun University, Changchun 130022, China)

  • Yongfa Chen

    (School of Mathematics and Statistics, Changchun University, Changchun 130022, China)

  • Jiageng Ma

    (School of Mathematics and Statistics, Changchun University, Changchun 130022, China
    Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China)

  • Dan Zhang

    (School of Landscape Architecture, Changchun University, Changchun 130022, China)

Abstract

Comprehensively clarifying the influencing factors of carbon emissions is crucial to realizing carbon emission reduction targets in China. To address this issue, this paper develops a four-level carbon emission influencing factor system from six perspectives: population, economy, energy, water resources, main pollutants, and afforestation. To analyze how these factors affect carbon emissions, we propose an improved partial least squares structural equation model (PLS-SEM) based on a random forest (RF), named RF-PLS-SEM. In addition, the entropy weight method (EWM) is employed to evaluate the low-carbon development level according to the results of the RF-PLS-SEM. This paper takes Shandong Province as an example for empirical analysis. The results demonstrate that the improved model significantly improves accuracy from 0.8141 to 0.9220. Moreover, water resources and afforestation have relatively small impacts on carbon emissions. Primary and tertiary industries are negative influencing factors that inhibit the growth of carbon emissions, whereas total energy consumption, the volume of wastewater discharged and of common industrial solid waste are positive and direct influencing factors, and population density is indirect. In particular, this paper explores the important role of fisheries in reducing carbon emissions and discusses the relationship between population aging and carbon emissions. In terms of the level of low-carbon development, the assessment system of carbon emission is constructed from four dimensions, namely, population, economy, energy, and main pollutants, showing weak, basic, and sustainable stages of low-carbon development during the 1997–2012, 2013–2020, and 2021–2022 periods, respectively.

Suggested Citation

  • Yingjie Zhu & Yinghui Guo & Yongfa Chen & Jiageng Ma & Dan Zhang, 2024. "Factors Influencing Carbon Emission and Low-Carbon Development Levels in Shandong Province: Method Analysis Based on Improved Random Forest Partial Least Squares Structural Equation Model and Entropy ," Sustainability, MDPI, vol. 16(19), pages 1-21, September.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:19:p:8488-:d:1488868
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