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<p>The accurate quantification of carbon footprints in the coal industry is critical for high-quality energy transition. However, traditional Life Cycle Assessment (LCA) methodologies predominantly rely on static emission coefficients, failing to capture the dynamic fluctuations inherent in mining operations. To address this limitation, this paper develops a time-dependent carbon emission assessment framework covering the entire coal extraction lifecycle. The study first characterizes the temporal variability of emission sources, identifying that carbon intensities are driven by fluctuating production loads, energy consumption profiles, and fugitive methane release. Consequently, emission factors exhibit significant heterogeneity across different operational phases. To precisely capture these shifts, a dynamic evaluation model is established, supported by an IoT-based continuous data acquisition infrastructure. This system enables the iterative recalibration of emission factors at discrete intervals, thereby replacing static estimates with high-frequency, empirically grounded data. This approach significantly enhances the temporal resolution and precision of emissions tracking. Based on the quantitative trends identified, the paper proposes data-driven decarbonization strategies, including process parameter optimization and energy recovery, providing a verifiable technical foundation for low-carbon mining operations.</p>

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Research on carbon emission models and emission characteristics in the coal development process

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月20日

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  • Wu Gang,
  • Wu Enguo,
  • Zhang Fukai,
  • Liang Minfu,
  • Chen Ningning,
  • Feng Haotian,
  • He Dexing,
  • Song Yang

摘要

The accurate quantification of carbon footprints in the coal industry is critical for high-quality energy transition. However, traditional Life Cycle Assessment (LCA) methodologies predominantly rely on static emission coefficients, failing to capture the dynamic fluctuations inherent in mining operations. To address this limitation, this paper develops a time-dependent carbon emission assessment framework covering the entire coal extraction lifecycle. The study first characterizes the temporal variability of emission sources, identifying that carbon intensities are driven by fluctuating production loads, energy consumption profiles, and fugitive methane release. Consequently, emission factors exhibit significant heterogeneity across different operational phases. To precisely capture these shifts, a dynamic evaluation model is established, supported by an IoT-based continuous data acquisition infrastructure. This system enables the iterative recalibration of emission factors at discrete intervals, thereby replacing static estimates with high-frequency, empirically grounded data. This approach significantly enhances the temporal resolution and precision of emissions tracking. Based on the quantitative trends identified, the paper proposes data-driven decarbonization strategies, including process parameter optimization and energy recovery, providing a verifiable technical foundation for low-carbon mining operations.