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<p>With the global transition toward carbon neutrality, the primary industry, secondary industry, and tertiary industry (referred to as the three major industries) are the primary sources of carbon emissions, making the formulation of differentiated carbon reduction pathways both critical and complex. However, there are few studies that combine data-driven prediction and expert decision-making methods to explore emission reduction paths. This study addresses this gap by integrating scenario analysis and decision-making methods to develop a comprehensive analytical framework. First, key factors influencing carbon emissions are identified, and emission levels are predicted using an extended Stochastic Impacts by Regression on Population, Affluence and Technology model and ridge regression analysis. Next, scenario analysis is employed to simulate the carbon peak timing and emission levels of the three major industries under baseline and optimized scenarios. Finally, a novel multi-attribute decision-making model—combining the Best–Worst Method with hesitant multiplicative preferences—is developed to evaluate and select the optimal emission reduction path. The findings reveal substantial differences in the carbon peak timing and emission levels among the three industries across different scenarios. Optimized scenarios demonstrate considerable potential for carbon reduction, emphasizing that coordinating the transformation and upgrading of the three industries is essential for establishing and sustaining a low-carbon economic growth model in Anhui Province. This study provides a scientific basis for carbon reduction paths in the three major industries and offers valuable insights for low-carbon development strategies in other regions and sectors.</p>

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Combining the STIRPAT model with a novel best–worst method to explore carbon emissions prediction and reduction path of three major industries: a case study in Anhui Province China

  • 期刊论文

  • 发表日期：

    2025年5月22日

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  • Juan Ji,
  • Dayong Wang,
  • Ying Zhang,
  • Dongjun Wang

摘要

With the global transition toward carbon neutrality, the primary industry, secondary industry, and tertiary industry (referred to as the three major industries) are the primary sources of carbon emissions, making the formulation of differentiated carbon reduction pathways both critical and complex. However, there are few studies that combine data-driven prediction and expert decision-making methods to explore emission reduction paths. This study addresses this gap by integrating scenario analysis and decision-making methods to develop a comprehensive analytical framework. First, key factors influencing carbon emissions are identified, and emission levels are predicted using an extended Stochastic Impacts by Regression on Population, Affluence and Technology model and ridge regression analysis. Next, scenario analysis is employed to simulate the carbon peak timing and emission levels of the three major industries under baseline and optimized scenarios. Finally, a novel multi-attribute decision-making model—combining the Best–Worst Method with hesitant multiplicative preferences—is developed to evaluate and select the optimal emission reduction path. The findings reveal substantial differences in the carbon peak timing and emission levels among the three industries across different scenarios. Optimized scenarios demonstrate considerable potential for carbon reduction, emphasizing that coordinating the transformation and upgrading of the three industries is essential for establishing and sustaining a low-carbon economic growth model in Anhui Province. This study provides a scientific basis for carbon reduction paths in the three major industries and offers valuable insights for low-carbon development strategies in other regions and sectors.