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<p>Mitigating carbon emissions from industrial sectors necessitates a deeper comprehension of the socio-economic and energy determinants influencing sectoral carbon intensity. This research establishes a machine learning framework to analyze and explain the factors influencing value-added carbon intensity across several economic sectors. A panel dataset including 83 nations and 11 industrial sectors from 2000 to 2021 was utilized to construct sector-specific predictive models employing Extreme Gradient Boosting (XGBoost). To preserve the temporal structure of the panel dataset, a chronological train–validation–test split was implemented, while lagged explanatory variables and lagged carbon intensity terms were incorporated to capture temporal persistence. Model interpretability was further evaluated using SHapley Additive exPlanations (SHAP). The models included economic, energy, and structural indicators, and model performance was evaluated using root mean square error (<i>RMSE</i>) and the coefficient of determination (<i>R</i><sup>2</sup>), alongside benchmarking against ordinary least squares linear regression (OLS) and Random Forest (RF) models. The findings indicate that XGBoost attained high predictive accuracy across all sectors, with test <i>R</i><sup>2</sup> values ranging from approximately 0.80 to 0.95. Although linear regression was competitive in certain domains, the machine learning methodology offered enhanced adaptability for identifying nonlinear associations among predictors. The SHAP analysis demonstrated that lagged carbon intensity consistently served as the most significant predictor across sectors, signifying robust temporal persistence in emissions intensity. Indicators of economic activity, including GDP per capita, industry value added, and per capita power consumption, were recognized as significant determinants of sectoral carbon intensity. These findings underscore the significance of economic structure and energy consumption patterns in influencing industrial emissions performance. The findings provide insights that can facilitate targeted decarbonization initiatives and guide policy formulation to mitigate industrial emissions while preserving economic output.</p>

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Machine learning modeling of value-added carbon intensity across global industrial sectors

  • 期刊论文

  • 发表日期：

    2026年7月4日

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  • Nnaemeka Achara,
  • Davor Antanasijević,
  • Viktor Pocajt

摘要

Mitigating carbon emissions from industrial sectors necessitates a deeper comprehension of the socio-economic and energy determinants influencing sectoral carbon intensity. This research establishes a machine learning framework to analyze and explain the factors influencing value-added carbon intensity across several economic sectors. A panel dataset including 83 nations and 11 industrial sectors from 2000 to 2021 was utilized to construct sector-specific predictive models employing Extreme Gradient Boosting (XGBoost). To preserve the temporal structure of the panel dataset, a chronological train–validation–test split was implemented, while lagged explanatory variables and lagged carbon intensity terms were incorporated to capture temporal persistence. Model interpretability was further evaluated using SHapley Additive exPlanations (SHAP). The models included economic, energy, and structural indicators, and model performance was evaluated using root mean square error (RMSE) and the coefficient of determination (R2), alongside benchmarking against ordinary least squares linear regression (OLS) and Random Forest (RF) models. The findings indicate that XGBoost attained high predictive accuracy across all sectors, with test R2 values ranging from approximately 0.80 to 0.95. Although linear regression was competitive in certain domains, the machine learning methodology offered enhanced adaptability for identifying nonlinear associations among predictors. The SHAP analysis demonstrated that lagged carbon intensity consistently served as the most significant predictor across sectors, signifying robust temporal persistence in emissions intensity. Indicators of economic activity, including GDP per capita, industry value added, and per capita power consumption, were recognized as significant determinants of sectoral carbon intensity. These findings underscore the significance of economic structure and energy consumption patterns in influencing industrial emissions performance. The findings provide insights that can facilitate targeted decarbonization initiatives and guide policy formulation to mitigate industrial emissions while preserving economic output.