Deep Graph Learning for Industrial Carbon Emission Analysis and Policy Impact

• URL: http://arxiv.org/abs/2507.02912v3
• Date: Wed, 05 Nov 2025 19:25:04 GMT
• Title: Deep Graph Learning for Industrial Carbon Emission Analysis and Policy Impact
• Authors: Xuanming Zhang,
• Abstract summary: We propose a novel graph-based deep learning framework DGL to analyze and forecast industrial CO emissions.<n>Unlike traditional regression or clustering methods, our approach uses a Graph Neural Network (GNN) to learn long-range patterns.<n>The proposed model achieves superior predictive performance - reducing error by over 15% compared to baseline deep models.
• Score: 5.000188333305898
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Industrial carbon emissions are a major driver of climate change, yet modeling these emissions is challenging due to multicollinearity among factors and complex interdependencies across sectors and time. We propose a novel graph-based deep learning framework DGL to analyze and forecast industrial CO_2 emissions, addressing high feature correlation and capturing industrial-temporal interdependencies. Unlike traditional regression or clustering methods, our approach leverages a Graph Neural Network (GNN) with attention mechanisms to model relationships between industries (or regions) and a temporal transformer to learn long-range patterns. We evaluate our framework on public global industry emissions dataset derived from EDGAR v8.0, spanning multiple countries and sectors. The proposed model achieves superior predictive performance - reducing error by over 15% compared to baseline deep models - while maintaining interpretability via attention weights and causal analysis. We believe that we are the first Graph-Temporal architecture that resolves multicollinearity by structurally encoding feature relationships, along with integration of causal inference to identify true drivers of emissions, improving transparency and fairness. We also stand a demonstration of policy relevance, showing how model insights can guide sector-specific decarbonization strategies aligned with sustainable development goals. Based on the above, we show high-emission "hotspots" and suggest equitable intervention plans, illustrating the potential of state-of-the-art AI graph learning to advance climate action, offering a powerful tool for policymakers and industry stakeholders to achieve carbon reduction targets.

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