Deep Graph Learning for Industrial Carbon Emission Analysis and Policy Impact

TL;DR

A novel graph-based deep learning framework DGL is proposed to analyze and forecast industrial CO_2 emissions, addressing high feature correlation and capturing industrial-temporal interdependencies, and is believed to be the first Graph-Temporal architecture that resolves multicollinearity by structurally encoding feature relationships.

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.

Figures (5)

• Figure 1: Deep Graph Learning architecture for industrial emission modeling. The framework first constructs a graph where nodes represent industrial entities and edges represent relationships. Graph Neural Network with graph attention layers produces embedded representations for each node that encode influences from connected nodes. Simultaneously, Temporal Module processes time-series of each node’s features to capture temporal patterns. The outputs are fused in spatio-temporal interaction layer, combining information across industries and time. Finally, a prediction layer forecasts emissions, and interpretability module provides insights into feature importance and scenario analysis.
• Figure 2: Excerpt Global Industrial‑Sector Correlation Graph used as GNN backbone. Nodes represent 25 macro‑sectors of world economy; edges connect pairs of sectors whose historical $CO_2$‑emission time‑series (1990–2020) exhibit moderate‑to‑strong positive Pearson correlation. Edge colour and thickness jointly encode the correlation magnitude. The resulting adjacency matrix $A_0$ is reused across country‑specific batches, providing prior structure for GNN.
• Figure 3: DGL Spatio‑temporal architecture combining multi‑layer Graph Attention Network with multi‑scale Temporal Transformer to predict node‑level industrial emissions.
• Figure 4: Attention weight matrix ($\alpha_{ij}$) learned for China.
• Figure 5: Feature importance for emission prediction. The relative importance scores of key features as determined by explainability analysis. “Coal Consumption” and “Oil Consumption” emerge as the most influential factors, which aligns with domain expectations that coal and oil are carbon-intensive fuels. “Gas Consumption” also has a moderate impact, while “Electricity Use” and a macro-economic indicator “GDP per Capita” show contributions. Such insights allow policymakers to identify which factors (fuel types, economic drivers) are most critical to address for reducing emissions.