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Toward Explainable and Policy-Aware AI for Carbon Credit Price Prediction: A Research Framework for Emerging Carbon Markets

Summaiya Unnisa Begum, Mohammed Nadeem Ullah, Mohammed Abdul Ghani Khan

arXiv:2609.01765v1cs.LG

TL;DR

Carbon-price forecasting remains difficult in emerging markets because research is geographically narrow, policy text is weakly integrated, and evaluation often omits calibration and explanation stability. This paper proposes EPA-CarbonNet, which fuses market and policy inputs with cross-attention and produces forecasts, explanations, and policy attributions; testing yields largely negative results, except for leading directional accuracy.

  • Problem

    Carbon-price forecasting research largely centers on EU and Chinese markets, weakly represents policy text, and leaves explanation stability and joint evaluation insufficiently addressed.

  • Method

    EPA-CarbonNet aligns structured and unstructured inputs, encodes market sequences and policy text, fuses them through cross-attention, and produces probabilistic forecasts, explanations, and policy-impact attributions.

  • Results

    The architecture places seventh of eight on point accuracy, while directional accuracy reaches 58.6% against 54.4% for the autoregressive baseline and policy attention misses documented regulatory events.

  • Takeaways & Limitations

    The findings support measuring policy attribution directly rather than inferring it from cross-attention, while explainability remains relevant to transparency and access for smaller emerging-market buyers.

  • Takeaways & Limitations

    The evidence is a first probe covering one index pair, one horizon, one held-out window, and only two policy events inside the test window.

Abstract

from arXiv · show

Carbon markets put a price on emissions, yet that price remains hard to forecast. Work in this area clusters on the EU and Chinese schemes, compresses regulatory text into a sentiment score, and reports accuracy without calibration or explanation stability. We distil ten recurring gaps into an impact-feasibility matrix and propose EPA-CarbonNet, a six-layer architecture that fuses market series with policy text by cross-attention and calibrated intervals alongside policy-attributed explanations. We then build and test it on eleven years of daily S and P carbon index data. The findings are largely negative, and reported as measured: a random walk beats the model on five-day RMSE (0.0365 against 0.0475), SHAP rankings agree at rho = 0.54 across resampled backgrounds, and policy attention never coincides with documented regulatory events. Directional accuracy, at 58.6 percent, leads every baseline. Code, data documentation and all result artifacts are available at https://github.com/Kimalice/Toward-Explainable-and-Policy-Aware-AI-for-Carbon-Credit-Price-Prediction

I. INTRODUCTION: MOTIVATION THROUGH THE CARBON MARKET LENS

Carbon markets support emissions pricing but remain difficult to forecast because prices respond to policy, energy, economic, environmental, and market factors. The paper organizes recurring gaps into a prioritized agenda and proposes EPA-CarbonNet with a corresponding evaluation protocol.

  • Carbon markets convert emission-reduction activity into tradable financial instruments whose prices attract forecasting interest from regulators, investors, and compliance buyers.
  • I. INTRODUCTION: MOTIVATION THROUGH THE CARBON MARKET LENS: Policy shocks, market differences, and limited data complicate deployment in newly formed carbon markets.The motivating scenario involves estimating the price impact of caps, court rulings, or budget announcements within days using data from a different, more liquid market.
  • The research question asks whether carbon-price systems can be accurate, explainable, policy-aware, and viable under emerging-market data constraints.
  • The paper distils ten research gaps into an impact–feasibility matrix and presents EPA-CarbonNet as a conceptual six-component architecture traced to those gaps.
  • The proposed evaluation protocol includes metric families for calibration and policy attribution, grounded in India’s CCTS case application.

II. RELATED WORK: COMPARATIVE ANALYSIS WITH GAP IDENTIFICATION

The reviewed literature progresses from classical machine learning toward nonlinear carbon-price forecasting, but its criteria remain tied to abundant historical data and omit long-range dependence and policy text.

  • Random Forest, Support Vector Regression, and XGBoost model nonlinear relationships without strong distributional assumptions.Random Forest provides feature-importance measures, while SVR is described as suitable for smaller datasets.
  • Classical machine-learning criteria target supervised regression with abundant historical data and do not model long-range temporal dependence well.
  • None of the reviewed classical-machine-learning studies incorporate unstructured policy text.

B. Deep Learning Architectures

Deep and hybrid architectures extend temporal and multivariate modeling, while Transformers offer parallel sequence modeling and partial interpretability through attention. The reviewed work still lacks calibrated uncertainty, joint structured–unstructured fusion, and cross-market transfer evaluation.

  • B. Deep Learning Architectures: LSTM and GRU networks address temporal dependence through gating, while CNNs mainly extract features from multivariate market panels.
  • B. Deep Learning Architectures: Deep models generally outperform classical machine learning with sufficient data but reduce interpretability and do not report calibrated uncertainty alongside point forecasts.
  • C. Hybrid and Ensemble Models: Hybrid models report accuracy gains by combining numerical architectures, but none fuse structured and unstructured branches within one trainable architecture.
  • D. Transformer-Based Architectures: Transformers model dependencies across entire sequences in parallel, and inspectable attention weights provide a partial route to interpretability.
  • D. Transformer-Based Architectures: No reviewed Transformer study evaluates cross-market transfer to a data-scarce regime, while adoption remains recent and concentrated on EU ETS and China.

E. Explainable AI

Explainable-AI work in carbon-price forecasting commonly uses SHAP and LIME, while policy text is usually reduced to sentiment features or handled outside the forecasting loop. The review identifies instability and missing empirical tests as key gaps.

  • E. Explainable AI: SHAP and LIME are the explainability techniques most consistently paired with carbon-price forecasters.
  • E. Explainable AI: Both methods are known to produce unstable local explanations and to be susceptible to adversarial manipulation.
  • E. Explainable AI: The reviewed carbon-pricing studies cite these risks but do not empirically test them within carbon-price forecasting.
  • Policy and news text is typically converted into a sentiment score, while LLMs mainly serve summarization or classification roles.
  • No reviewed study embeds an LLM inside the forecasting loop or evaluates whether its policy understanding improves predictive accuracy.

B. Research Gap Prioritization

The paper distils ten recurring gaps from prior carbon-price forecasting research and prioritizes them by impact and feasibility. G1–G3 receive the highest-impact ratings, while G7 is judged most feasible but foundational.

  • Ten recurring gaps are synthesized from the reviewed literature and positioned in an impact–feasibility matrix.Figure 2 separates equal scores visually so both remain visible.
  • G7, shared benchmarks, is rated highest-feasibility but lower-impact because it is foundational rather than direct.
  • The proposed forecasting problem targets distributional forecasts, calibrated uncertainty, and human-interpretable attribution to structured features and policy or news events.The design specification is intended for low-liquidity, short-history emerging markets, including India’s CCTS.
  • EPA-CarbonNet is explicitly a design specification rather than a solved optimization problem with reported convergence guarantees.

D. EPA-CarbonNet Architecture

EPA-CarbonNet aligns structured and unstructured inputs, encodes them in separate branches, fuses them through cross-attention, and produces forecasting, explanation, and policy-attribution outputs. India’s CCTS serves as a design case because its price history is currently too short for empirical training or evaluation.

  • EPA-CarbonNet aligns heterogeneous inputs, encodes market sequences and policy/news text separately, and fuses both representations through cross-attention.
  • The fused representation feeds three parallel outputs: a probabilistic forecast, an explanation set, and a policy-attribution report.Algorithm 1 describes the forward pipeline from structured and unstructured input windows to these outputs.
  • The architecture is traced to the identified research gaps rather than presented as a generic forecasting pipeline.
  • India’s CCTS is used as a design case for an emerging, thinly traded, policy-sensitive market, not as an empirical benchmark.Its price history is presently too short for training or evaluation, so Table V lists candidate future data categories.

B. Evaluation Protocol

The evaluation protocol emphasizes joint reporting of point accuracy, calibration, explanation robustness, policy attribution, and transfer. It combines a qualitative literature-positioning framework with a reproducible implementation and documented index data, while distinguishing design targets from measured results.

  • Future EPA-CarbonNet evaluations should report calibration, explanation robustness, policy attribution, and transfer alongside point accuracy.
  • Figure 4 compares paradigms across six dimensions, but its proposed-framework values are design targets rather than measured benchmark results.
  • The reference implementation uses roughly 36,000 parameters, a purpose-built reverse-mode autodifferentiation engine, and verified gradient and Kernel SHAP calculations.
  • Experiments use daily closes of three S&P carbon credit indices from 31 July 2014 to 17 February 2026, yielding 3,014 aligned business days.Chronological splits include a ten-day embargo at each boundary to prevent leakage from overlapping multi-step targets.
  • The measured evaluation forecasts five-trading-day EU allowance-index log returns over a held-out window of 423 business days.

VI. FIRST EMPIRICAL FINDINGS

On the held-out five-day forecasting task, EPA-CarbonNet produced a largely negative evaluation: it lagged on point accuracy, led on directional accuracy, and exposed weaknesses in explanation stability, policy attribution, and cross-market transfer.

  • Point accuracy: Every model returned a negative R2, with EPA-CarbonNet placing seventh of eight and no model beating the unconditional mean.The results indicate near-martingale five-day returns over this index and window, with greater capacity associated with overfitting rather than signal.
  • Directional accuracy: 58.6% directional accuracy led the architecture above the autoregressive baseline at 54.4%.This pattern is consistent with capturing regime information without capturing return magnitude.
  • The level-versus-return distinction: Level scoring produced R2 = 0.84 for the model versus 0.91 for the random walk, showing why level accuracy requires a random-walk comparison.The reported level R2 largely reflects price-series autocorrelation rather than forecasting skill.
  • Explanation robustness (G8): SHAP feature rankings agreed at a mean Spearman correlation of 0.54, while the single most important feature changed in 40% of runs.The background sample varied across five runs, so explanation production and explanation stability were evaluated separately.
  • Policy attribution (G3): Policy attention led on 42 days, but none occurred within ten days of documented EU regulatory events against a chance expectation of 1.4.The result was unchanged across nine flagrate and window settings, although only two EU events fell inside the test window.
  • Cross-market transfer (G10): Zero-shot transfer from the EU to the Californian index degraded RMSE by 59%.This quantifies the cost of crossing a regulatory regime boundary identified as under-tested in prior research.
  • Reading these results: The evaluation protocol did not validate EPA-CarbonNet; it returned a largely negative verdict on this architecture and dataset.The paper presents the protocol as discriminating rather than as evidence that the proposed architecture succeeds.

VII. DISCUSSION

EPA-CarbonNet is designed to combine capabilities not found together in the reviewed methods, but whether it achieves that combination in practice remains an empirical question.

  • VII. DISCUSSION: No reviewed method combines multi-source fusion, explainability, structured policy integration, uncertainty quantification, and emerging-market applicability.EPA-CarbonNet is designed to occupy this combination, without claiming that it does so in practice.

B. Deployment Readiness Considerations

Deployment readiness remains conditional: the paper identifies human verification, limited empirical coverage, and constrained emerging-market data as important boundaries, while its broader agenda targets explainable forecasting for markets outside the mature-market evidence base.

  • Evidence boundaries: The empirical results are a first probe rather than validation, covering one index pair, one horizon, and one held-out window.The policy branch also uses a 26-event corpus with only two events inside the test window.
  • Data boundaries: Limited historical CCTS price data constrains training the structured branch on domestic data rather than transferred data.The limitation is tied to the scheme's early stage and current data availability.
  • Deployment safeguards: Human verification is required before policy-impact outputs inform regulatory or investment decisions, particularly during early deployment.The rationale is the paper's stated hallucination risk in policy-impact outputs.
  • Deployment context: The paper frames emerging markets as underserved by trustworthy, explainable forecasting evidence despite their practical need for such tools.Its motivating contrast is between data-rich mature markets and emerging markets that remain largely outside the evidence base.
  • Research agenda: The proposed agenda combines a five-paradigm synthesis, ten-gap prioritization matrix, traceable EPA-CarbonNet architecture, and India-CCTS evaluation protocol.These contributions are positioned as an organized response to gaps in the existing literature.

B. Future Research Directions •

Future work prioritizes empirical baselines, expert validation, robustness testing, and cross-domain generalization, while keeping EPA-CarbonNet extensible across encoders and low-resource markets.

  • Empirical validation: A first empirical baseline should compare EPA-CarbonNet or a reduced version on a data-rich reference market before low-data transfer to CCTS-like settings.The proposed example is the EU ETS.
  • Expert validation: A structured Delphi panel should validate the impact–feasibility gap-prioritization matrix with carbon-market and AI researchers.This addresses the matrix's status as an authors' synthesis rather than formal expert elicitation.
  • Explainability robustness: The explainability head should be stress-tested against adversarial fragility documented in the wider XAI literature.The proposed direction targets robustness rather than adding another forecasting model.
  • Cross-domain generalization: Future studies should test whether the gap taxonomy and prioritization method generalize to other policy-driven, thinly traded environmental and commodity markets.The proposed scope extends beyond CCTS-like settings.
  • Architecture extensibility: EPA-CarbonNet is designed for extensibility, allowing alternative sequence encoders, LLMs, and independently tuned lightweight variants.These substitutions support adaptation to other low-resource markets.
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