Mengxiang Zhu | Searcharxiv

arXiv2026

Green Finance and Carbon Emissions: A Nonlinear and Interaction Analysis Using Bayesian Additive Regression Trees

As a core policy tool for China in addressing climate risks, green finance plays a strategically important role in shaping carbon mitigation outcomes. This study investigates the nonlinear and interaction effects of green finance on carbon emission intensity (CEI) using Chinese provincial panel data from 2000 to 2022. The Climate Physical Risk Index (CPRI) is incorporated into the analytical framework to assess its potential role in shaping carbon outcomes. We employ Bayesian Additive Regression Trees (BART) to capture complex nonlinear relationships and interaction pathways, and use SHapley Additive exPlanations values to enhance model interpretability. Results show that the Green Finance Index (GFI) has a statistically significant inverted U-shaped effect on CEI, with notable regional heterogeneity. Contrary to expectations, CPRI does not show a significant impact on carbon emissions. Further analysis reveals that in high energy consumption scenarios, stronger green finance development contributes to lower CEI. These findings highlight the potential of green finance as an effective instrument for carbon intensity reduction, especially in energy-intensive contexts, and underscore the importance of accounting for nonlinear effects and regional disparities when designing and implementing green financial policies.

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arXiv2025

Bayesian Additive Regression Trees (BART) in Food Authenticity: A Classification Approach to Food Fraud Detection

Feature engineering plays a critical role in handling hyperspectral data and is essential for identifying key wavelengths in food fraud detection. This study employs Bayesian Additive Regression Trees (BART), a flexible machine learning approach, to discriminate and classify samples of olive oil based on their level of purity. Leveraging its built-in variable selection mechanism, we employ BART to effectively identify the most representative spectral features and to capture the complex interactions among variables. We use network representation to illustrate our findings, highlighting the competitiveness of our proposed methodology. Results demonstrate that when principal component analysis is used for dimensionality reduction, BART outperforms state-of-the-art models, achieving a classification accuracy of 96.8\% under default settings, which further improves to 97.2\% after hyperparameter tuning. If we leverage a variable selection procedure within BART, the model achieves perfect classification performance on this dataset, improving upon previous optimal results both in terms of accuracy and interpretability. Our results demonstrate that three key wavelengths, 1160.71 nm, 1328.57 nm, and 1389.29 nm, play a central role in discriminating the olive oil samples, thus highlighting an application of our methodology in the context of food quality. Further analysis reveals that these variables do not function independently but rather interact synergistically to achieve accurate classification, and improved detection speed.

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