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This paper investigates whether and how fintech development is associated with environmental quality. A machine learning technique—the principal component analysis (PCA), is adopted to construct an index that measures fintech development. The two‐step system generalised method of moments (GMM) addresses the potential endogeneity in the established nexus. Analysing a panel of 42 countries from 2012 to 2021, we found that fintech development can improve environmental quality by encouraging low‐carbon energy consumption and mitigating anthropogenic polluting emissions. Our analysis further reveals that high‐income inequality, a socioeconomic issue, counteracts the favourable features of fintech development on the environment. Our findings are robust across various additional analyses and environmental quality indicators. This study, therefore, contributes to the scarce but growing literature on the fintech‐environment nexus. [ABSTRACT FROM AUTHOR]

This paper studies how return connectedness exhibits potential linkages among 17 economies over a 20-year period starting in 2001. We obtained three main results by employing the dynamic connectedness approach, which is based on vector auto-regression (VAR), to calculate generalized forecast error decompositions. First, although the financial crisis (2007–2008) experienced a high level of connectedness, the spillover index spiked during the early stages of the COVID-19 outbreak. Second, the "return shock sender" is a community of countries that includes the United States, Australia, and European countries, while Vietnam is immune to financial linkages. Third, we discovered the predictive power of U.S. economic policy uncertainty and disease fear with market volatility for the Vietnamese return connectedness. As a result, our research identifies a range of relevant policies to mitigate spillover risks in the context of financial stability. [ABSTRACT FROM AUTHOR]

Recent abundance of moderate-to-high spatial resolution satellite imagery has facilitated land-cover map production. However, in cloud-prone areas, building high-resolution land-cover maps is still challenging due to infrequent satellite revisits and lack of cloud-free data. We propose a classification method for cloud-persistent areas with high temporal dynamics of land-cover types. First, compositing techniques are employed to create dense time-series composite images from all available Landsat 8 images. Then, spectral–temporal features are extracted to train an ensemble of five supervised classifiers. The resulting composite images are clear with at least 99.78% cloud-free pixels and are 20.47% better than their original images on average. We classify seven land classes, including paddy rice, cropland, grass/shrub, trees, bare land, impervious area, and waterbody over Hanoi, Vietnam, in 2016. Using a time series of composites significantly improves the classification performance with 10.03% higher overall accuracy (OA) compared to single composite classifications. Additionally, using time series of composites and the ensemble technique, which combines the best of five experimented classifiers (eXtreme Gradient Boosting, logistic regression, Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel – SVM–RBF and Linear kernel – SVM–Linear, multilayer perceptron), performed best with 84% OA and 0.79 kappa coefficient. [ABSTRACT FROM PUBLISHER]