This study rigorously examines the impact of political instability (POI) on firm performance (FPER) using high-dimensional panel data from 2006 to 2022, drawn from the World Bank Enterprise Survey (WBES) for 60 economies. Using advanced machine learning-based econometric techniques, including Double-Selection LASSO Regression (DSLR) and Partialing-Out LASSO Regression (POLR), the analysis reveals that POI significantly reduces FPER across the sampled countries. These findings remain robust across a series of validation tests, including alternative estimation approaches such as Cross-Fit Partialing-Out LASSO Regression (CF-POLR) and Bayesian Model Averaging (BMA), the use of alternative FPER proxies—employment growth (FEMG), innovation (IINN), and labor productivity (FLP)—and the substitution of POI with government regulation (REG). Mediation analysis further indicates that operational costs (OCOST) and firm investment (FINV) significantly and partially mediate the total effect of POI on FPER. In contrast, financial constraints (FCST) do not emerge as a significant mediator. The moderation analysis shows that political connections (PC) substantially attenuate the negative impact of POI on FPER. Heterogeneity analyses demonstrate that small, young, and capital-intensive firms are more severely affected by POI than medium and large, older, technology-intensive, and labor-intensive firms. Additionally, firm ownership-based heterogeneity indicates that state-owned enterprises experience slightly stronger adverse effects from fluctuations in POI than non-state-owned firms. Based on these empirical insights, policymakers need to promote institutional stability and provide direct support for vulnerable young and small firms to reduce the adverse effects of POI on FPER. Ultimately, this boosts economic flexibility in politically unstable markets by managing key growth drivers.

Accurate nighttime land surface temperature (LSTn) forecasting in data-scarce, topographically complex regions remains a significant challenge for satellite remote sensing and climate monitoring. To address this, the present study introduces the Physics-Guided Transformer Bidirectional Long Short-Term Memory (PG-ST-BiLSTM) deep learning model. For comparison, we employ four baseline models: three deep learning models (TCN, LSTM, and BiLSTM) and one statistical model (linear regression). Using Afghanistan, a topographically challenging and data-scarce region, as a “hard” test bed, the proposed model is evaluated across five major river basins over the period 2001-2024. The proposed PG-ST-BiLSTM achieves an R² of 0.977, RMSE of 2.023 °C, and MAE of 1.534 °C, outperforming baseline models including TCN (R² = 0.961), LSTM (R² = 0.966), BiLSTM (R² = 0.966), and linear regression (R² = 0.926). The model demonstrates strong alignment between observed and fitted LSTn values along the reference line of y = 0.98x + 0.21 closely approaching the 1:1 reference line. All Sen’s slope values are positive, confirming a universal nighttime warming trend across the projected period (2025-2040), with a grand mean of +0.0160 °C/year across all basin-season combinations, suggesting a marginal acceleration of LSTn warming under a trend-stationary climatological assumption, wherein historical warming rates derived from Mann-Kendall and Theil–Sen analyses are assumed to persist through 2040. At the seasonal level, Winter exhibits the highest projected warming rate (+0.1174 °C/year), followed by Summer (+0.0984 °C/year), Autumn (+0.0545 °C/year), and Spring (+0.0504 °C/year), indicating that cold-season and peak-summer nighttime temperatures are projected to remain the most thermally vulnerable through 2040. At the basin level, the hyper-arid Helmand Basin records the highest projected mean Sen’s slope (+0.0796 °C/year), consistent with its minimal vegetation buffering and high nocturnal thermal emissivity, while the Amu-Darya Basin exhibits the lowest warming rate (+0.0460 °C/year), attributable to orographic cooling and comparatively higher soil moisture retention. These results demonstrate the generalizability and reproducibility of the PG-ST-BiLSTM model for nighttime LST forecasting in data-scarce, mountainous regions, with direct relevance to climate adaptation, water resource planning, and cryosphere monitoring worldwide.

Non-Linear Impact of Global FDI and Chinese OFDI on Green Growth in Belt and Road Economies: A Comparative Panel Data Analysis-Green Growth, Global Foreign Direct Investment, Chinese Outward Foreign Investment, Belt and Road Initiative, Environmental Kuznets Curve.