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This study evaluates the influence of land use and water stress on ecosystem resilience, using panel data for thirty-three European countries from 2007 to 2024, following the identification of a research gap in the literature on this topic. The dependent variable is the bioclimatic ecosystem resilience index (BER), and the explanatory variables are Agricultural Land Share (ALS), Forest Land Share (FLS), and the Level of Water Stress (WS). The estimated models are a fixed-effects panel regression with Driscoll-Kraay standard errors, robust to autocorrelation, heteroscedasticity, and spatial dependence, and a kernel-based regularized least squares model, which offers a new, nonlinear, heterogeneous, and sensitive to local contexts perspective on ecosystem resilience. The results indicate a significant positive effect of FLS on ecosystem resilience, ALS has a mixed influence, while WS has a negative impact. Robustness checks using cluster-robust standard errors and alternative model specifications confirmed the stability and direction of the estimated coefficients. The conclusions support the promotion of forest conservation policies, sustainable water resource management, and ecosystem-friendly agriculture practices as main directions for enhancing the capacity of ecosystems to respond to human and climate pressures.

Purpose The purpose of this study is to empirically examine the impact of natural resource rents on income inequality in Ethiopia from 1981 to 2022 and investigate whether investments in manufacturing moderate this relationship. Design/methodology/approach Dynamic autoregressive distributed lag simulation and Kernel-based regularized least squares (KRLS) models are used to analyses short- and long-run relationships, as well as the potential moderating role of manufacturing. Findings The bounds test indicates natural resource rents have a long-run positive effect on inequality but a short-run negative impact. The KRLS model finds manufacturing conditions for this linkage in the short run. In the long run, economic growth decreases inequality following an inverted Kuznets pattern, while government expenditures reduce disparities when directed at priority social services. Research limitations/implications The findings provide mixed support for theories while highlighting nuances not fully captured without local analyses. Strategic sectoral investments may help optimize outcomes from resource dependence. Practical implications The results imply Ethiopia should prudently govern resources, productively invest revenues and prioritize social spending to equitably manage industrialization and uphold stability. Social implications Reducing disparities through inclusive development aligned with empirical evidence could help Ethiopia sustain peace amid transformation and realize its goals of shared prosperity. Originality/value This study applies innovative econometrics to provide novel insights into Ethiopia's experience, resolving inconsistencies in the literature on relationships between key determinants and inequality.

Environmental exposure assessment seeks to quantify exposure to potentially toxic environmental stressors, especially airborne pollutants. Health effects are typically estimated by regressing health outcome data on estimates of pollution exposure. Environmental exposure assessment is concerned with measuring or estimating exposure for the population of interest. The formulation of exposure assessment as a supervised learning regression problem naturally led to the use of machine learning models. A wide variety of machine learning models have been used, including generalized additive models, multivariate adaptive regression splines, kernel‐based regularized least squares, support vector regression, deletion/substitution/addition, cubist regression, cluster‐based bagging of mixed‐effect models, random forest, gradient boosting, neural networks, Bayesian regularized neural networks, generative adversarial networks. Model comparison, tuning, averaging, and feature selection all require reliable evaluation procedures. Cross‐validation and the bootstrap are two such procedures that are commonly used to evaluate exposure models.