While socioeconomic gradients in regional health inequalities are firmly established, the synergistic interactions between socioeconomic deprivation and climate vulnerability within convenient proximity and neighbourhood locations with health disparities remain poorly explored and thus require deep understanding within a regional context. Furthermore, disregarding the importance of spatial spillover effects and nonlinear effects of covariates on childhood stunting are inevitable in dealing with an enduring issue of regional health inequalities. The present study aims to investigate the spatial inequalities in childhood stunting at the district level in Pakistan and validate the importance of spatial lag in predicting childhood stunting. Furthermore, it examines the presence of any nonlinear relationships among the selected independent features with childhood stunting. The study utilized data related to socioeconomic features from MICS 2017–2018 and climatic data from Integrated Contextual Analysis. A multi-model approach was employed to address the research questions, which included Ordinary Least Squares Regression (OLS), various Spatial Models, Machine Learning Algorithms and Explainable Artificial Intelligence methods. Firstly, OLS was used to analyse and test the linear relationships among selected variables. Secondly, Spatial Durbin Error Model (SDEM) was used to detect and capture the impact of spatial spillover on childhood stunting. Third, XGBoost and Random Forest machine learning algorithms were employed to examine and validate the importance of the spatial lag component. Finally, EXAI methods such as SHapley were utilized to identify potential nonlinear relationships. The study found a clear pattern of spatial clustering and geographical disparities in childhood stunting, with multidimensional poverty, high climate vulnerability and early marriage worsening childhood stunting. In contrast, low climate vulnerability, high exposure to mass media and high women’s literacy were found to reduce childhood stunting. The use of machine learning algorithms, specifically XGBoost and Random Forest, highlighted the significant role played by the average value in the neighbourhood in predicting childhood stunting in nearby districts, confirming that the spatial spillover effect is not bounded by geographical boundaries. Furthermore, EXAI methods such as partial dependency plot reveal the existence of a nonlinear relationship between multidimensional poverty and childhood stunting. The study’s findings provide valuable insights into the spatial distribution of childhood stunting in Pakistan, emphasizing the importance of considering spatial effects in predicting childhood stunting. Individual and household-level factors such as exposure to mass media and women’s literacy have shown positive implications for childhood stunting. It further provides a justification for the usage of EXAI methods to draw better insights and propose customised intervention policies accordingly.

More than 800 million people worldwide suffer from chronic kidney disease (CKD). It stands as one of the primary causes of global mortality, uniquely noted for an increase in death rates over the past twenty years among non-communicable diseases. Machine learning (ML) has promise for forecasting such illnesses, but its opaque nature, difficulty in explaining predictions, and difficulty in recognizing predicted mistakes limit its use in healthcare. Addressing these challenges, our research introduces an explainable ML model designed for the early detection of CKD. Utilizing a multilayer perceptron (MLP) framework, we enhance the model’s transparency by integrating Local Interpretable Model-agnostic Explanations (LIME), providing clear insights into the predictive processes. This not only demystifies the model’s decision-making but also empowers healthcare professionals to identify and rectify errors, understand the model’s limitations, and ascertain its reliability. By improving the model’s interpretability, we aim to foster trust and expand the utilization of ML in predicting CKD, ultimately contributing to better healthcare outcomes.

Energy consumption is defined as one of the main determinants of environmental degradation. Therefore, this issue becomes one of the main points of debate to achieve sustainable development. This research examines how a set of economic factors determine the energy consumption in Organization for Economic Cooperation and Development countries, for which second-generation econometric methods have been used that control cross-sectional dependence issues. Therefore, the results, using nonlinear methods, suggest the presence of Jevons' paradox in these countries. However, under the Jevons' paradox scenario, technological innovation becomes a factor that mitigates energy demand. Contrary to the foreign direct investment that contributes significantly to the increase in energy consumption, from the results found, some policy implications are derived in the framework of achieving sustainable development.

This research attempts to model the association of crude oil imports with several macroeconomic factors such as renewable energy, transport services, trade, industrial value-added, and patents, using Germany’s annual data covering the period of 1990–2020. Employing the Autoregressive Distributed Lag model, this study finds a significant co-integration relationship among targeted variables. Moreover, this study provides empirical evidence on the influence of given macroeconomic factors in determining crude oil imports of Germany. Results reveal that transport services and industrial value-added positively and significantly influence crude oil imports in the long and short run. Similarly, trade is discovered to have a significant positive impact on oil imports only in the long run. In contrast, findings reveal a significant negative association of renewable energy with crude oil imports. Hence, this research implies that the transportation sector and industrial production strongly depend on crude oil consumption. At the same time, promoting renewable energy in these segments could significantly help economies control crude oil demand and achieve sustainability by reducing the economic burden and protecting the environment.

Environmental performance is a key aspect of business for both shareholders and stakeholders. However, it is necessary to examine whether current practices in corporate governance protect the key interests of shareholders and environmental stakeholders. This study examines how corporate governance affects a company’s sustainability and environmental performance. The study takes a novel approach by dividing businesses into three categories based on various business scenarios for environmental sustainability and evaluating the effect of corporate governance on each scenario in businesses. According to the study, corporate governance is a relative phenomenon whose effectiveness depends on assumptions about how long a company can continue operating under its current environmental conditions. Empirical results show that corporate governance is only effective in business-as-usual environmentally sustainable or highly environmentally sustainable scenarios.

Recent literature has explored the nexus between macroeconomic policy uncertainty (MPU) and the environment in compliance with Sustainable Development Goals (SDGs). This study contributes to the literature by exploring the possible or negative environmental effects of MPU. The present study reviewed 117 research articles published from 2020 to 2025 to understand the multifaceted association between MPU and environmental sustainability, having considered sectoral and spatial dynamics, asymmetric responses, and heterogeneous responses from different countries and regions. The findings suggested that the relationship was complex, and varied upon the economic sector, emissions source, policy regime, and geographical location. MPU reduced the speed of transition from the first to the second phase of the Environmental Kuznets Curve (EKC). In the short run, MPU can reduce emissions due to temporary economic slowdowns. Nevertheless, it can be responsible for negative long-term environmental performance by delaying green investments, increasing fossil fuel reliance, and weakening institutional effectiveness. Sectoral analyses revealed that MPU raised emissions in the energy and industrial sectors and reduced them in the agricultural sector. While strong institutional quality helped to mitigate emissions, weak institutions raised environmental problems. The findings of this review suggested that policymakers should design adaptive, sector-sensitive, and regionally coordinated environmental strategies to protect the environment from macroeconomic policy volatility.