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.

Pakistan ranks 149th in the maternal mortality ratio (MMR) and has failed to keep pace with other countries in the region, except Afghanistan, with respect to health indicators. Home deliveries are linked to a higher risk of maternal death; therefore, discouraging home deliveries is imperative to improve maternal health. This study provides a holistic view and analyses factors affecting home birth decisions within the context of maternal socio-demographic characteristics in Pakistan. The study exploits the latest data from the Pakistan Demographic and Health Survey (2012–2013), which includes a nationally representative sample of 13,558 women aged 15–49 years. However, the sample was reduced to 6977 women who had given birth in the 5 years preceding the survey. Statistical techniques, including bi-variate and multivariate logistic regression, were used to analyse the data. The dependent variable was dichotomous and coded as 0 for home deliveries and 1 for deliveries at a health facility. The dependent variable was constructed based on information regarding the most recent birth in the 5 years preceding the survey. The study reveals that giving birth at home is highly prevalent among mothers in Pakistan (Baluchistan, 74%; Khyber-Pakhtunkhwa, 53%; Gilgit Baltistan, 46%; Punjab, 45% and Sindh, 34%) because of their difficulty obtaining permission to visit a health facility, financial barriers, the distance to health facilities and transportation. Substantial variation is observed when geo-demographic characteristics are considered. Higher home childbirth rates have been recorded in rural areas compared with those in urban areas (OR 1.32; p ≤ 0.000). The likelihood of home birth is highest (OR 2.67; p = 0.000) among women in Baluchistan province and lowest (OR 0.48; p = 0.000) among mothers in Punjab province. After controlling for all odds ratios and demographic characteristics, the parents’ education levels remain a significant factor (p = 0.000) that affects women’s decisions to deliver at home rather than at a health facility. The study findings provide a better understanding of why women prefer to give birth at home. These results can help policymakers to introduce appropriate interventions to increase the number of deliveries at health facilities. These findings are expected to reduce maternal and neonatal mortality in Pakistan.

| Agriculture in Pakistan is extremely vulnerable to climate change. Adaptation of the agriculture sector is imperative as it contributes 20 percent to the GDP and supports directly or indirectly about 68 percent of the population for their sustenance. Farmers’ perception about climate change, current adaptation measures, and decision-making process is important for farmers’ successful adaptation strategies. Using data of 205 conventional farmers from three district of Punjab province, this study provides insights into farmers’ perceptions about climate change, on-going adaptation practices, and constraints to various adaption strategies. We found that majority of the farmers do not consider climate change as a potential threat to agriculture; therefore, they do not make any intentional efforts to change their farming practices. However, a few adaptation measures like use of different crop varieties, tree planting, and drip irrigation methods are prevalent in the research area. The major hurdles that farmers perceived in adaptation are limited access to credit, lack of access to information, and institutional support. As expected, no significant variations were found across districts in the characteristics of respondents (age and income) that can affect their perceptions, their adaptation strategies, and obstacles faced in adaptation. The results of the study need to be interpreted with caution, as we did not focus on other determinants like education, role of media, and government policies that can affect farmers’ choices to adopt climate resilient farming practices.