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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. [ABSTRACT FROM AUTHOR]

In the recent times agriculture sector has been considered as one of the most affected sectors by climate change. Climatic factors that have direct influence on agricultural productivity are: rise in temperature, heavy rainfall, precipitation, floods and drought etc. This paper has empirically examined the effect of climatic variations on cotton productivity in Pakistan. In order to evaluate the climate change, three climatic variables (rainfall, max. temp and min temp) and three non-climatic variables (technology, fertilizer and area) had been used for analysis. Historical climate data from 1981-2015, was collected from Pakistan Meteorological Department, while data of Technology, fertilizer and cotton area has been gathered from Pakistan Economic Survey. ARDL bound testing econometric approach was employed to gauge the empirical estimates. The outcomes of cointegration shows that all climate variables have statistically substantial effect on cotton productivity in Pakistan. Both rain-fall and maximum temperature have positive effects while minimum temperature has negative effects on cotton production. Furthermore, both fertilizer and area have positive impact on cotton production. long run cointegrating relationship holds among climate change variables and cotton productivity. mitigating negative impressions of climate change requires strategies focusing on the adaptation of climate resistant cultivars. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Increment in atmospheric CO2, variations in temperature and precipitation are helping tools to forecast scenarios for the climate in an area. Changes in these tools from the past due to any unavoidable circumstances for today and in the future is refers to climate change (CC). Besides other living being, crops are either benefited or adversely affected by these changes in the climate of an area. Maize is one of the widely grown crops in the world and even in Pakistan has also influenced in either way. Current study employed vector auto regression models (VAR) along with times series data from 1980 to 2013 for Pakistan to estimate the potential effects of climate change on Maize production. Result professed that average temperature will affect Maize crop negatively bringing about 6% reduction in maize production till the year 2030. Average minimum temperature brought about 9% increases in maize production till 2021. Increase in overall rainfall will also benefits Maize crop production. Maize production will increase by 2% by the year 2030 from the base year i.e. 1980. Water availability in the crop growth periods and fertilizer application (today or in the future) will certainly increase production. This scenario demands devising adaptation strategies for farmers to be made available, this should be the key policy intervention of the government to cater with the climate change on agriculture and particularly on maize. Fertilizer management and crop variety choices according to the changing climate will address this serious concern expected in the future. [ABSTRACT FROM AUTHOR]