This study investigates the factors that affect farmers’ access to agricultural credit and its role in adopting improved agricultural technologies in the rain-fed zone of Khyber Pakhtunkhwa (KP), Pakistan. Using logistic models, we assess and compare the relative role of farmers’ socioeconomic attributes in their access to credit and adoption strategies. The results indicate a moderate positive association between farmers’ access to agricultural credit and their adoption of improved agricultural technologies. The binary logit model’s results indicate that farmers with a large-sized farm, high farm income, better access to information, and large physical asset ownership showed a positive influence on credit access. However, farming experience showed a negative effect on farmers’ access to agricultural credit. Regarding farmers’ credit sources, this study found that asset-rich farmers with more farming experience and better access to information relied more on banks than on input providers and informal credit sources. Similarly, older farmers with more education, larger farm sizes and high farm income were more likely to have borrowed from input providers than banks. We conclude that the role of the effective provision of information on credit and agricultural technology is imperative and requires separate policies that are specifically aimed at different groups of farmers with different socioeconomic and farm-related characteristics.

Using survey information of 150 randomly selected households across 21 villages of the forest-rich district of Swat, Pakistan, this study assessed households’ decision-making behaviors in depending on income from nearby forested land using socio-economic attributes. The evidence from the study may aid in making the existing policies be better targeted toward families that depend on the forest for income. Descriptive statistics and econometric techniques such as logit and tobit were used to analyze the data. Respondent households obtained the highest share of their income from off-farm activities (37%) and least from forest activities (16%). Fuelwood constitutes the biggest share (66%) of forest income, followed by medical plants (20%) and fodder (13%). We found that households with more physical assets, more family members working in off-farm jobs, and households earning more income from off-farm jobs were significantly and negatively associated with households’ decision to depend on forest income and total income obtained. We also found that households with less distance to the market and membership to joint forest management committees (JFMCs) were significantly and negatively associated with households’ total income obtained. However, household size was significantly and positively related to households’ decision of forest dependency. The study recommends the creation of off-farm opportunities and inclusion of local people in the management of forests through establishment of JFMCs, particularly for large and poor families.

Understanding the drivers of herbaceous productivity is crucial for managing vulnerable mountain ecosystems under changing climate and land use pressures. This study investigates the direct and indirect drivers governing herb biomass in the Hindukush Himalaya (HKH) using field measurements from 225 sample plots from multiple sites. We employed bivariate correlation analysis and structural equation modeling (SEM) to quantify the effects of available soil nitrogen, precipitation, temperature, degradation, elevation, and biodiversity. The results indicated that herb biomass was positively and directly associated with soil available nitrogen ( β = 0.7***), precipitation ( β = 0.17***), and species richness ( β = 0.101*). However, elevation had a significant negative direct effect on herb biomass ( β = − 0.151**). Indirectly, degradation was positively associated with both soil available nitrogen ( β = 0.255***) and species richness ( β = 0.477***), consistent with a model pathway in which these variables mediate degradation’s association with herb biomass. Precipitation was also indirectly associated with herb biomass through its positive association with soil-available nitrogen ( β = 0.333***). These findings highlight the importance of considering both direct and indirect (interactive) pathways among environmental variables when interpreting productivity patterns in Himalayan pastoral systems.

Air pollution is among the major causes of death and disease all around the globe. The prime impact of ambient air pollution is on the lungs through the respiratory system. This study aims to estimate the health cost due to air pollution from a Sugar Mill in the Mardan district of Khyber Pakhtunkhwa, Pakistan. To determine the impact of pollution on respiratory illness, primary data were collected from 1141 individuals from 200 households living within a 3 km radius of the mill. The Household Production Method was used to drive the reduced-form Dose–Response Function and the Mitigation Cost Function for assessing the impact of pollution on health and then estimating the monetary cost associated with mitigating such illnesses. The results indicate that about 60% of the respondents living in the surrounding area of the mill suffered from different respiratory illnesses. The study estimates that by reducing the suspended particulate matter (SPM) level by 50%, the expected annual welfare gains to an individual living within a 3 km radius of the mill are US $20.21. The whole community residing within a 3 km radius of the mill will enjoy an estimated welfare gain of PKR. 70.67 million (US $0.511 million). If the pollution standard limits prescribed by the World Health Organization are followed, the expected monetary benefits to all the individuals living within a 3 km radius of the mill are PKR. 114.48 million (US $0.27 million) annually.

Mountain ecosystems are under increasing pressure from forest degradation, which can alter key ecological indicators, including herbaceous biomass production. This study considered how herbaceous above-ground biomass (AGB) is influenced by degradation intensity, nitrogen availability, elevation, and species richness in temperate and subtropical forests of the Hindu Kush Himalayas. Data from 200 systematically placed plots were analyzed using bivariate and correlation methods, followed by structural equation modeling (SEM) to assess both direct and indirect pathways affecting herbaceous biomass. The results showed that available nitrogen was the strongest predictor of biomass (β = 0.77, p < 0.001), followed by species richness (β = 0.18, p < 0.05). Degradation reduced biomass indirectly by decreasing nitrogen (β = -0.72, p < 0.001) and species richness (β = -0.59, p < 0.001). Moderate degradation also negatively influenced subtropical habitat (β = -0.82, p < 0.001) and nitrogen (β = -0.43, p < 0.01). Higher nitrogen levels were significantly associated with less degradation (β = -0.47, p < 0.001), suggesting a protective effect of nitrogen-rich soils. Elevation had no direct effect on biomass but reduced subtropical forests (β = -0.82, p < 0.001) and increased moist temperate forests (β = 0.35, p < 0.01). The model identified nitrogen as the main driver of productivity, while degradation disrupted soil fertility and biodiversity, especially in vulnerable low-elevation forests.