University of Kashmir

Incorporating environmental protections in bilateral and regional trade agreements is crucial for fostering sustainable economic development. Early trade agreements focused on economic liberalization, whereas contemporary frameworks increasingly integrate environmental obligations to tackle climate change, resource depletion, and ecological degradation. This paper examines the development of environmental provisions in trade agreements, enforcement mechanisms, and optimal practices for sustainability-oriented trade policy. Prominent case studies such as the CPTPP, EU-Mercosur, and ACFTA, underscore both achievements and obstacles in the execution of environmental requirements. Moreover, market incentives like carbon pricing, border tax adjustments, and green subsidies are essential for harmonizing economic and environmental goals. The research offers policy suggestions for enhancing environmental protections, assuring adherence, and promoting international collaboration. The incorporation of comprehensive sustainability measures in trade agreements will be essential for attaining enduring environmental and economic resilience.

India's National Education Policy 2020 integrates Artificial Intelligence to address educational disparities across heterogeneous contexts—rural-urban divides, resource-constrained government schools, and technologically advanced academies. The chapter employs qualitative policy examination, institutional readiness assessments, and critical synthesis of empirical research on intelligent tutoring systems and learning analytics within Indian educational ecosystems. Analysis reveals AI's multifaceted potential: personalised learning pathways, augmented teacher capacity in under-resourced contexts, and equitable access in geographically dispersed communities. The 2026-27 mandatory AI curriculum presents developmental opportunities; success requires substantial investment in teacher preparation and critical AI literacy. Realising transformative potential demands balanced integration of technological innovation with institutional capacity building, teacher empowerment, and transparent governance, prioritising educational equity and resource allocation.

The socio-economic traits and constraints that impact the fruit supply in market centres in the Kashmir Valley, India, were evaluated. The logistic regression modelling and Garrett ranking method were used to assess the significance and relative importance of various factors affecting fruit supply. The findings revealed that important socio-economic factors impacting fruit supply in market centres are—market information, landholding size, distance to market, quantity produced, access to credit and extension services. The constraints were—inadequate market infrastructure and information, financial insecurities, management insufficiencies and inadequate cold storage facilities affecting fruit supply chains. These findings provided valuable policy insights by identifying and understanding the socio-economic factors that affect fruit supply, appropriate interventions and policies required. JEL Classification: Q11, Q12, Q13, Q14, Q16

ABSTRACT Gaining insight into local communities’ perception of Joint Forest Management (JFM) is crucial for sustainable forest management and livelihood improvement. However, limited research exists on people’s perceptions of JFM, particularly in the North-western Himalayas. This study evaluates the perceptions of local people as key stakeholders in the JFM program, based on primary data collected from 434 households using structured and semi-structured questionnaires comprising both close- and open-ended questions. The data were analyzed using descriptive statistics and Weighted Mean Scores (WMS). Findings reveal a moderate perception level regarding the socio-economic aspects (WMS: 3.51) and forest governance (WMS: 3.05) of JFM. In contrast, perceptions of ecological aspects were low (WMS: 2.32), likely due to the indirect link between ecological services and immediate economic benefits for forest dwellers. Additionally, respondents largely disagreed with the notion that JFM promotes forest tourism and water-harvesting mechanisms to mitigate water scarcity. The findings hold important policy implications, emphasizing the need to integrate community awareness initiatives, equitable benefit-sharing, and ecological education into JFM frameworks. For forest managers and policymakers, the results provide practical guidance for formulating locally tailored strategies that strengthen community engagement, enhance governance, and promote the sustainable management of forest resources in this fragile Himalayan region.

Soil erosion is a serious threat to land and water resources, affecting groundwater availability and agricultural productivity on a global scale. Accurate data on soil erosion rates is important for organising effective land conservation strategies. This study investigates the spatiotemporal patterns of soil erosion in the Sindh catchment of Kashmir Valley, India, over a decade (2013–2023) using Revised Universal Soil Loss Equation (RUSLE). The results reveal significant spatial variability in soil erosion rates, with elevated erosion observed in areas with sparse vegetation, steep slopes, and intensive agricultural practices. Between 2013 and 2023, the mean annual soil loss rose from moderate (17.04 t/ha/yr) to moderately high (33.12 t/ha/yr), corresponding to the standard RUSLE erosion severity classification (very slight: 0–5; slight: 5–10; moderate: 10–20; moderately high: 20–40; high: 40–80; severe: 80–120; very severe: >120 t/ha/yr). Different Land Cover/Land Use (LULC) classes exhibited varying rates of soil erosion. In 2013 and 2023, it rose from 13.18 to 53.63 t/ha/yr in agricultural land, from 20.82 to 45.94 t/ha/yr in riverine cover, from 21.96 to 37.72 in barren land, from 10.19 to 27.29 t/ha/yr in forest cover, and from 13.02 to 41.49 t/ha/year in build-up area. However, in glaciated terrain, the average soil loss decreased from 25.47 to 11.93 t/ha/yr in 2023 and from 14.66 to 13.81 t/ha/yr in the waterbody class of land use. These changes highlight the significant role of LULC dynamics, particularly the expansion of build-up areas and agricultural land, in influencing soil erosion patterns. The study highlights the critical implementation of integrated soil conservation measures, reduce soil erosion, and ensure sustainable land management in Sindh catchment.

ABSTRACT This study applies the Water Poverty Index (WPI) framework to evaluate seasonal variations in water poverty across eight tehsils of Leh district, using five components: resources, access, capacity, use, and environment, using primary and secondary data. To calculate WPI, scores were assigned to each component and then aggregated. Initially, equal weights were assigned to each component, followed by an alternative weighting. The findings indicate distinct seasonal variations in water poverty across the study area. The overall WPI score increases from 52.90 in winter to 70.85 in summer. Component-wise analysis shows that resource access, capacity, and use components decline sharply during winter, while the environment component remains stable across seasons. In summer, Sumoor and Leh experienced medium water poverty, while other tehsils showed low levels of water scarcity. However, in the winter, Leh experienced severe water poverty, and the other tehsils exhibit high levels of water scarcity, with Saspol consistently maintaining low water poverty in both seasons. Further, the study shows that the resource component is the dominant determinant of water poverty during summer, whereas the resource, capacity, and environment components significantly control winter water scarcity. The present study provides useful insights for effective water management in high-priority areas.

Purpose This paper investigates the impact of exchange rate movements on firm value, settling a long-standing argument on the magnitude of its effect. We aim to shed light on the mystery of exchange rate exposure. Design/methodology/approach An analysis of monthly data for ten (10) years from 1st April 2012 to 31st March 2022 for 567 import and export-oriented Indian firms has been undertaken. Econometric models like auto-regressive integrated moving average (ARIMA), vector error correction model (VECM), and autoregressive distributed lag (ARDL) have been used to derive the unanticipated component of the exchange rate to estimate for each firm separately using ordinary least squares. Findings The study has found that 80.95% (459/567) of the sample firms have exhibited significant exposure to unanticipated exchange rate changes derived from the ARIMA model. The level of exposure varies from 74.56% to 54.85% using several other models and different exchange rate measures. Research limitations/implications The study found, confirmed and emphasised the various possible reasons for the ongoing debate of the “Exchange Rate Exposure Puzzle.” An approach for measuring exchange rate exposure has been suggested. Financial managers may use the recommended method to measure exchange rate exposure. Originality/value The study has added value to the existing literature by arguing that measurement bias committed by the existing literature is primarily due to the usage of actual exchange rates and argues how unanticipated exchange rate changes are an effective and superior measurement tool for exchange rate exposure.

Field-based seasonal and annual mass-balance observations collected over the past one decade from four benchmark glaciers in the Ladakh region of the western Himalaya, Viz., Pensilungpa, Drang-Drung, Kangrez, and Machoi, indicate a consistent and significant loss of glacier mass across all elevation bands. Mass-balance estimates, obtained from stake networks and snow-pit observations exhibit persistently negative annual values, with enhanced ablation in lower and mid-elevation zones and limited accumulation at higher elevations. The average annual glacier mass balance ranges from −0.4 to −1.4 m w.e. yr⁻¹, with glacier-specific mass balance ranges of −0.8 to −1.4 m w.e. (Pensilungpa), −0.6 to −1.2 m w.e. (Drang-Drung), −0.5 to −1.1 m w.e. (Kangrez), and −0.4 to −0.9 m w.e. (Machoi). Winter accumulation across these four benchmark glaciers ranges from +0.3 to +1.1 m w.e., while summer ablation varies between −0.8 and −2.0 m w.e., reflecting strong altitude-dependent glacier-melt. All glaciers show steep mass-balance gradients, with a pronounced melt in lower ablation zones, and limited but persistent accumulation at higher elevations in the accumulation zones. Drang-Drung and Kangrez exhibit relatively stronger winter mass gains at higher elevations, while Pensilungpa and Machoi display the most intense summer ablation. Although the accumulation zones still gain seasonal mass, but it is not enough to offset the significant ablation as the ablation zones dominate glacier area, causing cumulative negative mass balances. The upward-shifted equilibrium-line altitudes and the dominance of ablation over accumulation indicate the increasing glacier sensitivity to regional warming in the cold desert Ladakh region.Based on typical uncertainties associated with stake measurements, density sampling, and spatial interpolation of point observation, the uncertainty in annual mass-balance is estimated at ±0.25–0.40 m w.e. yr⁻¹. Despite this uncertainty, the results robustly demonstrate significant glacier mass loss in the Ladakh region, underscoring enhanced cryospheric vulnerability to climate change and potential impacts on hydrological regimes in the upper Indus basin. These findings are consistent with regional trends in the Himalaya showing accelerated ice loss and rising ELAs, underscoring the growing sensitivity of cold-arid glaciers to climate warming, once considered relatively resilient. Continued mass loss has significant implications for water security, climate adaptation, and glacier hazard risk management across the upper Indus basin.

With increased Internet of Things (IoT) devices, complexity and protection are more challenging. Lightweight cryptographic algorithms are secure and suitable for limited-resource environments; however, their hash functions provide encrypted data but not integrity. Strong security features are available, but setup is difficult and expensive. Network security mechanisms increase power consumption and latency. As IoT networks grow, managing cryptographic keys and securely authenticating large numbers of devices become complex tasks. Efficient key management strategies are required to ensure the scalability required. Existing state-of-the-art solutions lack standardization, scalability, complex and costly. Thus, this research proposes a secure solution for IoT resource-constrained devices, combining strong data integrity and lightweight encryption, and is thus named a hybrid. This hybrid approach integrates SHA-512 and the present cipher in our proposed approach and thus ensuring higher security than state-of-the-art models. This intelligent combination not only enhances the algorithm’s resistance against cryptographic attacks but also improves its processing speed. The proposed approach is used to reduce the processing time for encryption in the IoT platform and to preserve the trade-off between security and efficiency. In terms of memory use, execution time, and precision, the proposed approach is compared with recent state-of-the-art research. The experimental results indicate that our approach is efficient using the avalanche, authentication success rate, collision events, and execution time. The efficiency is 53% to 65%, and the avalanche effect indicates sensitivity to input variations, suggesting moderate-to-considerable reactivity to small data changes. The experimental tests conducted across 10,000 and 80,000 runs reveal no collisions and found that the proposed approach is resilient in managing unique IDs. Moreover, our approach performs consistently, with an average execution time of 0.088246 s, ranging from 0.075954 to 0.094583 s. Finally, our approach provides a practical and scalable solution for securing IoT devices in resource-constrained environments, addressing practical problems for IoT devices.