CEPT University, formerly the Centre for Environmental Planning and Technology, is an academic institution located near university area in Ahmedabad, India offering undergraduate, postgraduate and doctoral programmes in areas of natural and developed environment of human society and related disciplines.
Modern test methods for air conditioners and heat pumps must reconcile two competing demands: accurately reflecting real operating behavior and ensuring practicality, repeatability, and interlaboratory comparability. Conventional fixed-speed, steady-state rating procedures fall short of this objective, as they exclude control dynamics and interactions with buildings and distribution systems, thereby limiting their representativeness of in-use performance.This review critically examines the limitations of current testing standards and synthesizes recent research and technical advances aimed at improving performance characterization and seasonal efficiency assessment. Emphasis is placed on load-based testing methodologies, emulator-based approaches, and hardware-in-the-loop (“field test in the lab”) concepts, which enable active-control operation under reproducible yet realistic conditions. Evidence from laboratory demonstrations, interlaboratory comparisons, and emerging standardization efforts is consolidated to assess the technical maturity, robustness, and scalability of these methods. The discussion also reflects ongoing international initiatives, including International Energy Agency Annex 88, “Evaluation and Demonstration of Actual Energy Efficiency of Heat Pump Systems in Buildings,” and the International Organization for Standardization Informal Meeting on “Load-based Test Methods.”Based on the reviewed methods, the paper identifies pathways toward next-generation testing frameworks that better recognize advanced system architectures and control strategies, support evidence-based policy and standardization, and provide consumers with performance metrics that more closely align with real-world energy outcomes.
The urban thermal dynamics of a city comprise of Urban Heat Island Effect (UHI) and the Urban Cool Island Effect (UCI) that influence the local climate. This research employs a spatial and comparative approach to analyze thermal variations in Pune, India, by studying the conditions across three distinct years-2016, 2020, and 2024, to capture yearly variations influenced by changes in human activity and environmental dynamics, particularly during the 2020 period marked by the COVID-19 lockdown in India. To evaluate the urban thermal patterns, the research integrates four indices (i) Land Surface Temperature (LST), (ii) Normalized Difference Vegetation Index (NDVI), (iii) Normalized Difference Moisture Index (NDMI), and (iv) Normalized Difference Anthropogenic Impervious Surface Index (NDAISI). The spatial mapping has been done using Q-GIS, presenting diurnal and seasonal patterns for all indices. Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 8-9 satellite data were utilized for April (summer) and November (winter) for the analysis. Statistical correlation techniques have been used to evaluate the interrelationship between LST with the other three indices. To capture the temporal disparity in heat retention and dissipation, it assesses interactions between these indices with diurnal and seasonal variations. Surface Urban Heat Island Intensity (SUHII) variation has been plotted using a Box and Whisker plot. The result suggests that Pune shows the UCI effect during the day, while the UHI effect during the night. During UCI (daytime), the results indicate a positive correlation between LST and NDAISI, whereas NDVI and NDMI exhibit strong negative correlations with LST, highlighting their cooling effects. The study highlights intra-urban thermal variation as an urban heat spread (UHS) effect. These findings provide valuable insights for urban planners and policymakers in developing heat action plans and climateresilient urban strategies for Pune and similar cities.
Urban expansion systematically alters land use, producing cumulative impacts on surface-level environmental quality. Addressing these processes requires a computational framework capable of observing and modeling environmental dynamics within a unified urban system. This study formulates a computational framework to model trajectories of surface environmental quality in a metropolitan region, using the Kolkata Urban Agglomeration as a representative case. Multi-temporal Landsat data (1991–2021) were employed to model land-use and land-cover (LULC) transition processes and their relationships with key land surface parameters. Urban environmental quality was quantified using a Land Surface Environmental Quality Index (LSEQI) derived through an aggregate application of multiple multi-criteria decision analysis (MCDA) techniques, including AHP, TOPSIS, EDAS, and VIKOR, followed by ensemble integration to enhance robustness. Model performance was evaluated using statistical consistency measures and proxy validation based on community perception data. To extend the analysis beyond historical assessment, a three-stage predictive architecture was developed, incorporating artificial neural networks for future LULC simulation, dummy regression to quantify LULC–environmental parameter relationships, and MCDA-based synthesis to generate projected LSEQI for 2030. Results indicate a nonlinear urban transformation, with built-up areas expanding from 17.33% in 1991 to 59.80% in 2021, accompanied by continuous losses of vegetative and agricultural land and progressive wetland degradation. Ensemble LSEQI outputs reveal increasing spatial concentration of low environmental quality within dense metropolitan zones. Predictive simulations suggest built-up areas will exceed 68% by 2030, implying further environmental stress. The proposed framework offers transferable computational tools for diagnosing and forecasting environmental consequences of metropolitan growth.
Building digital infrastructure is no longer the challenge, ensuring its meaningful use is. In the age of digital transformation, the true test of digital public services lies not in their technical deployment, but in their societal adoption. This paper investigates India’s DigiLocker, an ambitious data exchange Digital Public Infrastructure (DPI) that allows citizens to access, store, and share verified documents seamlessly. Despite its robust architecture, DigiLocker’s potential hinges on public trust, behavioural intent to use, and actual usage. To decode these adoption dynamics, this study introduces an adapted assessment framework inspired by the Unified Theory of Acceptance and Use of Technology (UTAUT), reimagined for data exchange platforms. Through a survey-based analysis of key stakeholders, the research evaluates the influence of factors like social norms, performance expectations, privacy concerns, and voluntariness on user behaviour. Findings reveal that while ease of use and institutional mandates support adoption, voluntary engagement and user confidence remain limited. Issuers, meanwhile, report technical and administrative constraints that hinder proactive participation. By applying the UTAUT model to both user and institutional perspectives, this research offers a holistic understanding of adoption barriers and enablers in the context of DigiLocker. The research contributes to the growing discourse on inclusive digital transformation and provides a framework for evaluating DPI adoption in emerging and advanced digital societies alike.
