Climate change presents an urgent global challenge, necessitating innovative and data-driven solutions for carbon mitigation. Universities, as hubs of research and innovation, are uniquely positioned to lead the implementation of sustainable energy transitions. This study develops a comprehensive methodological framework to evaluate and optimize rooftop solar energy potential across Sakarya University, using high-resolution Unmanned Aerial Vehicle (UAV) data integrated with Multi-Criteria Decision-Making (MCDM) techniques. A total of 70 buildings were identified as suitable for photovoltaic (PV) installation, yielding a usable rooftop area of approximately 44,500 m² after filtering out structural constraints and shading elements. Rooftop Global Horizontal Irradiance (GHI) ranged from 875 to 1,300 kWh/m² annually. The estimated cumulative solar potential across all rooftops reached ~53,000 MWh/year, with individual building potential varying between 21 and 2,300 MWh/year. Educational and administrative buildings emerged as the primary contributors, together accounting for more than 70% of the total solar yield, with educational facilities alone offering ~27,000 MWh/year. To prioritize rooftop installations, two criteria weighting techniques—Ordinal Priority Approach (OPA) and Ranking Comparison (RANCOM)—were applied, followed by the implementation of five recent and efficient MCDM methods: RAM, PROBID, MARCOS, SPOTIS, and EDAS. The final ranking of alternatives was synthesized using the Footrule Aggregation method. All computations were carried out in the Google Colab environment utilizing the Python libraries PyMCDM, pyDecision, and pyRankMCDA. With the deployment of around 16,500 solar panels (500 W, 24% efficiency), the university could install an 8.2 MW PV system, capable of generating approximately 22.18 GWh annually—more than double the current campus electricity demand of 9.7 GWh. This surplus of 12.48 GWh creates opportunities for future energy expansion and integration with electric mobility systems. Financial analysis revealed a Net Present Cost (NPC) of ~36,000 TRY/kW (~900–950 USD/kW), confirming the economic viability of rooftop PV infrastructure, especially given the remarkably low Levelized Cost of Energy (LCOE). The integration of UAV-derived geospatial data, open-source modeling tools, and MCDM-based decision frameworks offers a scalable, transferable model for data-informed policy-making in higher education institutions. This study aligns with key Sustainable Development Goals (SDGs), particularly SDG 7 (Affordable and Clean Energy) and SDG 11 (Sustainable Cities and Communities), and positions universities as strategic actors in advancing climate action and energy resilience. These findings contribute practical insights into the operationalization of campus-scale decarbonization strategies, offering a replicable roadmap for sustainability leadership in the academic sector.

This paper presents a comprehensive theoretical design framework and efficiency analysis for a small-scale hydropower turbine intended to support sustainable, decentralized, and low-impact energy generation. As global energy demand continues to rise and the effects of climate change intensify, the need for resilient renewable power systems has become increasingly urgent. Small hydropower (SHP) technologies offer a practical solution for rural, remote, and off-grid regions by providing reliable, low-cost electricity without the ecological disruption commonly associated with large dam infrastructure. In response to this need, the present study develops and evaluates a simplified impulse-turbine model-representative of Pelton- and Turgo-type micro-hydro systems using fundamental fluid mechanics principles, Bernoulli-based jet velocity predictions, and classical momentum-transfer turbine equations.

The analysis investigates the relationship between hydraulic head, flow rate, jet formation, and mechanical power output while accounting for real-world loss mechanisms introduced by penstock friction, nozzle inefficiencies, turbulence, flow separation, jet dispersion, mechanical drag, and misalignment. By integrating analytical modeling with insights from the contemporary computational fluid dynamics (CFD) literature, this study identifies how nozzle geometry, jet coherence, bucket shape, and runner design strongly influence momentum transfer and overall turbine efficiency. Loss mechanisms are categorized into penstock, nozzle, runner, and mechanical components, each quantified with established efficiency factors and combined multiplicatively to provide a realistic system-level performance estimate.

Results demonstrate that while power output increases with hydraulic head and flow rate, diminishing returns emerge at high operating extremes due to intensifying turbulence and mechanical losses. The analysis confirms that peak efficiency occurs only within a narrow bucket-to-jet speed-ratio range, consistent with established impulse-turbine theory. This study additionally highlights practical considerations essential for real-world applications, including material selection, erosion resistance, sediment effects, manufacturability, site-specific turbine adaptation, and the influence of seasonal flow variability on long-term system reliability. Overall, the findings provide a detailed foundation for optimizing micro-hydropower systems intended for sustainable, decentralized energy deployment.

Renewables are crucial for climate and socioeconomic sustainability, reducing the environmental footprint of fossil fuels, and supporting the green transition. Wind energy is one of the most competitive renewable energy sources, playing a pivotal role in achieving the United Nations Sustainable Development Goals (SDGs; namely, SDG 7 and 13—Affordable and Clean Energy and Climate Action). Moreover, the potential of Aeolian energy is acknowledged as a key factor in enhancing energy security, fostering socioeconomic resilience, and contributing to climate change mitigation. This work investigates the mean efficiency of the Coupled Model Intercomparison Project Phase 6^th (CMIP6) models (ΜΜ) in capturing and reproducing distributional and temporal wind energy potential (WEP) features over a geographical window that covers the climatically vulnerable region of the central Eastern Mediterranean (cEMed). Outputs from eighteen (18) CMIP6 model simulations are utilized to compute the CMIP6 MM WEP, which is subsequently evaluated against the fifth-generation global climate reanalysis dataset from ECMWF (ERA5 reanalysis). In addition, WEP projections under two Shared Socioeconomic Pathways (namely, moderate, SSP2-4.5, and extreme, SSP5-8.5, scenarios) are studied, focusing on the changes between the last period of the 21st century and a basis period that covers the historical period from 1970 to 2000. Results show that CMIP6 MM demonstrates low performance (in terms of Kling–Gupta efficiency index; KGE ~ -0.22) in capturing and reproducing the WEP features over cEMed. Both SSP scenarios show WEP increasing over the central, and slightly decreasing over the south, area of the southeastern Mediterranean, respectively. CMIP6 show high multi-model variability in model simulations WEP outputs over cEMed area. Considering the model simulations that show better performance in reproducing WEP over cEMed (four out of eighteen model simulations; KGE>0.35), a reduction in WEP over the southeastern Mediterranean is shown. However, due to high multi-model variability and uncertainty in the WEP results, further investigation combining different datasets is needed.

To address the long-term operational challenges of space environment monitoring buoys under extreme Arctic conditions—characterized by polar day–night alternations, ultra-low temperatures (often dropping below -40°C), strong winds, and highly variable renewable energy availability—this paper proposes an energy management optimization method based on deep reinforcement learning (DRL) algorithms. A comprehensive buoy system model is constructed, integrating renewable energy (photovoltaic panels and wind turbines) and lithium-ion battery power supply units, lead–acid battery energy storage units, and multi-sensor load units (including ionospheric detectors, geomagnetic instruments, high-precision attitude sensors, temperature and humidity sensors, and satellite communication modules). Critically, the model incorporates Arctic-specific environmental constraints, particularly low-temperature-induced battery efficiency degradation patterns that can reduce energy storage performance by 30–50% in extreme cold, as well as dynamic fluctuations in solar irradiance (zero during polar nights) and wind speed (frequent gusts exceeding 25 m/s). To balance operational stability and energy efficiency, a scientific reward function is designed to minimize unsupplied energy while strictly ensuring the functional integrity of multi-sensor monitoring tasks, with penalty terms for both energy surplus (wasted renewable generation) and deficit (compromised sensing operations). Using the Twin Delay Deep Deterministic Policy Gradient (TD3) algorithm on the MATLAB simulation platform, comparative experiments are conducted to verify the effectiveness of two energy storage configurations: a photovoltaic-lithium battery–lead–acid battery system and a hybrid photovoltaic–wind–lithium battery–lead–acid battery system for long-term Arctic observation. Based on 180 days of real Arctic environmental data (irradiance, wind speed, temperature) sourced from the NSF Data Center, the results demonstrate that the proposed method dynamically optimizes charging/discharging strategies of energy storage systems and power allocation of supply units in real time. It not only reduces lithium battery power demand by 87.5% (from 61.44 kWh to 7.685 kWh) compared to photovoltaic-only systems but also extends the buoy’s continuous operational duration to over six months—overcoming the 60-day limit of traditional solar-powered buoys once polar nights begin. Additionally, the TD3 algorithm’s dual-critic networks and target policy smoothing mechanisms enhance scheduling intelligence and robustness, effectively mitigating the uncertainties of renewable energy in extreme environments. This research provides a robust technical solution for overcoming energy bottlenecks in polar monitoring equipment, reduces operational costs and environmental risks associated with lithium battery overreliance, and offers new insights for energy management in intelligent polar observation systems operating in harsh, dynamic environments.

A crucial element in the worldwide shift towards cleaner energy, renewable sources present a lasting alternative to traditional energy, significantly reducing carbon emissions and mitigating climate change. Solar energy, in particular, stands out as a highly advantageous resource, fostering technological progress, economic growth, and energy self-sufficiency. However, in order to accurately predict power generation, reliable and robust predictive models are required for improving accuracy and model performance in real-world practical solar applications. Sophisticated forecasting techniques must be developed due to the inherent intermittency of solar radiation and the growing demand for renewable energy. This study proposes a hybrid method for solar power prediction that combines Artificial Neural Networks (ANN) and the Fast Fourier Transform (FFT). The dataset used comprises 30 days of measurements, with 15 sampled values of global irradiance, ambient temperature (AT), and module temperature (MT) per day. To extract dominant periodic components, daily power profiles are first converted into the frequency domain using the FFT. In order to minimize noise while maintaining the crucial daily patterns of solar production, a frequency filtering procedure is then used to retain important low-frequency components. The input vector of the ANN model is then created by combining these extracted frequency features with meteorological data. The nonlinear relationship between environmental factors and photovoltaic power output is captured by the ANN through supervised learning. Statistical indicators such as RMSE (0.092), MAE (0.067), and the coefficient of determination (R² = 99.44%) are used to evaluate the model using independent test data. An improvement over a traditional ANN model is demonstrated by a comparative analysis (R² = 98.67%). Nevertheless, because of the comparatively small dataset and the lack of thorough testing under various operating conditions, the validation of the suggested approach is still restricted despite these encouraging results. To properly illustrate the robustness and generalization potential of the suggested approach, more research on bigger and more diverse datasets is required.

Buildings account for a substantial share of global energy consumption and operational carbon emissions, and a significantly high value of this impact is formed long before mechanical systems are specified. The building envelope, particularly glazing and shading design, controls how solar radiation and heat exchange, along with daylight, are managed across different climates. Despite this, façade optimization research has largely been conducted within isolated climatic contexts, producing results that are difficult to transfer beyond their original location. This fragmentation leaves designers without robust and comparative guidance for climate-responsive façade decisions.

Rather than focusing on a single city or environmental condition, this study aimed to develop and evaluate an AI-driven, multi-city optimization methodology for glazing and shading systems capable of operating across diverse global climate zones.

A standardized prototype-based simulation methodology was adopted to ensure consistency across all analyses. The building model was developed in DesignBuilder, with EnergyPlus performing the underlying energy calculations. The prototype was tested in representative cities corresponding to major Köppen climate classifications, including hot–arid, hot–humid, Mediterranean, temperate, and cold climates. An AI-driven optimization workflow was then used to change the ratios of glazing to walls, the fixed external shading configurations, and the building's orientation. Performance was assessed using annual energy use intensity, heating and cooling demand, and operational carbon emissions, enabling direct comparison of façade strategies under contrasting climatic conditions.

The analysis showed that façade performance varied significantly across different climatic conditions. AI-optimized solutions consistently outperformed baseline configurations and achieved measurable reductions in energy use and associated operational carbon emissions. Climates that were mostly cool were the most sensitive to shading depth and glazing proportion. Climates that were mixed or mostly warm had more complex interactions between solar gains and thermal losses. No single glazing or shading configuration emerged as optimal across all climates, highlighting the limitations of uniform façade design practices.

In conclusion, these findings support a shift away from standardized façade solutions toward façade possibilities that respond directly to the climate in which they are built. This is achieved by framing façade design as an adaptive problem rather than a fixed solution; this study contributes a transferable, AI-assisted methodology suitable for early stage decisions. The approach provides a scalable foundation for future research on more complex building typologies and advanced façade systems. It also provides climate-responsive pathways toward operational carbon reduction.

Green innovation and carbon pricing have gained renewed attention as credible pathways for achieving low-carbon and sustainable economic growth, particularly following the maturation of energy transition policies across major economies. Both developed and developing countries increasingly prioritize green technological advancement and market-based carbon pricing instruments to meet long-term carbon neutrality commitments. Against this backdrop, this study aims to empirically examine the interlinkages between energy transition, green innovation, carbon pricing, and zero-carbon outcomes, with the objective of clarifying how these mechanisms jointly contribute to carbon neutrality.

To achieve this objective, the study employs a Dynamic Simulated Autoregressive Distributed Lag (DS-ARDL) framework, which allows for the assessment of both short-run dynamics and long-run adjustments under simulated policy shocks. Using annual data for China spanning 1990 to 2024, sourced from the World Development Indicators, the model captures nonlinear and forward-looking responses relevant to long-term decarbonization strategies. China offers a compelling case due to its rapid economic expansion, evolving energy structure, and increasing reliance on green policy instruments.

The empirical findings confirm that energy transition, green innovation, and carbon pricing play statistically significant and economically meaningful roles in advancing carbon neutrality. Specifically, the DS-ARDL estimates indicate that a 1% increase in next-generation energy deployment, clean and green technological innovation, and carbon pricing intensity leads to reductions in carbon emissions of 0.637%, 0.534 %, and 0.531 %, respectively. These results underscore the effectiveness of coordinated policy interventions in reducing emissions without undermining long-term economic performance.

From a policy perspective, the findings highlight the necessity for policymakers and legislators to adopt integrated and forward-looking strategies that simultaneously promote renewable energy adoption, incentivize green innovation, and enforce stringent carbon pricing mechanisms. Aligning these instruments is particularly critical for economies following an Environmental Kuznets Curve trajectory, where structural transformation and technological progress are essential for decoupling growth from emissions. Consequently, comprehensive and transformative policy measures are required across environmental regulation, energy system reform, green technology diffusion, and clean energy utilization to ensure the realization of carbon neutrality over the long term.

Introduction

The decarbonisation of hard-to-abate industrial sectors such as cement, lime, and steel production requires CO₂ capture technologies capable of operating efficiently under high-temperature, dust-laden, and compositionally variable flue gas conditions, while simultaneously delivering CO₂ streams of sufficiently high purity for transport, storage, or utilisation. Calcium looping (CaL) is a promising CO₂ capture technology due to the fast kinetics, low cost, and wide availability of CaO-based sorbents. However, conventional CaL concepts face major challenges associated with the high energy demand of CaCO₃ calcination and the need for downstream CO₂ purification. This work presents the experimental validation of a novel packed-bed calcium looping process incorporating steam-enhanced calcination, designed to exploit the heat released during carbonation to regenerate the sorbent via a rapid CO₂ partial-pressure swing, enabling the direct production of ultra-high-purity CO₂ without costly purification steps.

Methods

The proposed process was experimentally investigated in a laboratory-scale packed-bed reactor (1.2 m bed height, 50 mm internal diameter) loaded with up to 4.4 kg of natural limestone as CaO precursor. The reactor was operated in cyclic carbonation–calcination mode. During carbonation, CO₂-containing gas mixtures (15–40 vol% CO₂ in air) were fed at temperatures between 500 and 750 °C, allowing exothermic CaO carbonation to proceed and store heat within the solid bed. Calcination was subsequently initiated by injecting preheated steam, inducing a rapid decrease in CO₂ partial pressure and triggering CaCO₃ decomposition. Gas compositions were continuously monitored, and axial temperature profiles were measured using a multipoint thermocouple. Additional experiments integrated chemical looping combustion (CLC) stages using CuO-based oxygen carriers to provide in situ heat generation under conditions where carbonation alone could not achieve sufficiently high temperatures.

Results

The experimental results demonstrate that the heat released during carbonation can raise the bed temperature well above 800 °C when CO₂ concentrations exceed approximately 25 vol%, enabling rapid and effective sorbent regeneration during subsequent steam-assisted calcination. CO₂ capture efficiencies above 95% were consistently achieved during carbonation for feed gases containing up to 40 vol% CO₂, with gas residence times of only a few seconds. During calcination, the injection of steam produced outlet gas streams composed of virtually 100% CO₂ on a dry basis, confirming that high-purity CO₂ can be obtained without downstream purification beyond water condensation. Temperature profiles showed sharp and uniform drops during calcination, indicating effective decomposition of CaCO₃ throughout the bed. Multi-cycle experiments confirmed stable operation over successive carbonation–calcination cycles, with capture efficiencies remaining between 85% and 95%, despite the non-adiabatic limitations of the laboratory-scale setup. Integration of CLC stages successfully increased bed temperatures beyond 850 °C, improving calcination rates and enabling robust operation even with low CO₂ inlet concentrations or highly cycled sorbents.

Conclusions

This study provides experimental proof of concept for a novel packed-bed calcium looping process with steam-enhanced calcination capable of delivering ultra-pure CO₂ while significantly reducing the energy penalty associated with sorbent regeneration. By exploiting the heat stored during carbonation and combining it with rapid CO₂ partial-pressure swings and optional in situ heat generation via chemical looping combustion, the process enables near-autothermal operation and eliminates the need for costly CO₂ purification units. The results highlight the strong potential of this technology for decarbonising hard-to-abate industrial sectors and provide a solid experimental basis for further scale-up, optimisation of multi-cycle stability, and future techno-economic assessment.

Introduction
Access to reliable drinking water remains limited in many rural and non-interconnected areas of lower-income countries, where conventional water treatment infrastructure is absent or insufficient. In these contexts, solar water disinfection has been recognized as a low-cost and decentralized alternative that relies on the availability of adequate solar irradiance. However, the assessment of solar energy potential is frequently constrained by the lack of ground-based radiometric measurements, particularly in rural regions. Empirical models for solar radiation estimation provide a practical solution under such data-scarce conditions. The Colombian Caribbean region presents favorable climatic characteristics for solar applications, yet comprehensive evaluations of solar energy availability in its rural zones remain limited. This study estimates the solar energy potential in rural and non-interconnected areas of the Colombian Caribbean region using sunshine-based empirical models and evaluates its suitability for solar water disinfection applications.

Methods
Global solar radiation was estimated using five empirical models based on sunshine duration: Angstrom–Prescott, Glover and McCulloch, Dogniaux and Lemoine, Page, and Bahel. Monthly average meteorological data were obtained from the nearest available stations operated by the Institute of Hydrology, Meteorology and Environmental Studies of Colombia, IDEAM, due to the absence of local measurements in several evaluated areas. The analysis focused on rural zones across 92 municipalities located in the departments of Atlántico, Bolívar, Cesar, Magdalena, Córdoba, Sucre, and La Guajira. Estimated irradiance values were evaluated on monthly and annual bases and compared with satellite-derived solar radiation data obtained from the Prediction Of Worldwide Energy Resources (POWER) platform. Model performance was assessed using statistical indicators including Mean Bias Error, Mean Absolute Error, Root Mean Square Error, coefficient of determination, and Pearson correlation coefficient. Spatial distribution maps of annual average solar radiation were generated for each empirical model to evaluate regional consistency.

Results
The estimated annual solar energy potential in rural areas of the Colombian Caribbean region ranged between approximately 4.8 and 6.2 kW m⁻². All evaluated models exhibited consistent seasonal behavior with moderate monthly variability, reflecting the relatively stable solar resource characteristic of low-latitude regions. The coefficient of determination values were approximately 0.22 across models, indicating limited temporal variability, while additional statistical indicators showed no significant systematic bias and satisfactory predictive performance. Root Mean Square Error values remained within acceptable ranges, and Pearson correlation coefficients indicated moderate positive linear relationships between estimated and satellite-derived irradiance values. Among the evaluated formulations, the Page model presented the lowest error metrics, although all models showed comparable performance. Spatial analysis revealed a relatively homogeneous distribution of solar radiation across the evaluated rural zones, with minor geographic variations associated with location and sunshine duration inputs.

Conclusions
The results indicate that rural and non-interconnected areas of the Colombian Caribbean region exhibit solar energy availability compatible with the operational requirements reported for solar water disinfection systems. Sunshine-based empirical models provide a practical and reliable approach for assessing solar radiation in data-limited regions, supporting preliminary feasibility analyses for decentralized solar water treatment technologies. The integration of empirical estimations with satellite-derived reference data enhances confidence in regional-scale assessments and offers a transferable framework for evaluating solar-based solutions in rural environments lacking direct radiometric measurements.

Tessellated geometric façades are increasingly employed as performative building envelopes capable of modulating daylight, controlling glare, and mediating solar exposure. Composed of repetitive or rule-based geometric units, tessellated systems enable controlled permeability, pattern differentiation, and modular fabrication, allowing façades to operate simultaneously as environmental regulators and architectural expressions. Advances in parametric design and digital fabrication have further expanded their application, making tessellated façades attractive as climate-responsive shading systems. Despite growing academic interest and extensive simulation-based evaluation of their daylighting potential, their practical application remains limited by the lack of design-oriented guidance that translates parametric results into operational façade strategies. While existing studies successfully quantify daylight performance, the step from numerical evaluation to actionable architectural decision-making remains underdeveloped. This paper addresses this gap by focusing on the practical implications of tessellated geometric façades for daylighting performance, with particular emphasis on how parametric findings can inform façade design decisions. The study adopts a performance-informed design synthesis methodology, aimed at translating validated parametric daylighting results into design-operational knowledge. Rather than conducting new simulations, the research is based on a secondary analytical reinterpretation of an existing, peer-reviewed parametric dataset, allowing the study to shift from performance generation to design translation. This methodological approach positions simulation outputs as evidence for architectural reasoning, rather than as isolated numerical results. The methodological process reframes computational parameters, such as perforation ratio, geometric pattern, and spatial configuration, as architectural design variables that directly correspond to façade openness, geometric organization, and functional daylight demand. A comparative analytical framework is employed to examine how variations in façade geometry and porosity interact with different spatial functions, revealing recurring performance tendencies rather than singular optimized solutions. To support holistic interpretation, a composite daylight performance indicator is used as a synthesis tool to integrate multiple daylight metrics, while illuminance thresholds are applied as external design constraints to ensure functional adequacy. Through this interpretive process, tessellated façades are understood not as static decorative screens but as tunable daylight-modulating systems, whose effectiveness depends on the alignment between geometric logic, façade openness, and spatial use. The synthesis highlights how façade porosity and pattern organization jointly influence the balance between daylight sufficiency and visual comfort across varying interior conditions. The outcomes are consolidated into a simplified decision-support logic that enables rapid comparison of façade alternatives without reliance on complex multi-objective optimization workflows. By providing design-operational rules and a transparent evaluation logic, this study offers actionable guidance for architects and façade designers and supports the integration of tessellated façade systems into daylighting standards, design guidelines, and performance-driven architectural practice.