Search Results (637)

The German industrial and energy sectors accounted for over 52% of national greenhouse gas emissions in 2024. This is influenced both by an ongoing demand for fossil fuels and the usage of emission-intensive raw and processed materials. With the current European directive on corporate sustainability reporting, a push is being made for companies to publish annual emission reports. However, as per a study conducted by the authors, small and medium-sized companies have difficulties accurately calculating emissions across their supply chain without relying on external service providers. As a scientific institute with a real production facility for metal machining, the ETA (Energy Technologies and Applications) Factory bridges the gap between academia and manufacturing enterprises. The authors have used this disposition to calculate scope 1–3 emissions for the factory as per the Greenhouse Gas Protocol across three years, while progressively attempting to automate data collection for all scopes. CO₂e emissions for the years 2022–2024 were 86.3 tCO₂e, 146.9 tCO₂e, and 86.1 tCO₂e, respectively. Emission categories were assessed in terms of relevance to the institute and subsequently used to analyze the emission activities of the factory. The highest contributor to emissions was electricity purchasing for 2022 and 2024, along with business travel for 2023. Within scope 3, the emissions produced by business travel showed the highest impact across all years, followed by either energy-related activities or purchased goods. The sensitivity of CO₂e factors was also investigated, showing discrepancies between 25% and 130% for the utilized CO₂e factor for steel. Automation of data collection benefits largely from implemented manufacturing systems, such as manufacturing execution systems or enterprise resource planning systems. Full article

In the Kingdom of Saudi Arabia (KSA) and other arid regions, higher education institutions account for a significant share of energy consumption and greenhouse gas (GHG) emissions. Improving the environmental performance of higher education institutions is important to achieving nationwide impact reduction. This study evaluates the water, energy, and carbon (WEC) footprint of higher education campuses in arid environments. Qassim University (QU), KSA, is a leading public institution of higher education and research in Buraydah City and was selected for this study. A comprehensive assessment based on the GHG Protocol was conducted for the period 2022–2025, covering Scope I, II, and III emissions. This study analyzed institutional data on water use, wastewater, electricity consumption, transportation, waste generation, and air travel. The results show that total water consumption increased from 354,747 m³ in 2022 to 547,268 m³ in 2025, with per capita use rising from 46.2 to 61.7 L/c/day. Net water demand, including irrigation, reached 877,456 m³ in 2025. The declining trend in energy consumption between 2022 and 2025 reflects significant (33%) energy savings with the use of sensors and the overall tendency towards sustainability. Correspondingly, Scope II emissions decreased significantly from 147.2 million kg CO₂/year to 99.1 million kg CO₂/year and were the dominant CO₂ contributor (60–75% of total emissions). In contrast, Scope III emissions from commuting staff and students increased, with transport-related emissions rising from 36.4 million kg CO₂/year in 2022 to 52.2 million kg CO₂/year in 2025. This study also evaluated current and potential CO₂ emission reduction scenarios targeting energy and transportation systems on the QU campus. The findings indicate that the deployment of a 5.1 MW solar energy system can generate approximately 8.6 million kWh annually, resulting in a reduction of around 4000 tCO₂ and contributing to nearly 43% of the 2030 emission reduction target. In addition, transportation-focused strategies—including modal shift, vehicle electrification, and hybrid learning approaches—demonstrate significant mitigation potential, with total reductions reaching up to 18,700 tCO₂ by 2030. Overall, this study contributes to the limited body of knowledge on WEC footprint assessments on university campuses in arid regions and provides a baseline for future sustainability planning. Full article

Agricultural crop residue burning is a major driver of seasonal PM2.5 pollution and greenhouse gas (GHG) emissions in Northern Thailand. This study quantified GHG emissions from the open burning of rice, maize, and sugarcane residues across six provinces (Chiang Mai, Mae Hong Son, Lampang, Uttaradit, Nakhon Sawan, and Kamphaeng Phet) from 2019 to 2024 using the 2006 IPCC emission methodology. Spatiotemporal patterns of fire hotspots were characterized using MODIS and VIIRS satellite data, combined with kernel density estimation (KDE) and land-use classification in ArcGIS Pro. Total non-CO₂ GHG emissions (CH₄ and N₂O, expressed as CO₂-eq using GWP100 from IPCC AR5) over the six years totaled 2,599,551 tCO₂-eq, with major rice contributing the largest share (35%), followed by sugarcane (24%), second rice (21%), and maize (20%). Nakhon Sawan was the leading emitter (41%), reflecting its extensive rice and sugarcane cultivation. Pearson correlation analysis revealed consistently positive relationships between daily fire hotspot counts and PM2.5 concentrations (r = 0.30–0.84), with the strongest correlations observed in Mae Hong Son, where basin topography traps pollutants. Time-series analysis confirmed pronounced seasonal PM2.5 peaks that exceeded Thailand’s 24-h NAAQS limit (37.5 μg/m³) by 7–9 times in severe years. Biochar production via pyrolysis was evaluated as a zero-burning alternative, with an estimated annual carbon sequestration potential of 2.3–3.5 million tCO₂-eq, substantially exceeding emissions from open burning. These findings indicate that crop-residue valorization options—including biochar production, composting, and biochar co-compost—could theoretically offset agricultural GHG emissions and reduce field-burning PM2.5 emissions in Northern Thailand. However, the realized mitigation will depend on (i) verification of biochar long-term stability in tropical Thai soils through dedicated in situ trials, (ii) economic incentives that offset biochar production costs of approximately 1500–3500 THB per tonne, and (iii) integration within a policy mix that combines burning bans, mechanization support, and farmer extension services. Without these enabling conditions, biochar should be regarded as a future-perspective option rather than an immediately deployable solution. Full article

This study presents a nonlinear optimization framework for the hourly economic dispatch of interconnected multi-zone power systems integrating thermal, hydroelectric, wind, photovoltaic, and battery energy storage resources. The proposed formulation explicitly models zonal power balance, interzonal power exchange, thermal ramp-rate limits, battery state-of-charge dynamics, storage operating bounds, and hydroelectric energy quotas in order to minimize total system operating cost while preserving technical feasibility. The methodology was implemented in MATLAB and applied to a three-zone interconnected test system under two operating conditions: autonomous zonal operation and coordinated interconnected operation with battery storage support. The results show that the coordinated strategy reduces total operating cost from USD 8.23 million/day to USD 6.60 million/day, corresponding to a 19.8% reduction and an estimated annual saving of USD 595 million. In parallel, the optimized interconnected dispatch increases wind generation from 14.46 to 16.44 GWh/day and reduces thermal generation from 8.12 to 6.08 GWh/day, thereby improving the effective use of renewable resources. A complementary sustainability assessment further shows that coordinated operation increases the renewable share from 71.81% to 78.68%, decreases the carbon intensity of supplied electricity from 189.4 to 146.3 kgCO₂-e/MWh, and yields estimated avoided emissions of 1241.0 tCO₂-e/day. These findings demonstrate that the joint use of interzonal coordination and battery energy storage provides simultaneous economic, operational, and environmental benefits, thereby supporting sustainability-oriented operation of modern multi-zone power systems. Full article

This study examines corporate carbon emissions of Korean firms from an ESG perspective and develops an AI-based screening framework to improve the identification of firms likely to exceed regulatory emission thresholds. As global climate policies and carbon pricing mechanisms expand, understanding the emission profiles of listed companies has become increasingly important for regulators, investors, and policymakers. Despite growing ESG disclosure, reliable firm-level screening tools for carbon emissions remain limited. Using a pooled annual panel of KOSPI-listed non-financial firms from 2019 to 2024, the study constructs a dataset of 552 firm-year observations. Firms are classified as high-emission when annual emissions exceed the Korean Emissions Trading Scheme (K-ETS) regulatory threshold of 125,000 tCO₂e. To evaluate predictive performance, the analysis compares multiple machine learning models (RF, SVM, XGBoost, LightGBM, and CatBoost) and deep learning models (CNN, RNN, GAN, LSTM, and Transformer). In addition, a hybrid ensemble combining CatBoost, GAN, and Transformer is proposed to enhance predictive reliability. The empirical results show that ESG-augmented models consistently outperform financial-only baselines across AUC and F1 metrics. Among individual models, the ESG-enhanced Transformer achieves the strongest discriminatory power, while the proposed hybrid ensemble delivers the best overall predictive performance. The findings contribute to the literature by demonstrating the incremental value of ESG information in predicting corporate carbon emissions and by presenting a practical AI-based framework for compliance-oriented screening under carbon regulation. From a policy and investment perspective, the model provides a useful decision support tool for anticipating potential inclusion in emissions trading schemes, assessing transition exposure, and supporting data-driven decarbonization strategies. Full article

The growing demand for sustainable energy solutions in the built environment has increased interest in hybrid envelope retrofits that integrate vegetation systems with on-site photovoltaics (PVs). This study presents a comparative assessment of two integrated vegetation–PV envelope retrofit strategies for an educational building in a cooling-dominated hot-humid climate relevant to Nearly Zero Energy Building (NZEB) applications. A calibrated dynamic simulation model was developed to quantify annual net electricity savings, operational CO₂ emission reductions, and cost-effectiveness using the levelized cost of saved electricity (LCOS). Two configurations were assessed: a solar green roof and a façade system combining green walls with glazing-integrated photovoltaics (GIPVs), enabling a consistent evaluation of roof-based and façade-based hybrid systems under identical conditions. Both strategies deliver comparable energy and environmental performance. The solar green roof achieves annual net electricity savings of 231.0 MWh and avoids 163.3 tCO₂, while the green walls–GIPV system provides 228.3 MWh and 161.4 tCO₂. However, significant differences are observed in economic performance. The LCOS of the solar green roof is approximately 0.07 $/kWh, compared with 0.28 $/kWh for the façade-integrated system. The results demonstrate that vegetation–PV hybrid retrofits can effectively support NZEB pathways in hot-humid climates, while highlighting that the solar green roof provides a more cost-effective solution under the studied conditions. The study contributes a consistent, decision-oriented comparison of integrated vegetation–PV strategies, linking energy, environmental, and economic performance within a unified modeling framework. Full article

Electric two-wheelers (E2Ws) are promoted as lower-emission options in emerging economies. Their long-term cost competitiveness depends mainly on battery durability and how batteries are managed at the end of their life. This research examines Li-ion and nickel-cobalt-manganese (NCM)-type batteries versus the previously common lead-acid batteries in these markets. The study uses a 12-year total cost of ownership (TCO) framework that includes battery degradation, estimated first-life duration, and alternative lifecycle pathways. It covers three sensitivity analysis cases: conservative, base case, and optimistic. Three scenarios are evaluated: (1) no lifecycle management, (2) refurbishment for first-life extension, and (3) integrated lifecycle management with refurbishment, second-life utilisation, and recycling. Results show that managing the battery lifecycle can reduce TCO. The amount of reduction depends on first-life duration, ownership horizon, refurbishment cost, downstream residual value, and use intensity. The greatest TCO gains are found in battery categories with short first-life duration, allowing substantial residual value recovery during ownership. Batteries with first-life durations of 12 years or more provide smaller benefits. These findings support optimising lifecycle pathways for maximum residual value. Improved TCO performance, along with supportive infrastructure, policies, and market development, is critical for broader E2W adoption. Full article

Tanker cargo operations involve hazardous cargo environments, complex technical systems and stringent operational procedures. These conditions make accident analysis particularly demanding and require analytical approaches that consider the specific operational context of tanker cargo handling. Existing Human Factors Analysis and Classification System (HFACS) adaptations used in maritime safety research provide a useful framework for analysing human and organisational factors, but they do not fully capture the operational characteristics of tanker cargo operations. As a result, some factors specific to tanker cargo handling remain insufficiently represented in existing HFACS-based analyses. Therefore, this study develops and validates a domain-specific HFACS framework for tanker cargo operations (HFACS-TCO) and applies it to the analysis of accident investigation reports. The framework was developed through an iterative process based on accident report analysis, expert evaluation and the development of structured coding guidelines. The reliability of the coding procedure was assessed using Fleiss’s kappa coefficient to evaluate inter-rater agreement. The proposed framework extends existing HFACS adaptations by incorporating cargo operation-specific organisational, operational and environmental factors. A total of 27 accident investigation reports related to tanker cargo operations were analysed. From these reports, 333 causal factors were identified and classified using the HFACS-TCO framework. The results show that tanker cargo accidents rarely arise from a single cause and usually involve multiple interacting organisational, operational and human factors. Most factors were identified at the levels of Preconditions for Unsafe Acts, Organisational Influences and External Factors, indicating that many accident conditions are established before unsafe acts occur at the operational level. The analysis also shows that most accidents involve factors across several HFACS levels, indicating that tanker cargo incidents develop through interactions between different system levels. The proposed HFACS-TCO framework provides a structured, domain-specific approach to analysing tanker cargo accidents and supports a more systematic identification of organisational and human factors in tanker cargo-related operations. Full article

The decarbonization of multimodal transport systems requires assessment approaches that simultaneously address environmental impacts and economic performance at dynamic operational conditions. Conventional Life Cycle Assessment (LCA) and Life Cycle Costing (LCC), including Total Cost of Ownership (TCO), are widely used for this purpose; however, they often rely on static assumptions and averaged data, limiting their ability to capture real-world variability. This study proposes an AI-enhanced LCA–LCC/TCO framework for the integrated evaluation of decarbonised multimodal Door-to-Port transport systems. Artificial intelligence is embedded directly into the life cycle inventory and cost inventory stages to generate scenario-specific estimates of energy consumption, greenhouse gas emissions, and operational costs. The framework is demonstrated through a case study of a multimodal Door-to-Port transport chain comprising road pre-haulage, rail line-haul, and port terminal operations. Three scenarios are analysed: conventional, partially decarbonised, and fully decarbonised configurations. The results indicate that partial decarbonization reduces greenhouse gas emissions by more than 60% compared to the baseline while achieving the lowest total cost of ownership. Full decarbonization achieves emission reductions exceeding 95% but is associated with slightly higher costs under current assumptions. Sensitivity analysis verifies the robustness of the relative scenario ranking under different energy prices, carbon pricing, and electricity carbon intensity. The proposed framework provides a structured decision-support framework for logistics operators, port authorities, and policymakers seeking cost-effective pathways to low-emission multimodal transport systems. Full article

The article provides a comprehensive overview of the role of hydrogen as a key vector in the decarbonization of the global transport sector. The study situates hydrogen within the broader context of energy transition and climate neutrality targets, emphasizing its potential to replace fossil fuels in road, rail, maritime, and aviation applications. The analysis integrates a review of current technological, infrastructural, and policy developments, covering both combustion-based and fuel-cell hydrogen propulsion systems. Quantitative and qualitative data were assessed from international reports, scientific publications, and ongoing industrial projects to evaluate performance, efficiency, safety, and cost parameters such as Levelized Cost of Hydrogen (LCOH) and Total Cost of Ownership (TCO). The results indicate that while hydrogen remains economically challenging, technological progress in electrolysis, fuel cells, and refueling infrastructure significantly improves its competitiveness, particularly in heavy-duty and long-range transport. The paper highlights the critical role of international strategies, including the European Hydrogen Strategy and Fit for 55 package, in driving market adoption and regulatory alignment. The conclusions suggest that by 2050, hydrogen could contribute up to one-quarter of total transport energy demand, positioning it as a cornerstone of sustainable mobility and a bridge toward a fully decarbonized transport ecosystem. Full article

Zinc oxide (ZnO) is currently one of the most significant wide-bandgap semiconductor materials, attracting extensive research across diverse fields including materials science, chemistry, physics, medicine, electronics, and power engineering. Its exceptional properties, such as high optical transparency, high electron mobility, chemical stability, and compatibility with low-cost fabrication techniques, have established ZnO as a versatile material with immense application potential. A critical application for ZnO is its role as a transparent conducting oxide (TCO) in modern optoelectronic and photovoltaic devices, as well as in sensors, transparent electronics, and spintronics. To meet the requirements of these advanced applications, precise control over the structural, optical, and electrical properties of ZnO thin films is essential. This is effectively achieved through the selection of specific synthesis methods and intentional modification techniques, such as doping. This review provides a comprehensive overview of the synthesis and modification of ZnO thin films, with a particular focus on how various dopants influence their fundamental characteristics. The work discusses a range of deposition techniques, including physical vapor deposition (PVD), chemical vapor deposition (CVD), atomic layer deposition (ALD), sol–gel methods, spray pyrolysis, and other solution-based approaches. The novelty of this review lies in its comparative analysis of different doping strategies combined with various thin-film deposition techniques, highlighting how specific synthesis routes influence dopant incorporation and ultimately determine functional properties. Furthermore, recent advances in tailoring ZnO thin films are summarized, alongside the identification of key challenges and future research directions. Ultimately, this work aims to provide researchers with a systematic perspective on the synthesis–structure–property relationships in doped ZnO thin films to support the development of optimized materials for next-generation electronic and optoelectronic devices. This review, thus, serves as a comprehensive reference for researchers and engineers seeking to optimize the functionality of ZnO-based thin films for emerging technological applications. Full article

Office buildings are significant contributors to energy consumption and carbon emissions due to high occupancy density and prolonged operation. To balance decarbonization with indoor environmental quality, this study proposes a simulation-driven multi-strategy optimization framework for a three-story office building in Jinan. This study integrates EnergyPlus 23.2, jEPlus+EA 2.3.2, and the NSGA-II algorithm to co-optimize building performance. We evaluate the synergistic effects of roof photovoltaic coverage ratio, night ventilation turn-on temperature difference, and HVAC control strategies on carbon emissions and thermal comfort, while ensuring that CO₂ concentrations remain within health thresholds. The results indicate that the night ventilation temperature turn-on temperature difference is the most influential parameter. It yields standardized regression coefficients (SRCs) of 0.7456 for carbon emissions and 0.5325 for thermal discomfort. The Pareto-optimal solution achieves a carbon footprint of approximately 477 tCO₂, with only 8.8% indoor discomfort hours. This framework provides a robust, practical approach for the low-carbon and healthy operation of office buildings. Full article

The shift toward Smart Cities heavily relies on adopting energy-efficiency strategies to meet ambitious decarbonization targets. However, the rebound effect, where improvements in technical efficiency are partly offset by increased energy consumption, often reduces the expected environmental and economic benefits. Traditional Marginal Abatement Cost Curves (MACC) often ignore this behavioral feedback, which can lead to an overestimation of mitigation potential. This paper introduces a data-driven probabilistic framework for assessing the influence of the rebound effect on a portfolio of urban mitigation strategies by integrating behavioral feedback into a bottom-up MACC. By combining Monte Carlo (MC) simulations to address parametric uncertainty with Bayesian Networks (BN) for conditional inference, the robustness of nine strategies is examined across residential, commercial, and transportation sectors. The results demonstrate that even a moderate rebound effect ($\eta =0.5$) causes a $10.09\%$ decrease in total net abatement, dropping from 24.86 to 22.35 tCO₂e, and significantly raises costs. Notably, the number of strictly cost-effective strategies ($MAC<0$) decreases from six to three, highlighting the fragility of certain “win–win” measures. This framework introduces the concepts of Financial Backfire Probability (FBP) and Environmental Backfire Probability (EBP) as new metrics for urban planning. These findings emphasize that rebound tolerance is a critical factor in climate policy, indicating that additional measures, such as Internet of Things (IoT)-based monitoring and demand-side management, may be necessary to prevent performance erosion amid behavioral uncertainty. Full article

Purpose: The posterior tibial plateau offset (PTPO) is a parameter of sagittal plane bony tibia morphology with high variability and clinical relevance, particularly in cases involving stemmed tibial implants, where posterior tibial cortex interference may occur. However, its change during total knee arthroplasty (TKA), and its relationship to tibial component positioning remain unknown. Methods: Pre- and postoperative sagittal radiographs of 98 patients undergoing primary, mechanically aligned TKA using a single implant system were retrospectively analyzed. PTPO was measured as the distance between the tibial anatomical axis and the center of the tibial plateau or tibial component. Tibial component placement (TCP) was assessed anteriorly and posteriorly and categorized as anatomical (0–1 mm), mild (1–3 mm), or moderate (>3 mm) underhang (TCU) or overhang (TCO). Pre- and postoperative changes in PTPO were analyzed, preoperative PTPO was compared across TCP categories. Correlations with absolute anterior and posterior deviation from anatomical component placements were calculated. Results: PTPO showed high preoperative variability (mean 6.89 ± 3.69 mm) and was significantly reduced after TKA (5.89 ± 3.44 mm; mean change −1.06 ± 3.44 mm; p < 0.001). Higher preoperative PTPO was associated with anterior (p = 0.01) and posterior TCU (p = 0.02). PTPO showed a moderate correlation with anterior (r = 0.53, p < 0.01) and a strong correlation with posterior implant deviation (r = 0.68, p < 0.01). Conclusions: PTPO shows high variability among patients undergoing TKA, is significantly altered through surgery and correlates with tibial component malposition, particularly TCU. Surgeons should consider PTPO during preoperative planning to optimize tibial component positioning and reduce the risk of implant-to-bone conflict, especially when using stemmed implants. In patients with a high preoperative PTPO, accuracy-enhancing techniques such as computer navigation or robotic assistance may be considered. Full article

One of the main goals of heat and electricity producers in Latvia is to reduce the use of fossil fuels and introduce alternative fuel types that could help in reducing carbon dioxide emissions. This work focuses on addressing the set issue for a medium-capacity automated gas boiler plant, which provides heat for a local residential district. The following solutions were selected for boiler plant optimization: an electric boiler, a heat storage system, and solar collectors. Operating mode simulations were conducted for the electric boiler and solar collectors using Excel and Polysun (Standard) software. Simulations were created based on energy resource demand data obtained from a residential district located in Latvia and local energy resource prices/heat energy tariffs for the year 2024. The results from the simulations were used for technical and economic calculations to determine the payback period of the project. The electric boiler, together with the thermal energy storage tank and solar collectors, can produce 5903.04 MWh/year (~70% of local district heat demand) of thermal energy. This reduces the CO₂ emissions of the boiler plant by at least 1186.51 tCO₂ per year, which, at an emission quota price of 63.80 EUR/tCO₂, allows for savings of 75,699.34 EUR per year (12.82 EUR/MWh heat energy). The project’s discounted payback period is 4.12 years, considering the reduction in the cost of the CO₂ emission quota. The results of this study show that the chosen technologies are straightforward solutions that can be used to optimize existing boiler plants with limited space and can provide financial benefits to heat energy producers. Full article