Search Results (525)

The building sector is a critical component of the European Union’s strategy to achieve climate neutrality, as it accounts for 30–40% of total energy consumption and significant greenhouse gas emissions. While sustainability certification systems like BREEAM, LEED, DGNB, and HQE have established frameworks for environmental assessment, their widespread adoption in the residential sector faces challenges related to complexity and technical barriers. This paper provides a state-of-the-art literature review on streamlining sustainability certification for residential buildings in the EU. It examines the transition from established private schemes to harmonised frameworks such as Level(s), alongside the integration of Building Information Modelling (BIM) and Life Cycle Assessment (LCA). The review identifies key obstacles, including data interoperability issues, the need for automated quantity extraction, and the lack of technical expertise among stakeholders. Findings suggest that streamlining requires advancing semantic data models and digital twins to enable real-time performance monitoring and automated compliance checking. Furthermore, the alignment of national building codes with the Energy Performance of Buildings Directive (EPBD) and the European Green Deal is essential for fostering a more cohesive certification landscape. The study concludes by outlining pathways for reducing the administrative and technical burden of certification to support the EU’s decarbonisation and renovation goals. Full article

Between 2024 and 2025, Chinese land-based emission reduction projects frequently faced quality reviews and sanctions from Verra, a leading international standards-setting body. In addition to project stagnation and the withdrawal of carbon credits, China’s reputation as a host country in international efforts to reduce carbon emissions was severely damaged. These cases stem from a deeper social phenomenon: non-state actors like Verra have acquired rule-making power, and exercising this power has substantial implications for other entities, manifesting in the carbon emissions reduction field as tensions over the interests and reputations of project proponents and related parties. With non-state actors breaking the previous monopoly on rule-making power held solely by state actors, creating a “dualistic” confrontation, coordinating the relationship between the two becomes crucial, as promoting positive interaction becomes crucial. Otherwise, the dilemma of “compliant domestically, non-compliant internationally” and “international standards being difficult to implement domestically” will arise, as seen in these cases. This study used two cases of sanctions imposed by Verra on Chinese land-related projects as starting points. Then, taking China’s independent development of a methodology for silt-retention dam carbon sink projects to mitigate international sanctions as a third case. Following a research approach of “case analysis, in-depth investigation of bottlenecks, overcoming difficulties,” this study systematically examines the shortcomings and necessary efforts of both sides by exploring the various problems arising from the clash and conflict of rules between non-state actors and state actors. To address this issue, this study constructs a nested theoretical framework comprising two two-tiered theoretical structures. This study argues that both Verra and the government of China should work together to promote the legitimacy of emission reduction project standards and their effectiveness within host countries. The solutions proposed in this study can also provide experience and a reference for developing countries in addressing the expansion of power by non-state actors and the disconnect between domestic rules. Full article

The construction sector accounts for approximately 36% of global final energy consumption and close to 40% of total CO₂ emissions, making it a primary target of international climate policy. Despite this growing attention, the indigenous building traditions of the Ecuadorian Andes remain virtually absent from the international scientific literature on vernacular sustainability. This study presents a systematic field documentation and bioclimatic assessment of vernacular bahareque dwellings in the Kichwa-Saraguro community of Ilincho, canton of Saraguro, province of Loja, Ecuador (2700 m a.s.l.). A field survey of 30 dwellings identified five morphological typologies—I-1P, I-2P, 2B, L, and C—with typology C, a compact C-shaped block with a three-sided portal, accounting for 53.3% of the sample. A structured multi-criteria framework of 48 bioclimatic indicators distributed across eight categories, adapted to the cold-temperate mountain climate of the study area, was applied to quantify each typology’s bioclimatic performance. All typologies exceeded 75% overall compliance on the global Bioclimatic Performance Index (BPI), with typology C achieving the highest value (88.5%). Categories F (Materials and construction) and H (Cultural and social aspects) scored 100% across all typologies, reflecting system-level properties of the bahareque constructive system rather than morphological differences between typological variants; a supplementary morphological BPI restricted to Categories A–E and G is reported. An exploratory, uncalibrated energy simulation of typology C provided indicative evidence consistent with the expected thermal behavior of a high-thermal-mass bahareque envelope, with simulated minimum temperatures in the sleeping area within the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) 55-2013 comfort range (T-min 18.80 °C). Collectively, these findings contribute quantified bioclimatic documentation of vernacular bahareque architecture in Ilincho, identifying attributes—encompassing solar control, spatial compactness, high-thermal-mass envelope performance, and use of locally sourced low-embodied-energy materials—that may inform sustainable rural housing discussions in the Ecuadorian Andes and comparable high-altitude mountain contexts. Its documentation in the indexed scientific literature constitutes a step toward recognizing this constructive heritage as a practical resource for low-carbon building policy. Full article

The transition to a low-carbon economy is the cornerstone of global sustainability, requiring high-emission enterprises to shift from carbon-intensive production to genuine green innovation. However, this study uncovers a significant structural impediment to this transition: the “defensive greenwashing” response to climate stress. Focusing on listed companies in China’s high-emission industries (2009–2024), we employ a Debiased Machine Learning (DML) framework and Causal Forest analysis to capture the non-linear impacts of multi-dimensional climate risks. Our findings reveal a robust “threshold-trigger” mechanism: once climate pressures—whether physical shocks or policy-induced transition risks—exceed corporate endurance levels, firms aggressively pivot toward strategic “information arbitrage” rather than substantive decarbonization. We identify a profound “capability paradox” in sustainability governance, where firms with higher digital maturity and resource slack leverage their technical prowess to “calibrate” sophisticated narratives, thereby widening the monitoring gap and distorting green asset pricing. Furthermore, CEO risk preference acts as a psychological accelerator, amplifying strategic decoupling, particularly under transition-risk-induced uncertainty. By demonstrating how climate stress inadvertently incentivizes symbolic compliance over sustainable transformation, this research offers critical micro-level insights for policymakers. These findings are vital for refining sustainability oversight and ensuring that capital allocation fosters a resilient, equitable transition toward true ecological and economic decoupling. Full article

Large-scale wind integration introduces significant harmonic degradation and resonance risks. Traditional strategies primarily targeting Total Harmonic Distortion (THD) often struggle with individual node violations and high investment costs. To address these challenges, this paper proposes a two-stage sequential coordination strategy for Passive Power Filters (PPFs) and Active Power Filters (APFs). First, stochastic harmonic emission and frequency-domain power flow models are developed to characterize wind-induced harmonic propagation. Second, a sequential optimization framework is established to minimize Life Cycle Cost (LCC). In the first stage, PPF siting and sizing are optimized for cost-effective, system-wide mitigation of low-order harmonics while ensuring THD compliance. The second stage utilizes targeted APF deployment to precisely suppress residual high-order violations and localized resonance. Chance-constrained programming is incorporated to manage wind power uncertainty, enhancing the scheme’s robustness. Simulations on an IEEE 17-bus system demonstrate that the proposed method effectively balances harmonic suppression performance with economic efficiency, providing a robust and cost-effective solution for wind farm power quality management. Full article

A three-year (January 2020–December 2022) daily dataset of 16 polycyclic aromatic hydrocarbons (PAHs) collected in parallel with PM_2.5 and a suite of meteorological variables at a coastal Mediterranean urban site in southern Italy (Pomigliano d’Arco, Campania) is presented and analysed. Raw PAH time series were decomposed into a long-term trend component (LT), a seasonal component (ST), and a residual component (RT) using an iterative missing-value-robust Kolmogorov–Zurbenko (KZ) moving-average filter. Spearman rank correlations between PAH concentrations and four meteorological predictors (mean temperature, relative humidity, mean wind speed, and maximum wind speed) were computed for each congener. Diagnostic molecular ratios—Fla/(Fla + Pyr), BaP/BghiP, Indeno[1,2,3-cd]pyrene/(IcdP + BghiP), and BaA/(BaA + Chr)—were evaluated seasonally and interpreted jointly with an information-theoretic Bayesian mixture modelling procedure (SNOB/MML) and with the documented susceptibility of some PAH ratios, especially BaP-containing ratios, to atmospheric ageing, phase repartitioning and summer photodegradation. Total PAH concentrations (sum of 16 congeners) ranged from <1 ng m⁻³ in summer to 46 ng m⁻³ during winter high-pollution episodes, with BaP peaking at ≈6.7 ng m⁻³. Because BaP was measured in the PM_2.5 fraction, comparisons with the EU annual target value of 1 ng m⁻³ established for PM₁₀-bound BaP are treated as indicative context only, not as formal compliance statements. Pronounced seasonal variability was driven primarily by residential heating emissions, and the incremental lifetime cancer risk (ILCR) for inhalation exposure reached $1.03\times {10}^{-4}$ (95% CI: $0.88$–$1.20\times {10}^{-4}$) during the heating season under a continuous outdoor-exposure worst-case scenario. The absolute ILCR magnitude is conditional on the selected TEF scheme and on the adopted BaP unit-risk coefficient; under an additional indoor-dominated scenario (16 h day⁻¹, infiltration factor 0.6), the corresponding risk remained above the conventional ${10}^{-6}$ benchmark. An anomalous near-background PAH signal during spring 2020 is attributed to the COVID-19 national lockdown, which reduced total PAH concentrations by approximately 85% relative to the seasonal component predicted by the iterative moving-average filter for the same calendar window. Source apportionment via diagnostic ratios identifies residential/biomass combustion as the dominant cold-season source and vehicular emissions as the prevailing warm-season source. These results provide a novel characterisation of PAH pollution dynamics in the undersampled southern Mediterranean and provide evidence to support targeted abatement policies. Full article

Green shipping corridors have become a key strategic initiative for advancing maritime decarbonization. This study develops an AIS-based bottom–up framework for estimating carbon emissions and compliance costs in green shipping corridors. The framework combines corridor fleet identification, AIS-based energy consumption and emission estimation, and compliance-cost modeling under the IMO CII and GFI requirements. On this basis, eight alternative energy options—HFO, fossil LNG, bio-LNG, e-LNG, bio-methanol, e-methanol, green ammonia, and biofuel B100—together with carbon capture technology, are incorporated into the analysis and applied to the Shanghai–Los Angeles/Long Beach green shipping corridor. The results show that before 2035, the emission reduction requirements of CII can cover the basic compliance requirements of GFI. Without carbon capture, the combined use of fossil LNG and bio-LNG appears to be a relatively favorable transition pathway. When carbon capture is considered, LNG with carbon capture and HFO with carbon capture emerge as two relatively advantageous transition pathways. During 2025–2035, it is recommended that ships first adopt fossil LNG, then gradually introduce limited amounts of bio-LNG, and subsequently integrate carbon capture once the technology becomes mature. Full article

Industrial control of volatile organic compound (VOC) emissions from medium-density fibreboard (MDF) production remains constrained by a shortage of compound-resolved evidence from full-scale plants, where wood furnish, amino resin chemistry, heat transfer, gas flow, and wet gas cleaning act simultaneously. Here, we analysed more than 20,000 synchronized operating records from a full-scale single-stage flash-tube MDF dryer at an industrial SWISS KRONO production line and linked total VOC (TVOC) measurements from flame ionization detection with Fourier-transform infrared speciation on the cleaned stack. Five compounds—α-pinene, 3-carene, limonene, methanol, and formaldehyde—accounted for more than 80% of the resolved VOC signal. Process–state contrasts showed that higher digester residence time, discharge screw speed, adhesive amount, urea amount, dryer inlet temperature, and scrubber–water temperature increased one or more representative compounds, whereas higher hardwood share, additional flue-gas supply, and higher scrubber–water pH decreased them. Limonene, methanol, and formaldehyde were substantially more process-sensitive than α-pinene. An exploratory decorrelation step further showed that a drying/throughput domain explained about half of the variability of the screened process space. The study therefore identifies the small set of compounds and operating domains that most strongly govern the cleaned dryer-stack signature and provides a mechanistically grounded prioritization framework for follow-up causal experiments, source apportionment, and emission-mitigation design in industrial MDF manufacture. Unlike product or chamber emission studies, this work links the compound-resolved FTIR/FID chemistry of the final cleaned industrial stack with synchronized production variables; it therefore addresses a scale-integration gap by transforming routine compliance-type exhaust monitoring into a process-diagnostic framework for ranking emission sources, abatement-sensitive variables, and mitigation experiments. Full article

One approach to make the aviation sector climate-compatible is to minimize greenhouse gas emissions by employing electric and hybrid electric propulsion system concepts. The introduction of novel technologies introduces novel failure modes and consequently effects of failure conditions on the aircraft. This study examines the safety of distributed electrified aircraft propulsion systems and evaluates individual failure scenarios in the context of the relevant certification requirements. A comparison of the functional architectures of legacy and Electric Hybrid Propulsion Systems (EHPSs) is conducted and the existing aircraft-level requirements, that are based on experience with conventional propulsion systems, are assessed for their applicability to the certification of novel propulsion systems. Subsequently the relevant safety items from these requirements are identified in the context of a critical loss of thrust scenario. Analysis methods are assigned to these safety items in order to prove the compliance of the novel systems with the legacy certification documentation. This results in a validation concept for EHPS at the aircraft level in the context of a critical loss of thrust. In particular, the distribution of individual subsystems and components throughout the aircraft leads to reduced isolation of the respective propulsion systems and thus potential safety-critical interactions with adjacent systems. The analysis demonstrates that the use of distributed propulsion systems increases the risk of multiple failures of redundant systems and cascading failure propagation, highlighting the need to develop targeted means of prevention and the mitigation of failure conditions for these systems. Full article

Coal-fired power units remain integral to electricity supply in many regions while facing increasingly stringent environmental expectations. Bridging reliable generation with sustainability requires more than end-of-pipe controls; it demands continuous intelligence embedded in plant operations. This study introduces an industry-oriented monitoring framework that transforms historical operational records into actionable foresight, enabling on-the-fly orchestration of combustion conditions to anticipate sulfur oxide (SOx) concentrations. Leveraging 919 empirical data points collected in 2019 from Unit 8 of the Taichung Thermal Power Plant, the framework integrates robust data governance, targeted feature curation, and a neural network-based analytics core. Eight process variables—sulfur content, coal feed rate, fixed carbon, grinding rate, calorific value, excess air, air flow, and boiler efficiency—emerge as the most influential drivers through systematic selection and feature importance attribution. The resulting forecasting module exhibits near-perfect alignment with observed emissions (R² = 0.99), enabling near-real-time guidance for setpoint adjustments and facilitating compliance strategies under varying load and fuel-quality conditions. Beyond accuracy, the system is architected for scalability and portability, aligning with Industry 4.0 paradigms by coupling continuous sensing, data-driven decision support, and stakeholder transparency. By reframing emission oversight as a proactive, intelligent service rather than a static reporting function, the proposed approach advances operational resilience, regulatory compliance, and community trust, with direct implications for resource efficiency and circular economy initiatives across heavy industry. The framework reduces potential SOx emissions and improves energy utilization efficiency under varying operational conditions. This approach contributes to environmental sustainability by enabling proactive emission reduction and cleaner production practices. It supports regulatory compliance and aligns with global sustainability goals, including SDG 7 and SDG 13. Full article

This review examines an environmental LCA of solar modules and cell technologies across 18 environmental indicators to assess the performance of current solar module types. It provides a more comprehensive analysis by including studies that account for recycling credits in end-of-life PV waste management. The literature search covered seven databases up to 20 November 2025, resulting in the selection of 43 papers focused on solar modules, LCA, and recycling for data extraction. The methodological quality and risk of bias of included studies were evaluated based on compliance with ISO 14040 and ISO 14044 requirements. Due to the diversity of methodologies, functional units, and system boundaries across studies, it is observed that there is a wide range of emission values for the various solar PV technologies, with some studies reporting very high or very low figures. Considering one of the main impact categories, GHG emissions of key PV technologies, including mono c-Si, multi c-Si, CdTe, and PERC modules, for the study period 2015–2025 have been reported in the ranges of 493–2760, 640–2418, 312–2140, and 425–1759 kg CO₂-eq/kW_p. It can be generally expected that when assessed under comparable LCA frameworks, emerging solar cells are more likely to exhibit lower emissions than conventional silicon-based solar cells across most indicators. Moreover, results from recent studies show an environmental improvement for the various module and cell types, largely due to advancements in material efficiency during the optimized manufacturing process. A major limitation of this study is the omission of service lifetime considerations, as most sustainability assessments are ideally based on lifetime energy generation (kWh). However, given the assumption of comparable lifetimes among the assessed module types, the use of kW_p-based emissions remains suitable for relative comparisons, although possible differences in long-term performance may not be fully captured. Full article

This paper presents a comprehensive methodology for assessing supply chain transparency and resiliency using a data-driven approach. Leveraging global trade data and Harmonized System (HS) codes, the methodology maps each stage of the supply chain to enhance regulatory compliance and mitigate operational risks. Transparency is evaluated using a novel classification system that categorizes branches as fully transparent, highly transparent, moderately transparent, or non-transparent. This enables raw material traceability, Scope 3 greenhouse gas (GHG) emission estimation, and identification of high-emission nodes for targeted reductions. Resiliency is assessed through graph analytics and stress testing, incorporating metrics such as the Giant Connected Component (GCC) and probabilistic simulations to analyze vulnerabilities and develop recovery strategies. A case study on the Cr-13 Steel Drill Pipe supply chain highlights the benefits of incorporating scrap materials for sustainability, alongside challenges related to traceability due to regulatory gaps and non-transparent networks. Monte Carlo simulations identify critical nodes whose disruption significantly affects network connectivity; therefore, resiliency, and transparency. This methodology delivers actionable insights to improve supply chain resiliency, sustainability, and operational efficiency. It is scalable across industries, enabling stakeholders to optimize management strategies, align with global climate initiatives, and build resilient and transparent networks. 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

Classification of vehicle engines using the chemical composition of the exhaust from these engines can be used to identify the engine’s design and verify compliance with environmental regulations through the vehicle’s emissions. This paper describes a method to identify the type of vehicles using machine learning (ML), where low-cost MQ series sensors measure the gases and particle emissions from a vehicle exhaust system, while simultaneously collecting and measuring the vehicle’s temperature and humidity levels. A custom-designed multi-sensor exhaust sensing module is employed to capture real-time exhaust emissions prior to entering the atmosphere. Exhaust samples are collected from vehicles representing three major engine categories: petrol, diesel, and compressed natural gas (CNG). In addition, fresh air samples are collected as a baseline environmental reference for comparison. All exhaust measurements are collected under controlled and consistent engine operating conditions to ensure comparable emission profiling across vehicle classes. To ensure consistent combustion-based emission profiling, this study focuses on conventional fuel-powered vehicles. MQ-series gas sensors are sensitive to combustion by-products emitted during engine operation, such as carbon monoxide (CO), hydrocarbons (HC), while also exhibiting cross-sensitivity to other gaseous components present in exhaust mixtures. Nevertheless, the proposed system performs pattern-based classification using relative sensor response signatures. Standardization of data is achieved through z-score normalization. The best models developed (based on three separate experimental designs) are trained and validated using six supervised machine learning algorithms such as Logistic Regression, Support Vector Machine (RBF), k-Nearest Neighbors, Random Forest, Gradient Boosting Decision Tree, and XGBoost and are compared against one another. Evaluation of the tested algorithms using various evaluation metrics demonstrated that ensemble models outperformed all other algorithms, achieving the highest accuracy of 99.5%. Furthermore, noise analysis confirms that the proposed solution maintains high classification accuracy (more than 89%) even under substantial sensor perturbations mimicking the real-world deployment. The solution proposed below illustrates how using gas sensors and advanced algorithms can provide accurate exhaust identification and identify engines in real-time. Full article

The accelerated processes of industrialization and urbanization have led to increasingly prominent environmental risks by emerging organic contaminants (EOCs) in wastewater. These contaminants are characterized by low concentrations, high toxicity, and complex composition, making their efficient removal crucial for safeguarding ecological security and human health. Advanced oxidation processes exhibit significant potential for the removal of EOCs due to their high degradation efficiency. However, current treatment paradigms remain constrained by several critical issues. Notably, the routine over-oxidation of low-toxicity small-molecule organics solely aims to satisfy chemical oxygen demand (COD) compliance standards. This unnecessary practice not only increases operational costs and carbon footprint but also leads to energy waste and reduced overall treatment efficiency. Based on the current technological landscape, this paper analyzes the core challenges in the removal of EOCs at present. In light of policy orientations and technological trends, it outlines future research directions and industrial development pathways, providing insights for achieving the synergistic goals of efficient removal of EOCs, low carbon emissions, and cost-effective operation. Full article