Search Results (19,019)

The construction sector is responsible for a significant share of greenhouse gas emissions in Europe, making the decarbonisation of the existing building stock a critical priority. In this context, Digital Building Logbooks (DBLs) are increasingly promoted as digital tools to support renovation planning, data continuity, and circular economy practices across the building lifecycle. Despite growing policy attention, the adoption of DBLs in renovation projects remains limited in practice. This study provides one of the first empirical rankings of perceived barriers, benefits, and drivers influencing DBL adoption in renovation projects across Europe. An exploratory quantitative survey was conducted with a purposively selected sample of stakeholders involved in renovation-related activities. Likert-scale responses were analysed using descriptive ranking statistics and reliability testing, while qualitative data from open-ended responses were analysed using directed content analysis. The results indicate that stakeholders strongly recognise the benefits of DBLs, particularly in terms of improved access to reliable building information, informed decision-making, and support for circular renovation practices. However, adoption is constrained by regulatory uncertainty, limited awareness, and unclear governance and operational frameworks. The most influential drivers identified relate to interoperability with existing digital tools, rising awareness of DBLs among stakeholders, regulatory support, and the availability of standardised and operationally clear frameworks for DBL implementation. Full article

Urban business-to-business distribution in Casablanca relies heavily on light commercial vehicles (LCVs) operating in a constrained street environment where loading/unloading access, intersection capacity, and recurring bottlenecks jointly shape performance and environmental impacts. However, high-resolution freight origin–destination (OD) observations and junction calibration data are limited, which complicates direct estimations of congestion and externalities attributable to commercial activity. This study develops a reproducible, large-scale modeling workflow that couples tour-based freight demand generation in order units with simulation-based traffic assignment (SBA) on a metropolitan network and translates network performance into emissions and monetary losses. Warehouses are modeled as primary producers and commercial activity zones as attractors via sector-tagged production and attraction functions; the resulting order distribution is converted to OD vehicle trips using the tour-based trip generation procedure with the mean targets-per-tour fixed to one to ensure numerical stability, yielding a direct-shipment approximation appropriate for stress–response analysis. Junction impedance is represented through turn-type volume–delay relationships and node-level impedance procedures, and congestion is evaluated using vehicle kilometers traveled/vehicle hours traveled (VKT/VHT)-based indicators, delay-intensity measures, and link/node bottleneck rankings. Across demand-scaling scenarios, VKT increases from 302,159 to 1,017,686 veh·km/day, while network delay rises nonlinearly from 392.5 to 2738.4 veh·h/day, indicating saturation-driven amplification of time losses. The Handbook of Emission Factors for Road Transport (HBEFA)-compatible emission estimates scale with activity: total carbon dioxide (CO₂) increases from 154.1 to 519.5 t/day, and nitrogen oxides (NOx) and particulate matter (PM_2.5) totals rise proportionally under fixed fleet assumptions. Monetizing delay with a purchasing-power-adjusted value-of-time range yields a congestion cost per trip that increases from approximately 0.20 to 0.41 Moroccan dirham, MAD/trip (at 60 MAD/veh·h), consistent with rising delay intensity. Bottleneck extraction shows welfare losses to be structurally concentrated on a small persistent corridor set, led by ‘Boulevard de la Résistance’, with recurrent hotspots including ‘Rue d’Arcachon’ and ‘Rue d’Ifni’. The framework supports policy-relevant reporting of congestion, emissions, and welfare impacts under data scarcity, with explicit sensitivity bounds. Full article

Hydropower refurbishment is increasingly recognized as a key strategy for maintaining renewable electricity generation and minimizing the environmental and social impacts of developing new infrastructure. With much of the global hydropower fleet approaching or exceeding its original design life, refurbishment decisions must strike complex trade-offs between technical performance, environmental impacts, economic viability, and social acceptability. This review provides a comprehensive summary of the scientific and policy literature on sustainable hydropower refurbishment, with a particular focus on the integration of life cycle assessment (LCA) and multi-criteria decision analysis (MCDA) from a circular economy perspective. The study systematically reviews the latest results in the fields of environmental LCA, life cycle costing (LCC), social LCA (S-LCA), and integrated life cycle sustainability assessment (LCSA), highlighting their application in the refurbishment and modernization of hydropower plants. The results show that construction-related impacts, particularly those associated with concrete and steel, dominate the environmental load over the life cycle, making refurbishment and component recycling highly effective strategies for reducing embodied emissions. The integration of LCA and MCDA allows for the transparent prioritization of refurbishment alternatives by explicitly considering stakeholder preferences and trade-offs between environmental, economic, social, and technical criteria. Overall, the results support the use of integrated, multi-criteria life cycle frameworks as reliable decision-making tools for managing sustainable hydropower refurbishment and long-term energy system resilience. Full article

Warehouses are energy-intensive nodes in a logistics chain and critical hotspots for decarbonization efforts. Digitalization and Industry 4.0 technologies are increasingly promoted as enablers of greener warehousing; however, environmental benefits are often implied rather than empirically quantified. This study examines how digitalization, automation, and robotization support the implementation of green logistics principles in warehousing operations. The research combines a scientific literature review and document content analysis with semi-structured interviews with company managers and logistics professionals. The results indicate that implementing a warehouse management system (Vision Equinox), integrating information systems, and adopting RFID technology reduce paper-based processes, improve picking accuracy and internal routing, shorten loading and unloading times, and may decrease the risk of human error. Consequently, these technologies enable more efficient resource use and can contribute to lower energy consumption and a reduced environmental footprint associated with warehouse activities. The study concludes that digital technologies already serve as a systematic enabler of green logistics within the organization; however, their environmental benefits have not yet been quantified. Future research should therefore focus on measuring changes in energy use and CO₂ emissions under different warehousing scenarios. Full article

The Indian Himalayan Region is an important ecological location, but it is now suffering from serious air pollution due to activities like vehicular emissions, industrial activities, biomass burning, and regional atmospheric circulation, which have led to increased air pollution and threatened ecosystems, human health, and the climate. This paper employs qualitative document analysis through reviews of the national climate policies, institutional frameworks, state documents, and technology-based solutions. It concludes that despite comprehensive national policies, many gaps exist between the policy design and ground-level implementation. Our findings reveal three critical governance gaps: (i) altitude-specific regulatory failures in vehicular emission standards, (ii) Institutional fragmentation limiting enforcement capacity, particularly for diffuse sources, (iii) economic barriers preventing sustained adoption of clean fuels despite subsidy programs. According to this research, we propose a three-pillar framework integrating (i) investment in sustainable technology and green infrastructure, (ii) strengthening institutions and policies, and (iii) fostering behavioral change and public awareness. The study contributes to the limited literature on region-specific air quality governance and offers a strategic framework to support climate resilience in the Himalayas. Full article

To address the problem of excessive sulfur in high-sulfur magnetite concentrates when used directly, this study systematically investigated the desulfurization behavior and mechanism during oxidative roasting. Green pellets were prepared by mixing high-sulfur iron concentrate fines with 1% bentonite, followed by roasting experiments in air at 800–1200 °C. Thermogravimetric analysis (TG), real-time flue gas analysis (DOAS), X-ray diffraction (XRD), and scanning electron microscopy–energy dispersive spectroscopy (SEM–EDS) were employed to characterize the process and products. The results show that sulfur release is mainly concentrated in two stages: intensive oxidative decomposition of FeS/FeS₂ in the range of 480–580 °C and release of reacted sulfur originally encapsulated within the pellets in the range of 940–1080 °C. It was found that alkali metal oxides CaO and MgO in the feed can fix sulfur at a high temperature. They react with released SO₂ and iron oxides to form Ca/Mg sulfate–iron oxide composite phases, such as (Ca_0.75Mg_0.25)SO₄·0.38Fe₂O₃ and (Ca_0.91Mg_0.09)SO₄·3.66Fe₂O₃·1.47MgO, which slow the SO₂ emission rate. A desulfurization ratio above 99% can be achieved when roasting at 1100 °C and above. This study clarifies the sulfur migration mechanism during the roasting of high-sulfur iron concentrate pellets, providing a theoretical basis for optimizing the roasting process to achieve efficient desulfurization and recovery of iron resources. Full article

Hydrological drought projections are crucial for climate-resilient water management; however, many basins lack calibrated process-based models that can readily be forced with climate scenarios. This study develops a purely data-driven framework to forecast the Streamflow Drought Index (SDI) from standardized meteorological indices and to assess future drought regimes under different emission pathways. We used a 60-year monthly record (1961–2020) of the Standardized Precipitation Index (SPI), the Standardized Temperature Index (STI), the Standardized Precipitation–Evapotranspiration Index (SPEI), and the SDI for the Sava River Basin. Correlation analysis showed that the SDI is primarily controlled by the short-lag SPI (0–1 months), whereas the STI and SPEI play a minor role. Several machine learning models were tested for one-month-ahead SDI prediction; a Random Forest (RF) with hyperparameters optimized by TimeSeriesSplit cross-validation, combined with linear-scaling bias correction, clearly outperformed XGBoost, Elastic Net, support vector regression, and a multilayer perceptron. On the independent test period (2009–2020), the RF achieved MAE ≈ 0.62, RMSE ≈ 0.83, NSE ≈ 0.49, and KGE ≈ 0.65. Using SPI/STI/SPEI projections from RCP2.6, RCP4.5, and RCP8.5, the RF produced monthly SDI projections for 2021–2050, revealing increasingly frequent, severe, and persistent streamflow droughts with higher emissions. The results demonstrate that carefully tuned ensemble tree models driven solely by standardized climate indices can provide skilful and interpretable SDI projections for drought risk assessment, supporting sustainable, climate-resilient water resources planning and adaptation in this transboundary basin. Full article

Challenges caused by climate change increase concern for achieving global sustainability. Although citizen awareness is increasing, ensuring sustainability in key sectors like construction is necessary. Achieving sustainability requires essential actions that, however, could have a negative impact on economic performance. Studies on renewable energy installations tend to prioritize performance or sustainability, rather than facing the strategic challenge to find the balance between both. The present work fits this framework through managing renewable energy operations in a construction materials factory of Grupo Puma, located in Spain. The objective of the proposed methodology is to identify key performance indicators (KPIs) for the FV installation and to simulate energy flows using a validated model within a digital simulation environment. This study proposes a trinomial of KPIs—self-consumption, solar utilization, and avoided CO₂ emissions—as more stable indicators than conventional metrics. The Pareto front analysis shows that self-consumption can be increased by up to 20% with only an approximate 10% reduction in solar utilization. This finding offers a clear strategic recommendation: prioritizing higher self-consumption is a viable industrial strategy to enhance Grupo PUMA’s sustainability performance while maintaining acceptable economic efficiency. Full article

Aiming at the problem of non-uniform and non-universal evaluation criteria of machine tools’ carbon emissions in the whole life-cycle analysis, an evaluation method of life-cycle carbon-emission analysis of machine tool based on declared unit was put forward by analyzing and summarizing the existing carbon emission evaluation models. A universal evaluation system for machine-tool life-cycle carbon-emission analysis was first established, and an appropriate declared unit was then selected according to industry characteristics and machine-tool types. Subsequently, an information-flow-based iERWC boundary division method was proposed to support data collection and carbon-emission calculation across five life-cycle stages. To better reflect carbon emissions in the phase of application, the life-cycle inventory incorporates the use of coated cutters, including the associated cutters’ consumption and replacement demands. Two heavy duty floor-milling and boring machine tools produced by Qiqihar No. 2 Machine Tool (Group) Co., Ltd. Were taken as examples to calculate and evaluate life-cycle carbon-emission analysis of machine tools, and the uncertainty analysis was carried out; the possible influencing factors were pointed out to ensure the comprehensive carbon-emission assessment of the whole life cycle. Full article

While sustainability is set as a goal by a broad range of international organizations, its definition varies, and there is still a lack of practical criteria for product designers to evaluate the degree of (un)sustainability in the design phase. Life cycle assessment (LCA) can allow quantification of the environmental impacts of a product but is often carried out post-design, when the manufacturing process is already settled. Finally, while significant advances have been made towards standardizing LCA calculations by providing product category rules, large uncertainties remain in the calculation results due to a lack of transparency regarding the choices of databases, system boundaries, allocation, cut-off rules, and level of data granularity. A practical way to improve in those areas is to share with the semiconductor community a parametrizable life cycle inventory (LCI) model based on a target device to (1) identify knowledge gaps in LCA methods for such products, (2) identify the main process variables, and (3) provide a starting point for LCA calculations by the designers themselves. With this aim, a parametrizable cradle-to-gate manufacturing LCI model was developed based on the peer-reviewed process flow of a trench field-stop silicon insulated gate bipolar transistor (IGBT) semiconductor power device. The model allows computation of the environmental impacts of the IGBT manufacturing process based on different tunable parameters such as die size, wafer diameter, manufacturing yield, abatement efficiency, wafer fab throughput, wafer fab location, and associated electricity mix. Embedding a high level of data granularity, it helps identify, at elementary process levels, key environmental hotspots and associated technical levers for their reduction. Analysis of the IGBT manufacturing process tends to demonstrate the importance of an impact assessment approach considering multiple environmental categories, going beyond the sole focus on greenhouse gas emissions and accounting for potential transfers of impact. With an open-source mindset and in a continuous improvement prospective, the manufacturing inventory model and its associated tools are freely available from a public GitHub repository and open for comments and consolidation from users. Full article

Based on 2011–2022 panel data covering 278 Chinese cities, a panel fixed-effects model, a mediating effect model, and a threshold regression model are used to conduct an empirical analysis of the influence of the digital economy (DE) on urban carbon emission performance from the quantitative and efficiency perspectives. The key findings include the following: (1) An inverted U-relationship is observed between the DE development and urban per capita carbon emissions (PCE), while the nexus between the DE and carbon emission efficiency (CEE) follows a U-shaped pattern. (2) The DE yields a stronger carbon reduction effect once green technology innovation attains elevated levels; conversely, under conditions of nascent green innovation, its principal impact manifests through improvements in CEE. Only when green technology innovation surpasses a critical threshold does the DE begin to reduce carbon emissions. (3) Heterogeneity analysis indicates that, in optimization and upgrading agglomerations, carbon emissions are reduced by DE at a later time point. In growth and expansion agglomerations, the impact of DE on CEE is more evident. Moreover, policy priorities should include fostering innovation-driven digitalization, expanding green technology diffusion, and optimizing regional mechanisms for coordinated low-carbon growth. Full article

The Mexican Tropical Pacific (MTP) is a key component of the Eastern Tropical North Pacific Oxygen Minimum Zone, yet its carbonate system variability remains poorly constrained. This study examines wind-driven circulation effects on dissolved oxygen (DO) and the carbonate system —dissolved inorganic carbon (DIC), total alkalinity (TA), total-scale pH (pH_T), partial pressure of CO₂ in seawater (pCO_2w) and air–sea CO₂ fluxes (FCO₂)— in the Gulf of Tehuantepec (GT) and Tehuantepec Bowl (TB). Hydrographic data and discrete water samples were collected at 50 oceanographic stations during March 2020. Principal Component Analysis (PCA) identifies wind-driven circulation as the primary control of biogeochemical variability. Tehuano wind events and mesoscale eddies promoted upwelling of low-oxygen (DO < 20 µmol kg⁻¹) and high-DIC (>2200 µmol kg⁻¹) waters to 50 m depth in the central GT, while downwelling conditions prevailed in the TB. Stoichiometric analysis revealed DIC-DO coupling (slope = −1.39). Overall, the MTP acted as CO₂ source (FCO₂ ranging from −1.92 to 24.11 mmol m⁻² d⁻¹), with enhanced emissions linked to eddy-induced upwelling. This study provides the first integrated characterization of the carbonate system across both the GT and TB. Full article

In response to growing concerns over global warming and energy sustainability, transitioning from fossil-fuel-based heating systems to renewable alternatives is essential. This study evaluates the economic and environmental performance of geothermal heat pumps for building heating and compares it with conventional coal-fired boilers, natural-gas boilers, and diesel furnaces. Using the heating degree-day (HDD) method, heating energy demand was analyzed for four U.S. cities—Anchorage (AK), San Francisco (CA), Salt Lake City (UT), and Las Vegas (NV)—representing diverse climatic zones. The analysis integrates thermodynamic and economic parameters, including the coefficient of performance (COP = 2–5) and annual fuel-utilization efficiency (AFUE = 80–97%), to evaluate heating-system performance and operational cost across different climatic regions. Sensitivity analysis with ±10% variations in fuel and electricity prices and system efficiencies demonstrates that geothermal heating remains the most stable and emission-efficient option under all scenarios. Results indicate that geothermal systems, despite higher reported initial investment, achieve lower operational and emissions-related costs and offer a robust and sustainable solution for decarbonizing building-heating systems. For example, the estimated seasonal geothermal heating cost is $370.59 in Anchorage compared with $646.48 for coal heating and $3375.65 for diesel systems. Furthermore, policy evaluation indicates that federal and state incentives, such as investment tax credit under the Inflation Reduction Act and rebate programs, can reduce installation costs by 25–40%, improving economic feasibility, particularly in colder regions. The analysis focuses exclusively on energy and emissions-related costs and does not explicitly model capital investment or levelized cost metrics. Full article

The paper examines the energy transition using Poland as a case study. The model was estimated based on annual data for Poland for the period of 1990–2024 (n = 35). The estimation was carried out using the OLS method with HAC correction, and the statistical significance of parameters was assessed using statistical tests. Based on econometric analysis, the impact was examined throughout the entire research period, with additional analysis of the structural break dummy for 2015. It was verified whether this impact had changed since 2015 compared to the earlier period. The data were used to calculate indicators, arranged in three groups: (1) capacity availability indicators (for the availability of the overall power system and for the renewable energy sources (RES)); (2) indicator of emission intensity (the indicator was defined as the ratio of total greenhouse gases emission to real GDP); (3) indicator of the economy’s energy intensity (the indicator was defined as primary energy consumption per unit of GDP). Annual summaries of these indicators constituted the input data for econometric modelling. The aim of the empirical analysis was to deepen the identification of mechanisms shaping greenhouse gas emission intensity by incorporating into the model indicators of generation capacity availability and measures of the economy’s energy intensity. The data collection based on constructed greenhouse gas emission intensity and energy intensity indicators of the economy enables the analysis of the increase in emission intensity regardless of the scale of the economy, in the system of power availability for the entire energy system, as well as for renewable energy sources. This approach makes it possible to move away from the analysis of absolute volumes toward a structural perspective that better reflects the real production capabilities of the power system as well as the efficiency of energy use in the economy. The results indicate that economic energy intensity is the dominant determinant of greenhouse gas emission intensity in Poland during the research period. The econometric analysis estimates show a positive and statistically significant relationship between energy intensity and emissions intensity, whereas generation capacity availability indicators—both for the total power system and for renewable energy sources—do not exhibit statistically significant effects. However, it was found that this impact was not constant throughout the entire period (β is 0.455 for pre-2015 and 0.325 for post-2015). Sensitivity analysis based on point elasticities reveals that a 1% increase in energy intensity of GDP leads to an increase in greenhouse gas emission intensity (by approximately 1.18% pre-2015 and 0.85% post-2015), whereas analogous changes in total capacity availability and RES availability are associated with substantially smaller effects (0.10% and 0.20%, respectively). These findings suggest that improvements in economy-wide energy efficiency played a more decisive role in reducing emissions intensity than short-term variations in generation capacity availability. Full article

The conflict between rapid industrialization and ecological deterioration constitutes a critical bottleneck for developing regions, particularly concerning industrial wastewater governance. The primary purpose of this study is to investigate whether industrial robotization (IR) can break this deadlock. This study proposes the central hypothesis that adopting IR significantly mitigates industrial wastewater emissions (IWE). Utilizing comprehensive panel data from 30 Chinese provinces from 2013 to 2022, this proposition is rigorously tested using fixed effects models. The main results clearly demonstrate that IR acts as a robust suppressant against IWE. Importantly, mechanism verification shows that this pollution reduction effect is propelled by stimulating green patents and amplifying technical expenditure. The empirical evidence reveals distinct nonlinear features regarding how IR affects IWE. Crucially, heterogeneity analysis indicates that the emission reduction utility of IR becomes significantly more pronounced in territories with robust financial depth and targeted policy backing. Consequently, this study provides vital strategic blueprints for policymakers to leverage industrial automation to navigate the sustainability crisis. Full article