Search Results (2,566)

The industrial sector in Liaoning Old Industrial Base exhibits energy consumption per unit of added value substantially above the national average, with carbon emissions from industrial energy consumption characterized by a large baseline and stringent reduction constraints. To address these challenges, this study applies the IPCC emission-factor method to construct a long time-series accounting of provincial carbon emissions from industrial energy consumption and develops a multi-dimensional carbon intensity evaluation system. An extended Logarithmic-Mean Divisia Index (LMDI) decomposition framework is employed to integrate five driving factors—energy structure, energy intensity, economic structure, per-capita economic output, and population scale—alongside differentiated mechanisms across four fossil-fuel categories (raw coal, crude oil, coke, and diesel). The results identify per-capita economic output as the dominant positive driver, while energy intensity and economic structure serve as primary abatement factors, with their cumulative contributions exhibiting notable phase transitions over the study period. These findings provide a quantitative foundation for designing industry- and region-specific emission-reduction policies and offer practical insights for reconciling industrial revitalization with the “dual-carbon” strategy in old industrial bases. Full article

The suspension polyvinyl chloride (PVC) industry is characterized by intensive water and energy consumption, making process integration an attractive strategy for improving resource efficiency. Previous studies have demonstrated the technical, environmental, and safety benefits of direct water recycling in suspension PVC production; however, the thermodynamic implications of this integration have not been evaluated. Therefore, this study presents an exergy-based sustainability assessment of an industrial-scale suspension PVC production process incorporating direct water recycling through mass integration using a Computer-Aided Process Engineering (CAPE) approach implemented in Aspen Plus^®. Physical and chemical exergy were calculated for all material and utility streams under pseudo-steady-state conditions, and exergy balances were performed for the main process sections to determine exergy destruction and exergetic efficiency. The integrated configuration achieved an aggregated stage-level exergetic efficiency of 93.0%, representing a 16.8% improvement over the conventional process, while freshwater consumption and wastewater generation were reduced by 16.0% and 26.3%, respectively. Recovery and purification accounted for the largest share of total exergy destruction (39.2% and 36.4%), indicating that downstream separation operations dominate the thermodynamic inefficiencies of the process. Although direct water recycling introduced additional local irreversibilities associated with recycle operations, the integrated configuration improved the utilization of available exergy while reducing water consumption and wastewater discharge. These results demonstrate that exergy analysis provides complementary information beyond conventional water–energy–product indicators and constitutes an effective tool for identifying thermodynamic improvement opportunities in industrial PVC production systems. Full article

Traditional household-based decentralized agro-processing is an important source of air pollution in rural China, yet the pollution–carbon co-benefits of centralization remain poorly quantified. In this paper, a LEAP-based scenario framework (2018–2035) is developed for Yibin’s Camphora longepaniculata essential oil processing industry under four scenarios: baseline, policy (27% centralization share), enhanced scenario 1 (ENH1; 50% centralization share), and enhanced scenario 2 (ENH2; 50% centralization share with a 2% efficiency retrofit). The framework simulates energy demand and emissions of CO₂, PM₁₀, SO₂, NOx, and VOCs based on specified emission factors and technological assumptions. The results indicate that centralization reduces unit energy consumption by 51.20% and CO₂ intensity by 63.12%. The 27% penetration rate (current policy) lowers emission levels but cannot alter the growth trajectory during output expansion, whereas the 50% penetration rate achieves net CO₂ and energy reductions of 31.56% and 25.60%, respectively, relative to the baseline. All pollutants exhibit synergy elasticity coefficients (ε) above unity (PM₁₀ > SO₂ > VOCs > NOx); these coefficients are governed by the emission factor structure rather than the penetration rate. Thus, scaling centralization drives absolute reductions, whereas co-benefit enhancement requires process or end-of-pipe upgrades. All of the reported results are scenario-based model outputs, not field measurements, and biogenic CO₂ is included only for cross-scenario comparability, not as a climate impact claim. Full article

The Internet of Things has increasingly enabled advancing real-time environmental monitoring through the integration of Low-Power Wide-Area Networks. Among these technologies, LoRa (Long Range) has emerged as a prominent communication technology due to its combination of long-range communication, low energy consumption, and affordability, making it particularly suitable for remote and infrastructure-limited environments. Its adaptability is further enhanced through the use of open-source hardware, renewable energy sources, and intelligent algorithms. Despite LoRa’s limitations in bandwidth and data rate, recent innovations enabled increasingly data-intensive applications, including image transmission. This review critically examines recent advances in image transmission over LoRa networks, synthesising approaches across four interconnected strategies: image compression, packetisation and reliability, communication optimisation, and application-specific techniques. The analysis evaluates trade-offs among image size, transmission latency, energy consumption, coverage, and reconstructed image quality. These considerations are particularly relevant for environmental sensing applications, including water quality assessment, air pollution monitoring, wildlife tracking, and underground mining. This review synthesises recent advances in LoRa-based environmental and visual sensing and highlights persistent challenges, including duty-cycle restrictions, limited throughput, and energy constraints, that must be addressed for broader adoption in data-intensive sensing applications. By analysing current strategies and proposing future directions, including adaptive encoding, lightweight encryption, and energy-aware scheduling, the review demonstrates the potential of LoRa to play an increasingly important role in enabling sustainable, scalable, and accessible Internet of Things solutions across diverse environmental settings. Full article

Achieving net-zero energy and zero-emission buildings is a critical pathway toward decarbonizing the built environment, particularly in cooling-dominated regions where operational energy demand remains exceptionally high. Existing university buildings in hot–humid climates face significant challenges due to intensive cooling requirements, limited passive cooling potential, and the economic burden associated with large-scale renewable energy deployment. This study develops and evaluates a climate-responsive retrofit framework that integrates sequential energy optimization, CFD-based passive-cooling analysis, and on-site renewable energy systems to transform an operational university building in Jeddah, Saudi Arabia, into a nearly net-zero energy building (NZEB). A high-fidelity DesignBuilder–EnergyPlus model was calibrated using three years of monthly measured electricity consumption data, achieving strong agreement with utility records (NMBE = 2.19%, CV(RMSE) = 7.93%). The proposed framework prioritizes demand-side load reduction through optimized HVAC operation, envelope enhancement, daylight-responsive lighting control, natural ventilation, and Passive Downdraught Evaporative Cooling (PDEC) before renewable energy integration. The baseline building exhibited an Energy Use Intensity (EUI) of 613 kWh/m²·year, with cooling accounting for approximately 70% of total electricity consumption. Sequential optimization reduced annual energy demand by 58%, while CFD-supported passive cooling strategies provided an additional 17% reduction in cooling energy and improved indoor airflow performance. Crucially, nearly 80% of total energy savings were realized prior to photovoltaic (PV) deployment. A 1586-kW rooftop photovoltaic system subsequently offset the residual annual demand, achieving a nearly net-zero annual energy balance. Over 25 years, the proposed retrofit pathway reduced life-cycle costs from 7.51 million SAR to 3.13 million SAR. The findings demonstrate that climate-responsive demand reduction is the primary enabler of NZEBs in hot–humid regions, substantially reducing renewable energy requirements and long-term economic costs while providing a scalable pathway to decarbonize existing campus infrastructure. Full article

Conventional air stripping for ammonia–nitrogen wastewater is often hampered by packing clogging and low mass transfer efficiency. To address these limitations, this study proposes a jet impact negative pressure reactor (JI-NPR) featuring an optimized scatter-pattern (D7) multi-orifice configuration. Computational Fluid Dynamics (CFD) simulations were employed to systematically investigate the effects of Reynolds number (Re = 5503.4~9651.0, corresponding to 2.76~4.84 m/s) on the hydrodynamic characteristics and deamination performance. Results indicate that increasing jet velocity significantly enhances the water volume fraction, resultant velocity, and pressure core intensity within the impact zone. Notably, these enhancements are maximized at the second row (z = 146 mm), attributed to reduced interference from the negative-pressure flash evaporation region. While a higher Re promotes interfacial renewal and vortex evolution, thereby enhancing mass transfer, it also intensifies energy dissipation and reduces the uniformity of the turbulent kinetic energy distribution. This work elucidates a critical trade-off between mass transfer enhancement and energy consumption, establishing a quantitative structure: the Re–flow field-performance relationship. The findings provide a theoretical foundation for the design and optimization of energy-efficient, high-performance wastewater treatment systems. Full article

The intensive development of road transportation and the increasing number of vehicles have led to significant challenges related to road safety, traffic congestion, travel time, energy consumption, and negative environmental impacts. In this context, intelligent transport systems (ITS) represent a significant approach to improving the efficiency and sustainability of modern transportation systems. The aim of this paper is to present and systematize the application of modern ITS technologies for improving road safety, reducing emissions, and shortening travel time. Based on an analysis of the relevant literature, the fundamental components and architecture of ITS are presented, including sensor systems, V2X communication, IoT, cloud and edge computing, as well as the application of artificial intelligence in traffic data processing and prediction. The analyzed studies demonstrate that ITS enables dynamic traffic flow management, route optimization, reduction in congestion and emissions, and more efficient responses to emergency situations. Particular attention is devoted to the possibility of simultaneously considering travel time, energy consumption, emissions, noise, and road safety. As a synthesis of the analyzed findings, an integrated algorithm for intelligent traffic management is proposed, operating as a closed feedback loop encompassing data collection, state assessment, prediction, optimization, and control. Future ITS development is expected to focus on the integration of AI, IoT, 6G, and edge computing technologies and their validation using real-world traffic data. Full article

The retinal pigment epithelium (RPE) is a monolayer of cells located between retinal photoreceptors and the choroid, playing a critical role in maintaining visual function by protecting the retina and supporting photoreceptor metabolism. Damage to RPE cells can lead to visual disorders, including macular degeneration. Chronic exposure to high-energy blue light has been shown to elevate intracellular reactive oxygen species (ROS) in RPE cells, causing oxidative stress and cellular damage. In this study, a microfluidic platform incorporating a gradient-generating structure was developed to establish controllable and stable gradients of blue light intensity and chemical concentrations. This platform was used to investigate the effects of varying blue light intensities and antioxidant concentrations on oxidative stress in human RPE cells ARPE-19. Cells cultured within the microfluidic channels were exposed to different blue light intensities in combination with chemical treatments. Results demonstrated that ROS production increased with higher blue light intensity, whereas higher antioxidant concentrations effectively reduced ROS accumulation, supporting the ability of these antioxidants to attenuate blue-light-induced intracellular oxidative stress. The present microfluidic device enables simultaneous evaluation of multiple conditions within a single experiment, reducing reagent consumption and enhancing experimental efficiency. This in vitro microfluidic platform integrates chemical and light gradients to assess retinal oxidative damage and antioxidant effects, offering significant potential for ophthalmic drug screening and investigations of retinal protective mechanisms. Full article

The wine sector is increasingly required to improve its environmental performance by reducing the consumption of key resources such as water and energy. However, these two dimensions are often analyzed separately, even though winery operations may generate interdependent demands for both resources. This study explores the water–energy nexus in the wine sector by investigating the relationships among annual water consumption, electrical energy use, outsourcing intensity and production configuration in a sample of 21 Italian wineries. A dedicated dataset was developed from company-level operational data, including winery reference models, outsourcing and packaging indices, production volumes, electricity consumption, water consumption and specific performance indicators. The analysis combined descriptive statistics on winery-average data with Pearson correlation analysis, exploratory pooled multiple regression, Principal Component Analysis and a log-linear mixed-effects model applied to 74 winery-year observations nested within 21 wineries. Pearson correlation analysis showed strong positive associations between electrical energy consumption and water consumption (r = 0.891), between wine outsourcing index (WOI) and electrical energy consumption (r = 0.796), and between WOI and water consumption (r = 0.780). The complete within–between mixed-effects model showed that a 1% higher-than-usual electricity use within a winery was associated with 0.595% higher annual water use (95% CI: 0.418–0.772; p < 0.001) after accounting for within-winery variation in annual production. In the conventional mixed-effects model, the corresponding electricity coefficient was 0.571 (95% CI: 0.406–0.735; p < 0.001), conditional on production and the other covariates. Given the strong collinearity between electricity and production (r = 0.955; VIFs = 11.6 and 13.2), the 0.571 estimate should not be interpreted as an independent electricity effect. These findings indicate that aggregate water and electricity consumption covary strongly across the sampled wineries and that this relationship persists after accounting for repeated winery observations and annual production volume. WOI and production configuration contribute to the structural interpretation of resource demand, although their conditional associations are less stable. The results support the joint assessment of absolute resource demand and specific performance indicators for more context-sensitive winery benchmarking and managerial decision making. Full article

The construction phase of sports buildings is characterized by high carbon emission intensity, yet existing studies have largely focused on operational energy consumption and static life-cycle accounting, with no systematic investigation of the dynamic interactions among labor, materials, machinery, and environmental factors during construction. To fill this gap, this study develops an agent-based modeling (ABM) framework for a university natatorium in Shaanxi, China, to assess carbon emissions during the civil engineering construction phase and analyzes the independent and synergistic effects of labor, materials, machinery, and environmental factors. The simulation results show that, among single factors, material recycling achieves the highest reduction efficiency (57.93%, under the avoided-burden approach, representing a technical upper-bound estimate), followed by labor skill improvement (3.08%) and machinery maintenance (0.26%), while adverse weather increases carbon emissions by 13.20%. Multi-factor synergy analysis reveals that labor skill improvement buffers weather-induced increases (synergy: +610.49 t, 4.26%), and the full-intervention scenario achieves a 52.33% net reduction under adverse weather, though weather impacts cannot be fully offset. The proposed framework provides methodological support and a decision-making basis for low-carbon construction planning of natatoriums and similar buildings. Full article

Computing capacity has become a metered factor of production for digitally intensive enterprises, yet its consumption exhibits strong temporal heterogeneity—tidal intraday cycles, weekly contrasts, seasonal surges, and project-driven regime shifts—so that individually provisioned capacity is structurally underutilized. This paper proposes a park-level computing power sharing center (CPSC) as an institutional mechanism that converts the temporal complementarity of co-located enterprises into measurable cost savings. We develop a general mode-design framework that separates CPU core-hours from GPU card-hours, characterizes demand via deterministic tides and stochastic modulations, and derives optimal pooled capacity commitments through a newsvendor-type quantile condition. A parametric calibration protocol maps observable temporal features—peak-to-trough ratios, inter-tenant phase spreads, and residual volatility—into closed-form diversity-factor expressions with Monte Carlo confidence intervals. The procurement model covers a multi-option contract menu (on-demand, one–three-year reserved instances, savings plans, and spot), region-specific pricing, hardware class tariffs, and ancillary costs, including network egress, migration, and data sovereignty compliance; benefits are measured relative to each tenant’s individually optimal reserved portfolio, not naive retail procurement. A mechanism design analysis incorporating Shapley value allocation, Bayesian incentive compatibility, and penalty structures ensures individual rationality and robustness to misreporting and strategic load shifting. We further develop an energy model—with utilization-dependent power draw, facility PUE, embodied carbon, and marginal grid emission factors—showing that financial savings translate into genuine emission reductions only when pooling enables physical capacity retirement rather than mere billing reallocation. The framework is applied to twenty representative Chinese parks spanning seven functional categories; all park-level data are reconstructed from public sources using the calibration methodology, and the reported figures are model-derived projections, not empirical measurements. The model yields procurement saving estimates of 4.6–20.2% relative to individually optimal reserved-procurement portfolios, with high-diversity parks at the upper end. Sensitivity analyses across regional tariffs, hardware mixes, and cross-country utilization benchmarks (Uptime Institute, US DOE, EU Commission) confirm robustness and delineate boundary conditions. This paper concludes with a data provenance taxonomy and a phased implementation roadmap. Full article

Chinese home-cooked dishes (CHCDs) link agricultural production and daily consumption, but their resource dependence and sustainability remain poorly quantified. We standardized 341 recipes from China’s 34 provincial-level administrative regions, conducted a production-to-consumption analysis using emergy (solar-equivalent available energy directly and indirectly required for a product or service), and evaluated six optimization scenarios. Mean emergy input was 5.93 × 10¹² sej·dish⁻¹, with nonrenewable purchased inputs accounting for 77.99%. Ingredients, oil and sauces, and cooking contributed 69.17%, 21.69%, and 9.14%, respectively. Plant-based production relied on labor and chemical inputs; animal-based production relied on feed. Ruminant meats had relatively high nonrenewable inputs and environmental loads; vegetables had lower values. Across CHCDs, the emergy sustainability index (ESI), transformity, emergy consumption intensity, and food processing intensity averaged 0.26, 1.02 × 10⁶ sej·J⁻¹, 1.45 × 10¹² sej·serving⁻¹, and 4.64 × 10¹¹ sej·serving⁻¹, respectively. Sustainability declined from plant-based through mixed to animal-based dishes. Shandong cuisine ranked highest and Zhejiang cuisine lowest in combined sustainability and efficiency. Combining crop–livestock recycling, mechanization and energy-efficiency improvements, technological advancement and scaling up, moderate oil and sauce reduction, and cooking-efficiency improvements was projected to increase ESI by 19.34% relative to baseline. These results support dish-level emergy accounting for improving agri-food sustainability. Full article

The increasing demand for sustainable outdoor lighting has accelerated the development of solar-powered lighting systems. However, conventional solar lamps typically employ fixed illumination levels and simple day–night switching mechanisms, resulting in inefficient battery utilization and limited adaptability to changing environmental conditions. This study proposes a TRIZ-guided intelligent solar-powered lighting system that integrates photovoltaic energy harvesting, adaptive pulse-width modulation (PWM)-based illumination control, ultrasonic sensing, wireless communication, and embedded control into a unified standalone platform. The TRIZ contradiction matrix was employed during the conceptual design stage to systematically resolve key engineering contradictions involving illumination performance, energy efficiency, hardware complexity, battery lifetime, and user convenience. The proposed prototype was developed using an AT89S51 microcontroller to coordinate battery charging protection, environmental sensing, adaptive brightness regulation, and manual wireless operation. Experimental validation demonstrated stable photovoltaic charging with a regulated battery charging voltage of 14.4 V, reliable execution of embedded control functions, seamless transition between manual and autonomous operating modes, and adaptive LED brightness regulation according to real-time environmental conditions. The integrated PWM control strategy reduced unnecessary energy consumption by dynamically adjusting illumination intensity based on object detection rather than maintaining constant full-power operation. The experimental results further verified the feasibility of combining software-driven adaptive control with renewable energy harvesting to achieve intelligent energy management without increasing hardware complexity. Overall, the proposed system demonstrates that the integration of TRIZ-based systematic innovation with embedded intelligent control provides a practical, energy-efficient, and cost-effective solution for autonomous outdoor lighting. The proposed architecture offers valuable engineering insights for future smart lighting applications in off-grid infrastructure, sustainable communities, and smart city environments. Full article

Natural gas consumption in urban residential buildings is important for household energy affordability and the low-carbon transition of the residential sector. Using panel data from 30 Chinese provinces from 2005 to 2020, this study employs a panel regression model to examine the impact of residential natural gas prices (NPs) on per capita natural gas consumption (NGC) in urban residential buildings. We apply the model to estimate price elasticity and analyze the transmission mechanism of energy consumption intensity (EI), while examining the differentiated characteristics of price effects from both regional and urbanization-stage perspectives. The main findings are as follows: (1) NP shows a significantly negative association with residential NGC, with a price elasticity of −1.182; (2) EI serves as a potential transmission channel between NP and NGC, with its indirect effect offsetting 12.5%; (3) heterogeneity analysis shows that price elasticity is statistically significant only in the western region. Across urbanization quartiles, the absolute magnitude of price elasticity is greatest in Q2. This study provides empirical evidence to optimize tiered natural gas pricing, refine targeted subsidy policies, and inform energy utilization policies in urban residential areas. Full article

This study investigates the key drivers of CO₂ emissions in Gulf Cooperation Council (GCC) countries, focusing on energy consumption, economic growth, urbanization, trade openness, and institutional quality over the period 1980–2023. The analysis adopts a structured econometric framework in which each technique serves a distinct analytical purpose. Fixed-effects regression with Driscoll–Kraay standard errors is used to estimate contemporaneous relationships while accounting for cross-sectional dependence. LASSO regression is employed as an exploratory variable-selection tool to identify the most relevant predictors of emissions, while Common Correlated Effects Mean Group (CCEMG) and instrumental-variable fixed-effects (IV-FE) estimators are used as robustness checks to account for cross-sectional dependence, slope heterogeneity, and potential endogeneity. The results consistently identify energy consumption as the dominant determinant of CO₂ emissions across all specifications, reflecting the persistent dependence of GCC economies on fossil fuel-intensive energy systems. GDP per capita also exhibits a positive relationship with emissions, suggesting a possible non-linear income–emissions relationship; however, the results do not provide robust support for the conventional EKC hypothesis. Population density is negatively associated with emissions, suggesting potential efficiency gains from urban concentration. In contrast, regulatory quality does not show a statistically robust effect and is excluded from the preferred specification by the LASSO procedure. The impact of trade openness appears model-dependent, indicating that its environmental effects vary according to the balance between scale and technology-transfer effects. Overall, the findings provide policy-relevant insights for balancing economic growth, energy transition, and decarbonization objectives in hydrocarbon-dependent economies. Full article