Search Results (6,569)

Energy consumption plays a significant role in global warming. In order to achieve carbon neutrality and enhance energy efficiency through a stable energy supply, it is necessary to pursue the development of innovative architectures designed to optimize and analyze time series data. Therefore, this study presents a new architecture that highlights the critical role of preprocessing in improving predictive performance and demonstrates its scalability across various energy domains. The architecture, which discerns patterns indicative of time series characteristics, is founded on three core components: data preparation, process optimization methods, and prediction. The core of this architecture is the identification of patterns within the time series and the determination of optimal data processing techniques, with a strong emphasis on preprocessing methods. The experimental results for heat energy demonstrate the potential for data optimization to achieve performance gains, thereby confirming the critical role of preprocessing. This study also confirms that the proposed architecture consistently enhances predictive outcomes, irrespective of the model employed, through the evaluation of five distinct prediction models. Moreover, experiments extending to electric energy validate the architecture’s scalability and efficacy in predicting various energy types using analogous input variables. Furthermore, this research employs explainable artificial intelligence to elucidate the determinants influencing energy prediction, thereby contributing to the management of low-carbon energy supply and demand. Full article

Purpose: This study employed a Life Cycle Assessment (LCA) methodology to evaluate the environmental impacts of a novel plant cell-based biomanufacturing process for producing plant natural product ingredients. The primary purpose was to assess the relative sustainability of the process and to provide insights into potential areas of improvement in the biomanufacturing process. Method: The LCA method used an MS Excel (Ver. 2407) -based approach with a cradle-to-gate system boundary covering raw material sourcing (A1), raw material transportation (A2), and product extract manufacturing (A3) stages. Energy use and material inventory data are presented for different unit operations, and environmental impact factors were obtained from the Ecoinvent database. The study included a Material Circularity Index (MCI) calculation to assess the circularity of the biomanufacturing process for the production of saponin emulsifiers that are normally extracted from the woody tissue of the Chilean soapbark tree (Quillaja saponaria). Comparative analyses were performed against a wild-harvest approach for plant tannin extraction from spruce (Picea abies) tree bark. Key Results: The environmental impact assessment focused on determining relative Global Warming Potential (GWP), Acidification Potential (AP), Freshwater Eutrophication (FE), Particulate Matter Formation (PMF), and Ozone Depletion Potential (ODP). Results indicated that the extract manufacturing stage (A3) contributed significantly to adverse environmental impacts, with varying levels of effects based on the energy source used. Comparative analysis with the wild harvest approach highlights the lower environmental impact of the alternative biomanufacturing process. The biomanufacturing process showed a 23% reduction in GWP, AP, and FE and a 25% reduction in PMF and ODP relative to the wild harvest approach. However, the MCI for the biomanufacturing process was estimated to be 0.186, indicating a low material circularity. Conclusions: The results revealed that the extract manufacturing stage, particularly energy consumption, significantly influences the relative environmental impacts of the alternative production processes. Different energy sources exhibit varying effects, with renewable energy sources showing lower environmental impacts. The Material Circularity Index indicated a low circularity for the biomanufacturing process, suggesting opportunities for improvement, such as incorporating recycled or reused materials. Compared with the tannin extraction process, the plant cell-based biomanufacturing process demonstrated lower environmental impacts, emphasising the importance of sustainable practices and the use of renewable energy sources in future plant natural product sourcing. Recommendations include implementing more sustainable practices, optimising raw material choices, and extending product life spans to enhance circularity and overall environmental benefits. Full article

Solar energy is an important alternative energy source, and it is essential to forecast solar power generation for efficient power management. Due to the seasonal characteristics of weather features, seasonal data partition strategies help develop prediction models that perform better in extreme weather-related situations. Most existing studies rely on fixed season partitions, such as meteorological and astronomical, where the start and end dates are specific. However, even if the countries are in the same Northern or Southern Hemisphere, seasonal changes can occur due to abnormal climates such as global warming. Therefore, we propose a novel unfixed seasonal data partition based on Symbolic Aggregate Approximation (SAX) to forecast solar power generation. Here, symbolic representations generated by SAX are used to select seasonal features and obtain seasonal criteria. We then employ two-layer stacked LSTM and combine predictions from various seasonal features and partitions through ensemble methods. The datasets used in the experiments are from real-world solar panel plants such as in Gyeongju, South Korea; and in California, USA. The results of the experiments show that the proposed methods perform better than non-partitioned or fixed-partitioned solar power generation forecasts. They outperform them by 2.2% to 3.5%; and 1.6% to 6.5% in the Gyeongju and California datasets, respectively. Full article

With the rapid development of industrialization and urbanization in China, civil aviation plays an increasingly important role in the transportation industry. However, pollutants and greenhouse gas (GHG) emissions from civil aviation are becoming an increasingly concerning environmental problem. In order to mitigate the resulting environmental pollution, such as air quality deterioration, regional and global climate warming, and declining human health, more and more efforts have been devoted to reducing both pollutants and GHG emissions. Among these efforts, emissions inventories from civil aviation provide a basis for quantifying pollutants and GHG emissions, establishing evaluation standards of environmental impact, and formulating management policies for both air quality improvement and climate change mitigation. In this paper, we reviewed both compilation approaches and data collection methods for civil aviation emissions inventories, introduced several typical calculation methods for aviation emissions inventories, and analyzed specific cases of actual application based on typical methods of inventory compilation. We also described in detail the activity level and emission index calculation methods of several pollutants and greenhouse gases. Furthermore, based on the above research methods, four typical application cases were investigated, including a specific airport, the landing and takeoff (LTO) cycle of a nation, the entire period with the LTO cycle and the climb–cruise–descent (CCD) phase of a country, and global emissions inventories from civil aviation. The results suggest that, in addition to quantifying the emissions of both pollutants and GHG produced by civil aviation, the selection of inventory compilation methods is likely to be important for improving aviation emission inventory accuracy and for further reducing the environmental, economic, and health impacts resulting from aviation emissions. Moreover, this paper can also provide a reference and theoretical basis for the development of aviation emission inventory compilation methods in the future. Full article

The Fen River Basin (FRB), an ecologically fragile region in China, exemplifies the intricate interplay between vegetation dynamics and both climatic and human-driven factors. This study leverages a 40-year (1982–2022) dataset, utilizing the kernel-based normalized difference vegetation index (kNDVI) alongside key climatic variables—rainfall (PRE), temperature (TMP), and solar radiation (SRAD)—to investigate vegetation variations and their drivers in the FRB, particularly in relation to the Grain for Green Program (GGP). Our analysis highlights significant greening across the FRB, with the kNDVI slope increasing by 0.0028 yr⁻¹ and green-covered areas expanding by 92.8% over the study period. The GGP facilitated the greening process, resulting in a notable increase in the kNDVI slope from 0.0005 yr⁻¹ to 0.0052 yr⁻¹ and a marked expansion in the area of significant greening from 24.6% to 95.8%. Regional climate shifts, characterized by increased warming, heightened humidity, and a slight rise in SRAD, have further driven vegetation growth, contributing 75%, 58.7%, and 23.6% to vegetation dynamics, respectively. Notably, the GGP has amplified vegetation’s sensitivity to climatic variables, with areas significantly impacted by multiple climate factors expanding from 4.8% to 37.5%. Specially, PRE is the primary climatic influence, impacting 71.3% of the pertinent regions, followed by TMP (60.1%) and SRAD (30%). The integrated effects of climatic and anthropogenic factors, accounting for 47.8% and 52.2% of kNDVI variations, respectively, collectively influence 96% of the region’s vegetation dynamics. These findings underscore the critical role of climate change and human interventions in shaping vegetation patterns and provide a robust foundation for refining ecological conservation strategies, particularly in the context of global warming and land-use policies. Full article

Rubber (Hevea brasiliensis Muell.) plantations are vital agricultural ecosystems in tropical regions. These plantations provide key industrial raw materials and sequester large amounts of carbon dioxide, playing a vital role in the global carbon cycle. Climate change has intensified droughts in Southeast Asia, negatively affecting rubber plantation growth. Limited in situ observations and short monitoring periods hinder accurate assessment of drought impacts on the gross primary productivity (GPP) of rubber plantations. This study used GPP data from flux observations at four rubber plantation sites in China and Thailand, along with solar-induced chlorophyll fluorescence (SIF), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), near-infrared reflectance of vegetation (NIRv), and photosynthetically active radiation (PAR) indices, to develop a robust GPP estimation model. The model reconstructed eight-day interval GPP data from 2001 to 2020 for the four sites. Finally, the study analyzed the seasonal drought impacts on GPP in these four regions. The results indicate that the GPP prediction model developed using SIF, EVI, NDVI, NIRv, and PAR has high accuracy and robustness. The model’s predictions have a relative root mean square error (rRMSE) of 0.22 compared to flux-observed GPP, with smaller errors in annual GPP predictions than the MOD17A3HGF model, thereby better reflecting the interannual variability in the GPP of rubber plantations. Drought significantly affects rubber plantation GPP, with impacts varying by region and season. In China and northern Thailand (NR site), short-term (3 months) and long-term (12 months) droughts during cool and warm dry seasons cause GPP declines of 4% to 29%. Other influencing factors may alleviate or offset GPP reductions caused by drought. During the rainy season across all four regions and the cool dry season with adequate rainfall in southern Thailand (SR site), mild droughts have negligible effects on GPP and may even slightly increase GPP values due to enhanced PAR. Overall, the study shows that drought significantly impacts rubber the GPP of rubber plantations, with effects varying by region and season. When assessing drought’s impact on rubber plantation GPP or carbon sequestration, it is essential to consider differences in drought thresholds within the climatic context. Full article

Global warming and urban heat island effects negatively impact the development of urban thermal environments, making them very uncomfortable to live in. Green space plays an essential role in controlling and improving air pollution, regulating the microclimate, and enforcing compliance with public health requirements. Therefore, this study explored the relationship between green space and thermal comfort in the historical neighborhood of Dazhalan in Beijing through questionnaires, observational interviews, and numerical simulations. The current situation of the microclimate environment in the green space of the block was observed first. Then, the microclimate environment was simulated by the ENVI-met 5.6 software. The thermal comfort of the three types of space, such as enclosed space, strip space, and corner space, was also evaluated to explore the coupling relationship between different green space elements and microclimate evaluation factors. It was found that the thermal comfort PET had a positive correlation with the sky openness SVF. The green space morphology was quantitatively measured, and it was found that the thermal comfort PET had a negative correlation with the three-dimensional green quantity of green space. The paper developed managing strategies for optimizing the layout and construction mode of the green space. The ultimate goal was to rationally match the greening planting, improve the pavement of the underlying surface of the block, and optimize the design of the internal space topography. Full article

The advance of spring phenology and the delay of autumn phenology caused by global warming have been documented by many studies. However, most research has focused on natural areas, with limited studies conducted on phenological observations in urban environments. Here, we selected the first flowering date (FFD), first leaf date (FLD), and leaf coloring date (LCD) at three sites (Beijing, Harbin, and Mudanjiang) from the China Phenological Observation Network. We analyzed the phenological changes of 84 species between 1963–1991 and 1992–2020 to examine their response to urban warming. We then quantified the correlations and regressions between phenological events and preseason temperature. The results show the following: (1) Among the three sites, the mean FFD and FLD were earliest in Beijing, while the mean LCD occurred earliest in Harbin and latest in Beijing. (2) FFD and FLD exhibited a significant trend towards earlier occurrences at all three sites, while LCD showed a significant delay trend except for the Mudanjiang site. Specifically, at the Beijing, Harbin, and Mudanjiang sites, the mean FFD advanced by 8.32 days, 6.11 days, and 2.60 days in the latter period (p < 0.05), whereas the mean FLD advanced by 11.30 days, 7.21 days, and 5.02 days (p < 0.05), respectively. (3) In Beijing, Harbin, and Mudanjiang, both FFD and FLD were significantly negatively correlated with preseason temperature. However, no consistent relationship was observed between LCD and preseason temperature. These results enhance our understanding of the response of plant phenology to urban warming. Full article

Facing global warming’s consequences is a major issue in the present times. Regarding the climate, projections say that heavy rainfalls are going to increase with high probability together with temperature rise; thus, the hazard related to rainfall-induced shallow landslides will likely increase in density over susceptible territories. Different modeling approaches exist, and many of them are forced to make simplifications in order to reproduce landslide occurrences over space and time. Process-based models can help in quantifying the consequences of heavy rainfall in terms of slope instability at a territory scale. In this study, a narrative review of physically based deterministic distributed models (PBDDMs) is presented. Models were selected based on the adoption of the infinite slope scheme (ISS), the use of a deterministic approach (i.e., input and output are treated as absolute values), and the inclusion of new approaches in modeling slope stability through the ISS. The models are presented in chronological order with the aim of drawing a timeline of the evolution of PBDDMs and providing researchers and practitioners with basic knowledge of what scholars have proposed so far. The results indicate that including vegetation’s effects on slope stability has raised in importance over time but that there is still a need to find an efficient way to include them. In recent years, the literature production seems to be more focused on probabilistic approaches. Full article

With few measurement sites and a great need to validate satellite data to characterize the exchange of energy and carbon fluxes in tropical forest areas, quantified by the Net Ecosystem Exchange (NEE) and associated with phenological measurements, there is an increasing need for studies aimed at characterizing the Amazonian environment in its biosphere–atmosphere interaction, considering the accelerated deforestation in recent years. Using data from a flux measurement tower in the Caxiuanã-PA forest (2005–2008), climatic data, CO₂ exchange estimated by eddy covariance, as well as Gross Primary Productivity (GPP) data and satellite vegetation indices (from MODIS), this work aimed to describe the site’s energy, climatic and carbon cycle flux patterns, correlating its gross primary productivity with satellite vegetation indices. The results found were: (1) marked seasonality of climatic variables and energy flows, with evapotranspiration and air temperature on the site following the annual march of solar radiation and precipitation; (2) energy fluxes in phase and dependent on available energy; (3) the site as a carbon sink (−569.7 ± 444.9 gC m⁻² year⁻¹), with intensity varying according to the site’s annual water availability; (4) low correlation between productivity data and vegetation indices, corroborating data in the literature on these variables in this type of ecosystem. The results show the importance of preserving this type of environment for the mitigation of global warming and the need to improve satellite estimates for this region. NDVI and EVI patterns follow radiative availability, as does LAI, but without direct capture related to GPP data, which correlates better with satellite data only in the months with the highest LAI. The results show the significant difference at a point measurement to a satellite interpolation, presenting how important preserving any type of environment is, even related to its size, for the global climate balance, and also the need to improve satellite estimates for smaller areas. Full article

In the context of global warming, temperature increases have led to frequent drought events and a sharp increase in economic losses and social risks. In this study, five medium- and high-emission scenario models, the SSP245 and SSP585, CMIP6 monthly scale temperature and precipitation datasets under different global warming contexts (1.5 °C and 2 °C), and the 1984–2014 weather station observations were selected. The latter dataset was used to improve the ability of the CMIP6 to simulate surface drought accuracy. A standardized precipitation–evapotranspiration index dataset was generated. The latest intensity–area–duration framework was adopted to identify regional drought events by considering their continuity and spatial dynamic characteristics. The parameters of intensity, area, and duration were used to characterize the dynamic evolution of drought events. Under the medium- to high-emission scenario model, with a continuous increase in global temperature to 1.5 °C, in the southeastern Qinghai–Tibet Plateau (QTP) and southern Xinjiang (XJ) there is a significant increase in intensity, extent, and duration of drought events and some drought exacerbation in northeastern China. Under the high-emission SSP585 scenario model, the severity of these drought events is reduced when compared with the SSP245 scenario model, but this also shows an increasing trend, especially with the 2 °C global warming background. Significant drought aggravation trends were observed in southern XJ, northern QTP, and northern Northwest. In contrast, a small but significant drought-weakening trend was observed in southwestern south China. The results of this study provide a reference for society and government departments to make decisions in response to future drought events. Full article

The release of greenhouse gases (GHGs) is a major contributor to global warming, endangering both human and nonhuman well-being, environmental integrity, economic development, and the planet’s long-term survival. This study delves into the interplay between crop production, livestock production, fertilizer utilization, and agricultural land usage on CO₂ emissions in four Asian economies: China, India, Pakistan, and Bangladesh. Employing panel data analysis techniques, the research uncovers the significant impacts of various agricultural activities on environmental degradation. The findings derived from the panel autoregressive distributed lag (PARDL) estimation reveal that crop production in these emerging economies contributes to CO₂ emissions, as evidenced by the positive coefficients and statistically significant results. Similarly, livestock production and agricultural land used for crop production exhibit a substantial impact on CO₂ emissions, further highlighting their role in environmental degradation. While fertilizer usage also displays a positive coefficient, its impact on CO₂ emissions is not statistically significant. The results of our study highlight the critical importance of addressing the environmental impacts of agricultural practices, particularly in emerging economies. Crop and livestock production, along with the expansion of agricultural land, significantly contribute to CO₂ emissions, which underscores the urgent need for sustainable agricultural practices. These findings suggest that policymakers should prioritize the development and implementation of strategies that mitigate the environmental impacts of agriculture. This could include promoting sustainable land management practices, investing in technology that reduces emissions from crop and livestock production, and encouraging the adoption of eco-friendly fertilizers. Full article

Global warming and climate change are major concerns across multiple disciplines. Population growth, urbanization, and industrialization are significant contributing factors to such problems due to the escalating use of fossil fuels required to meet growing energy demands. The building sector uses the largest share of total global energy production and produces tons of greenhouse gas emissions. Emerging eco-friendly technologies, such as solar and wind energy harvesting, are being extensively explored; however, they are insufficient. Nature-inspired technologies could offer solutions to our problems. For instance, algae are microorganisms that use water, light, and CO₂ to produce energy and sustain life, and the exploitation of these characteristics in a built environment is termed algae building technology, which is a very efficient and green application suitable for a sustainable future. Algae-integrated façades show great versatility through biomass and energy production, wastewater treatment, shading, and thermal and acoustic insulation. In this paper, algae will be introduced as a robust tool toward a greener and more sustainable future. Algae building technology and its implementation will be demonstrated. Furthermore, steps for applying this sustainable strategy in Egypt will be discussed. Full article

This study aimed to assess the potential of soil organic carbon (SOC) production through organic amendments. SOC sequestration would help to achieve the net-zero emissions targets set by the Intergovernmental Panel on Climate Change (IPCC). Given the urgency to reduce greenhouse gas emissions, traditional methods that estimate SOC over 100 years must be revised. Hence, a novel fate transport numerical model was developed to forecast SOC levels relevant to individual countries’ net-zero targets in various time frames. The simulation results revealed that most countries had sufficient organic amendment to mitigate the CO₂ emission of that country for a year if the organic amendment was applied on 20% of the arable land. However, if a significant fraction of the total CO₂ emissions needs to be mitigated before reaching the net zero target, the requirements of organic amendments need to be increased several folds. All the available agricultural land should also be brought under the organic amendment regime. Later, the dynamic LCA approach was undertaken for estimating Global Warming (GWP) from land-applied organic residue. It was observed that, depending on the dynamic LCA model, the estimated GWP was different. However, the estimated dynamic GWP was very close to the residual SOC calculated through the fate transport model. The mass of organic residues generated from a biorefinery was examined by employing a waste biorefinery model to explore further the routes of acquiring additional organic amendment. Simulated results showed that while a waste biorefinery could not provide additional organic residue compared to the original organic waste input, it was highly efficient for nutrient recovery and its uses. This study demonstrated that organic amendment-based carbon sequestration adequately mitigated residual GHG at the net-zero target. Full article

The trinity of global warming, climate change, and air pollution casts an ominous shadow over society and the environment. At the heart of these threats lie carbon emissions, whose reduction has become paramount. Blockchain technology and the internet of things (IoT) emerge as innovative tools for establishing an efficient carbon credit exchange. This paper presents a blockchain and IoT-centric platform for carbon credit exchange, paving the way for transparent, secure, and effective trading. IoT devices play a pivotal role in monitoring and verifying carbon emissions, safeguarding the integrity and accountability of the trading process. Blockchain technology, with its decentralized and immutable nature, empowers the platform with transparency, reduced fraud, and enhanced accountability. This platform aims to arm organizations and individuals with the ability to actively curb carbon emissions, fostering collective efforts towards global pollution reduction goals. Full article