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This study aims to address a critical gap in the literature by examining the incorporation of uncertainty in measuring carbon emissions using the greenhouse gas (GHG) Protocol methodology across all three scopes. By comprehensively considering the various dimensions of CO2 emissions within the context of organizational activities, our research contributes significantly to the existing body of knowledge. We address challenges such as data quality issues and a high prevalence of missing values by using information entropy, techniques for order preference by similarity to ideal solution (TOPSIS), and an artificial neural network (ANN) to analyze the contextual variables. Our findings, derived from the data sample of 56 companies across 18 sectors and 13 Brazilian states between 2017 and 2019, reveal that Scope 3 emissions exhibit the highest levels of information entropy. Additionally, we highlight the pivotal role of public policies in enhancing the availability of GHG emissions data, which, in turn, positively impacts policy-making practices. By demonstrating the potential for a virtuous cycle between improved information availability and enhanced policy outcomes, our research underscores the importance of addressing uncertainty in carbon emissions measurement for advancing effective climate change mitigation strategies.

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Although anthropogenic activities are the primary drivers of increased greenhouse gas (GHG) emissions, it is crucial to acknowledge that wetlands are a significant source of these gases. Brazil's Pantanal, the largest tropical inland wetland, includes numerous lacustrine systems with freshwater and soda lakes. This study focuses on soda lakes to explore potential biogeochemical cycling and the contribution of biogenic GHG emissions from the water column, particularly methane. Both seasonal variations and the eutrophic status of each examined lake significantly influenced GHG emissions. Eutrophic turbid lakes (ET) showed remarkable methane emissions, likely due to cyanobacterial blooms. The decomposition of cyanobacterial cells, along with the influx of organic carbon through photosynthesis, accelerated the degradation of high organic matter content in the water column by the heterotrophic community. This process released byproducts that were subsequently metabolized in the sediment leading to methane production, more pronounced during periods of increased drought. In contrast, oligotrophic turbid lakes (OT) avoided methane emissions due to high sulfate levels in the water, though they did emit CO2 and N2O. Clear vegetated oligotrophic turbid lakes (CVO) also emitted methane, possibly from organic matter input during plant detritus decomposition, albeit at lower levels than ET. Over the years, a concerning trend has emerged in the Nhecolândia subregion of Brazil's Pantanal, where the prevalence of lakes with cyanobacterial blooms is increasing. This indicates the potential for these areas to become significant GHG emitters in the future. The study highlights the critical role of microbial communities in regulating GHG emissions in soda lakes, emphasizing their broader implications for global GHG inventories. Thus, it advocates for sustained research efforts and conservation initiatives in this environmentally critical habitat.

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Although benthic microbial community offers crucial insights into ecosystem services, they are underestimated for coastal sediment monitoring. Sepetiba Bay (SB) in Rio de Janeiro, Brazil, holds long-term metal pollution. Currently, SB pollution is majorly driven by domestic effluents discharge. Here, functional prediction analysis inferred from 16S rRNA gene metabarcoding data reveals the energy metabolism profiles of benthic microbial assemblages along the metal pollution gradient. Methanogenesis, denitrification, and N2 fixation emerge as dominant pathways in the eutrophic/polluted internal sector (Spearman; p < 0.05). These metabolisms act in the natural attenuation of sedimentary pollutants. The methane (CH4) emission (mcr genes) potential was found more abundant in the internal sector, while the external sector exhibited higher CH4 consumption (pmo + mmo genes) potential. Methanofastidiosales and Exiguobacterium, possibly involved in CH4 emission and associated with CH4 consumers respectively, are the main taxa detected in SB. Furthermore, SB exhibits higher nitrous oxide (N2O) emission potential since the norB/C gene proportions surpass nosZ up to 4 times. Blastopirellula was identified as the main responsible for N2O emissions. This study reveals fundamental contributions of the prokaryotic community to functions involved in greenhouse gas emissions, unveiling their possible use as sentinels for ecosystem monitoring.

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Agricultural and animal farming practices contribute significantly to greenhouse gas (GHG) emissions such as NH3, CH4, CO2, and NOx, causing local environmental concerns involving health risks and water/air pollution. A growing need to capture these pollutants is leading to the development of new strategies, including the use of solid adsorbents. However, commonly used adsorbent materials often pose toxicity and negative long-term environmental effects. This study aimed to develop responsive eco-friendly cryogels using xylan extracted from coffee parchment, a typical residue from coffee production. The crosslinking in cryogels was accomplished by "freeze-thawing" and subsequent freeze-drying. Cryogels were characterized in terms of morphology by using scanning electron microscopy, porosity, and density by the liquid saturation method and also moisture adsorption and ammonia adsorption capacity. The analysis showed that the porosity in the cryogels remained around 0.62-0.42, while the apparent densities varied from 0.14 g/cm3 to 0.25 g/cm3. The moisture adsorption capacity was the highest at the highest relative humidity level (80%), reaching 0.25-0.43 g of water per gram of sample; the amount of water adsorbed increased when the xylan content in the cryogel increased up to 10% w/v, which was consistent with the hygroscopic nature of xylan. The ammonia adsorption process was modeled accurately by a pseudo-second-order equation, where the maximum adsorption capacity in equilibrium reached 0.047 mg NH3/g when xylan reached 10% w/v in cryogels, indicating a chemisorption process. The cryogels under investigation hold promise for ammonia adsorption applications and GHG separation, offering a sustainable alternative for gas-capturing processes.

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BACKGROUND: Assessing the trends in dietary GHGE considering the social patterning is critical for understanding the role that food systems have played and will play in global emissions in countries of the global south. Our aim is to describe dietary greenhouse gas emissions (GHGE) trends (overall and by food group) using data from household food purchase surveys from 1989 to 2020 in Mexico, overall and by education levels and urbanicity. METHODS: We used cross-sectional data from 16 rounds of Mexico's National Income and Expenditure Survey, a nationally representative survey. The sample size ranged from 11,051 in 1989 to 88,398 in 2020. We estimated the mean total GHGE per adult-equivalent per day (kg CO2-eq/ad-eq/d) for every survey year. Then, we estimated the relative GHGE contribution by food group for each household. These same analyses were conducted stratifying by education and urbanicity. RESULTS: The mean total GHGE increased from 3.70 (95%CI: 3.57, 3.82) to 4.90 (95% CI 4.62, 5.18) kg CO2-eq/ad-eq/d between 1989 and 2014 and stayed stable between 4.63 (95% CI: 4.53, 4.72) and 4.89 (95% CI: 4.81, 4.96) kg CO2-eq/ad-eq/d from 2016 onwards. In 1989, beef (19.89%, 95% CI: 19.18, 20.59), dairy (16.87%, 95% CI: 16.30, 17.42)), corn (9.61%, 95% CI: 9.00, 10.22), legumes (7.03%, 95% CI: 6.59, 7.46), and beverages (6.99%, 95% CI: 6.66, 7.32) had the highest relative contribution to food GHGE; by 2020, beef was the top contributor (17.68%, 95%CI: 17.46, 17.89) followed by fast food (14.17%, 95% CI: 13.90, 14.43), dairy (11.21%, 95%CI: 11.06, 11.36), beverages (10.09%, 95%CI: 9.94, 10.23), and chicken (10.04%, 95%CI: 9.90, 10.17). Households with higher education levels and those in more urbanized areas contributed more to dietary GHGE across the full period. However, households with lower education levels and those in rural areas had the highest increase in these emissions from 1989 to 2020. CONCLUSIONS: Our results provide insights into the food groups in which the 2023 Mexican Dietary Guidelines may require to focus on improving human and planetary health.

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Edaphoclimatic conditions influence nitrous oxide (N2O) emissions from agricultural systems where soil biochemical properties play a key role. This study addressed cumulative N2O emissions and their relations with soil biochemical properties in a long-term experiment (26 years) with integrated crop-livestock farming systems fertilized with two P and K rates. The farming systems consisted of continuous crops fertilized with half of the recommended P and K rates (CCF1), continuous crops at the recommended P and K rates (CCF2), an integrated crop-livestock system with half of the recommended P and K rates (ICLF1), and an integrated crop-livestock at the recommended P and K rates (ICLF2). The ICLF2 may have promoted the greatest entry of carbon into the soil and positively influenced the soil's biochemical properties. Total carbon (TC) was highest in ICLF2 in both growing seasons. The particulate and mineral-associated fractions in 2016 and 2017, respectively, and the microbial biomass fraction in the two growing seasons were also very high. Acid phosphatase and arylsulfatase in ICLF1 and ICLF2 were highest in 2016. The soil properties correlated with cumulative N2O emissions were TC, total nitrogen (TN), particulate nitrogen (PN), available nitrogen (AN), mineral-associated organic carbon (MAC), and microbial biomass carbon (MBC). The results indicated that ICLF2 induces an accumulation of more stable organic matter (OM) fractions that are unavailable to the microbiota in the short term and result in lower N2O emissions.

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The Sargassum phenomenon is currently affecting the Caribbean in several ways; one of them is the increase of greenhouse gases due to the decomposition process of this macroalgae; these processes also produce large amounts of pollutant leachates, in which several microbial communities are involved. To understand these processes, we conducted a 150-day study on the Sargassum spp environmental degradation under outdoor conditions, during which leachates were collected at 0, 30, 90, and 150 days. Subsequently, a metagenomic study of the microorganisms found in the leachates was carried out, in which changes in the microbial community were observed over time. The results showed that anaerobic bacterial genera such as Thermofilum and Methanopyrus were predominant at the beginning of this study (0 and 30 days), degrading sugars of sulfur polymers such as fucoidan, but throughout the experiment, the microbial communities were changed also, with the genera Fischerella and Dolichospermum being the most predominant at days 90 and 150, respectively. A principal component analysis (PCA) indicated, with 94% variance, that genera were positively correlated at 30 and 90 days, but not with initial populations, indicating changes in community structure due to sargassum degradation were present. Finally, at 150 days, the leachate volume decreased by almost 50% and there was a higher abundance of the genera Desulfobacter and Dolichospemum. This is the first work carried out to understand the degradation of Sargassum spp, which will serve, together with other works, to understand and provide a solution to this serious environmental problem in the Caribbean.

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Assessing the impact of greenhouse gas (GHG) emissions on agricultural soils is crucial for ensuring food production sustainability in the global effort to combat climate change. The present study delves to comprehensively assess GHG emissions in Cuba's agricultural soil and analyze its implications for rice production and climate change because of its rich agriculture cultivation tradition and diverse agro-ecological zones from the period of 1990-2022. In this research, based on Autoregressive Distributed Lag (ARDL) approach the empirical findings depicts that in short run, a positive and significant impact of 1.60 percent % in Cuba's rice production. The higher amount of atmospheric carbon dioxide (CO2) levels improves photosynthesis, and stimulates the growth of rice plants, resulting in greater grain yields. On the other hand, rice production index raising GHG emissions from agriculture by 0.35 % in the short run. Furthermore, a significant and positive impact on rice production is found in relation to the farm machinery i.e., 3.1 %. Conversely, an adverse and significant impact of land quality was observed on rice production i.e., -5.5 %. The reliability of models was confirmed by CUSUM and CUSUM square plot. Diagnostic tests ensure the absence of serial correlation and heteroscedasticity in the models. Additionally, the forecasting results are obtained from the three machine learning models i.e. feed forward neural network (FFNN), support vector machines (SVM) and adaptive boosting technique (Adaboost). Through the % MAPE criterion, it is evident that FFNN has achieved high precision (91 %). Based on the empirical findings, the study proposed the adoption of sustainable agricultural practices and incentives should be given to the farmers so that future generations inherit a world that is sustainable, and healthy.

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The cattle sector plays a pivotal role in the economies of numerous Latin American and Caribbean countries. However, it also exerts a significant impact on environmental degradation, including substantial contributions to greenhouse gas emissions (accounting for 23.5 % of global livestock emissions) and deforestation (70 % attributed to livestock in South America). This article aims to investigate the complex, long-term, and short-term relationships between population growth, pastureland expansion, deforestation, and the cattle sector in 15 countries across the region, focusing on their effects on greenhouse gas emissions as well as beef and dairy production. Utilizing data from FAOSTAT spanning the period from 1990 to 2019, a cointegrated panel model was developed using the Pooled Mean Group technique, resulting in the estimation of six models. The aggregate-level results for the region reveal the presence of relatively stable long-term relationships. This implies that over time, the influence of population growth, pastureland expansion, and deforestation on greenhouse gas emissions from cattle production tends to diminish in significance. This long-term behavior may be particularly pronounced in countries with more developed cattle sectors, where efforts to mitigate the environmental impacts of cattle production, such as promoting improved forage technologies, silvo-pastoral systems, grazing management practices, and the implementation of policies, regulatory frameworks, and incentives, have gained traction. These progressive countries can serve as regional benchmarks, and the lessons they have learned hold valuable insights for the sustainable intensification of cattle production in countries with less-developed cattle sectors.

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Enteric methane emission is the main source of greenhouse gas contribution from dairy cattle. Therefore, it is essential to evaluate drivers and develop more accurate predictive models for such emissions. In this study, we built a large and intercontinental experimental dataset to: (1) explain the effect of enteric methane emission yield (g methane/kg diet intake) and feed conversion (kg diet intake/kg milk yield) on enteric methane emission intensity (g methane/kg milk yield); (2) develop six models for predicting enteric methane emissions (g/cow/day) using animal, diet, and dry matter intake as inputs; and to (3) compare these 6 models with 43 models from the literature. Feed conversion contributed more to enteric methane emission (EME) intensity than EME yield. Increasing the milk yield reduced EME intensity, due more to feed conversion enhancement rather than EME yield. Our models predicted methane emissions better than most external models, with the exception of only two other models which had similar adequacy. Improved productivity of dairy cows reduces emission intensity by enhancing feed conversion. Improvement in feed conversion should be prioritized for reducing methane emissions in dairy cattle systems.

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The aim of the present study was to evaluate the effects of marine microalgae (Dunaliella salina) as a food additive on biogas (BG), methane (CH4), carbon monoxide (CO), and hydrogen sulfide (H2S) production kinetics, as well as in in vitro rumen fermentation and the CH4 conversion efficiency of different genotypes of maize (Zea mays L.) and states of forage. The treatments were characterized by the forage of five maize genotypes (Amarillo, Montesa, Olotillo, Tampiqueño, and Tuxpeño), two states of forage (fresh and ensiled), and the addition of 3% (on DM basis) of microalgae (with and without). The parameters (b = asymptotic production, c = production rate, and Lag = delay phase before gas production) of the production of BG, CH4, CO, and H2S showed an effect (p < 0.05) of the genotype, the state of the forage, the addition of the microalgae, or some of its interactions, except for the time in the CO delay phase (p > 0.05). Moreover, the addition of microalgae decreased (p < 0.05) the production of BG, CH4, and H2S in most of the genotypes and stages of the forage, but the production of CO increased (p < 0.05). In the case of fermentation characteristics, the microalgae increased (p < 0.05) the pH, DMD, SCFA, and ME in most genotypes and forage states. With the addition of the microalgae, the fresh forage from Olotillo obtained the highest pH (p < 0.05), and the ensiled from Amarillo, the highest (p < 0.05) DMD, SCFA, and ME. However, the ensiled forage produced more (p < 0.05) CH4 per unit of SFCA, ME, and OM, and the microalgae increased it (p < 0.05) even more, and the fresh forage from Amarillo presented the highest (p < 0.05) quantity of CH4 per unit of product. In conclusion, the D. salina microalga showed a potential to reduce the production of BG, CH4, and H2S in maize forage, but its effect depended on the chemical composition of the genotype and the state of the forage. Despite the above, the energy value of the forage (fresh and ensiled) improved, the DMD increased, and in some cases, SCFA and ME also increased, all without compromising CH4 conversion efficiency.

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Accurate measurements of methane (CH4) and carbon dioxide (CO2) fluxes from tree stems are important for understanding greenhouse gas emissions. Closed chamber methods are commonly employed for this purpose; however, leaks between the chamber and the atmosphere as well as gas accumulation, known as the concentration buildup effect, can impact flux measurements significantly. In this study, we investigated the impacts of concentration buildup and leaks on semi-rigid closed chamber methods. Field measurements were conducted on six tree species, including three species from a Mexican mangrove ecosystem and three species from a Magellanic sub-Antarctic forest. Systematic observations revealed significant leak flow rates, ranging from 0.00 to 465 L h-1, with a median value of 1.25 ± 75.67 L h-1. We tested the efficacy of using cement to reduce leaks, achieving a leak flow rate reduction of 46-98 % without complete elimination. Our study also demonstrates a clear and substantial impact of concentration buildup on CH4 flux measurements, while CO2 flux measurements were relatively less affected across all tree species studied. Our results show that the combined effects of leaks and concentration buildup can lead to an underestimation of CH4 emissions by an average of 40 ± 20 % and CO2 emissions by 22 ± 22 %, depending on the bark roughness. Based on these findings, we recall a straightforward yet effective method to minimize experimental errors associated with these phenomena, previously established, and reiterated in the current context, for calculating emissions that considers effects of leaks and concentration buildup, while eliminating the need for separate determinations of these phenomena. Overall, the results, combined with a literature review, suggest that our current estimates of GHG flux from tree stems are currently underestimated.

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Brazil is the second-largest ethanol producer in the world, primarily using sugar cane as feedstock. To foster biofuel production, the Brazilian government implemented a national biofuel policy, known as RenovaBio, in which greenhouse gas (GHG) emission reduction credits are provided to biofuel producers based on the carbon intensities (CI) of the fuels they produce. In this study, we configured the GREET model to evaluate life cycle GHG emissions of Brazilian sugar cane ethanol, using data from 67 individual sugar cane mills submitted to RenovaBio in 2019/2020. The average CI per megajoule of sugar cane ethanol produced in Brazil for use in the U.S. was estimated to be 35.2 g of CO2 equivalent, a 62% reduction from U.S. petroleum gasoline blendstock without considering the impacts of land use change. The three major GHG sources were on-field N2O emissions (24.3%), sugar cane farming energy use (24.2%), and sugar cane ethanol transport (19.3%). With the probability density functions for key input parameters derived from individual mill data, we performed stochastic simulations with the GREET model to estimate the variations in sugar cane ethanol CI and confirmed that despite the larger variations in sugar cane ethanol CI, the fuel provided a robust GHG reduction benefit compared to gasoline blendstock.

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New agricultural practices and land-use intensification in the Cerrado biome have affected the soil carbon stocks. A major part of the native vegetation of the Brazilian Cerrado, a tropical savanna-like ecoregion, has been replaced by crops, which has caused changes in the soil carbon (C) stocks. To ensure the sustainability of this intensified agricultural production, actions have been taken to increase soil C stocks and mitigate greenhouse gas emissions. In the last two decades, new agricultural practices have been adopted in the Cerrado region, and their impact on C stocks needs to be better understood. This subject has been addressed in a systematic review of the existing data in the literature, consisting of 63 articles from the Scopus database. Our review showed that the replacement of Cerrado vegetation by crop species decreased the original soil C stocks (depth 0-30 cm) by 73%, with a peak loss of 61.14 Mg ha-1. However, when analyzing the 0-100 cm layer, 52.4% of the C stock data were higher under cultivated areas than in native Cerrado soils, with a peak gain of 93.6 Mg ha-1. The agricultural practices implemented in the Brazilian Cerrado make low-carbon agriculture in this biome possible.

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The transition to a neutral carbon and sustainable urban water cycle requires improving eco-efficiency in wastewater treatment processes. To support decision-making based on eco-efficiency evaluations, reliable estimations are fundamental. In this study, the eco-efficiency of a sample of 109 WWTPs was evaluated using efficiency analysis tree method. It combines machine learning and linear programming techniques and therefore, overcomes overfitting limitations of non-parametric methods used by past research on this topic. Results from the case study revealed that optimal costs and greenhouse gas emissions depend on the quantity of organic matter and suspended solids removed from wastewater. The estimated average eco-efficiency is 0.373 which involves that the assessed WWTPs could save 0.32 €/m3 and 0.11 kg of CO2 equivalent/m3. Moreover, only 4 out of 109 WWTPs are identified as eco-efficient which implies that the majority of the evaluated facilities can achieve substantial savings in operational costs and greenhouse gas emissions.

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Forest restoration mitigates climate change by removing CO2 and storing C in terrestrial ecosystems. However, incomplete information on C storage in restored tropical forests often fails to capture the ecosystem's holistic C dynamics. This study provides an integrated assessment of C storage in above to belowground subsystems, its consequences for greenhouse gas (GHG) fluxes, and the quantity, quality, and origin of soil organic matter (SOM) in restored Atlantic forests in Brazil. Relations between SOM properties and soil health indicators were also explored. We examined two restorations using tree planting ('active restoration'): an 8-year-old forest with green manure and native trees planted in two rounds, and a 15-year-old forest with native-planted trees in one round without green manure. Restorations were compared to reformed pasture and primary forest sites. We measured C storage in soil layers (0-10, 10-20, and 20-30 cm), litter, and plants. GHG emissions were assessed using CH4 and CO2 fluxes. SOM quantity was evaluated using C and N, quality using humification index (HLIFS), and origin using δ13C and δ15N. Nine soil health indicators were interrelated with SOM attributes. The primary forest presented the highest C stocks (107.7 Mg C ha-1), followed by 15- and 8-year-old restorations and pasture with 69.8, 55.5, and 41.8 Mg C ha-1, respectively. Soil C stocks from restorations and pasture were 20% lower than primary forest. However, 8- and 15-year-old restorations stored 12.3 and 28.3 Mg ha-1 more aboveground C than pasture. The younger forest had δ13C and δ15N values of 2.1 and 1.7‰, respectively, lower than the 15-year-old forest, indicating more C derived from C3 plants and biological N fixation. Both restorations and pasture had at least 34% higher HLIFS in deeper soil layers (10-30 cm) than primary forest, indicating a lack of labile SOM. Native and 15-year-old forests exhibited higher soil methane influx (141.1 and 61.9 µg m-2 h-1). Forests outperformed pasture in most soil health indicators, with 69% of their variance explained by SOM properties. However, SOM quantity and quality regeneration in both restorations approached the pristine forest state only in the top 10 cm layer, while deeper soil retained agricultural degradation legacies. In conclusion, active restoration of the Atlantic Forest is a superior approach compared to pasture reform for GHG mitigation. Nonetheless, the development of restoration techniques to facilitate labile C input into deeper soil layers (>10 cm) is needed to further improve soil multifunctionality and long-term C storage.

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In this study, we evaluate the effects of intercropping pigeon pea (Cajanus cajan (L.) Millsp.) with tropical pastures for feeding Nellore cattle and compared animal performance and enteric CH4 emissions with other pasture-based systems during the dry and rainy seasons of 2021. Thirty-six Nellore steers (with a body weight of 221 ± 7 kg and an age of 15-16 months) were randomly distributed in three treatments with three replicates (in paddocks of 1.5 hectares each): (1) a degraded pasture of Urochloa spp. (DEG); (2) a recovered and fertilized pasture of Urochloa spp. (REC); and (3) pigeon pea intercropped with Urochloa spp. (MIX). Enteric CH4 emissions were estimated using the sulfur hexafluoride (SF6) tracer gas technique, and dry matter intake (DMI) was determined using internal (iNDF) and external (TiO2) markers. Forages were collected by hand plucking after observations of ingestive behavior, and feces was collected after voluntary defecation. The proportion of grass and legume intake was estimated by C stable isotopes, and the forage nutritional quality was determined, while animal performance was monitored monthly, and the stocking rate was adjusted by the "put and take" technique. The results indicated that intercropping pigeon pea with tropical grasses is an interesting strategy for sustainable livestock production based on pastures. The MIX treatment was able to meet the nutritional requirements of the animals, which presented higher performance. In addition, there was a reduction in CH4 emissions up to 70% when expressed per average daily weight gain in comparison to the DEG treatment.

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This study aimed to evaluate methane emission, milk yield and behavior of ewes kept exclusively on irrigated pasture of Tifton 85 grass (Cynodon spp.) or supplemented with ground corn or whole cottonseed (WCS) based concentrates. Twelve Lacaune x Santa Ines ewes (43.07±0.83 kg of body weight, 77±24 days after parturition, on average) were distributed in replicated 3x3 Latin square. Treatments consisted of three diets: pasture (no concentrate supplementation); corn (pasture + corn-based supplement); whole cottonseed (pasture + whole cottonseed-based supplement), offering 0.5 kg/ewe/day. The WCS group showed the highest concentrate dry matter intake (DMI) (p=0.049) and crude protein (CP) intake (p=0.001) compared to the others. There was no difference on total DMI (p=0.115) for the tested diets. Animals exclusively kept on pasture had the greatest forage DMI (p=0.004), lowest CP digestibility (p=0.015), showed longer grazing time (p=0.01) and shorter idle time (p=0.01) compared to the supplemented groups. Milk yield (0.36 kg/ewe/day) (p=0.15) and methane emission (33.12 g/ewe/day) (p=0.95) were similar for all three evaluated groups. Supplementation with concentrate based on corn or whole cottonseed does not improve productive performance nor decrease methane emission. However, lactating ewes kept exclusively in pasture show longer grazing time, without changes in milk yield and methane emission.

Objetivou-se avaliar a emissão de metano, a produção de leite e o comportamento de ovelhas mantidas exclusivamente em pastagem irrigada de Tifton 85 (Cynodon spp.) ou suplementadas com concentrados à base de milho grão ou caroço de algodão. Doze ovelhas Lacaune x Santa Inês (43,07±0,83 kg de peso corporal e 77±24 dias após a parição, em média) foram distribuídas em quadrados latinos 3x3 replicados. Os tratamentos consistiram em três dietas: pastagem (sem suplementação concentrada); milho (pastagem + suplemento à base de milho); caroço de algodão integral (pastagem + suplemento à base de caroço de algodão), oferecendo 0,5 kg/ovelha/dia. O grupo caroço de algodão apresentou maior consumo de matéria seca (CMS) (P=0,049) e consumo de proteína bruta (PB) (P=0,001) em relação aos demais. Não houve diferença no CMS total (P=0,115) para as dietas testadas. Os animais exclusivamente em pastagem apresentaram maior consumo de forragem (P=0,004), menor digestibilidade da PB (P=0,015), maior tempo em pastejo (P=0,01) e menor tempo em ócio (P=0,01), em relação aos grupos suplementados. A produção de leite (0,36 kg/ovelha/dia) (P=0,15) e a emissão de metano (33,12 g/ovelha/dia) (P=0,95) foram semelhantes nos três grupos avaliados. A suplementação com concentrado à base de milho grão ou caroço de algodão não melhorou o desempenho produtivo e não reduziu a emissão de metano. Entretanto, as ovelhas lactantes mantidas exclusivamente em pastagem apresentaram maior tempo em pastejo, sem alterações na produção de leite e na emissão de metano.

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The world scenario regarding consumption and demand for products based on fossil fuels has demonstrated the imperative need to develop new technologies capable of using renewable resources. In this context, the use of biomass to obtain chemical intermediates and fuels has emerged as an important area of research in recent years, since it is a renewable source of carbon in great abundance. It has the benefit of not contributing to the additional emission of greenhouse gases since the CO2 released during the energy conversion process is consumed by it through photosynthesis. In the presented review, the authors provide an update of the literature in the field of biomass transformation with the use of niobium-containing catalysts, emphasizing the versatility of niobium compounds for the conversion of different types of biomass.

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The European Union has identified the Textile and Clothing industry as one of the essential objectives towards carbon neutrality in 2050 in line with the "European Green Deal". There are no previous research papers focused on analysing the drivers and inhibitors of the past greenhouse gas emission changes of the textile and clothing industry in Europe. This paper aims to analyse the determinants of the changes in these emissions, and the disassociation level between emissions and economic growth, throughout the 27 Member States of the European Union, from 2008 to 2018. A Logarithmic Mean Divisia Index that explains the key drivers of the changes in greenhouse gas emissions of European Union Textile and Cloth industry and a Decoupling Index have been applied. The results generally conclude that the intensity and carbonisation effects are key factors that contribute to reducing greenhouse gas emissions. The lower relative weight of the textile and clothing industry throughout the EU-27 was noteworthy, and favours lower emissions, partially counteracted by the activity effect. Also, most Member States have been decoupling the industry's emissions from economic growth. Our policy recommendation shows that if further reductions in greenhouse gas emissions are to be achieved, energy efficiency improvements and cleaner use of energy sources would offset the potential increase in emissions of this industry as a result of a relative increase in its gross value added.

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