Sustainability synergies and trade-offs considering circularity and land availability for bioplastics production in Brazil.

Alongside the concerns of waste management, plastic production represents a future problem for managing greenhouse gas emissions. Advanced recycling and bio-based production are paramount to face this challenge. The sustainability of bio-based polyethylene (bioPE) depends on the feedstock, avoiding stress on natural resources. This work discusses Brazil's potential to meet future global bioPE demand by 2050, using sugarcane as feedstock and considering environmental sustainability for production expansion. From the assessed 35.6 Mha, 3.55 Mha would be exempt from trade-offs related to land use change (dLUC), biodiversity, and water availability. The scenario with the highest circularity efficiency would require 22.2 Mha to meet the global demand, which can be accommodated in areas with positive impacts in carbon stocks, neutral impacts in water availability, and medium impacts on biodiversity. Here, we show that dropping demand is essential to avoid trade-offs and help consolidate bioPE as a sustainable alternative for future net-zero strategies.

Determinants of greenhouse gas emissions by Brazilian agricultural sector.

The agricultural sector is one of the most polluting economic activities, contributing significantly to greenhouse gas (GHGs) emissions. Brazil is one of the largest agricultural producers worldwide and plays a major role in reducing the environmental impact of this sector. Here, we aimed to determine the impact of the agricultural sector, with special attention to production, prices, and trade openness, on the short- and long-term GHGs emissions of Brazilian agriculture. Employing data from 1974 to 2019, we tested the cointegration of variables and compared the determinants of GHG emissions using Autoregressive Distributed Lag (ARDL) and Vector Error Correction Model (VECM) methods. Our results show a long-term equilibrium trend for Brazilian agricultural GHGs emissions, a result that correlates with emerging environmental compliance, and society demands the adoption of sustainable technologies, processes, and policies. In the short run, both cattle herds and Agricultural Added Value to GDP per capita showed an expected positive and significant contribution to GHGs emissions, while agricultural crop area demonstrated an inverse relationship. The trade openness index confirmed that foreign trade plays an important role in reducing GHGs emissions. The price index is not significant in our models. Both the private and public sectors have important roles in sustainable agriculture, especially in increasing system efficiency through the adoption of management and technologies that reduce GHG emissions levels.

Environmental impacts of extensive beef production in Colombia by life cycle assessment: a case study.

The increase in the negative effects of global change promotes the search for alternatives to supply the demand for food worldwide aligned with the Sustainable Development Goals (SDGs) to ensure food security. Animal protein, which is a main source of nutrients in the diet of today's society, especially beef, which is one of the most demanded products nowadays, has been criticized not only for its high water consumption and land occupation for production but also for the emission of greenhouse gases (GHG) from enteric methane generated in the fermentation process within the bovine rumen and deforestation for the adaptation of pastures. This study is mainly motivated by the lack of quantifiable scientific information in Colombia on the environmental impacts of beef production. Therefore, it is intended to estimate some of the impacts of beef production in extensive systems using the life cycle assessment (LCA) method under a particular scenario considering all the production phases (from raw material to fattening, where the cattle are ready to be slaughtered). The study was conducted with data supplied by a farm in Antioquia, Colombia, and the functional unit (FU) was defined as 1 kg of live weight (LW). The scope of this study was gate-to-gate. "The 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories" (IPCC  2006; IPCC 2019) was used to calculate methane and nitrous oxide emissions. LCA modeling was developed with Ecoinvent database v3.8 and the Umberto LCA + software. It was found that the most affected category of damage was ecosystem quality, which represents 77% of the total, followed by human health at 17% and resources at 6%. The category impact of agricultural land occupation is the one that represents the most significant contribution to the ecosystem quality endpoint, with a percentage of 87%, due to the soil's compaction and the loss of the soil's properties. Additionally, the obtained carbon footprint for the system was 28.9 kg of CO2-eq/kg LW.

Assessing the carbon footprint of soybean transportation in Brazil: a network equilibrium model approach.

Brazil has historically invested few resources in its transport infrastructure, leaving gaps and reducing its efficiency. The country presents a high dependence on road transport, which results in increased operational costs and higher greenhouse gas (GHG) emissions. Consequently, the performance of cargo transportation in Brazil has been deteriorating, accompanied by a rise in the consumption of fossil fuels and noteworthy levels of GHG emissions. This article assesses the carbon intensity of soybean transport operations within Brazil. Utilizing a network equilibrium model, this study estimated the soybean transportation flows that minimize the total cost of transporting this product across the origins and destinations within the grain handling system. The modeling also calculated the GHG emissions in transportation. The results show that the transportation of soybeans produced 2.74 million tonnes of CO2 equivalent annually, with road transport accounting for 81.7% of these emissions. The state of Mato Grosso, responsible for 44.08 kg CO2 equivalent per tonne of soybeans transported, contributed almost 49% of the total emissions due to the extensive distances involved. In contrast, states like Paraná and Rio Grande do Sul, located closer to southern ports, exhibited the lowest emissions, with rates of 11.55 kg CO2 eq/t and 12.52 kg CO2 eq/t, respectively. The analysis highlights the significant potential for reducing GHG emissions by increasing the use of rail and barge transport, particularly in high-emission regions such as Mato Grosso.

Leveraging the trend analysis for modeling of the greenhouse gas emissions associated with coal combustion.

In this paper, it is aimed, for the first time, at deriving simple models, leveraging the trend analysis in order to estimate the future greenhouse gas emissions associated with coal combustion. Due to the expectations of becoming the center of global economic development in the future, BRICS-T (Brazil, the Russian Federation, India, China, South Africa, and Turkiye) countries are adopted as cases in the study. Following the models' derivation, their statistical validations and estimating accuracies are also tested through various metrics. In addition, the future greenhouse gas emissions associated with coal combustion are estimated by the derived models. The results demonstrate that the derived models can be successfully used as a tool for estimating the greenhouse gas emissions associated with coal combustions with accuracy ranges from at least 90% to almost 98%. Moreover, the estimating results show that the total amount of greenhouse gas emissions associated with coal combustions in the relevant countries and in the world will increase to 14 BtCO2eq and 19 BtCO2eq by 2035, with an annual growth of 2.39% and 1.71%, respectively. In summary, the current study's findings affirm the usefulness of trend analysis in deriving models to estimate greenhouse gas emissions associated with coal combustion.

Green missing spots: Information entropy on greenhouse gas emission disclosure by Brazilian companies.

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.

Impacts of the global food system on terrestrial biodiversity from land use and climate change.

The global food system is a key driver of land-use and climate change which in turn drive biodiversity change. Developing sustainable food systems is therefore critical to reversing biodiversity loss. We use the multi-regional input-output model EXIOBASE to estimate the biodiversity impacts embedded within the global food system in 2011. Using models that capture regional variation in the sensitivity of biodiversity both to land use and climate change, we calculate the land-driven and greenhouse gas-driven footprints of food using two metrics of biodiversity: local species richness and rarity-weighted species richness. We show that the footprint of land area underestimates biodiversity impact in more species-rich regions and that our metric of rarity-weighted richness places a greater emphasis on biodiversity costs in Central and South America. We find that methane emissions are responsible for 70% of the overall greenhouse gas-driven biodiversity footprint and that, in several regions, emissions from a single year's food production are associated with global biodiversity loss equivalent to 2% or more of that region's total land-driven biodiversity loss. The measures we present are relatively simple to calculate and could be incorporated into decision-making and environmental impact assessments by governments and businesses.

The role of microbial communities in biogeochemical cycles and greenhouse gas emissions within tropical soda lakes.

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.

Global biomass maps can increase the precision of (sub)national aboveground biomass estimates: A comparison across tropical countries.

Countries within the tropics face ongoing challenges in completing or updating their national forest inventories (NFIs), critical for estimating aboveground biomass (AGB) and for forest-related greenhouse gas (GHG) accounting. While previous studies have explored the integration of map information with local reference data to fill in data gaps, limited attention has been given to the specific challenges presented by the clustered plot designs frequently employed by NFIs when combined with remote sensing-based biomass map units. This research addresses these complexities by conducting four country case-studies, encompassing a variety of NFI characteristics within a range of AGB densities. Examining four country case-studies (Peru, Guyana, Tanzania, Mozambique), we assess the potential of European Space Agency's Climate Change Initiative (CCI) global biomass maps to increase precision in (sub)national AGB estimates. We compare a baseline approach using NFI field-based data with a model-assisted scenario incorporating a locally calibrated CCI biomass map as auxiliary information. The original CCI biomass maps systematically underestimate AGB in three of the four countries at both the country and stratum level, with particularly weak agreement at finer map resolution. However, after calibration with country-specific NFI data, stratum and country-level AGB estimates from the model-assisted scenario align well with those obtained solely from field-based data and official country reports. Introducing maps as a source of auxiliary information fairly increased the precision of stratum and country-wise AGB estimates, offering greater confidence in estimating AGB for GHG reporting purposes. Considering the challenges tropical countries face with implementing their NFIs, it is sensible to explore the potential benefits of biomass maps for climate change reporting mechanisms across biomes. While country-specific NFI design assumptions guided our model-assisted inference strategies, this study also uncovers transferable insights from the application of global biomass maps with NFI data, providing valuable lessons for climate research and policy communities.

Greenhouse gas emission potential of tropical coastal sediments in densely urbanized area inferred from metabarcoding microbial community data.

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.

Microalgae-mediated biofixation as an innovative technology for flue gases towards carbon neutrality: A comprehensive review.

Microalgae-mediated industrial flue gas biofixation has been widely discussed as a clean alternative for greenhouse gas mitigation. Through photosynthetic processes, microalgae can fix carbon dioxide (CO2) and other compounds and can also be exploited to obtain high value-added products in a circular economy. One of the major limitations of this bioprocess is the high concentrations of CO2, sulfur oxides (SOx), and nitrogen oxides (NOx) in flue gases, according to the origin of the fuel, that can inhibit photosynthesis and reduce the process efficiency. To overcome these limitations, researchers have recently developed new technologies and enhanced process configurations, thereby increased productivity and CO2 removal rates. Overall, CO2 biofixation rates from flue gases by microalgae ranged from 72 mg L-1 d -1 to over 435 mg L-1 d-1, which were directly influenced by different factors, mainly the microalgae species and photobioreactor. Additionally, mixotrophic culture have shown potential in improving microalgae productivity. Progress in developing new reactor configurations, with pilot-scale implementations was observed, resulting in an increase in patents related to the subject and in the implementation of companies using combustion gases in microalgae culture. Advancements in microalgae-based green technologies for environmental impact mitigation have led to more efficient biotechnological processes and opened large-scale possibilities.

A systematic evaluation of seven different scores representing the EAT-Lancet reference diet and mortality, stroke, and greenhouse gas emissions in three cohorts.

Different approaches have been used for translation of the EAT-Lancet reference diet into dietary scores that can be used to assess health and environmental impact. Our aim was to compare the different EAT-Lancet diet scores, and to estimate their associations with all-cause mortality, stroke incidence, and greenhouse gas emissions. We did a systematic review (PROSPERO, CRD42021286597) to identify different scores representing adherence to the EAT-Lancet reference diet. We then qualitatively compared the diet adherence scores, including their ability to group individuals according the EAT-Lancet reference diet recommendations, and quantitatively assessed the associations of the diet scores with health and environmental outcome data in three diverse cohorts: the Danish Diet, Cancer and Health Cohort (DCH; n=52 452), the Swedish Malmö Diet and Cancer Cohort (MDC; n=20 973), and the Mexican Teachers' Cohort (MTC; n=30 151). The DCH and MTC used food frequency questionnaires and the MDC used a modified diet history method to assess dietary intake, which we used to compute EAT-Lancet diet scores and evaluate the associations of scores with hazard of all-cause mortality and stroke. In the MDC, dietary greenhouse gas emission values were summarised for every participant, which we used to predict greenhouse gas emissions associated with varying diet adherence scores on each scoring system. In our review, seven diet scores were identified (Knuppel et al, 2019; Trijsburg et al, 2020; Cacau et al, 2021; Hanley-Cook et al, 2021; Kesse-Guyot et al, 2021; Stubbendorff et al, 2022; and Colizzi et al, 2023). Two of the seven scores (Stubbendorff and Colizzi) were among the most consistent in grouping participants according to the EAT-Lancet reference diet recommendations across cohorts, and higher scores (greater diet adherence) were associated with decreased risk of mortality (in the DCH and MDC), decreased risk of incident stroke (in the DCH and MDC for the Stubbendorff score; and in the DCH for the Colizzi score), and decreased predicted greenhouse gas emissions in the MDC. We conclude that the seven different scores representing the EAT-Lancet reference diet had differences in construction, interpretation, and relation to disease and climate-related outcomes. Two scores generally performed well in our evaluation. Future studies should carefully consider which diet score to use and preferably use multiple scores to assess the robustness of estimations, given that public health and environmental policy rely on these estimates.

Assessing methane emissions from paddy fields through environmental and UAV remote sensing variables.

Concerns about methane (CH4) emissions from rice, a staple sustaining over 3.5 billion people globally, are heightened due to its status as the second-largest contributor to greenhouse gases, driving climate change. Accurate quantification of CH4 emissions from rice fields is crucial for understanding gas concentrations. Leveraging technological advancements, we present a groundbreaking solution that integrates machine learning and remote sensing data, challenging traditional closed chamber methods. To achieve this, our methodology involves extensive data collection using drones equipped with a Micasense Altum camera and ground sensors, effectively reducing reliance on labor-intensive and costly field sampling. In this experimental project, our research delves into the intricate relationship between environmental variables, such as soil conditions and weather patterns, and CH4 emissions. We achieved remarkable results by utilizing unmanned aerial vehicles (UAV) and evaluating over 20 regression models, emphasizing an R2 value of 0.98 and 0.95 for the training and testing data, respectively. This outcome designates the random forest regressor as the most suitable model with superior predictive capabilities. Notably, phosphorus, GRVI median, and cumulative soil and water temperature emerged as the model's fittest variables for predicting these values. Our findings underscore an innovative, cost-effective, and efficient alternative for quantifying CH4 emissions, marking a significant advancement in the technology-driven approach to evaluating rice growth parameters and vegetation indices, providing valuable insights for advancing gas emissions studies in rice paddies.

Forage peanut legume as a strategy for improving beef production without increasing livestock greenhouse gas emissions.

The transformation of pastures from a degraded state to sustainable productivity is a major challenge in tropical livestock production. Stoloniferous forage legumes such as Arachis pintoi (forage peanut) are one of the most promising alternatives for intensifying pasture-based beef livestock operations with reduced greenhouse gas (GHG) emissions. This 2-year study assessed beef cattle performance, nutrient intake and digestibility, and balance of GHG emissions in three pasture types (PT): (1) mixed Palisade grass - Urochloa brizantha (Hochst. ex A. Rich.) R.D. Webster (syn. Brachiaria brizantha Stapf cv. Marandu) and forage peanut (A. pintoi Krapov. & W.C. Greg. cv. BRS Mandobi) pastures (Mixed), (2) monoculture Palisade grass pastures with 150 kg of N/ha per year (Fertilised), and (3) monoculture Palisade grass without N fertiliser (Control). Continuous stocking with a variable stocking rate was used in a randomised complete block design, with four replicates per treatment. The average daily gain and carcass gain were not influenced by the PT (P = 0.439 and P = 0.100, respectively) and were, on average, 0.433 kg/animal per day and 83.4 kg/animal, respectively. Fertilised and Mixed pastures increased by 102 and 31.5%, respectively, the liveweight gain per area (kg/ha/yr) compared to the Control pasture (P < 0.001). The heifers in the Mixed pasture had lower CH4 emissions (g/animal per day; P = 0.009), achieving a reduction of 12.6 and 10.1% when compared to the Fertilised and Control pastures, respectively. Annual (N2O) emissions (g/animal) and per kg carcass weight gain were 59.8 and 63.1% lower, respectively, in the Mixed pasture compared to the Fertilised pasture (P < 0.001). Mixed pasture mitigated approximately 23% of kg CO2eq/kg of carcass when substituting 150 kg of N/ha per year via fertiliser. Mixed pastures with forage peanut are a promising solution to recover degraded tropical pastures by providing increased animal production with lower GHG emissions.

Compilations and updates on residual feed intake in sheep.

The increasing global demand for food and the strong effect of climate change have forced animal science to advance regarding new methods of selection in search of more efficient animals in production systems. Feed consumption represents more than 70% of the costs of sheep farms, and more efficient animals can increase the farmers' profitability. One of the main measures of feed efficiency is estimated residual feed intake (RFI), created in 1963 by Robert Koch for estimation in cattle and later adapted for sheep. Animals with negative RFI values (RFI-) are more efficient than animals with positive values (RFI+), with influence on the variables of performance, carcass quality and production of enteric gases. The RFI is the most common and accepted metric of the feed efficiency trait for genetic selection, since it is independent of growth traits, unlike the feed conversion ratio. The purpose of this review article was to present updated literature information on the relationship of RFI estimates with performance measures, molecular markers, greenhouse gas production and feed efficiency, the technical aspects and physiological basis of metabolic in sheep.

Soil greenhouse gas emissions from dead and natural mangrove forests in Southeastern Brazil.

Mangroves forests may be important sinks of carbon in coastal areas but upon their death, these forests may become net sources of carbon dioxide (CO2) and methane (CH4) to the atmosphere. Here we assessed the spatial and temporal variability in soil CO2 and CH4 fluxes from dead mangrove forests and paired intact sites in SE-Brazil. Our findings demonstrated that during warmer and drier conditions, CO2 soil flux was 183 % higher in live mangrove forests when compared to the dead mangrove forests. Soil CH4 emissions in live forests were > 1.4-fold higher than the global mangrove average. During the wet season, soil GHG emissions dropped significantly at all sites. During warmer conditions, mangroves were net sources of GHG, with a potential warming effect (GWP100) of 32.9 ± 10.2 (±SE) Mg CO2e ha-1 y-1. Overall, we found that dead mangroves did not release great amounts of GHG after three years of forest loss.

Trends in the contribution of greenhouse gas emissions from food and beverage purchases in Mexico: 1989-2020.

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.

Fine-Scale Spatial Variability of Greenhouse Gas Emissions From a Subantarctic Peatland Bog.

Peatlands are recognized as crucial greenhouse gas sources and sinks and have been extensively studied. Their emissions exhibit high spatial heterogeneity when measured on site using flux chambers. However, the mechanism by which this spatial variability behaves on a very fine scale remains unclear. This study investigates the fine-scale spatial variability of greenhouse gas emissions from a subantarctic Sphagnum peatland bog. Using a recently developed skirt chamber, methane emissions and ecosystem respiration (as carbon dioxide) were measured at a submeter scale resolution, at five specific 3 × 3 m plots, which were examined across the site throughout a single campaign during the Austral summer season. The results indicated that methane fluxes were significantly less homogeneously distributed compared with ecosystem respiration. Furthermore, we established that the spatial variation scale, i.e., the minimum spatial domain over which notable changes in methane emissions and ecosystem respiration occur, was <0.56 m2. Factors such as ground height relative to the water table and vegetation coverage were analyzed. It was observed that Tetroncium magellanicum exhibited a notable correlation with higher methane fluxes, likely because of the aerenchymatous nature of this species, facilitating gas transport. This study advances understanding of gas exchange patterns in peatlands but also emphasizes the need for further efforts for characterizing spatial dynamics at a very fine scale for precise greenhouse gas budget assessment.

A recipe for change: Analyzing the climate and ecosystem impacts of the Brazilian diet shift.

Diet shift is an opportunity to mitigate the impacts of food systems, which are responsible for about a third of greenhouse gas (GHG) emissions globally and exert various environmental pressures on ecosystems. This study evaluates the mitigation potential of both global and local environmental impacts through dietary changes within the Brazilian context. Furthermore, the study aims to identify the potential benefits and trade-offs that may arise from these dietary transitions, thus providing a comprehensive analysis of the overall environmental implications. To this end, a life cycle assessment (LCA) was performed to evaluate the environmental impacts of a conventional diet in Brazil and seven alternatives, namely adjusted-EAT-Lancet, pescatarian, vegetarian, entomophagic (insect-based food), mycoprotein (microbial-based food), and synthetic (cell-based food) diets. Results indicate a substantial mitigation potential for GHG emissions (4-9 kg CO2e/cap/day) (39 % to 86 %) and land use (4-9 m2/cap/day) (38 % to 82 %) through a diet shift from a conventional diet to any of the seven alternative diets. However, certain trade-offs exist. A diet shift demonstrates no mitigation potential of soil acidification, and opportunities to reduce water eutrophication (0.02-0.2 g Pe/cap/day) (2 % to 24 %) and water consumption (0.2-0.5 m3/cap/day) (7 % to 14 %) were only found by completely substituting animal products for insect-based food, microbial-based food, and cell-based food. This study highlights the considerable potential of dietary changes to mitigate global environmental impacts associated with food systems. By revealing opportunities and challenges, this study supports science-based decision-making and guides efforts toward sustainable and environmentally friendly food consumption patterns.

Trade-offs in the externalities of pig production are not inevitable.

Farming externalities are believed to co-vary negatively, yet trade-offs have rarely been quantified systematically. Here we present data from UK and Brazilian pig production systems representative of most commercial systems across the world ranging from 'intensive' indoor systems through to extensive free range, Organic and woodland systems to explore co-variation among four major externality costs. We found that no specific farming type was consistently associated with good performance across all domains. Generally, systems with low land use have low greenhouse gas emissions but high antimicrobial use and poor animal welfare, and vice versa. Some individual systems performed well in all domains but were not exclusive to any particular type of farming system. Our findings suggest that trade-offs may be avoidable if mitigation focuses on lowering impacts within system types rather than simply changing types of farming.