Variability Of Greenhouse Gas Emissions Research Articles

Analyzing the variation of greenhouse gas emissions from typical municipal wastewater treatment plants in Beijing during 2007–2021

With the proposal of dual carbon goals and stringent effluent standards, the path of mitigating greenhouse gas (GHG) emissions from wastewater treatment plants (WWTPs) has gained significant research attention. Here, we evaluate the impact of season, elevated standards, operating parameters, and using clean energy on GHG emissions from 8 typical WWTPs in Beijing based on 180 monthly monitoring data. Coupled with the increasing demand for wastewater treatment and 77% more chemical oxygen demand being removed in 2017, total GHG emissions from 5 WWTPs increased by 89% compared to the status quo in 2007, and after energy structure reform total GHG emissions decreased by 17% in 2021. Scenario analysis reveals that energy recovery and clean energy utilization provide 64% and 48% mitigation potential by 2050, respectively. We argue stricter effluent standard leads to GHG emissions growth in WWTPs; meanwhile, process optimization, proper temperature and targeted policies at WWTPs can reduce GHG emissions.

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.

Variability of Greenhouse Gas Emissions in Relation to Economic and Ecological Indicators from Cattle Farms

Agricultural production, including animal husbandry, is associated with greenhouse gas (GHG) emissions, which have a negative impact on the environment. The purpose of this study was to determine methane and nitrous oxide emissions at the level of individual farms and to identify differences in emissions by type of production. Analyses were conducted using data from the FADN agricultural accounting system, covering the farms in Poland engaged in dairy production and the breeding of other grazing livestock for the period from 2012 to 2021. The relationships and trends of the indicators during the study period were also analyzed. The statistical analysis confirmed the relationship between greenhouse gas emissions and economic performance. In the case of dairy farms, the emissions averaged 137.09 kg·ha−1 of methane and 94.05 kg·ha−1 of nitrous oxide, which were found to increase as the net-added value and farm income increased. The results of the presented research highlight the existence of a link between economic goals and GHG emissions, which is an important contribution to the development of mitigation strategies in livestock production.

Is Democracy the Answer to Intractable Climate Change?

Abstract Climate change is the greatest governance challenge humanity has ever faced. Understanding why some governments successfully reduce greenhouse gas emissions and others fail is thus imperative. While regime type is often hypothesized to be a source of variation in greenhouse gas emissions, empirical findings about the effects of democracy and autocracy on climate action are contradictory. This research note reconciles these inconsistencies and adopts a quasi-experimental approach to investigate the relationship between democratization and greenhouse gas emissions. A fixed effects model with a synthetic control estimator is used to construct appropriate counterfactuals and evaluate the effect of regime type on emissions with data from the World Bank and Varieties of Democracy Project. The analysis shows that movement toward democracy does not have a significant effect on emissions, suggesting that research on the politics of emissions reduction should focus on factors other than regime type.

High Intra‐Seasonal Variability in Greenhouse Gas Emissions From Temperate Constructed Ponds

AbstractInland waters play a major role in global greenhouse gas (GHG) budgets. The smallest of these systems (i.e., ponds) have a particularly large—but poorly constrained—emissions footprint at the global scale. Much of this uncertainty is due to a poor understanding of temporal variability in emissions. Here, we conducted high‐resolution temporal sampling to quantify GHG exchange between four temperate constructed ponds and the atmosphere on an annual basis. We show these ponds are a net source of GHGs to the atmosphere (564.4 g CO2‐eq m−2 yr−1), driven by highly temporally variable diffusive methane (CH4) emissions. Diffusive CH4 release to the atmosphere was twice as high during periods when the ponds had a stratified water column than when it was mixed. Ebullitive CH4 release was also higher during stratification. Building ponds to favor mixed conditions thus presents an opportunity to minimize the global GHG footprint of future pond construction.

Insights into greenhouse gas emissions from a wastewater treatment plant in vulnerable water areas of China

Wastewater treatment plants (WWTPs) are a significant anthropogenic source of greenhouse gas (GHG), but the quantitative assessment of GHG emissions from WWTPs in vulnerable water areas under stricter discharge limits remains unclear. Herein, depending on a case WWTP in southern China, we investigated the impacts of discharge standard improvement and key drivers of GHG emissions using daily operating data. We demonstrated that the stricter discharge limits increased the total GHG emission intensity by 18.2 %, with direct emissions increasing more than indirect GHG emissions. The GHG emissions were negatively correlated with water quantity, showing the scale effect, which became more pronounced after the discharge standard improvement. Increasing influent chemical oxygen demand and total nitrogen concentrations significantly drove the variations in GHG emissions, which were accelerated under stricter discharge limits. This study provides insights into the evaluation of GHG emission from WWTPs in vulnerable water areas and carbon-neutral wastewater management policies.

Addressing dairy industry's scope 3 greenhouse gas emissions by efficiently managing farm carbon footprints

Corporate climate action is increasingly considered important in driving the transition towards a low-carbon economy. Especially in the food processing industry indirect greenhouse gas (GHG) emissions upstream in a producer's value chain (scope 3 emissions) often represent the largest portion of the GHG inventory. In order to manage those GHG emissions efficiently there is a need for simple GHG calculation models ready for large scale application on supplying farms. The aim of this study is to describe a procedure for the development of a simplified GHG calculation model specific for dairy farms based on detailed GHG emission calculations. The results presented are obtained from a case study of supplying farms of a Bavarian organic dairy. The simplified model shall reduce the number of input parameters significantly while representing the variations in GHG emissions across farms and allowing the quantification of GHG mitigation measures. The median farm carbon footprint (CCF) of the case study farms calculated with the detailed calculation model is 441.7 t CO2e/a (mean number of dairy cows per farm is 57). The total GHG mitigation potential per farm ranges from 6.51 t CO2e/a to 112.29 t CO2e/a. The simplification of the calculation model results in a less complex and time consuming questionnaire covering 57 parameters instead of up to 467 in the detailed model. The total CCF results of the simplified model give highly comparable results to the detailed calculation model (R2 = 99.7% , standard error = 2.3% of the mean CCF). The correlation between the total mitigation potential calculated per farm by the simplified and detailed calculation model is also high (R2 = 98.92%, standard error = 7.0% of the mean total mitigation potential per case study farm). For the quantification of some mitigation measures with the simplified model, attention must be paid to the question whether calculated GHG emission reductions are significantly higher than the standard error of GHG emissions calculated for the relevant life cycle stage. Summarizing the procedure for the development of a simplified GHG calculation model proposed in this paper, leads to a model that meets the defined requirements.

Greenhouse gas emissions from natural ecosystems of the Norilsk Industrial District

This paper describes the results of field measurements of methane and carbon dioxide fluxes from natural and anthropogenic-modified ecosystems located on the Norilsk industrial district. Previously, such studies have not been conducted in the area. The study points are located in different landscape regions determined by various conditions of permafrost formation. Most of the study area is located within the zone of predominantly continuous permafrost distribution. Six landscape regions were identified. Methane and carbon dioxide fluxes were measured at selected key sites typical for each region. When selecting the study points, the variety of conditions affecting methane and carbon dioxide emissions was considered. First, these include soil type (mineral or peat) and local moisture conditions. Methane and carbon dioxide fluxes were measured by dart static chamber method. Measurements of greenhouse gas concentrations in the chamber were carried out by a portable gas analyzer Li-7810 (Li-COR, USA). The results obtained show significant variability in greenhouse gas emissions for different ecosystem types. Methane uptake by soils is recorded on mineral soils and dry parts of bog ecosystems. Positive methane emissions are typical for watered areas of bog ecosystems and lakes with maximum values in the hollows. Methane fluxes range from slightly negative in dry bog areas with a median of -0.026 mgCH4/m2/h to emissions of 0.802 mgCH4/m2/h as the median for watered areas of bog ecosystems. Carbon dioxide fluxes are inversely correlated with the ecosystem moisture content and have a range from 51.6 mgCO2/m2/h (median for lakes) to 576 mgCO2/m2/h (median for mineral soils). A medium strength correlation was found between surface air temperature and intensity of methane uptake by mineral soils was found. Probability density distributions of methane and carbon dioxide fluxes have different forms.

Nitrous oxide and methane emissions from coffee agroforestry systems with different intensities of canopy closure

Agroforestry-based coffee production systems (AFs) contribute to climate change mitigation through carbon sequestration. However, it is unclear whether AFs produce lower nitrous oxide (N2O) and methane (CH4) emissions than the open-shade coffee production system. In addition, little to no evidence is available to explain the relationship between canopy cover levels and greenhouse gas (GHG) emissions in AFs. The aim of this study was to investigate N2O, CH4 and yield-scaled emissions in AFs with differing shade-tree canopy levels. Three canopy cover levels were identified: (i) dense shade (80 % canopy closure), (ii) medium shade (49 % canopy closure), and (iii) open-shade (full sun) production. To determine the effect of canopy cover on GHG emissions under varying soil fertility management practices, three soil fertilization strategies were included: (i) mineral fertilizer, (ii) compost, and (iii) control (i.e., without soil amendment). The results showed that N2O emissions were two-to-three times greater when there was dense canopy cover than from open-shade production. The effect of canopy cover on N2O emission was more pronounced under the mineral fertilizer treatment. CH4 emissions were 44–64 % greater under the open-shade production system than under AFs. The yield-scaled global warming potential of 1 kg of fresh coffee cherries was 0.72 kg CO2eq for open-shade production, 0.58 kg CO2eq for medium canopy cover and 0.52 kg CO2eq for dense canopy cover. This study provides the first evidence of the importance of considering canopy cover intensity when determining the spatial-temporal variations in GHG emissions from agroforestry systems.

Assessment of Spatial and Temporal Modeling on Greenhouse Gas Emissions From Electricity Generation

This paper highlights the importance of precise assessments of greenhouse gas (GHG) emissions associated with power generation for effective policy making in environmental sustainability. The current assessment approaches based on historical data or estimated generation using energy models may not accurately reflect the reality of future power systems due to the impact of spatial-temporal and techno-economic characteristics of generation mix and load demands. To address this, the paper presents a comprehensive methodology for accurately quantifying the geographical and temporal variations in GHG emissions associated with generating units’ operation, startup, and shutdown at an hourly resolution. The methodology is based on a detailed electricity model that considers various sources of generation, techno-economic, and spatial-temporal characteristics of system components. The study demonstrates the effectiveness of the methodology in quantifying GHG emissions in the IEEE RTS-GLMC system, with a focus on CO2, N2O, and CH4. The analysis reveals significant variations in GHG emissions among different generation buses and hours of the year, attributed to the high proportion of renewable energy in the generation mix. The paper emphasizes the inadequacy of examining marginal environmental impacts based on GHG emission intensity alone and suggests a more thorough analysis based on total GHG emissions generation. Finally, the paper emphasizes the crucial role of time-varying and marginal assessment techniques in identifying effective strategies for reducing GHG emissions in the electricity sector, including optimizing the operation and capacity of generation units, energy storage systems, and electric vehicles, including their locations.

Sources of Variation in the Carbon Footprint of Hemodialysis Treatment.

Greenhouse gas emissions from hemodialysis treatment in the United States have not been quantified. In addition, no previous studies have examined how much emissions vary across facilities, treatments, and emission contributors. To estimate the magnitude and sources of variation in the carbon footprint of hemodialysis treatment, we estimated life-cycle greenhouse gas emissions in carbon dioxide equivalents (CO2-eq) associated with 209,481 hemodialysis treatments in 2020 at 15 Ohio hemodialysis facilities belonging to the same organization. We considered emissions from electricity, natural gas, water, and supply use; patient and staff travel distance; and biohazard and landfill waste. Annual emissions per facility averaged 769,374 kg CO2-eq (95% CI, 709,388 to 848,180 kg CO2-eq). The three largest contributors to total emissions were patient and staff transportation (28.3%), electricity (27.4%), and natural gas (15.2%). Emissions per treatment were 58.9 kg CO2-eq, with a three-fold variation across facilities. The contributors with the largest variation in emissions per treatment were transportation, natural gas, and water (coefficients of variation, 62.5%, 42.4%, and 37.7%, respectively). The annual emissions per hemodialysis facility are equivalent to emissions from the annual energy use in 93 homes; emissions per treatment are equivalent to driving an average automobile for 238 km (149 miles). Similar medical treatments provided in a single geographic region by facilities that are part of the same organization may be expected to have small variations in the determinants of greenhouse gas emissions. However, we found substantial variation in carbon footprints across facilities, treatments, and emission contributors. Understanding the magnitude and variation in greenhouse gas emissions may help identify measures to reduce the environmental effect of hemodialysis treatment.

Characterizing and quantifying uncertainty in projections of climate change impacts on air quality

Climate change can aggravate air pollution, with important public health and environmental consequences. While major sources of uncertainty in climate change projections—greenhouse gas (GHG) emissions scenario, model response, and internal variability—have been investigated extensively, their propagation to estimates of air quality impacts has not been systematically assessed. Here, we compare these uncertainties using a coupled modeling framework that includes a human activity model, an Earth system model of intermediate complexity, and a global atmospheric chemistry model. Uncertainties in projections of U.S. air quality under 21st century climate change are quantified based on a climate-chemistry ensemble that includes multiple initializations, representations of climate sensitivity, and climate policy scenarios, under constant air pollution emissions. We find that climate-related uncertainties are comparable at mid-century, making it difficult to distinguish the impact of variations in GHG emissions on ozone and particulate matter pollution. While GHG emissions scenario eventually becomes the dominant uncertainty based on the scenarios considered, all sources of uncertainty are significant through the end of the century. The results provide insights into intrinsically different uncertainties in projections of air pollution impacts and the potential for large ensembles to better capture them.

Political governance, socioeconomics, and weather influence provincial GHG emissions in Canada

Quantifying the effects of political, socioeconomic, and weather factors on greenhouse gas (GHG) emissions is vital for successful climate change mitigation and adaptation. We modeled these effects on provincial per-capita GHG emissions across Canada from 1990 to 2019. The results showed the percentage of variation in GHG emissions explained by the models ranged from 75.3% to 98.8% across the ten Canadian provinces. Socioeconomics was associated with most of the emission variation (46.1%), followed by weather (1.4%). The effect of political governance on GHG emissions was minor (0.7%) and inconsistent but had a strong interaction with socioeconomic factors. Energy use efficiency was identified to be the most influential factor, contributing to lowering emissions in eight provinces. We conclude that socioeconomic factors are most important in causing GHG emissions across Canada, while the importance of political governance is trivial, much to the chagrin of those making election promises. Investment in energy efficient technologies should have the highest return in reducing emissions.

More sustainable vegetable oil: Balancing productivity with carbon storage opportunities

Intensive cultivation and post-harvest vegetable oil production stages are major sources of greenhouse gas (GHG) emissions. Variation between production systems and reporting disparity have resulted in discordance in previous emissions estimates. The aim of this study was to assess global systems-wide variation in GHG emissions resulting from palm, soybean, rapeseed and sunflower oil production. Such an analysis is critical to understand the implications of meeting increasing edible oil demand. To achieve this, we performed a unified re-analysis of life cycle input data from diverse palm, soybean, rapeseed, and sunflower oil production systems, from a saturating search of published literature. The resulting dataset reflects almost 6000 producers in 38 countries, and is representative of over 71% of global vegetable oil production. Across all oil crop systems, median GHG emissions were 3.81 kg CO2e per kg refined oil. Crop specific median emissions ranged from 2.49 kg CO2e for rapeseed oil to 4.25 kg CO2e for soybean oil per kg refined oil. Determination of the carbon cost of agricultural land occupation revealed that carbon storage potential in native compared to agricultural land cover drives variation in production GHG emissions, and indicates that expansion of production in low carbon storage potential land, whilst reforesting areas of high carbon storage potential, could reduce net GHG emissions whilst boosting productivity. Nevertheless, there remains considerable scope to improve sustainability within current production systems, including through increasing yields whilst limiting application of inputs with high carbon footprints, and in the case of palm oil through more widespread adoption of methane capture technologies in processing stages.

Nutritional Quality, Environmental Impact and Cost of Ultra-Processed Foods: A UK Food-Based Analysis.

Food-based analyses of the healthiness, environmental sustainability and affordability of processed and ultra-processed foods are lacking. This paper aimed to determine how ultra-processed and processed foods compare to fresh and minimally processed foods in relation to nutritional quality, greenhouse gas emissions and cost on the food and food group level. Data from the National Diet and Nutrition Survey nutrient databank year 11 (2018/2019) were used for this analysis. Median and bootstrapped medians of nutritional quality (NRF8.3 index), greenhouse gas emissions (gCO2-equivalents) and cost (in GBP) were compared across processing categories. An optimal score based on the medians was created to identify the most nutritional, sustainable, and affordable options across processing categories. On a per 100 kcal basis, ultra-processed and processed foods had a lower nutritional quality, lower greenhouse gas emissions, and were cheaper than minimally processed foods, regardless of their total fat, salt and/or sugar content. The most nutritious, environmentally friendly, and affordable foods were generally lower in total fat, salt, and sugar, irrespective of processing level. The high variability in greenhouse gas emissions and cost across food groups and processing levels offer opportunities for food swaps representing the healthiest, greenest, and most affordable options.

Sediment Disturbance Negatively Impacts Methanogen Abundance but Has Variable Effects on Total Methane Emissions.

Methane emissions from aquatic ecosystems are increasingly recognized as substantial, yet variable, contributions to global greenhouse gas emissions. This is in part due to the challenge of modeling biologic parameters that affect methane emissions from a wide range of sediments. For example, the impacts of fish bioturbation on methane emissions in the literature have been shown to result in a gradient of reduced to enhanced emissions from sediments. However, it is likely that variation in experimental fish density, and consequently the frequency of bioturbation by fish, impacts this outcome. To explore how the frequency of disturbance impacts the levels of methane emissions in our previous work we quantified greenhouse gas emissions in sediment microcosms treated with various frequencies of mechanical disturbance, analogous to different levels of activity in benthic feeding fish. Greenhouse gas emissions were largely driven by methane ebullition and were highest for the intermediate disturbance frequency (disturbance every 7 days). The lowest emissions were for the highest frequency treatment (3 days). This work investigated the corresponding impacts of disturbance treatments on the microbial communities associated with producing methane. In terms of total microbial community structure, no statistical difference was observed in the total community structure of any disturbance treatment (0, 3, 7, and 14 days) or sediment depth (1 and 3 cm) measured. Looking specifically at methanogenic Archaea however, a shift toward greater relative abundance of a putatively oxygen-tolerant methanogenic phylotype (ca. Methanothrix paradoxum) was observed for the highest frequency treatments and at depths impacted by disturbance (1 cm). Notably, quantitative analysis of ca. Methanothrix paradoxum demonstrated no change in abundance, suggesting disturbance negatively and preferentially impacted other methanogen populations, likely through oxygen exposure. This was further supported by a linear decrease in quantitative abundance of methanogens (assessed by qPCR of the mcrA gene), with increased disturbance frequency in bioturbated sediments (1 cm) as opposed to those below the zone of bioturbation (3 cm). However, total methane emissions were not simply a function of methanogen populations and were likely impacted by the residence time of methane in the lower frequency disturbance treatments. Low frequency mechanical disruption results in lower methane ebullition compared to higher frequency treatments, which in turn resulted in reduced overall methane release, likely through enhanced methanotrophic activities, though this could not be identified in this work. Overall, this work contributes to understanding how animal behavior may impact variation in greenhouse gas emissions and provides insight into how frequency of disturbance may impact emissions.

Assessing the spatial and temporal variability of greenhouse gas emissions from different configurations of on-site wastewater treatment system using discrete and continuous gas flux measurement

Abstract. Global emissions linked to wastewater treatment are estimated to account for up to 1.5 % of total greenhouse gas (GHG) emissions globally. However, few studies have measured GHG emissions from domestic on-site treatment systems (DWWTSs) directly. In this study, two DWWTSs were monitored for 446 d and &gt; 42 000 gas flux measurements were conducted using both discrete spot measurements and continuous flux chamber deployments. The observed GHG fluxes from biological activity in the soil and water phase were found to be highly spatially and temporally variable and correlated to environmental factors, water usage patterns and system design. In total, the results show that a septic tank discharging effluent into a well-designed soil treatment unit is estimated to emit a net 9.99 kg-CO2eq.cap-1yr-1, with approximately 63 %, 27 % and 10 % of the total CO2-equivalent net emissions in the form of CO2, CH4 and N2O, respectively. Emissions from the septic tank surface contributed over 50 % of total emissions and tended to be strongly underestimated by one-off discrete measurements, especially when episodic ebullitive events are to be considered. Fluxes from the soil treatment unit (STU) stemmed from both the soil surface and the vent system. Soil fluxes were mostly influenced by temperature but peaked regularly under conditions of rapidly changing soil water content. Vent fluxes were mostly governed by effluent, quality and a low number of high-emission events were responsible for the majority of total observed vent emissions. Owing to the strong overall spatial and temporal heterogeneity of observed fluxes from DWWTSs across all modules, future studies should focus on continuous deployments of a number of flux chambers over discrete measurements to accurately assess GHG emissions from on-site systems. This study also provided insights into managing GHG emissions from DWWTSs by different system configuration design, as well as indicating that the current IPCC emission factors for CH4 and N2O significantly overestimate emissions for on-site wastewater treatment systems.

Softwood biochar and greenhouse gas emissions: a field study over three growing seasons on a temperate agricultural soil

Agricultural activities contribute to greenhouse gas emissions, but agroecosystems can also mitigate emissions by adding recalcitrant carbon sources such as biochar to soil. Our goal was to understand if greenhouse gas emissions decrease in soil amended with manure and biochar (MN) or with manure, nitrogen fertilizer, and biochar (MNB) than soil amended with manure and N fertilizer (MN) over the longer term. We hypothesized that biochar reduces the release of labile carbon and reactive nitrogen from organic amendments and nitrogen fertilizer, thereby reducing CO2 and N2O emissions and that soil temperature, moisture, and nitrogen availability are the strongest predictors for intra- and inter-annual variation in greenhouse gas emissions. Over three growing seasons, biweekly measurements of CO2 and N2O emissions were similar (P &lt; 0.05) among treatments. Although input of labile carbon and reactive nitrogen was highest in MN and MB, cumulative CO2 emissions were lowest (P &lt; 0.05) in MNB. The availability of nitrogen from fertilizer caused greater cumulative N2O emissions (P &lt; 0.05) in MN and MNB. We found that soil temperature, moisture, and nitrogen availability regulated intraannual variability (P &lt; 0.05) of CO2 and N2O emissions in all treatments, where emissions were greatest (P &lt; 0.05) in the spring followed by summer and autumn. We accepted our hypothesis and concluded that, for cumulative emissions, biochar reduces the release of labile carbon and reactive nitrogen, but we rejected this hypothesis for biweekly emissions. We also concluded and accepted our hypothesis that soil temperature, moisture, and nitrogen availability regulated intraannual variation of CO2 and N2O in all treatments.

Variations in greenhouse gas emissions of individual diets: Associations between the greenhouse gas emissions and nutrient intake in the United Kingdom.

BackgroundFood production accounts for 30% of global greenhouse gas (GHG) emissions. Less environmentally sustainable diets are also often more processed, energy-dense and nutrient-poor. To date, the environmental impact of diets have mostly been based on a limited number of broad food groups.ObjectivesWe link GHG emissions to over 3000 foods, assessing associations between individuals’ GHG emissions, their nutrient requirements and their demographic characteristics. We also identify additional information required in dietary assessment to generate more accurate environmental impact data for individual-level diets.MethodsGHG emissions of individual foods, including process stages prior to retail, were added to the UK Composition Of Foods Integrated Dataset (COFID) composition tables and linked to automated online dietary assessment for 212 adults over three 24-hour periods. Variations in GHG emissions were explored by dietary pattern, demographic characteristics and World Health Organization Recommended Nutrient Intakes (RNIs).ResultsGHG emissions estimates were linked to 98% (n = 3233) of food items. Meat explained 32% of diet-related GHG emissions; 15% from drinks; 14% from dairy; and 8% from cakes, biscuits and confectionery. Non-vegetarian diets had GHG emissions 59% (95% CI 18%, 115%) higher than vegetarian. Men had 41% (20%, 64%) higher GHG emissions than women. Individuals meeting RNIs for saturated fats, carbohydrates and sodium had lower GHG emissions compared to those exceeding the RNI.DiscussionPolicies encouraging sustainable diets should focus on plant-based diets. Substituting tea, coffee and alcohol with more sustainable alternatives, whilst reducing less nutritious sweet snacks, presents further opportunities. Healthier diets had lower GHG emissions, demonstrating consistency between planetary and personal health. Further detail could be gained from incorporating brand, production methods, post-retail emissions, country of origin, and additional environmental impact indicators.

Variability of greenhouse gas emissions and economic performances on 10 Piedmontese beef farms in North Italy

CONTEXTThe discussion on the sustainability of beef production has grown considerably, but the research on this topic has mainly been focused on farm-model studies considering a single year and only assessing the environmental impacts while neglecting the temporal representativeness of the data (variability) and the economic sustainability of beef production systems. OBJECTIVEThis work is aimed at evaluating the environmental impacts and economic performances of beef production through a joint assessment based on a dataset compiled on commercial farms to take into account the variability of the environmental impacts and economic performances over a 4-year period. Farms rearing suckler cows and fattening animals (whole cycle farms) in Northern Italy were taken as a case study. METHODSData have been collected from detailed on-farm questionnaires, field books, and invoices on 10 whole-cycle Piedmontese beef farms for four consecutive years (2016-2019). The collected data enabled to evaluate the environmental impacts by means of the global warming potential (GWP) LCA category, and economic efficiency indicators. Furthermore, correlations between the environmental impacts, economic performances and productive efficiency have been investigated. RESULTS AND CONCLUSIONSThe assessment of the environmental impacts showed a mean GWP value of 15.0 kg CO2 eq/kg LW, thus highlighting a high variability between farms and years. It has been found that farms characterized by a better productive efficiency had lower GWP emissions. The studied farms also showed high variability, in terms of economic performances, especially for the productivity of family work and farm household income. It has also been found that the environmental and economic sustainability of beef cattle farms is linked by a negative correlation (Pearson's r = −0.707 and P -value <0.001), thus implying that the farms that show lower environmental impacts tend to be characterized by better economic results. SIGNIFICANCEFrom the findings of this study, it therefore seems appropriate that efficient production practices, especially improvements in productive and reproductive efficiency, coupled with high farm feed self-sufficiency and low concentrate consumption, should be applied as mitigation strategies to reduce the environmental impacts of beef farms, thereby allowing good economic results to be achieved at the same time. Furthermore, to obtain more reliable results, the study indicates that it is advisable to carry out evaluations of environmental impacts and economic performances over several consecutive years. However further research involving a bigger farm sample and different production systems is needed to confirm the findings of this case study.