Impact Of Greenhouse Gases Research Articles

Simulation of Electronic Waste Reverse Chains for the Sao Paulo Circular Economy: An Artificial Intelligence-Based Approach for Economic and Environmental Optimizations.

The objective of this study was to apply simulation and genetic algorithms for the economic and environmental optimization of the reverse network (manufacturers, waste managers, and recyclers in Sao Paulo, Brazil) of waste from electrical and electronic equipment (WEEE) to promote the circular economy. For the economic evaluation, the reduction in fuel, drivers, insurance, depreciation, maintenance, and charges was considered. For the environmental evaluation, the impact of abiotic, biotic, water, land, air, and greenhouse gases was measured. It was concluded that the optimized structure of the WEEE reverse chains for Sao Paulo, Brazil provided a reduction in the number of collections, thus making the most of cubage. It also generated economic and environmental gains, contributing to the strategic actions of the circular economy. Therefore, the proposed approach is replicable in organizational practice, which is mainly required to meet the 2030 agenda of reducing the carbon footprint generated by transport in large cities. Thus, this study can guide companies in structuring the reverse WEEE chains in Sao Paulo, Brazil, and other states and countries for economic and environmental optimization, which is an aspect of great relevance considering the exponential generation of WEEE.

Decarbonization of Agriculture: The Greenhouse Gas Impacts and Economics of Existing and Emerging Climate-Smart Practices.

The worldwide emphasis on reducing greenhouse gas (GHG) emissions has increased focus on the potential to mitigate emissions through climate-smart agricultural practices, including regenerative, digital, and controlled environment farming systems. The effectiveness of these solutions largely depends on their ability to address environmental concerns, generate economic returns, and meet supply chain needs. In this Review, we summarize the state of knowledge on the GHG impacts and profitability of these three existing and emerging farming systems. Although we find potential for CO2 mitigation in all three approaches (depending on site-specific and climatic factors), we point to the greater level of research covering the efficacy of regenerative and digital agriculture in tackling non-CO2 emissions (i.e., N2O and CH4), which account for the majority of agriculture's GHG footprint. Despite this greater research coverage, we still find significant methodological and data limitations in accounting for the major GHG fluxes of these practices, especially the lifetime CH4 footprint of more nascent climate-smart regenerative agriculture practices. Across the approaches explored, uncertainties remain about the overall efficacy and persistence of mitigation-particularly with respect to the offsetting of soil carbon sequestration gains by N2O emissions and the lifecycle emissions of controlled environment agriculture systems compared to traditional systems. We find that the economic feasibility of these practices is also system-specific, although regenerative agriculture is generally the most accessible climate-smart approach. Robust incentives (including carbon credit considerations), investments, and policy changes would make these practices more financially accessible to farmers.

Herbivores influence biogeochemical processes by altering litter quality and quantity in a subarctic wetland

Global change drivers that modify the quality and quantity of litter inputs to soil affect greenhouse gas fluxes, and thereby constitute a feedback to climate change. Carbon cycling in the Yukon–Kuskokwim (Y–K) River Delta, a subarctic wetland system, is influenced by landscape variations in litter quality and quantity generated by herbivores (migratory birds) that create ‘grazing lawns’ of short stature, nitrogen-rich vegetation. To identify the mechanisms by which these changes in litter inputs affect soil carbon balance, we independently manipulated qualities and quantities of litter representative of levels found in the Y–K Delta in a fully factorial microcosm experiment. We measured CO2 fluxes from these microcosms weekly. To help us identify how litter inputs influenced greenhouse gas fluxes, we sequenced soil fungal and bacterial communities, and measured soil microbial biomass carbon, dissolved carbon, inorganic nitrogen, and enzyme activity. We found that positive correlations between litter input quantity and CO2 flux were dependent upon litter type, due to differences in litter stoichiometry and changes to the structure of decomposer communities, especially the soil fungi. These community shifts were particularly pronounced when litter was added in the form of herbivore feces, and in litter input treatments that induced nitrogen limitation (i.e., senesced litter). The sensitivity of carbon cycling to litter quality and quantity in this system demonstrates that herbivores can strongly impact greenhouse gas fluxes through their influence on plant growth and tissue chemistry.Graphical abstract

Regionalized estimates of enteric methane emissions from cattle raised in Italian territory

Within the framework of the Extended Partnership "Economic-Financial Sustainability of Systems and Territories" (GRINS Project Spoke 1, WP3), this work proposes a regional estimate of the amount of enteric methane produced by cattle raised in the Italian territory. These data will be used to create national and regional datasets that will be an integral part of the AMELIA platform and will be freely available for use by both public and private managers for environmental impact assessments in the livestock sector. To this purpose, the IPCC (level 2) provides statistical models capable of evaluating this parameter, considering population, daily weight gain, animal weight, type of breeding, and dietary intake for each considered breed. In particular, in the calculation of enteric emissions, 37 pure breeds with different attitudes (milk, meat, and dual-purpose) have been considered, whose population is significant (>1%) at the national and/or regional level. For each individual breed, six classes have been considered, considering age and gender of the cattle. In addition, 22 crossbred breeds have been added, 21 specifics to each region and one at the national level. Regional emissions have been estimated by considering the average population of each breed in the period 2019-2022, normalizing the contributions of the individual species in a delimited territory and/or geographic area. In this way, six contributions have been calculated for each Italian region, representing a statistical assessment of the emissions that will subsequently be confirmed in the field through direct measurements using innovative instrumental technologies. Overall, this analysis offers a comprehensive view of how regional factors, and the composition of cattle breeds can impact greenhouse gas emissions. These findings provide valuable guidance for advancing sustainability initiatives within the agricultural sector.

Diurnal greenhouse gas emissions and substrate temperatures from blue-green roofs in north-eastern Italy during a dry-hot summer season

Covering building rooftops with vegetation [Green roofs (GRs)] holds promise for lowering building temperatures, reducing stormwater runoff, and providing other ecosystem services, but it is unclear how this will impact greenhouse gas (GHG) emissions. The latter may also be influenced by vegetation type, substrate depth, and irrigation regime and we sought to test this by comparing daytime GHG emissions (i.e., CH4, CO2, and N2O) and daily substrate temperatures in 48 GR microcosms in North-eastern Italy during a dry-hot summer season (June to September). Four vegetation types (Sedum mixture, cold season grasses, warm season grasses, or wildflowers), two substrate depths (8 cm or 14 cm), and two irrigation levels (1 or 2 mm d−1) were evaluated, for a total of 16 treatments with 3 replicates. We found that vegetation type had a significant effect on temperature [average temp. of 24.8 °C (Sedum) vs 25.5 °C (warm season grasses)] and CH4, CO2, and N2O emissions. While all vegetation types had net CO2 emissions (median values from 147 to 671 mg m−2 h−1) and net N2O uptake (median values from -0.06 to -0.28 mg m−2 h−1), CH4 flux had net negative values (capture) only in microcosms with wildflowers (-0.07 mg m−2 h−1), whereas other treatments had a median CH4 emissions of 0.09 mg m−2 h−1. Substrate depth significantly affected CO2 and N2O fluxes with deeper substrate leading to higher CO2 emission (+ 60.7%) and greater N2O uptake (+ 30.8%). Irrigation level only significantly influenced N2O fluxes with 2 mm irrigation resulting in higher fluxes (-0.20 mg m−2 h−1) than 1 mm irrigation (-0.09 mg m−2 h−1). Our study suggests that under heat induced plant-stress conditions, GRs can improve N2O and CH4 capture but might increase the emissions of CO2 fixed by plants in the previous years in the substrate and that vegetation type and substrate depth can significantly alter emissions and are thus important design parameters.

Sustainability of lettuce production: A comparison of local and centralized food production

Communities are considering local food production in response to the pressing need to reduce food system greenhouse gas (GHG) emissions. However, local food systems can vary considerably in design and operation, including controlled environment agriculture (CEA), which refers to agricultural production that takes place within an enclosed space where environmental conditions, such as temperature, humidity, and light, are precisely controlled. Such systems require a considerable amount of energy and thus emissions; therefore, this study seeks to quantify these environmental impacts to determine how local CEA systems compare to alternative systems. For this study's methods, we apply life cycle assessment methodology to quantify the cradle-to-store-shelf GHG emissions and water consumption of four lettuce production systems: local indoor plant factory, local greenhouse, local seasonal soil, and conventional centralized production in California with transportation. Using geographically specific inputs, the study estimates the environmental impact of the different production systems including geospatially resolved growth modeling, emissions intensity, and transportation distances. The results include the major finding that baseline CEA systems always have higher GHG emissions (2.6–7.7 kg CO2e kg−1) than centralized production (0.3–1.0 kg CO2e kg−1), though water consumption is significantly less owing to hydroponic efficiency. In contrast, local seasonal soil production generally has a lower GHG impact than centralized production, though water consumption varies by crop yield and local precipitation during growing seasons. Scenario analyses indicate CEA facilities would need to electrify all systems and utilize low-carbon electricity sources to have equivalent or lower GHG impacts than California centralized production plus transportation. We conclude that these results can inform consumers and policy makers that local seasonal production and conventional supply chains are more sustainable than local CEA production in near-term food-energy-water sustainability nexus decision making.

Improved representation of cattle herd dynamics for bio-physical modelling of pathways to a climate neutral land sector

CONTEXTLivestock production contributes to food security and livelihood improvement globally but places a significant burden on the environment. In Ireland, an ongoing transition towards highly profitable dairy production after the phasing out of EU milk quotas has changed the composition of the national cattle herd with more dairy and fewer beef cows. This shift impacts greenhouse gas (GHG) accounting across different cattle cohorts, e.g. increasing the proportion of calves from the dairy herd. Dairy x beef crossbreeds (DxB) increasingly contribute to national beef output, leveraging larger average daily liveweight gain (ADG) traits from beef breeding bulls. OBJECTIVEProspective modelling of climate and land consequences arising from alternative cattle production strategies requires more accurate simulation of cohort-specific ADG and associated feed requirements and GHG emissions. METHODSA new COHORTS model was developed to improve national climate scenario mitigation modelling. COHORTS is capable of simulating 21 genetics-gender-age cohorts calibrated to Irish performance, using just a few basic input parameters (at minimum dairy- and beef-cow numbers). A cohort specific ADG and average standing liveweight is estimated for each genetic (pure dairy calves, DxB and pure beef calves), gender and age combination, enabling more accurate calculation of energy requirements and enteric fermentation emissions based on IPCC Tier 2 calculations. RESULTS AND CONCLUSIONSNational simulation of cattle numbers, enteric fermentation emissions and beef outputs were validated against relevant Irish inventories. For the period between 2006 and 2020, simulations resulted in total cattle numbers, emissions and beef production within 4%, 1.8% and 0.5%, respectively, of officially reported data. Our results indicate that climate projections based on average emission factors for pre-adult cattle cohorts may overestimate emissions in scenarios with projected growing dairy calf numbers and declining beef calf numbers. SIGNIFICANCEValidation using a 15-year data time series provides a high degree of confidence that COHORTS can be used to represent future herd dynamics in a wide range of scenarios, supporting robust policy regarding GHG mitigation, livestock production and land use – distinguishing between different levels of dairy or beef specialisation and across different levels of performance to predict forage (land) requirements and GHG emissions more accurately. COHORTS can be easily adapted for other countries, even when limited data are available.

Renewable-based charging in green ride-sharing

Governments, regulatory bodies, and manufacturers are proposing plans to accelerate the adoption of electric vehicles (EVs), with the goal of reducing the impact of greenhouse gases and pollutants from internal combustion engines on human health and climate change. In this context, the paper considers a scenario where ride-sharing enterprises utilize a 100%-electrified fleet of vehicles, and seeks responses to the following key question: How can renewable-based EV charging be maximized without disrupting the quality of the ride-sharing services? We propose a new mechanism to promote EV charging during hours of high renewable generation, and we introduce the concept of charge request, which is issued by a power utility company. Our mechanism is inspired by a game-theoretic approach where the power utility company proposes incentives and the ride-sharing platform assigns vehicles to both ride and charge requests; the bargaining mechanism leads to prices and EV assignments that are aligned with the notion of Nash equilibria. Numerical results show that it is possible to shift the EV charging during periods of high renewable generation and adapt to intermittent generation while minimizing the impact on the quality of service. The paper also investigates how the users’ willingness to ride-share affects the charging strategy and the quality of service.

How Does Intensive Land Use Affect Low-Carbon Transition in China? New Evidence from the Spatial Econometric Analysis

Anthropogenic land cover change is one of the primary sources of increasing carbon emissions and affects the potential of terrestrial ecosystems to store carbon and act as carbon sinks. As a necessary means to reduce land expansion, land-use intensification significantly impacts greenhouse gas emission reduction and the low-carbon transition of the economy. This paper constructs a framework for the relationship between intensive land use (ILU) and low carbon transition (LCT), considering direct and spatially driven effects. First, this paper constructs a multidimensional indicator to measure intensive land use and documents the spatial pattern of intensive land use levels in China. Second, this paper assesses the spatial driving effect of intensive land use on China’s economic low-carbon transition. Based on data from 283 Chinese cities from 2006–2019 and using a spatial Durbin model, the study provides empirical evidence that intensive land use can significantly promote low-carbon transition in neighboring and economically linked cities (especially in eastern cities, large and medium-sized cities, and veteran economic circles). Tests introducing exogenous policy shocks further confirm the robustness of the findings. In addition, industrial structure transformation and technology spillovers are identified as the dual mechanism channels of intensive land use for low-carbon transition in China, and the spatial driving effect on neighboring cities attenuating with geographic distance is also confirmed.

A Study on the Economic Impact of Greenhouse Gases in India

A Study on the Economic Impact of Greenhouse Gases in India

The impact of greenhouse gases on climate change

Climate change has become a threatening problem for all countries of the world. For Kazakhstan, one of the world’s largest grain exporters, with its previously predominantly arid climate, developed mining industry, metallurgy and mainly coalfired heat and electricity generation, slowing down warming is becoming an urgent task. The purpose of this study is to summarise various aspects of the impact of greenhouse gas emissions on climate change in the Republic of Kazakhstan and the possibilities of reducing greenhouse gas emissions through Kazakhstan’s transition to a new economic model based on the use of renewable energy sources. During the study, various methods of analysis were used – analytical selection based on a given or identified feature, comparative analysis of similar characteristics of various objects, statistical analysis of dynamic series, synthesis, representing the primary theoretical generalisation of empirical data. As a result of the study, a noticeable dependence of changes in average annual temperature and precipitation on total greenhouse gas emissions was identified. Moreover, the identified similarity of the dynamics of the main climatic indicators with the dynamics of individual greenhouse gases in Kazakhstan requires further research. It is also concluded that individual innovations in the form of individual renewable energy power plants or a Quota trading system determined by the highest values will not lead to a noticeable reduction in greenhouse gas emissions. Only the transition to a green economy, which should become a state priority, and its principles and main characteristics should be the reference when making any government decisions. This study can become a starting point for many theoretical and practical studies and show the need to reduce greenhouse gas emissions not as a purely mechanical process, but in conjunction with other phenomena, which can be useful in making managerial decisions

#2695 THE ENVIRONMENTAL IMPACT OF CHRONIC KIDNEY DISEASE INTERNATIONALLY: RESULTS OF A LIFE CYCLE ASSESSMENT

Abstract Background and Aims The environmental impact of healthcare is high, and kidney care for chronic kidney disease (CKD) contributes significantly to this. Haemodialysis represents the only viable kidney replacement option for certain patients with CKD, in whom the clinical benefits are lifesaving. Haemodialysis is also resource-intensive, requiring frequent sessions, and energy- and water-intensive equipment. In recognition of this, the ERA has implemented a green nephrology initiative aiming to minimize the environmental impact of kidney care. However, there is a paucity of up-to-date analysis on the environmental impact of different dialysis techniques and CKD overall. This study presents an international, holistic life cycle assessment (LCA) of the environmental impact of CKD in adults at all CKD stages. Method LCA methodology was used, conducted according to ISO 14040/14044 international standards, to estimate the annual environmental impacts of each stage (1–5) of CKD per patient. At CKD Stage 5, supportive care, haemodialysis, peritoneal dialysis, and transplantation pathways were considered. A literature review was performed to identify all studies reporting healthcare resource use in CKD, stratified by CKD stage. The study boundary is summarised in Figure 1. GaBi software was used to model the pathway based on ecoinvent Life Cycle Inventory database version 3.8. Environmental impact categories were reported according to the impact assessment methods, ReCiPE 2016 v1.1 and TRACI 2.1 (US only). Here, we describe the preliminary analysis of the international study, presenting results on the annual environmental impact of in-centre haemodialysis in the UK, based on one patient receiving haemodialysis three times weekly for four hours per session. The inputs for in-centre haemodialysis comprised dialysis consumables and their transport, energy/water used by the haemodialysis machine (including reverse osmosis), heating/cooling/lighting of the healthcare area, waste disposal, and patient transport. Results A total of 93,600 litres of water and 3,058 kWh electricity was estimated per patient for in-centre haemodialysis annually in the UK. The carbon footprint of in-centre haemodialysis in the UK per patient was estimated to be 3,900 kg CO2 equivalents, comparable to the average UK persons annual greenhouse gas footprint, effectively doubling their yearly greenhouse gas impact. Several other environmental impact categories were measured beyond the carbon footprint, including photochemical oxidation potential (ground-level ozone formation) and fine particulate matter, measured as PM2.5. Across each impact category, the parameters that drove each environmental impact differed (Figure 2). For example, dialysis consumables, haemodialysis machine, and patient transport were the main environmental contributors of fine particulate matter, while patient transport was the main driver of terrestrial ecotoxicity. Conclusion The results of this LCA build upon previous published research and demonstrate a high carbon footprint for in-centre haemodialysis, in line with other studies. The results also show that the environmental impact of in-centre haemodialysis goes beyond that of its carbon footprint, to other important environmental aspects such as PM2.5 emissions, in which long-term exposure is associated with health problems, such as cardiovascular and respiratory diseases, and an increased risk of CKD. This study hopes to highlight the need for evidence-based policy interventions around the implementation of green nephrology initiatives such as use of renewable energy to power haemodialysis, utilisation of water conserving reverse osmosis systems, and reduction in waste. Furthermore, healthcare policy initiatives that could help detect patients in the early stages of CKD and thereby enable them to be managed proactively could eventually reduce the need for resource-intensive dialysis therapy.

The impact of clustering strategies to site integrated community energy and harvesting systems on electrical demand and regional GHG reductions

The current work examines the Integrated Community Energy and Harvesting system (ICE-Harvest), an integration of thermal and electrical distributed energy resources. The system prioritizes the harvesting of community waste energy resources—for example, residual heat rejected from cooling processes and peak electricity fossil-fuel fired generators, as well as energy from curtailed clean grid electricity resources—to help in satisfying the heating demands of commercial and residential buildings. As such, ICE-Harvest systems provide a solution that can minimize greenhouse gas emissions from high-energy-consumption buildings in cold-climate regions such as North America and Northern Europe. The current work focuses on where to locate these systems and introduces different clustering methods for integrated energy systems that focus on thermal load diversity among buildings in each cluster. In the first technique, buildings with the highest amount of rejected heat from their cooling systems (anchor buildings) are clustered with nearby high heating demands buildings to create a large opportunity for harvesting wasted energy however, buildings not located close to anchor buildings are clustered using density-based clustering methods. The energy harvesting capabilities of this technique are compared to those provided by full DB clustering without specifying the anchor buildings, as well as to full density-based clustering with a post-processing step in which the nearest anchor building is added to each cluster. The selected clustering method also demonstrates the grid level potential of the ICE-Harvest systems to impact greenhouse gas emissions, heating, and electricity consumption by presenting a reduced model for the ICE-Harvest system that can be applied to any number of clusters on a municipal or provincial/state scale. Specifically, the model is applied to a database of 14,832 high-energy-consumption buildings with the potential for forming 1,139 clusters in the province of Ontario, Canada. The results of this case study reveal that density-based clustering with post processing resulted in the largest emission reduction per unit piping network length of 360 t CO2eq /km/year. In addition, the use of ICE-Harvest systems can displace the energy required from the gas-fired heating resources by 11 TWh, accounting for over 70% of the clusters’ total heating requirements. This results in a 1.9 Mt CO2eq reduction in overall sites’ emissions, which represents around 60% of the clusters’ emissions.

How Does Health Uncertainty Impact Greenhouse Gas Emissions in European Union Economies? A Blessing in Disguise.

The global outbreak of COVID-19 caused serious threats to public health and economic growth all around the world, but on the other hand, the betterment of the environment took place. How pandemics' health uncertainty will affect environmental quality is a crucial matter to address. The paper investigates the asymmetric association between pandemics-related health uncertainty and greenhouse gas emissions (GHG) in the top emitter European Union economies (Italy, Germany, France, Poland, Netherlands, Spain, Czech Republic, Belgium, Romania, and Greece). Employing data from 1996 to 2019, a unique approach called 'Quantile-on-Quantile', is adopted to evaluate the influence of various quantiles of the health uncertainty on GHG emissions. According to estimates, health uncertainty enhances environmental quality by minimizing GHG in most of our chosen nations at certain quantiles of data, which makes pandemics a blessing in disguise for environmental quality. Additionally, the estimations indicate that the grades of asymmetry between our variables varies by locality, accentuating the requisite for authorities to give specific consideration while executing health uncertainty and environmental quality policies.

An intelligent low carbon economy management scheme based on the genetic algorithm enabled replacement recommendation model

Enhancing energy efficiency, employing renewable energy sources, improving the performance of greenhouse gas (GHG) mitigation, creating innovative technologies to absorb greenhouse gases, and eliminating incentives for environmentally damaging operations are all tasks that support low-carbon advancement. Following the trend of the worldwide shift away from natural fuels as major energy providers to other forms of energy, the concept of a low-carbon economy (LCE) is being implemented. An LCE minimizes greenhouse gas emissions by replacing nonrenewable energy sources with renewable and natural energy resources. In recent years, LCEs have been globally implemented based on the impact of greenhouse gases on climatic conditions. This article introduces a genetic algorithm-enabled replacement recommendation model (GA-RRM) for managing LCEs in open environments. The proposed model identifies the links among energy exhaustion, requirements, and generation for emission management. The balancing process involves new weight assignments and recommendations for different resources. The proposed model generates multiple populating weights using the GA to mitigate the effects of certain factors. The proposed model uses current and previous climatic change factors to identify LCE management trends. GA-RRM is used to manage and analyze an LCE, and traditional energy sources are replaced with green gas-emitting and natural energy sources in an open environment. The performance of GA-RRM is evaluated based on emission control and energy usage scenarios, and high precision is observed.

The impact of policy and model uncertainties on emissions projections of the Paris Agreement pledges

Tracking progress towards the Paris Agreement climate goal requires understanding the 2030 emission levels implied by countries’ National Determined Contributions (NDCs). However, key uncertainties and assumptions impact greenhouse gas (GHG) emission projections implied by the NDCs. This study analyses this impact, both globally and for major emitting countries. We find that the assessed uncertainties markedly affect global GHG emission projections. Full achievement of NDC targets is estimated to result in a range of 46–60 GtCO2eq by 2030 (median estimate: 53 GtCO2eq). The uncertainty in measuring historical emissions, including land-use, as reflected by different datasets is the most important contributing factor. This is followed by two equally important factors globally: socio-economic baseline uncertainty and uncertainty about the emissions implied by current policies in case NDCs are less ambitious than these. Overall, the impact of policy uncertainty (i.e. uncertainty resulting from conditionality of or ranges in NDC targets and uncertainty in emissions resulting from current policies) is about equally important as model/technical uncertainty (i.e. uncertainty in historical emissions and socio-economic baseline variations). This new insight is important for decision makers and researchers because a larger share of the total uncertainty is now attributable to aspects that can be influenced by policy decisions compared to previous analyses of NDC uncertainty.

Influence of Clay Content on CO2-Rock Interaction and Mineral-Trapping Capacity of Sandstone Reservoirs

The injection of carbon dioxide (CO2) is an essential technology for maximizing the potential of hydrocarbon reservoirs while reducing the impact of greenhouse gases. However, because of the complexity of this injection, there will be many different chemical reactions between the formation fluids and the rock minerals. This is related to the clay content of sandstone reservoirs, which are key storage targets. Clay content and clay types in sandstone can vary substantially, and the influence of these factors on reservoir-scale CO2-water-sandstone interactions has not been managed appropriately. Consequently, by simulating the process of CO2 injection in two different clay-content sandstones (i.e., high- and low-clay content), we investigated the effect of the sandstone clay concentration on CO2-water-sandstone interactions in this article. High clay content (Bandera Grey sandstone) and low clay content (Bandera Brown sandstone) were considered as potential storage reservoirs and their responses to CO2 injection were computationally assessed. Our results indicate that the mineralogical composition of the sandstone reservoir significantly varies as a result of CO2-water-sandstone interactions. Clearly, the high clay-content sandstone (Bandera Grey) had a higher maximum CO2 mineral-trapping capacity (6 kg CO2/m3 sandstone) than Bandera Brown Sandstone (low clay content), which had only 3.3 kg CO2/m3 sandstone mineral-storage capacity after 400 years of storage. Interestingly, pH was decreased by ~3 in Bandera Grey sandstone and by ~2.5 in Bandera Brown sandstone. Furthermore, porosity increased in Bandera Grey sandstone (by +5.6%), more than in Bandera Brown Sandstone (+4.4%) after a 400-year storage period. Overall, we concluded that high clay-content sandstone shows more potential for CO2 mineral-trapping.

Consumers’ valuation for low - carbon emission and low – saturated fat butter

Currently, there is an ongoing debate related to the large environmental impact of livestock greenhouse gas (GHG) emissions and the negative effect of saturated fat contained in the animal foods on human health. In response to these adverse effects, dairy producers have adopted strategies to reduce these effects by modifying the conventional livestock feed composition. This study investigated, for the first time, Finnish consumers’ willingness to pay (WTP) and heterogeneous preferences for butter derived from milk produced by cows fed with lipid-rich rapeseed feed, hereafter called rapeseed feed, which can reduce the GHG emissions from cows, and the saturated fat content of dairy products. Using a hypothetical choice experiment (CE) involving butter that varied across four attributes (i.e. type of feed, saturated fat content, Carbon Trust label, and price), our results show that, on average, consumers preferred the low-price butter, produced from cows fed with regular feed, labelled with the claim “Reduced saturated fat”, and branded with the “Carbon Trust” label. Interestingly, we found that one-third of consumers were willing to pay a premium price for butter derived from milk produced by cows fed with rapeseed feed. Furthermore, we found that younger, and higher educated consumers tend to prefer butter derived from cows fed with rapeseed feed. These findings provide useful insights into the psychology of consumers’ level of acceptance and attitudes that can be used by dairy producers and marketers in communicating to the public the nature of the new feed practices to reduce the negative environmental emissions, and saturated fat content of dairy products.

The Environmental Footprint of Scientific Research: Proposals and Actions to Increase Sustainability and Traceability

In their work, scientists are responsible for stating a purpose, defining experimental conditions, producing data, describing and analyzing these data by statistical means, arguing by comparison with the literature, and drawing conclusions. When a manuscript is submitted for publication, no assessment of the direct and indirect environmental impacts of producing the work is reported. In this context, the aim of our study is to suggest schematic methods to assess, reduce, and mitigate the impact of greenhouse gas (GHG) emissions generated by three different papers (two scientific articles and a conference proceeding) previously published by the same authors. The results reported here show proposals and actions to contribute to the reduction of environmental impacts: a preliminary assessment of the inputs and outputs was initially converted into CO2 equivalents (CO2 eq.) and, subsequently, into partial mitigation action through the allocation of the planting of additional plants. Furthermore, real traceability and an opportunity to verify the close connection between initial works (papers and conference book) causing the environmental impact and new green life are suggested. Finally, we propose a new label (S-Paper to T-Plant) for eco-friendly guidelines.

Thermal Profiles: Novel phenotypic measurements of animal growth and metabolic efficiency

The costs of production for high density protein and the impacts food production have on the environment are becoming increasingly important issues in animal agriculture. The objective of the present study was to investigate the use of novel thermal profiles including a Thermal Efficiency Index (TEI) on the ability to identify efficient animals in a fraction of the time and at a significantly lower cost of conventional feed station and performance technology. Three hundred and fourty four high performance Duroc sires from a genetic nucleus herd were used in the study. The animals were monitored for feed consumption and growth performance using conventional feed station technology for a 72 day period. Animals were monitored in these stations between approximately 50 kg and 130 kg live body weight. An infrared thermal scan was performed on the animals at the end of the performance test by collecting automated dorsal thermal images and using these biometrics to measure both bio-surveillance values and a thermal phenotypic profile including the TEI (mean dorsal temperature /body weight 0.75). The thermal profile values were significantly correlated (r = 0.40, P < 0.0001) with a current industry best practice for performance in Residual Intake and Gain (RIG). The data from the current study suggest these rapid, real time, cost effective values for TEI constitute a useful precision farming tool for the animal industries to reduce the cost of production and green house gas (GHG) impact for high density protein production.