Assessment Of Greenhouse Gas Emissions Research Articles

Measurement-based carbon intensity of US offshore oil and gas production

The United States (US) produces oil and gas from six offshore regions: the North Slope of Alaska, Cook Inlet in Alaska, offshore California, and three Gulf of Mexico (GOM) sub-regions: state shallow, federal shallow, and deep waters. Measurement-based assessment of direct greenhouse gas emissions from this production can provide real-world information on carbon emissions to inform decisions on current and future production. In evaluating the climate impact of production, the carbon intensity (CI, the ratio of greenhouse gases emitted compared to the energy of fuels produced) is often used, though it is rarely quantified with measurements. Here, we complete an observational evaluation of the US offshore sector and present the largest current set of measurement-based CIs. We collected airborne measurements of methane, carbon dioxide, and nitrogen oxides from the North Slope, Cook Inlet, and California and combined with prior GOM results. For Alaska and California, we found emissions agree with facility-level inventories, however, the inventories miss some facilities. The US offshore CI, on a 100 year GWP basis, is 5.7 g CO2e/MJ[4.5, 6.8, 95% confidence interval]. This is greater than double the CI based on the national US inventory, with the discrepancy attributed primarily to methane emissions from GOM shallow waters, with a methane dominated CI of 16[12, 22] for GOM federal shallow waters and 43[25–65] for state shallow waters. Regional intensities vary, with carbon dioxide emissions largely responsible for CI on the North Slope 11[7.5, 15], in Cook Inlet 22[13, 34], offshore California 7.2[3.2, 13], and in GOM deep waters 1.1[1.0, 1.1]. These observations indicate offshore operations outside of the GOM in the US have modest methane emissions, but the CI can still be elevated due to direct carbon dioxide emissions. Accurate assessment of different offshore basins, with differing characteristics and practices, is important for the climate considerations of expanded production.

Assessment of urban greenhouse gas emissions towards reduction planning and low-carbon city: a case study of Montreal, Canada.

The online version contains supplementary material available at 10.1186/s40068-024-00341-y.

A simplified open flux chamber method for the measurement of greenhouse gas emissions from activated sludge reactors

ABSTRACT Measuring greenhouse gas emissions from wastewater treatment plants is of utmost importance in the context of climate change. However, due to their variability and complexity, it is a particularly challenging task in aerated reactors. The current methods involve capturing gas emissions from the water surface, measuring gas flow rates, and determining the concentration of the emitted gas at that location. Our study proposes a new, more efficient method that eliminates the need for gas flow rate measurements and additional equipment. The proposed technique uses a gas analyzer and a specially designed floating chamber to measure the transient trend of gas concentration within the chamber from the moment it is deployed to when it reaches a new steady state. Our research shows that this method accurately determines methane and carbon dioxide emissions from aerated reactors and potentially other gases emitted in wastewater treatment plants. It is cost effective, versatile, and simplifies the measurement process. This method facilitates the assessment of greenhouse gas emissions in wastewater treatment plants. Our findings are backed by comprehensive testing in the aeration tanks of a full-scale activated sludge plant, across diverse conditions, including fine- and coarse-bubble aeration.

Assessment of Greenhouse Gas Emissions from Heavy-Duty Trucking in a Non-Containerized Port through Simulation-Based Methods

Seaports are perceived as multimodal hubs of the logistics supply chain where various transport modes intersect to exchange goods shipped by vessels. Increasing trade and capacity constraints are making this area a major contributor to GHG emissions. National and regional decision-makers perceive port sustainability as a concern while planning GHG mitigation projects. However, to plan and conduct successful GHG management programs, it is critical to first develop an appropriate assessment approach that fits well with the operating and geographical context of the given port. For heavy-duty trucking activities taking place within such ports, several models and methodologies for assessing GHG emissions are available, but their generalization is challenging for many reasons, notably because of the specific features of traffic within the port. Therefore, this paper presents an assessment model for heavy-duty trucking emissions within a non-containerized port based on an in-depth study of the traffic per port zone and on parameters drawn from several real data sources. The GHG model based on road traffic profiles by zone is implemented in a simulation model for emission evaluation and prediction. The output shows the pattern of GHG emissions by zone and provides an outlook on how decision-makers could achieve a GHG reduction plan.

The greenhouse gas emissions from meat sheep production contribute double of household consumption in a Eurasian meadow steppe

With the rapid development of the economy, household activities have emerged as an important source of greenhouse gas (GHG) emissions, making them a crucial focal point for research in the pursuit of sustainable development and carbon emission reduction. Hulunber, as a typical steppe region in eastern Eurasia, is representative of studying the GHG emissions from household ranches, which are the basic production units in this region. In this paper, based on survey data of 2018 and 2019, we quantified and assessed GHG emissions from household ranches by combining life cycle assessment (LCA) and structural equation modeling (SEM) approaches, with LCA to define household ranches system boundary and SEM to determine the key driving factors of emissions. The results showed that GHG emissions of meat sheep live weight was 23.54 kg CO2-eq/kg. The major contributor to household GHG emissions was enteric methane (55.23 %), followed by coal use (20.80 %) and manure management systems (9.16 %), and other contributing factors (14.81 %). The SEM results indicated that the GHG emissions from household ranches were derived primarily by economic level, while the economic level was significantly affected by income. This study also found a significant positive and linear correlation between household GHG emissions and the number of meat sheep (R2 = 0.89, P < 0.001). The GHG emissions from meat sheep production (67.52 %) were double times greater than household livelihood consumption (32.48 %). These findings emphasized the importance of reducing emissions from meat sheep production and adjusting the energy mix of household livelihood, contributing to the establishment of a low-carbon household livelihood operation.

X-ray chemical imaging for assessing redox microsites within soils and sediments

Redox reactions underlie several biogeochemical processes and are typically spatiotemporally heterogeneous in soils and sediments. However, redox heterogeneity has yet to be incorporated into mainstream conceptualizations and modeling of soil biogeochemistry. Anoxic microsites, a defining feature of soil redox heterogeneity, are non-majority oxygen depleted zones in otherwise oxic environments. Neglecting to account for anoxic microsites can generate major uncertainties in quantitative assessments of greenhouse gas emissions, C sequestration, as well as nutrient and contaminant cycling at the ecosystem to global scales. However, only a few studies have observed/characterized anoxic microsites in undisturbed soils, primarily, because soil is opaque and microsites require µm-cm scale resolution over cm-m scales. Consequently, our current understanding of microsite characteristics does not support model parameterization. To resolve this knowledge gap, we demonstrate through this proof-of-concept study that X-ray fluorescence (XRF) 2D mapping can reliably detect, quantify, and provide basic redox characterization of anoxic microsites using solid phase “forensic” evidence. First, we tested and developed a systematic data processing approach to eliminate false positive redox microsites, i.e., artefacts, detected from synchrotron-based multiple-energy XRF 2D mapping of Fe (as a proxy of redox-sensitive elements) in Fe-“rich” sediment cores with artificially injected microsites. Then, spatial distribution of FeII and FeIII species from full, natural soil core slices (over cm-m lengths/widths) were mapped at 1–100 µm resolution. These investigations revealed direct evidence of anoxic microsites in predominantly oxic soils such as from an oak savanna and toeslope soil of a mountainous watershed, where anaerobicity would typically not be expected. We also revealed preferential spatial distribution of redox microsites inside aggregates from oak savanna soils. We anticipate that this approach will advance our understanding of soil biogeochemistry and help resolve “anomalous” occurrences of reduced products in nominally oxic soils.

Resource Efficiency and Environmental Impact Assessment Method for Small-Scale Producers: A Case Study of Pond and In-Pond Raceway System Production for Growing Nile Tilapia

The purpose of this study was to employ Resource Value Mapping (REVAM) and carbon footprint assessments to evaluate the resource efficiency and environmental impact of the aquaculture sector in Thailand. Focusing on tilapia production, a case study that compares the innovative In-Pond Raceway System (IPRS) with the traditional pond system was conducted to serve as a guideline for small-scale producers toward achieving the Sustainable Development Goals (SDGs). The assessment applied lean classification to the agriculture context and used both machine and non-machine resources. Moreover, the Muda Index (MI) and the concurrent assessment of greenhouse gas (GHG) emissions through the proposed map were introduced to identify resource efficiencies and environmental impacts across activities, respectively. The analysis highlighted resources with the highest MI values, waste proportions, and emissions requiring improvement. This study shows that addressing specific resources in REVAM can enhance resource management, facilitate control over variable costs, and ensure sustainability in both of these dimensions. Finally, a feasibility analysis was incorporated to weigh investment between in-pond and IPRS systems, in addition to resource use efficiency and environmental impact dimensions, creating an opportunity to meet the increasing demands in the international market.

Assessing the Environmental Footprint: A Life Cycle Assessment of Greenhouse Gas Emissions from Energy Consumption in 3 and 4-Star Hotels in Pontianak, West Kalimantan

Persebaran usaha penginapan di Indonesia hingga tahun 2021 terus meningkat, di mana 48,95% merupakan hotel berbintang. Pada operasionalnya, hotel memerlukan energi dari berbagai jenis bahan bakar yang menghasilkan emisi berupa gas rumah kaca. Studi ini dilakukan pada kasus di Hotel X (bintang 4) dan Hotel Y (bintang 3) di Kota Pontianak, Kalimantan Barat yang menggunakan energi dari batubara, gas alam cair, dan bahan bakar minyak. Tujuan penelitian ini adalah untuk menganalisis dampak lingkungan dari gas rumah kaca menggunakan metode analisis daur hidup. Data inventori dilakukan terhadap jenis sumber energi, jumlah alat, dan lama penggunaan pada tiap fasilitas yang tersedia di hotel. Aplikasi OpenLCA 1.11.0 dan database CML-IA baseline digunakan untuk menganalisis dampak lingkungan yang dihasilkan. Kategori dampak yang dianalisis yaitu potensi dampak pemanasan global, penipisan abiotik, dan potensi oksidasi fotokimia. Dampak lingkungan terhadap pandemik COVID-19 dan strategi manajemen gas rumah kaca juga didiskusikan. Hasil penelitian menunjukkan bahwa jumlah tamu, fasilitas dan jenis bangunan berpengaruh pada emisi yang dihasilkan. Jumlah tamu pada tahun 2020 merupakan jumlah terkecil dibandingkan tahun 2019 dan 2021, yang sejalan dengan jumlah konsumsi energinya. Jumlah fasilitas pada Hotel X yang lebih banyak dibandingkan Hotel Y juga menyebabkan Hotel X menghasilkan lebih banyak emisi dibandingkan Hotel Y. Rekomendasi dari penelitian ini berupa perencanaan strategi pengelolaan konsumsi energi yang lebih baik, terutama pada penggunaan lampu dan pendingin ruangan. Untuk mengurangi konsumsi energi dari penggunaan alat tersebut dapat diterapkan sistem sensor untuk aktivasi listrik, serta penggunaan elektronik dengan daya yang lebih rendah. Diharapkan hasil riset ini dapat memberikan solusi bagi perencanaan strategi pengelolaan energi yang berkelanjutan di hotel berbintang dan diperlukan inventarisasi detail untuk mengetahui dampak lingkungannya secara komprehensif. Abstract The distribution of Indonesia lodging businesses in 2021 has been continuously increase, with 48.95% being star-rated hotels. In their operations, hotels require energy from various types of fuels that produce emissions in the form of Greenhouse Gases. This study was conducted on the cases of X Hotel (4-star) and Y Hotel (3-star) in Pontianak, West Kalimantan, which utilize energy from coal, liquefied natural gas, and oil fuels. The research objective is to analyze the environmental impact of greenhouse gases using the life cycle analysis method. Inventory data were collected for the types of energy sources, the number of appliances, and the duration of usage for each facility available in the hotels. OpenLCA 1.11.0 application and CML-IA baseline database were employed to analyze the resulting environmental impact. The impact categories analyzed include the global warming potential, abiotic depletion, and photochemical oxidation potential. The environmental impact of the Covid-19 pandemic and greenhouse gas management strategies are also discussed. The research results indicate that the number of guests, facilities, and building types influence the emissions produced. The number of guests in 2020 was the smallest compared to 2019 and 2021, correlating with the energy consumption. The higher number of facilities in X Hotel compared to Y Hotel also results in X Hotel producing more emissions than Y Hotel. Recommendations from this research include better planning for energy consumption management strategies, especially in the use of lighting and room cooling. To reduce energy consumption from lighting and air conditioning, the implementation of sensor systems for electrical activation and the use of electronic devices with lower power consumption can be applied. It is hoped that the findings of this research can provide solutions for sustainable energy consumption management strategies in star-rated hotels, and a more detailed inventory is needed to comprehensively understand its environmental impact.

Multi-Modal Life Cycle Assessment of Journeys by Aircraft, Train or Passenger Car

To reduce the carbon footprint of transport, policymakers are simultaneously stimulating cleaner vehicles and more sustainable mobility choices, such as a shift to rail for short-haul flights within Europe. The purpose of this study is to determine the climate impact of a journey within Europe by aircraft, train or passenger car, and to better understand what factors drive this impact in order to make smarter and more sustainable fact-based mobility choices. The study consists of a life cycle inventory (LCI) and life cycle impact assessment (LCA) of greenhouse gas emissions of specific vehicles in five case study travel scenarios in Europe. The energy and resulting direct emissions (including non-CO2) of the aircraft scenarios were calculated for the purpose of this study using the Mission Aircraft and Systems Simulation tool developed by the Royal Netherlands Aerospace Centre NLR. For other LCA phases and other modes of transport, the study relies on emission factors from public literature. A trip by train results in three to five times less emissions than a comparable trip by aircraft. In most scenarios, the passenger car with two people onboard emits significantly more than a train but slightly less than an aircraft. The study also shows what drives the climate impact of such a trip and how this is very different for different modes of transport. The study further highlights a lack of high-quality data, especially in the areas of indirect emissions and infrastructure, poor consistency among studies and a general under-documentation and lack of transparency regarding assumptions.

Low-carbon efficiency assessment for Hong Kong's oceanic artificial cities applying the novel prefabricated diaphragm wall technology

Oceanic artificial cities built by reclamation are vital for addressing the future sustainable development of land in Hong Kong. However, traditional cast-in-situ (CIS) construction methods face issues such as significant environmental impacts and low durability, making it challenging to meet the requirements of low-carbon and resilient development. To address these challenges, a novel low-carbon construction technology for oceanic artificial cities is proposed: prefabricated two-wall-in-one diaphragm wall (PTDW). This study aims to address the comprehensive assessment and decision-making issues of greenhouse gas (GHG) emissions and load-bearing performance. First, the adaptability of PTDW technology to oceanic artificial cities is introduced. Second, the Carbon-mechanics Dual-control Assessment Model-Section level (CDAM-SL) is established. Finally, CDAM-SL is used to assess the GHG reduction efficiency of construction technologies for oceanic artificial cities. The results reveal the following key findings: (1) The Bearing Property Indicators (BPI) of the PTDW-Cavity scheme surpass the CIS scheme across the construction and operation phases. (2) Comparing to the traditional CIS scheme, the GHG emissions of the PTDW-Cavity scheme are reduced by 33.31% (8.65 t CO2e). This reduction is primarily attributed to PTDW technology, which eliminates the temporary enclosure structure construction. Furthermore, the refined PTDW technology significantly decreases the GHG emissions of precast components and enhances drainage performance. (3) The GHG reduction efficiency of the PTDW-Cavity scheme consistently outperforms the CIS scheme across both phases. Consequently, PTDW technology demonstrates significant advantages in load-bearing performance and GHG emissions reduction. The research findings provide a theoretical foundation and applicable tool for the comprehensive assessment and decision-making of low-carbon construction technology for oceanic artificial cities.

Predicting and assessing greenhouse gas emissions during the construction of monorail systems using artificial intelligence.

Based on partial data, this paper uses BP neural network optimised by particle swarm optimisation algorithm to predict the total greenhouse gas (GHG) emissions of the line in the construction phase. The GHG emission efficiency is analysed by SBM (Slacks-Based Measure) super efficiency method. Finally, the grey relational analysis (GRA) is applied to sort the GHG emission correlation factors. Based on the existing design and quota document data of 16 stations and 16 sections of the Wuhu Monorail Line 1, we have employed a neural network optimized by particle swarm optimization algorithm to predict the total emissions of greenhouse gases during the construction phase of the entire line consisting of 25 stations and 24 sections. The GHG emissions of all stations and sections are 29,300 tons and 21,000 tons. The technical efficiency, pure technical efficiency, and scale efficiency of the stations and sections were high. As for stations, the order of influence degree is metal material consumption (0.9731) > cost (0.9486) > electric energy consumption (0.9481) > station area (0.9109) > concrete and cement consumption (0.9032) > other material consumption (0.8831) > gasoline and diesel consumption (0.7258). For the section, the order of influence degree is cost (0.9766) > concrete (0.9581) > steel reinforcement (0.9483) > other steels (0.874) > section length (0.8337) > power energy consumption (0.7169) > wood consumption (0.6684).

A new set of Lean indicators to assess Greenhouse Gas emissions related to industrial losses

PurposeThis paper presents a new and well-structured framework that aims to assess the current environmental impact from a Greenhouse Gas (GHG) emissions perspective. This tool includes a new set of Lean Key Performance Indicators (KPIs), which translates the well-known logic of Overall Equipment Effectiveness in the field of GHG emissions, that can progressively detect industrial losses that cause GHG emissions and support decision-making for implementing improvements.Design/methodology/approachThe new metrics are presented with reference to two different perspectives: (1) to highlight the deviation of the current value of emissions from the target; (2) to adopt a diagnostic orientation not only to provide an assessment of current performance but also to search for the main causes of inefficiencies and to direct improvement implementations.FindingsThe proposed framework was applied to a major company operating in the plywood production sector. It identified emission-related losses at each stage of the production process, providing an overall performance evaluation of 53.1%. The industrial application shows how the indicators work in practice, and the framework as a whole, to assess GHG emissions related to industrial losses and to proper address improvement actions.Originality/valueThis paper scrutinizes a new set of Lean KPIs to assess the industrial losses causing GHG emissions and identifies some significant drawbacks. Then it proposes a new structure of losses and KPIs that not only quantify efficiency but also allow to identify viable countermeasures.

Crop Residues Stimulate Yield-Scaled Greenhouse Gas Emissions In Maize-Wheat Cropping Rotation In A Semi-Arid Climate

Mitigating yield-scaled greenhouse gas emissions (YSE) is beneficial for enhancing crop yield, reducing greenhouse gas (GHG) emissions, and advancing climate-smart agronomic management practices. This study aims to evaluate the impact of different crop residue rates– 100% (R100), 50% (R50), and residue removal (R0) – on the YSE indicator within a maize-wheat cropping rotation under both conventional tillage (CT) and no-tillage (NT) systems in a semi-arid region. In the NT system, crop residues had a notable effect on the YSE indicator for wheat. Specifically, R0 exhibited a 39% and 20% decrease in YSE for wheat compared to R100 and R50, respectively. Interestingly, crop residue did not significantly influence YSE for maize under the NT system. On the other hand, in the CT system, YSE for maize in R0 was 33% and 25% lower than that in R100 and R50, respectively. Additionally, compared to R0, there were observed increases of 28% and 20% in YSE for wheat in R100 and R50 under the CT system, respectively. Our findings show that crop residue removal decreases YSE under both CT and NT systems. However, given that this practice degrades soil quality and results in lower yields, it is not considered a sustainable management practice compared to residue retention options. This research highlights the importance of evaluating GHG mitigation strategies by concurrently considering both emissions and crop production. Nevertheless, it is essential to conduct off-site assessments of GHG emissions from crop residue application and also engage in long-term studies to comprehend the full potential of crop residue management on YSE.

Life Cycle Assessment of a Gas Turbine Installation

Gas turbine installations (GTIs) are widely used to generate electrical and thermal energy, mainly by burning gaseous fuels. With the development of hydrogen energy technology, a current area of particular interest is the use of GTIs to burn hydrogen. In order to assess the prospects of using GTIs in this way, it is necessary to understand the carbon emissions of gas turbines within the larger context of the entire hydrogen life cycle and its carbon footprint. The article provides an overview of results from previously published studies on life cycle assessment (LCA) of complex technical devices associated with the production and consumption of fuel and energy, which are most similar to GTIs when it comes to the complexity of LCA. The subject of analysis was a set of GTIs located in Russia with a capacity of 16 MW. An assessment of greenhouse gas (GHG) emissions per MWh of electricity produced showed that at different stages of the GTI life cycle, the total carbon footprint was 198.1–604.3 kg CO2-eq., of which more than 99% came from GTI operation. Greenhouse gas emissions from the production and end-of-life management stages are significantly lower for GTIs compared to those for other complex technical devices used to generate electricity. This is an indicator of the strong prospects for the future use of GTIs.

Technologies to reduce greenhouse gas emissions in pig farms

The paper assesses greenhouse gas emissions in pig breeding in Russia as a whole and in the Novosibirsk Region, including the Kudryashovsky pig farm of Sibagro Holding. A comparative assessment of greenhouse gas emissions among all of Sibagro's pig farms was carried out. The best available technologies that can be applied in pig farms were studied in detail and the resulting assessments of the possibility of applying the best available technologies used in pig breeding were made.

Fifty years of environmental progress for United States dairy farms

Dairy farms in the United States (US) have changed in many ways over the past 50 years. Milk production efficiency has increased greatly with about 30% fewer cows producing about twice the amount of milk today. Other improvements include increases in crop yields, fuel efficiency of farm equipment and efficiency in producing most resources used on farms (electricity, fuel, fertilizer, etc.). These improvements have led to changes in the environmental impact of farms. Through simulation of representative dairy farms in 1971 and 2020, changes in nutrient losses and farmgate life cycle assessments of greenhouse gas (GHG) emissions, fossil energy use, and blue (ground and surface) water use were determined for 6 regions and the US For all environmental metrics studied, intensities expressed per unit of fat and protein corrected milk produced were reduced, but the total impacts over all farms or milk produced increased for 5 of the 13 environmental metrics. Reductions in the impacts of dairy farms in the eastern US were offset by large increases in western regions because of a major increase in cow numbers in the west. The national average intensity of GHG emissions decreased by 42%, which gave just a 14% increase in the total GHG emission of all dairy farms over the 50-yr period. The intensity of fossil energy use decreased by 54% with the total for all farms decreasing 9%. Water use related to milk production decreased in intensity by 28%, but due to the large increase in dairy production in the dry western regions with greater dependence on irrigated feed crops, total blue water use increased 42%. Major pathways of nitrogen loss included ammonia volatilization, leaching, and denitrification, where total ammonia emissions related to US dairy farms increased 29% while leaching losses decreased by 39% with little change in nitrous oxide emission. Simulated nitrogen and phosphorus runoff losses totaled for all dairy farms decreased 27 to 51% through more efficient fertilizer use, reduced tillage, and greater use of cover crops. Emissions of methane and reactive non-methane volatile organic compounds increased 32% and 53%, respectively, due to greater use of long-term manure storage and silage stored in bunkers and piles. Although much progress has been made in improving production efficiency, continued improvement with new strategies and technologies are needed to meet the demand for dairy products while mitigating total environmental impacts, particularly in view of projected climate variability.

An Assessment of Greenhouse Gas Emissions Caused by the Fire of Strategic Diesel Tanks and Providing an Environmental Management Plan Based on the McKinsey 7S Model

An Assessment of Greenhouse Gas Emissions Caused by the Fire of Strategic Diesel Tanks and Providing an Environmental Management Plan Based on the McKinsey 7S Model

Prediction of nitrous oxide emission of a municipal wastewater treatment plant using LSTM-based deep learning models.

Accurate assessment of greenhouse gas emissions from wastewater treatment plants is crucial for mitigating climate change. N2O is a potent greenhouse gas that is emitted from wastewater treatment plants during the biological denitrification process. In this study, we developed and evaluated deep learning models for predicting N2O emissions from a WWTP in Switzerland. Six key parameters were selected to obtain the optimal LSTM model by adjusting experimental parameter conditions. The optimal parameter condition was achieved with 150 neurons, the tanh activation function, the RMSprop optimization algorithm, a learning rate of 0.001, no dropout regularization, and a batch size of 128. Under the same conditions, we compared the performance of recurrent neural networks (RNNs) and Long Short-Term Memory (LSTM) networks. We found that LSTM models outperformed RNN models in predicting N2O emissions. The optimal LSTM model achieved a 36% improvement in mean absolute error (MAE), a 19% improvement in root mean squared error (RMSE), and a 6.92% improvement in R2 score compared to the RNN model. Additionally, LSTM models demonstrated better resilience to sudden changes in the target sequence, exhibiting a 9.54% higher percentage of explained variance compared to RNNs. These results highlight the potential of LSTM models for accurate and robust prediction of N2O emissions from wastewater treatment plants, contributing to effective greenhouse gas mitigation strategies.

Life Cycle Assessment of Greenhouse Gas Emission of Rice Cultivation under Minimum Tillage in the Gimje, South Korea

Life Cycle Assessment of Greenhouse Gas Emission of Rice Cultivation under Minimum Tillage in the Gimje, South Korea

Greenhouse gas emission reduction opportunities for the Norwegian salmon farming sector - can they outweigh growth?

Norwegian farmed Atlantic salmon is a nutritious type of food in increasing demand and although production has stagnated and is faced by various challenges, it is likely to continue to expand in the future. We present results from a detailed greenhouse gas emission assessment of the most important Norwegian farmed salmon export products along with improvement measures. By scaling up both baseline results and reduction opportunities, based on growth projections, we estimate current and future emissions of the sector as a whole. We show that export of gutted salmon to Europe by truck dominates Norwegian salmon exports, not only in volume and value, but also in emissions, followed by export of fresh gutted salmon and fillets to Asia and fillets to the US by air. The cumulative greenhouse gas emissions are dominated by feed production followed by emissions from overseas airfreighting of fresh products. The five most important emission reduction measures, based only on existing technology and without particular order were 1) slightly increased feed efficiency, 2) increased utilization of side streams occurring in secondary processing after export, 3) seafreight to market instead of road and air, 4) higher energy efficiency and cleaner energy sources, and 5) changed feed composition. Collectively, they have the potential to reduce greenhouse gas emissions of current production by 60%, from 5.2 to 2.1 million tonnes of CO2e, assuming the same relative importance of each supply chain.This implies that a medium growth-scenario, representing more than a doubling of the volume of salmon farmed to 3.3 million tonnes, would be possible while reducing total sector emissions by 16% if the improvement measures were fully implemented. For larger reductions, either lower growth or more ambitious implementation of improvement measures is needed. Although greenhouse gas emissions are often linked to resource efficiency and wider sustainability, this is not always the case, and it is important to avoid shifting burdens from climate to e.g. eutrophication or biodiversity impacts. However, many environmental impacts of salmon farming are centered around feed efficiency, and even problems with welfare, escapees, and in part eutrophication are reflected in lower feed efficiency resulting in higher greenhouse gas emissions. In addition to the systematic collection of robust data for more continuous monitoring of greenhouse gas emission performance over time, we therefore recommend identifying additional indicators to monitor to ensure the sector develops not only towards climate neutrality but also towards broader sustainability.