Prediction of enteric methane emissions from cattle

Agriculture has a key role in food production worldwide and it is a major component of the gross domestic product of several countries. Livestock production is essential for the generation of high quality protein foods and the delivery of foods in regions where animal products are the main food source. Environmental impacts of livestock production have been examined for decades, but recently emission of methane from enteric fermentation has been targeted as a substantial greenhouse gas source. The quantification of methane emissions from livestock on a global scale relies on prediction models because measurements require specialized equipment and may be expensive. The predictive ability of current methane emission models remains poor. Moreover, the availability of information on livestock production systems has increased substantially over the years enabling the development of more detailed methane prediction models. In this study, we have developed and evaluated prediction models based on a large database of enteric methane emissions from North American dairy and beef cattle. Most probable models of various complexity levels were identified using a Bayesian model selection procedure and were fitted under a hierarchical setting. Energy intake, dietary fiber and lipid proportions, animal body weight and milk fat proportion were identified as key explanatory variables for predicting emissions. Models here developed substantially outperformed models currently used in national greenhouse gas inventories. Additionally, estimates of repeatability of methane emissions were lower than the ones from the literature and multicollinearity diagnostics suggested that prediction models are stable. In this context, we propose various enteric methane prediction models which require different levels of information availability and can be readily implemented in national greenhouse gas inventories of different complexity levels. The utilization of such models may reduce errors associated with prediction of methane and allow a better examination and representation of policies regulating emissions from cattle.     

The importance of considering animal body mass in IPCC greenhouse inventories and the underappreciated role of wild herbivores

Methane is an important greenhouse gas, but characterizing production by source sector has proven difficult. Current estimates suggest herbivores produce ~20% (~76–189 Tg yr^?1) of methane globally, with wildlife contributions uncertain. We develop a simple and accurate method to estimate methane emissions and reevaluate production by wildlife. We find a strikingly robust relationship between body mass and methane output exceeding the scaling expected by differences in metabolic rate. Our allometric model gives a significantly better fit to empirical data than IPCC Tier 1 and 2 calculations. Our analysis suggests that (i) the allometric model provides an easier and more robust estimate of methane production than IPCC models currently in use; (ii) output from wildlife is much higher than previously considered; and (iii) because of the allometric scaling of methane output with body mass, national emissions could be reduced if countries favored more, smaller livestock, over fewer, larger ones.     

A hybrid approach to identify the risk priority of sources of greenhouse gases

Abstract

Examination of greenhouse gas emissions is crucial for understanding the global warming. For this reason, identification of the sources of greenhouse gas emissions is crucial. This paper presents a hybrid multi criteria decision making method, which combines analytic hierarchy process and technique for order preference by similarity to ideal solution and compares this method with actual data for identifying the risk priority of sources of greenhouse gas emissions. For this purpose, the historical data of 25-years, for six-greenhouse gas sources and three-greenhouse gases (CO₂, CH_4, N₂O) are considered. Consequently, it was found that while incineration of wastes caused the minimum GHG emissions, energy sector caused the maximum GHG emissions. The results of this paper show that use of this hybrid method is easy and intelligible, and has a good potential for sorting the risk priority of sources of greenhouse gas emissions.     

Greenhouse gas emissions and the technical efficiency of dairy farmers

Agriculture in Scotland is facing immense challenges for the future. Scottish policy now promotes sustainable economic growth, which to many implies both a growth in resource use and the requirement to meet targets for reducing greenhouse gas emissions. Attempts to increase economic gains can be in conflict with environmental aims and the question arises how both objectives can be met simultaneously.This paper examines the relationship between the technical and environmental efficiency of dairy farms in Scotland with respect to greenhouse gas emissions. It estimates the technical efficiency of a sample of dairy farms based on survey data, applying a number of approaches based on the non-parametric Data Envelopment Analysis (DEA) method. The farms’ greenhouse gas emissions are calculated using a variety of sources and were used to estimate environmental efficiency. This paper finds that, within the study sample, farms which are more technically efficient, are bigger or have higher yields are also more efficient in their emissions of GHG emissions.These results suggest that the potential exists for, Scottish dairying to improve its competitiveness and lower greenhouse gas emissions by increasing efficiency, though the extent of the environmental benefits achieved largely depends on how efficiency is increased.     

Development of a new tank-to-wheels methodology for energy use and green house gas emissions analysis based on vehicle fleet modeling

Background, aim and scope  

Tank-to-Wheels (TtW) makes the largest contribution to the total Well-to-Wheels (WtW) energy consumption and greenhouse gas (GHG) emissions from fossil-derived transportation fuels. The most commonly adopted TtW methodologies to obtain vehicle energy consumption, energy efficiency, and GHG emissions used to date all have significant limitations. A new TtW methodology, which combines micro-scale virtual vehicle simulation with macro-scale fleet modeling, is proposed in this paper. The models capabilities are demonstrated using a case study based on data from the passenger car sector in Great Britain.     

Policy options in addressing livestock's contribution to climate change

There is a great potential to reduce greenhouse gas (GHG) emissions related to livestock production. For achieving this potential will require new initiatives at national and international levels that include promoting research and development on new mitigation technologies; deploying, diffusing and transferring technologies to mitigate emissions; and enhancing capacities to monitor, report and verify emissions from livestock production. This study describes the sources of livestock-related GHG emissions and reviews available mitigation technologies and practices. We assess the main policy instruments available to curb emissions and promote carbon sinks, and discuss the relative merits of alternative approaches. We discuss recent experiences in countries that have enacted mitigation strategies for the livestock sector to illustrate some of the key issues and constraints in policy implementation. Finally, we explore the main issues and challenges surrounding international efforts to mitigate GHG emissions and discuss some possible ways to address these challenges in future climate agreements.     

Analysis of greenhouse gas emissions related to aluminium transport applications

Background, aim and scope  

Climate change is a subject of growing global concern. Based on International Energy Agency (IEA 2004) research, about 19% of the greenhouse gas emissions from fuel combustion are generated by the transportation sector, and its share is likely to grow. Significant increases in the vehicles fleets are expected in particular in China, India, the Middle East and Latin America. As a result, reducing vehicle fuel consumption is most essential for the future. The reduction of the vehicle weight, the introduction of improved engine technologies, lower air friction, better lubricants, etc. are established methods of improving fuel efficiency, reducing energy consumption and greenhouse gas emissions. Continued progress will be required along all these fronts with light-weighting being one of the most promising options for the global transport sector. This paper quantifies greenhouse gas savings realised from light-weighting cars with aluminium based on life cycle assessment methodology. The study uses a pragmatic approach to assess mass reduction by comparing specific examples of components meeting identical performance criteria. The four examples presented in this analysis come from practical applications of aluminium. For each case study, the vehicle manufacturer has supplied the respective masses of the aluminium and the alternative component.     

Effects of Using Heterogeneous Prices on the Allocation of Impacts from Electricity Use: A Mixed‐Unit Input‐Output Approach

Economic input‐output life cycle assessment (IO‐LCA) models allow for quick estimation of economy‐wide greenhouse gas (GHG) emissions associated with goods and services. IO‐LCA models are usually built using economic accounts and differ from most process‐based models in their use of economic transactions, rather than physical flows, as the drivers of supply‐chain GHG emissions. GHG emissions estimates associated with input supply chains are influenced by the price paid by consumers when the relative prices between individual consumers are different. We investigate the significance of the allocation of GHG emissions based on monetary versus physical units by carrying out a case study of the U.S. electricity sector. We create parallel monetary and mixed‐unit IO‐LCA models using the 2007 Benchmark Accounts of the U.S. economy and sector specific prices for different end users of electricity. This approach is well suited for electricity generation because electricity consumption contributes a significant share of emissions for most processes, and the range of prices paid by electricity consumers allows us to explore the effects of price on allocation of emissions. We find that, in general, monetary input‐output models assign fewer emissions per kilowatt to electricity used by industrial sectors than to electricity used by households and service sectors, attributable to the relatively higher prices paid by households and service sectors. This fact introduces a challenging question of what is the best basis for allocating the emissions from electricity generation given the different uses of electricity by consumers and the wide variability of electricity pricing.     

Decomposition of Emission Multipliers in a National Accounting Matrix Including Environmental Accounts

This study defines a linear model of emission multipliers through the use of a national accounting matrix including environmental accounts (NAMEA) for the Catalan economy that integrates the regional economic information with the greenhouse gas emissions. As in the model of income multipliers, emission multipliers can be divided into own effects, open effects, and circular effects. This decomposition shows the channels of income generation and their effects on regional greenhouse gas emissions. Our results reveal significant differences among the three gases analyzed as well as important asymmetries at a sectorial level.     

Bridging the climate mitigation gap with economy‐wide material productivity

Projections of UK greenhouse gas emissions estimate a shortfall in existing and planned climate policies meeting UK climate targets: the UK's mitigation gap. Material and product demand is driving industrial greenhouse gas emissions at a rate greater than carbon intensity improvements in the economy. Evidence shows that products can be produced with fewer carbon intensive inputs and demand for new products can be reduced. The economy‐wide contribution of material productivity and lifestyle changes to bridging the UK's mitigation gap is understudied. We integrate an input‐output framework with econometric analysis and case study evidence to analyse the potential of material productivity to help the UK bridge its anticipated emissions deficits, and the additional effort required to achieve transformative change aligned with 2 and 1.5°C temperature targets. We estimate that the emissions savings from material productivity measures are comparable to those from the Government's planned climate policy package. These additional measures could reduce the UK's anticipated emissions deficit up to 73%. The results demonstrate that material productivity deserves greater consideration in climate policy.     

Estimating Corporate Carbon Footprints with Externally Available Data

Corporate carbon footprints (CCFs) are a core tool in greenhouse gas emissions reporting. Established approaches for CCF calculation are based on an internal perspective that requires detailed corporate information. However, many firms do not publish information about their emissions. We seek to close this data gap by estimating scope 1 and 2 CCFs from an external perspective. The study uses a regression analysis approach, using actual firm‐internally computed CCFs to assess their degree of predictability from the outside. Data were collected from 93 European companies belonging to the chemicals, construction and engineering, and industrial machinery sectors. As predictors, we use five measures that are computed with publicly available corporate data: firm size; level of vertical integration; capital intensity; centrality of production; and carbon intensity of the national energy mix. The analysis shows that significant explanatory power for the CCF can be observed for size, capital intensity, and centrality of production. The best estimation results are achieved when data from different sectors are integrated into a comprehensive all‐sector model, while accounting for sector‐specific emission intensities by means of dummy variables. With an adjusted R² value of 0.817, the proposed procedure estimates CCFs in an accurate, yet also efficient, manner. Moreover, the study enhances trust in the current CCF calculation practices by showing that their results are plausible from a third‐party perspective.     

Projecting insect voltinism under high and low greenhouse gas emission conditions

We develop individual-based Monte Carlo methods to explore how climate change can alter insect voltinism under varying greenhouse gas emissions scenarios by using input distributions of diapause termination or spring emergence, development rate, and diapause initiation, linked to daily temperature and photoperiod. We show concurrence of these projections with a field dataset, and then explore changes in grape berry moth, Paralobesia viteana (Clemens), voltinism that may occur with climate projections developed from the average of three climate models using two different future emissions scenarios from the International Panel of Climate Change (IPCC). Based on historical climate data from 1960 to 2008, and projected downscaled climate data until 2099 under both high (A1fi) and low (B1) greenhouse gas emission scenarios, we used concepts of P. viteana biology to estimate distributions of individuals entering successive generations per year. Under the low emissions scenario, we observed an earlier emergence from diapause and a shift in mean voltinism from 2.8 to 3.1 generations per year, with a fraction of the population achieving a fourth generation. Under the high emissions scenario, up to 3.6 mean generations per year were projected by the end of this century, with a very small fraction of the population achieving a fifth generation. Changes in voltinism in this and other species in response to climate change likely will cause significant economic and ecological impacts, and the methods presented here can be readily adapted to other species for which the input distributions are reasonably approximated.     

Sectoral Aggregation Error in the Accounting of Energy and Emissions Embodied in Trade and Consumption

Correctly accounting for the energy and emissions embodied in consumption and trade is essential to effective climate policy design. Robust methods are needed for both policy making and research—for example, the assignment of border carbon adjustments (BCAs) and greenhouse gas emission reduction responsibilities rely on the consistency and accuracy of such estimates. This analysis investigates the potential magnitude and consequences of the error present in estimates of energy and emissions embodied in trade and consumption. To quantify the error of embodied emissions accounting, we compare the results from the disaggregated Global Trade Analysis Project (GTAP 8) data set, which contains 57 sectors to results from different levels of aggregation of this data set (3, 7, 16, and 26 sectors), using 5,000 randomly generated sectoral aggregation schemes as well as aggregations generated using several commonly applied decisions rules. We find that some commonly applied decision rules for sectoral aggregation can produce a large error. We further show that an aggregation scheme that clusters sectors according to their energy, emissions, and trade intensities (net exports over output) can minimize error in embodied energy and emissions accounting at different levels of aggregation. This sectoral aggregation scheme can be readily used in any input‐output analysis and provide useful information for computable general equilibrium modeling exercises in which sector aggregation is necessary, although our findings suggest that, when possible, the most disaggregated data available should be used.     

Greenhouse gas emissions of the production chain behind consumption of products in Austria: Development and application of a product‐ and technology‐specific approach

Globalization has been one main driver affecting our whole economy. Thus, greenhouse gas emissions (GHGs) associated with imports and exports should get addressed in addition to the national emission inventory according to the United Nations Framework Convention on Climate Change (UNFCCC), which is focused on territorial emissions only. To enable a correct calculation for imports and exports and to find the most emission‐intensive products and their origin, a product‐ and technology‐specific approach would be favorable which has not been applied up to now. This article addresses this research gap in developing and applying such an approach to calculate the GHGs behind consumption of products in Austria. It is based on physical flows combined with life‐cycle‐based emission factors and emission intensities derived from sector‐ and country‐specific energy mix, for calculating all emissions behind the production chain (resources to final products) of products consumed in Austria. The results have shown that consumption of products in Austria leads to about 60% more emissions than those of the national inventory and that the main part of these emissions comes from the provision of products. The most emission‐intensive products are coming from the chemical and the metal industry. In particular, imports are the main driver of these emissions and are more emission intensive than those produced in Austria. As a result, it is necessary to look at practical measures to reduce emissions along the production chain not only in Austria, but especially abroad as well.     

Key sectors in greenhouse gas emissions in Spain: An alternative input–output analysis

We develop an alternative input–output approach and apply it to the determination of key sectors in emissions. This methodology allows us to assess and classify the different productive sectors according to their greenhouse gas emissions and the role that they play in the productive structure, as well as the participation of their output in the total volume of production. In contrast with previous approaches, we do not focus on the responsibility of final demand, but on the responsibility of the total production of each sector. We apply our methodology to the 2014 input–output table for Spain provided by the World Input–Output Database (2016). The results show that the sectors that induce more emissions from other sectors are manufacture of food products, wholesale and retail trade, and construction. Those that are pulled to emit coincide with those that are relevant for their own final demand, being the most important electricity and gas provision, agriculture, and transportation. The classification obtained allows to orient the design of greenhouse gas emission mitigation policies for the different sectors.     

Evaluating the carbon footprint of the cork sector with a dynamic approach including biogenic carbon flows

Purpose

The aim of the present study is to assess the influence of two different attributional life cycle assessment (LCA) approaches, namely static LCA (sLCA) and dynamic LCA (dLCA), through their application to the calculation of the carbon footprint (CF) of the entire cork sector in Portugal. The effect of including biogenic carbon sequestration and emissions is considered as well.

Methods

sLCA is often described as a static tool since all the emissions are accounted for as if occurring at the same time which may not be the case in reality for greenhouse gases. In contrast, dLCA aims to evaluate the impact of life cycle greenhouse gas emissions on radiative forcing considering the specific moment when these emissions occur.

Results and discussion

The results show that the total CF of the cork sector differs depending on the approach and time horizon chosen. However, the greater it is the time horizon chosen, the smaller the difference between the CF results of the two approaches. Additionally, the inclusion of biogenic carbon sequestration and emissions also influences significantly the CF result. The cork sector is considered a net carbon source when biogenic carbon is excluded from the calculations and a net carbon sink when biogenic carbon is included in the calculations since more carbon is sequestered than emitted along the sector.

Conclusions

dLCA allows an overview of greenhouse gas emissions along the time. This is an advantage as it allows to identify and plan different management approaches for the cork sector. Even though dLCA is a more realistic approach, it is a more time-consuming and complex approach for long life cycles. The choice of time horizon was found to be another important aspect for CF assessment.

     

Estimate of life-cycle greenhouse gas emissions from a vertical subsurface flow constructed wetland and conventional wastewater treatment plants: A case study in China

This study aims to estimate the three greenhouse gas (GHG) emissions (i.e. CO₂, CH₄, N₂O) from a vertical subsurface flow constructed wetland (VSSF CW, 1000 m²) and a cluster of conventional wastewater treatment plants (WWTPs) in the city of Changzhou, China. The two estimated emissions are set up for comparison. The results show that the WWTP system emits 7.3 kg CO₂-eq to remove 1 kg BOD in the studied life cycle, while the VSSF system only emits 3.18 kg CO₂-eq, which is only half of the amount given off by the WWTP system. Especially at the treatment stage, the WWTP system's GHG emissions are almost 7 times higher than the VSSF system's. N₂O emissions in both systems are only a minor fraction of the total emissions. Therefore, this study has concluded that the VSSF system is an effective option for GHG emissions mitigation in the wastewater sector. The study further suggests that developing countries like China should extensively build up VSSF systems for decentralized wastewater treatment, which could also potentially reduce GHG emissions by 8-17 million ton CO₂-eq per year compared with the centralized scenario.     

The impact of landfilling and composting on greenhouse gas emissions – A review

Municipal solid waste is a significant contributor to greenhouse gas emissions through decomposition and life-cycle activities processes. The majority of these emissions are a result of landfilling, which remains the primary waste disposal strategy internationally. As a result, countries have been incorporating alternative forms of waste management strategies such as energy recovery from landfill gas capture, aerobic landfilling (aerox landfills), pre-composting of waste prior to landfilling, landfill capping and composting of the organic fraction of municipal solid waste. As the changing global climate has been one of the major environmental challenges facing the world today, there is an increasing need to understand the impact of waste management on greenhouse gas emissions. This review paper serves to provide an overview on the impact of landfilling (and its various alternatives) and composting on greenhouse gas emissions taking into account streamlined life cycle activities and the decomposition process. The review suggests greenhouse gas emissions from waste decomposition are considerably higher for landfills than composting. However, mixed results were found for greenhouse gas emissions for landfill and composting operational activities. Nonetheless, in general, net greenhouse gas emissions for landfills tend to be higher than that for composting facilities.     

Increasing Precision in Greenhouse Gas Accounting Using Real‐Time Emission Factors

For many companies, the greenhouse gas (GHG) emissions associated with their purchased and consumed electricity form one of the largest contributions to the GHG emissions that result from their activities. Currently, hourly variations in electricity grid emissions are not considered by standard GHG accounting protocols, which apply a national grid emission factor (EF), potentially resulting in erred estimates for the GHG emissions. In this study, a method is developed that calculates GHG emissions based on real‐time data, and it is shown that the use of hourly electricity grid EFs can significantly improve the accuracy of the GHG emissions that are attributed to the purchased and consumed electricity of a company. A model analysis for the electricity delivered to the Spanish grid in 2012 reveals that, for companies operating during the day, GHG emissions calculated by the real‐time method are estimated to be up to 5% higher (and in some special cases up to 9% higher) than the emissions calculated by the conventional method in which a national grid EF is applied, whereas for companies operating during nightly hours, GHG emissions are estimated to be as low as 3% below the GHG emissions determined by the conventional method. A significant error can therefore occur in the organizational carbon footprint (CF) of a company and, consequently, also in the product CF. It is recommended that hourly EFs be developed for other countries and power grids.     

Microorganisms and climate change: terrestrial feedbacks and mitigation options

Microbial processes have a central role in the global fluxes of the key biogenic greenhouse gases (carbon dioxide, methane and nitrous oxide) and are likely to respond rapidly to climate change. Whether changes in microbial processes lead to a net positive or negative feedback for greenhouse gas emissions is unclear. To improve the prediction of climate models, it is important to understand the mechanisms by which microorganisms regulate terrestrial greenhouse gas flux. This involves consideration of the complex interactions that occur between microorganisms and other biotic and abiotic factors. The potential to mitigate climate change by reducing greenhouse gas emissions through managing terrestrial microbial processes is a tantalizing prospect for the future.