Which of the following are classified as gases that can contribute to the greenhouse effect?

1.3 Greenhouse gases and the greenhouse effect

Greenhouse gases (GHG) are gaseous compounds that can emit ultraviolet radiation within a certain thermal infrared range [76]. Greenhouse gases retain high temperatures in the lower atmosphere, thus allowing less heat to escape back to space. This subsequently results in the greenhouse effect and global warming. The greenhouse effect is a natural process that warms the Earth's surface to a temperature above which it would be without the atmosphere. The intensity of the greenhouse effect depends largely on the temperature of the atmosphere, and on the presence of greenhouse gases in the atmosphere. Greenhouse gases are vital for supporting a habitable temperature for the Earth because if there were totally no greenhouse gases present in the atmosphere, the average surface temperature of the Earth would be about − 18°C [77]. Common greenhouse gases present in the atmosphere include water vapor, chlorofluorocarbons (CFCs), hydrofluorocarbons (HFCs), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3). However, researchers have pointed out that the four main greenhouse gases attracting serious global attention today are CO2, SO2, CH4, and N2O [78]. Although water vapor is arguably the most abundant greenhouse gas naturally present in the atmosphere, CO2 is the most emitted greenhouse gas. Hence, different techniques have been reported to capture CO2 [79–81]. A generalized classification of activities leading to the emission of greenhouse gases into the atmosphere is depicted in Fig. 1.4, while the main greenhouse gases and their major sources are presented in Table 1.1.

Table 1.1. Greenhouse gases and their major sources.

Modified from T.J. Blasing, Recent Greenhouse Gas Concentrations, Environmental System Science Data Infrastructure for a Virtual Ecosystem, Carbon Dioxide Information Analysis Center (CDIAC), Oak Ridge National Laboratory (ORNL), Oak Ridge, TN, United States, 2016.

Information provided in Table 1.1 reveals that because of the high dependence on burning fossil fuels for power generation and other industrial activities, CO2 is now the most emitted greenhouse gas while fluorinated gases (CFCs and HCFs) are the least emitted greenhouse gases because the use of refrigerants has been phased out in many refrigeration systems in recent times [82].

International Energy Agency’s Greenhouse Gas R&D Programme

The IEAGHG R&D Programme is an international collaborative research program established in 1991 as an Implementing Agreement under the International Energy Agency (IEA) (IEA Greenhouse Gas R&D Programme, n.d.). The program’s main activities are as follows:

To evaluate technologies aimed at reducing greenhouse gas emissions.

To help facilitate the implementation of potential mitigation options.

To disseminate the data and results from evaluation studies

To help facilitate international collaborative research, development, and demonstration activities. Currently IEAGHG members include 17 member countries, the European Commission and the Organization of Petroleum Exporting Countries (OPEC), as well as 21 multinational sponsors. The members are listed in Table 15.13 (IEA Greenhouse Gas R&D Programme, 2015).

Table 15.13. IEAGHG Members

2.3.3.2 Calculation of the environmental benefits

The calculation of GHG associated with avoidable HHFW in 2015 is based on a bottom-up approach. This method uses information on the greenhouse gas footprint and quantities of the types of food waste. The current report has updated estimates of greenhouse gas footprints used in previous reports in the following areas:

Landfill (based on data from MacCarthy et al., 2015)

The global warming potential of methane (based on data from IPCC, 2014)

The quantity of gas derived through Anaerobic Digestion (based on information from WRAP, 2014 and U.S. EPA, 2016)

An emission value of 4.4 metric tons per metric ton of food waste is calculated for avoidable HHFW in 2015. The avoidable fraction of HHFW in the United Kingdom is therefore responsible for 19 million metric tons of CO2e emissions. This is equivalent to the annual emissions of around 30% of cars on British roads. This figure increases to 25.5 million metric tons when the other HHFW fractions (possibly avoidable and unavoidable) are included (WRAP, 2017).

10.6 Case Studies

12.4.3 Greenhouse gas footprint of bio-oil reforming

GHG emissions from the bio-oil reforming of agricultural residues, forest residues and whole trees are 3.43, 2.83 and 1.92 kg CO2 eq/kg H2, respectively (Fig. 12.12). Feedstock production contributes considerably to the life cycle GHG emissions in the case of agricultural residues (45%). Bio-oil transportation has a massive impact on GHG emissions in all cases; it accounts for 36%–64% of the total. The high impact of bio-oil transportation is due to the high density of bio-oil, which is around 1200 kg/m3 compared with H2 gas with a density of 0.089 kg/m3. Pump operation, which accounts for more than 95% of the pipeline energy use, is the key driver of GHG emissions from transportation. The source of electricity, hence, has a large impact on overall emissions. Sourcing renewable-based electricity such as hydro would reduce pipeline emissions. An extensive study comparing pipeline and truck transport for large-scale bio-oil production was carried out by Pootakham and Kumar [107]. The results suggest that in a region like Alberta, where the grid mix is dominated by coal energy, truck transportation has considerably fewer GHG emissions than pipeline.

The contribution from plant operations is negligible, as the energy required to dry the biomass is provided from the combustion of char generated in the process.

OPGEE Version 1.1 Draft E

OPGEE was developed by Adam Brandt and his colleagues at Stanford University.9 The California Air Resources Board has supported its development and used the model in GHG emission rulemaking. Under California regulations, OPGEE reports GHG emission outputs in units of grams of CO2 equivalent per megajoule of petroleum products generated. 10 These outputs are converted into emissions per barrel by multiplying them by the lower heating value of each oil (in megajoules per barrel), determined by correlations between the crude’s API gravity and its energy density.

The oil field and oil data specified are used to generate OPGEE’s bulk assessment tool for each oil. The bulk assessment tool is then used to calculate base-run GHG emission outputs for each oil. OPGEE also considers the transport of oil to the refinery inlet. In Phase 2, the OCI continues to assume that all oil is transported from its country of origin to Houston via the mode that is nearest to the oil field.

The petcoke produced upstream during oil-sand and extra-heavy-oil upgrading, requires an offline calculation to estimate the net petcoke produced. This considers the OPGEE-derived portion of petcoke used as an upstream energy source and subtracts that from the total petcoke production reported by the Alberta Energy Regulator.11 Lacking detailed reporting in Venezuela, OPGEE uses the average relative amounts of petcoke production reported for Canadian oil sands for Venezuelan extra-heavy-oil upgrading.

The OCI web tool has several upstream oil field operating parameters that users can modify allowing users to make different operating assumptions and see how these affect GHG emissions.

Flaring emissions are estimated in OPGEE by aligning satellite measurements of flares with outlines of the oil fields. The flares and volumes were identified with data from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor, which, when combined with the Nightfire Algorithm, can detect and provide estimates to quantify flare location and volume.12 The Nightfire Algorithm, developed by the National Oceanic and Atmospheric Administration (NOAA), develops annual estimates for flaring volumes and aggregates the data into a spatial database that designates the type of flare as production, refining, or processing. The flaring rates are computed as the average of 2010–14 flaring volumes located in the fields within this analysis. A rare exception is made when government-reported flaring data or gas-to-oil ratios are lower than VIIRS-assessed flaring data; in this case, OCI Phase 2 uses the lower government data rather than VIIRS.

Abstract

Greenhouse gas continues to contribute negatively to the environment leading to an uncomfortable society. Of all the various activities leading to greenhouse gas emission, the activities emanating from the industries require special attention. This is because the economy cannot grow without the support of the industrial activities. These industrial activities are known as industrial processes. It is a known fact that industrial development is proportional to economic growth; however, “I do not care attitude” from our industries to the environment constitute a disaster to the growing economy. This chapter will touch on one of the leading literature studies on ways to analyze greenhouse gas emission in our industries to detect opportunities for mitigation improvement with a focus on Logarithmic Mean Divisia Index, a form of index decomposition analysis application. Applications were focused on the early oil crisis, times of intensified knowledge to solving greenhouse crisis, and the present times of finding lasting solution to the greenhouse crisis. Findings of this chapter pointed out at the decrease in energy intensity, which in turn is the increase in energy efficiency indicator is the backbone to mitigating greenhouse gas emissions. Policies along these indicators will be well suited to mitigating the emissions.

6.4.1.3.1 Global warming potential

Greenhouse gases contribute to the global climate change and global warming as they warm the earth by absorbing the incoming solar energy from the sun and trapping it within the atmosphere. Acting like a blanket insulating earth, they slow the rate at which energy escapes. Most common greenhouse gases that account for this include carbon dioxide (CO2), methane (CH4), and chlorofluorocarbons (CFCs). The element carbon is the common factor among the different greenhouse gases. Global warming potential (GWP) is a measure that was developed to compare the impact of different gases on the atmosphere. Specifically, it is a measure of how much energy is absorbed when 1 tonne of a specified gas is released to the atmosphere over a period, relative to the emission of 1 tonne of carbon dioxide. In this case, the larger the GWP, the more negative it is for the environment. CO2 equivalence (CO2-eq) is used as a measure for GWP. The time usually used for GWP is 100 years. Thus, the GWP indicator in this book considers the 100 year warming potential of all greenhouse gases throughout their life cycle. The following equation illustrates the calculation of the GWP score (Hacatoglu, 2014):

(6.9)YGWP=XGWP(T)XGWP

where XGWP represents the actual greenhouse gas emissions for the period of 100 years. XGWP(T) represents the target value for this time period, which is the minimum greenhouse gas emissions, achieved by solely relying on renewable energy sources. This means, conventional energy sources such as fossil fuels are not considered in any stage of the energy production of the system. These values can be extracted by SimaPro as part of the LCA.