The Clean Development Mechanism (CDM) is the only mechanism available for use by developing nations. It is there for highly important for the inclusion of these developing nations in the climate change regime. A consideration on the early implementation of the CDM in South Africa and China, being two countries with many similarities and differences and vastly different successes, provides important lessons on how to approach the climate change regime. Certain barriers exist purely due to the nature of countries in which the CDM applies as well as other barriers found within the CDM project life cycle and development. Through the comparison these barriers are explored and areas for development within South Africa are noted as well as weakness with the current climate change regime particularly the Kyoto Protocol. / Thesis (LL.M.)-University of KwaZulu-Natal, Pietermaritzburg, 2012.

Net ecosystem carbon dioxide exchange (NEE) was measured using the eddy covariance (EC) technique at a wetland tundra-sedge fen near Churchill, Manitoba, Canada during two consecutive growing seasons (2007 and 2008). Mean daily NEE at the fen (DOY 157-254) was -3.5 (± 0.26 S.E.) g CO2 m-2 d-1 in 2007 and -4.6 (± 0.36) g CO2 m-2 d-1 in 2008. The fen was a net carbon dioxide (CO2) sink during both the 2007 and 2008 growing seasons of -343 (± 79) and -450 (± 87) g CO2 m-2, respectively. Mean air temperature during the summer (June 1-August 31) was about 1°C greater than the historical average (1971-2000) in 2007 and about 2°C greater in 2008. Growing season precipitation was 107.5 mm below normal in 2007 and 359.5 mm above normal in 2008. These data suggest that if future climate change brings warmer temperatures and near-to-above average precipitation maintaining the water table near the surface, similar subarctic ecosystems will experience increased gross ecosystem productivity enhancing CO2 sequestration during the growing season.

Mathematical modeling in animal agriculture can be applied at various levels including at the tissue, organ, animal, farm, regional and global levels. The purposes of this research were i) to evaluate models used to estimate volatile fatty acid (VFA) and methane (CH4) production and assess their impact on regional enteric CH4 inventory, and ii) to develop a process-based, whole-farm model to estimate net farm GHG emissions. In the first study, four VFA stoichiometric models were evaluated for their prediction accuracy of rumen VFA and enteric CH4 production. Comparison of measured and model predicted values demonstrated that predictive capacity of the VFA models varied with respect to the type of VFA in rumen fluid which impacted estimated enteric CH4 production. Moving to a larger scale assessment, we examined the enteric CH4 inventory from Manitoba beef cattle (from 1990 to 2008) using two mechanistic rumen models that incorporate VFA stoichiometric models: COWPOLL and MOLLY, and two empirical models: Intergovernmental Panel on Climate Change (IPCC) Tier 2 and a nonlinear equation (Ellis). The estimated absolute enteric CH4 production varied among models (7 to 63%) indicating that estimates of GHG inventory depend on model selection. This is an important consideration if the values are to be used for management and/or policy-related decisions. Development of models at the individual farm component level (animal, soil, crop) does not accurately reflect net GHG emissions generated from the whole production system. We developed a process-based, whole-farm model (Integrated Components Model, ICM), using the existing farm component models COWPOLL, manure-DNDC and some aspects of IPCC to integrate farm components and their associated GHG emissions. Estimates of total farm GHG emissions and their relative contribution using the ICM were comparable to estimates using two other whole-farm models (Integrated Farm System Model and Holos model). Variation was observed among models both in estimating whole-farm GHG emissions and the relative contribution of the different sources in the production system. Overall, whole-farm models are required to explore management options that will mitigate GHG emissions and promote best management practices. However, for full assessment of the production system, other benefits of the system (e.g., carbon sequestration, ecosystem services), which are not part of current whole-farm models, must be considered.

The increased use of synthetic nitrogen fertilizers since the early 1900s has resulted in greater food production but also problems with nitrogen pollution in freshwaters. Nitrate (NO3-) is a common pollutant in rivers and groundwater in agricultural watersheds; the drinking water limit in Canada is 10 mg N/L. Microbial processing of NO3- and ammonium (NH4+) can produce nitrous oxide (N2O), a potent greenhouse gas responsible for about 5% of the greenhouse effect. Rivers provide a complex environment, where a variety of redox conditions, available substrates and microbial populations can co-exist on small spatial and temporal scales. Therefore, many questions remain about N cycling in river environments. N2O is produced during anoxic microbial NO3- or NO2- reduction to N2 (denitrification) and oxic microbial NH4+ oxidation to NO3- (nitrification). A significant portion (~25%) of global anthropogenic N2O is produced in rivers and estuaries, but mechanisms are not clear and predictability is poor. The United Nations Intergovernmental Panel on Climate Change (IPCC) provides default equations for calculating N2O emission estimates, in which annual NO3- loading to rivers is positively linearly related to N2O emissions. However, it is unclear how sound these linear relationships are and if measured N2O emissions are similar to IPCC estimates. The Grand River watershed is the largest in southern Ontario. Nutrient discharge to the Grand River is high due to extensive agriculture and high urban populations. The river often has a hypoxic water column due to high community respiration in summer. However, although nitrogen pollution is significant, N cycling is not well understood in the river. This thesis shows that NO3- and NH4+ do not typically change on the diel scale, with the exception of two sites downstream of wastewater treatment plants (WWTPs). However, N2O concentration changes dramatically. N2O concentrations are higher at night and lower during the day for most sites, but are reversed at very low-nutrient sites. N2O is therefore a sensitive indicator of changes in N cycling that may not be evident from NO3- and NH4+ concentrations or stable isotope ratios. Additionally, this work shows the importance of having a sampling design that captures diel variability in N2O. Previous work in rivers and streams worldwide focused on the appropriate N2O:NO3- ratio used to predict N2O emissions. In contrast, this thesis shows that there is a significant but very weak relationship between instantaneous N2O emissions and NO3- concentrations. However, there is a much stronger negative exponential relationship between DO and N2O. Annual N2O emissions tripled between 2006 and 2007 but NO3- masses in the river were only 10% higher, likely because river levels were lower and anoxia more prevalent in 2007. This research suggests that the IPCC needs a new conceptual model for N2O-NO3- relationships in rivers. N2O is produced in rivers, partially due to microbial processing of NO3- and NH4+ from WWTP effluent. However, WWTP effluent may also include dissolved N2O and CH4 but this previously had not been directly quantified. It was also unclear if stable isotopic ratios of NH4+, NO3-, N2O and CH4 in WWTP effluent were distinct from river sources and could be used for effluent tracing. N2O emissions from three WWTPs in the Grand River Watershed were measured over 24 hours in summer and winter. N2O emissions were similar to direct emissions from WWTPs but CH4 emissions were about an order of magnitude lower than direct WWTP emissions. This is a previously-ignored source of N2O and CH4 to the atmosphere. While stable isotopic ranges of NO3- and NH4+ were not always distinct from river sources, ??15N-N2O, ??18O-N2O and ??13C-CH4 were distinct, making them potentially useful tracers of WWTP effluent in rivers. N2O isotopic signatures may help determine production and removal processes in rivers, but isotopic effects of the major production pathway, denitrification, have not been characterized for river sediments. This was addressed by preparing anoxic laboratory incubations of river sediment from two sites (non-urban and urban) in the Grand River and measuring stable isotopic effects of N2O production via denitrification. Stable isotopic fractionations were similar to published values but, surprisingly, strongly negatively correlated to production rate, even though NO3- substrate was plentiful. This novel finding suggests that N2O reduction resulting in isotopic effects is more prevalent in high-substrate systems than previously thought, and that N2O reduction may be inhibited by high NO3- or NO2- or by lags in N2O reductase activity in high N2O-production incubations. This could explain why N2O emissions from the Grand River are lower than predicted by IPCC equations, which assume that N2O:(N2O+N2) ratios produced by denitrification are constant. Concern about NO3- export to freshwater lakes and to oceans is growing, but the role of large, eutrophic rivers in removing watershed NO3- loading via denitrification and biotic assimilation is not clear. To understand how much NO3- the Grand River receives, and how much it removes annually, a NO3- isotope mass balance for the Grand River was created. The river denitrified between 0.5% and 17% of incoming NO3-, less than the 50% suggested by the IPCC. This is surprising, as the river is well mixed, has moderate to high NO3- concentrations, experiences hypoxia (promoting denitrification), and has extensive biomass (biofilm and macrophytes) that assimilate N. However, the river???s short residence time (~3 days not counting reservoirs), organic carbon-poor sediment and mineralization of organic matter could contribute to low denitrification rates. These findings suggest that denitrification rates in rivers worldwide could be lower than previously estimated. Although error was high, most ??15N-NO3- values for losses were in the expected range for denitrification and most ??15N-NO3- values for gains were within ranges from tributaries, WWTP effluent and groundwater measured in the watershed. The model suggests that 68% to 83% of N loads to the watershed are lost before entering the Grand River, and 13% is exported to Lake Erie, leaving 5 to 19% lost in the Grand River from a combination of denitrification, assimilation and storage. These findings suggest that large rivers are much less efficient in denitrification than other locations in watersheds such as small streams, ponds, groundwater and riparian zones. They also indicate that agricultural NO3- loading is much higher than WWTP effluent, suggesting that N management strategies should focus on agricultural runoff and groundwater. Given that N2O:NO3- relationships are weak and non-linear in the Grand River, a new conceptual model for N2O:NO3- relationships is presented. First, the Grand River dataset was supplemented with data from high-oxygen streams in southern Ontario. Regression tree analysis shows a weak relationship between NO3- and N2O in these streams with no other factors (temperature, DO, NH4+, TP, DOC, etc.) improving fit. A conceptual model was then created, which posits that N2O emission variability (between and within sites) increases with NO3- concentration when NO3- concentrations are above the threshold for NO3- limitation. The global dataset does not dispute this model, though a NO3- threshold was not clear. The lack of sites with both high NO3- and high N2O may indicate a paucity of research on eutrophic sites. Alternatively, high NO3- may indicate oxic conditions (i.e. little to no denitrification to remove it) which are incompatible with very high N2O emissions. In this case, the conceptual model can be modified such that N2O variability decreases when NO3- > ~ 4 mg N/L. The work also shows that low DO consistently results in high N2O emissions but high temperatures result in a very large range of N2O emissions. This approach allows N2O emissions, which have very high variability and are difficult to predict, to be constrained to likely ranges.

Carbon capture and storage technologies are necessary to start lowering greenhouse gas emissions while continuing to utilize existing thermal power generation infrastructure. Calcium looping is a promising technology based on cyclic calcination/carbonation reactions which utilizes limestone as a sorbent. Steam is present in combustion flue gas and in the calciner used for sorbent regeneration. The effect of steam during calcination on sorbent performance has not been extensively studied in the literature. Here, experiments were conducted using a thermogravimetric analyzer (TGA) and subsequently a dual-fluidized bed pilot plant to determine the effect of steam injection during calcination on sorbent reactivity during carbonation. In a TGA, various levels of steam (0-40% vol.) were injected during sorbent regeneration throughout 15 calcination/carbonation cycles. All concentrations of steam were found to increase sorbent reactivity during carbonation. A level of 15% steam during calcination had the largest impact. Steam changes the morphology of the sorbent during calcination, likely by shifting the pore volume to larger pores, resulting in a structure which has an increased carrying capacity. This effect was then examined at the pilot scale to determine if the phase contacting patterns and solids heat-up rates in a fluidized bed were factors. Three levels of steam (0%, 15%, 65%) were injected during sorbent regeneration throughout 5 hours of steady state operation. Again, all levels of steam were found to increase sorbent reactivity and reduce the required sorbent make-up rate with the best performance seen at 65% steam.

Net ecosystem carbon dioxide exchange (NEE) was measured using the eddy covariance (EC) technique at a wetland tundra-sedge fen near Churchill, Manitoba, Canada during two consecutive growing seasons (2007 and 2008). Mean daily NEE at the fen (DOY 157-254) was -3.5 (± 0.26 S.E.) g CO2 m-2 d-1 in 2007 and -4.6 (± 0.36) g CO2 m-2 d-1 in 2008. The fen was a net carbon dioxide (CO2) sink during both the 2007 and 2008 growing seasons of -343 (± 79) and -450 (± 87) g CO2 m-2, respectively. Mean air temperature during the summer (June 1-August 31) was about 1°C greater than the historical average (1971-2000) in 2007 and about 2°C greater in 2008. Growing season precipitation was 107.5 mm below normal in 2007 and 359.5 mm above normal in 2008. These data suggest that if future climate change brings warmer temperatures and near-to-above average precipitation maintaining the water table near the surface, similar subarctic ecosystems will experience increased gross ecosystem productivity enhancing CO2 sequestration during the growing season.

Mathematical modeling in animal agriculture can be applied at various levels including at the tissue, organ, animal, farm, regional and global levels. The purposes of this research were i) to evaluate models used to estimate volatile fatty acid (VFA) and methane (CH4) production and assess their impact on regional enteric CH4 inventory, and ii) to develop a process-based, whole-farm model to estimate net farm GHG emissions. In the first study, four VFA stoichiometric models were evaluated for their prediction accuracy of rumen VFA and enteric CH4 production. Comparison of measured and model predicted values demonstrated that predictive capacity of the VFA models varied with respect to the type of VFA in rumen fluid which impacted estimated enteric CH4 production. Moving to a larger scale assessment, we examined the enteric CH4 inventory from Manitoba beef cattle (from 1990 to 2008) using two mechanistic rumen models that incorporate VFA stoichiometric models: COWPOLL and MOLLY, and two empirical models: Intergovernmental Panel on Climate Change (IPCC) Tier 2 and a nonlinear equation (Ellis). The estimated absolute enteric CH4 production varied among models (7 to 63%) indicating that estimates of GHG inventory depend on model selection. This is an important consideration if the values are to be used for management and/or policy-related decisions. Development of models at the individual farm component level (animal, soil, crop) does not accurately reflect net GHG emissions generated from the whole production system. We developed a process-based, whole-farm model (Integrated Components Model, ICM), using the existing farm component models COWPOLL, manure-DNDC and some aspects of IPCC to integrate farm components and their associated GHG emissions. Estimates of total farm GHG emissions and their relative contribution using the ICM were comparable to estimates using two other whole-farm models (Integrated Farm System Model and Holos model). Variation was observed among models both in estimating whole-farm GHG emissions and the relative contribution of the different sources in the production system. Overall, whole-farm models are required to explore management options that will mitigate GHG emissions and promote best management practices. However, for full assessment of the production system, other benefits of the system (e.g., carbon sequestration, ecosystem services), which are not part of current whole-farm models, must be considered.

The negative consequences of rising global energy use have led governments and businesses to pursue methods of reducing reliance on fossil fuels. Plug-In Hybrid Electric Vehicles (PHEVs) and wind power represent two practical methods for mitigating some of these negative consequences. PHEVs use large onboard batteries to displace gasoline with electricity obtained from the grid, while wind power generates clean, renewable power that has the potential to displace fossil-fuel power generation. The emissions reductions realized by these technologies will be highly dependent on the energy system into which they are integrated, and also how they are integrated. This research aims to assess to cost of reducing emissions through the integration of PHEVs and wind power in three Canadian jurisdictions, namely British Columbia, Ontario and Alberta. An Optimal Power Flow (OPF) model is used to assess the changes in generation dispatch resulting from the integration of wind power and PHEVs into the local electricity network. This network model captures the geographic distribution of load and generation in each jurisdiction, while simulating local transmission constraints. A linear optimization model is developed in the MATLAB environment and is solved using the ILOG CPLEX Optimization package. The model solves a 168-hour generation scheduling period for both summer and winter conditions. Simulation results provide the costs and emissions from power generation when various levels of PHEVs and/or wind power are added to the electricity system. The costs and emissions from PHEV purchase and gasoline displacement are then added to the OPF results and an overall GHG reduction cost is calculated. Results indicate that wind power is an expensive method of GHG abatement in British Columbia and Ontario. This is due to the limited environmental benefit of wind over the nuclear and hydro baseload mixtures. The large premium paid for displacing hydro or nuclear power with wind power does little to reduce emissions, and thus CO2e costs are high. PHEVs are a cheaper method of GHG abatement in British Columbia and Ontario, since the GHG reductions resulting from the substitution of gasoline for hydro or nuclear power are significant. In Alberta, wind power is the cheaper method of GHG abatement because wind power is closer in price to the coal and natural gas dominated Alberta mixture, while offering significant environmental benefits. PHEVs represent a more expensive method of GHG abatement in Alberta, since substituting gasoline for expensive, GHG-intense electricity in a vehicle does less to reduce overall emissions. Results also indicate that PHEV charging should take place during off-peak hours, to take advantage of surplus baseload generation. PHEV adoption helps wind power in Ontario and British Columbia, as overnight charging reduces the amount of cheap, clean baseload power displaced by wind during these hours. In Alberta, wind power helps PHEVs by cleaning up the generation mixture and providing more environmental benefit from the substitution of gasoline with electricity.

Global cities have recognized the need to reduce greenhouse gas (GHG) emissions and have begun to take action to balance of the carbon cycle. This thesis examines the nuances of quantification methods used and the implications of current policy for long-term emissions. Emissions from waste management, though relatively small when compared with building and transportation sectors, are the largest source of emissions directly controlled by municipal government. It is important that municipalities understand the implications of methodological selection when quantifying GHG emissions from waste management practices. The “Waste-in-Place” methodology is presented as the most relevant for inventorying purposes, while the “Methane Commitment” approach is best used for planning. Carbon sinks, divided into “Direct” and “Embodied”, are quantified using the Greater Toronto Area (GTA) as a case study. “Direct” sinks, those whose sequestration processes occur within urban boundaries, contribute the largest share of carbon sinks with regional forests providing a significant proportion. “Embodied” sinks, those whose sequestration processes (or in the case of concrete, the processes that enable sequestration) are independent of the urban boundary, can contribute to the urban carbon pool, but greater uncertainty exists in upstream emissions as the management/processing prior to its use as a sink are generally beyond the consumer’s purview. The Pathways to Urban Reductions in Greenhouse gas Emissions (or PURGE) model is developed as a means to explore emissions scenarios resulting from urban policy to mitigate climate change by quantifying future carbon sources/sinks (from changes in building stock, vehicle stock, waste treatment and urban/regional forests). The model suggests that current policy decisions in the GTA provide short-term reductions but are not sufficient in the long term to balance the pressures of economic and population growth. Aggressive reductions in energy demand from personal transportation and existing building stock will be necessary to achieve long-term emissions targets.

Global cities have recognized the need to reduce greenhouse gas (GHG) emissions and have begun to take action to balance of the carbon cycle. This thesis examines the nuances of quantification methods used and the implications of current policy for long-term emissions. Emissions from waste management, though relatively small when compared with building and transportation sectors, are the largest source of emissions directly controlled by municipal government. It is important that municipalities understand the implications of methodological selection when quantifying GHG emissions from waste management practices. The “Waste-in-Place” methodology is presented as the most relevant for inventorying purposes, while the “Methane Commitment” approach is best used for planning. Carbon sinks, divided into “Direct” and “Embodied”, are quantified using the Greater Toronto Area (GTA) as a case study. “Direct” sinks, those whose sequestration processes occur within urban boundaries, contribute the largest share of carbon sinks with regional forests providing a significant proportion. “Embodied” sinks, those whose sequestration processes (or in the case of concrete, the processes that enable sequestration) are independent of the urban boundary, can contribute to the urban carbon pool, but greater uncertainty exists in upstream emissions as the management/processing prior to its use as a sink are generally beyond the consumer’s purview. The Pathways to Urban Reductions in Greenhouse gas Emissions (or PURGE) model is developed as a means to explore emissions scenarios resulting from urban policy to mitigate climate change by quantifying future carbon sources/sinks (from changes in building stock, vehicle stock, waste treatment and urban/regional forests). The model suggests that current policy decisions in the GTA provide short-term reductions but are not sufficient in the long term to balance the pressures of economic and population growth. Aggressive reductions in energy demand from personal transportation and existing building stock will be necessary to achieve long-term emissions targets.