Uncertainty In Greenhouse Gas Emissions Research Articles

Assessing the Impact of Land Conversion on Carbon Stocks and GHG Emissions

With the recent thrust to convert forests in Ontario’s Clay Belt to agricultural land, a vital need arises to assess the attendant effects on carbon and greenhouse gas (GHG) emissions. This paper examines the possible effect of land conversion on soil organic carbon and GHG emissions within a study area in Northern Ontario, Canada, during the next two decades under different land management schemes. The study established a framework to conduct simulations with the DNDC model for agricultural lands and the CBM for forested areas. The methodology involves a unique change detection method for models’ land cover and disturbance inputs. The work highlights the improvement in carbon simulation accuracy from better inputs to carbon models. Furthermore, it addresses modalities to ensure fewer uncertainties are introduced while merging data from multiple geospatial data sources. The simulations demonstrated that the carbon sequestration potential in the forests was almost double the soil organic carbon accumulation in the agricultural lands. Validations done for the estimation of carbon sequestered included comparisons of the carbon model outputs from field survey data from 2018–2021. In most sites, the carbon amounts from the computer models compared to those from the field survey, within limits of error. The average uncertainties in GHG emissions ranged from ~0.5% to 12.8%.

Reducing uncertainties in greenhouse gas emissions from chemical production

AbstractUncertainties in greenhouse gas emissions estimates for petrochemical production have lacked quantification globally, impacting emissions reporting and decarbonization policymaking. Here we analyze cradle-to-gate emissions of 81 chemicals at 37,000 facilities worldwide, assessing 6 uncertainty sources. The results estimate a 34% uncertainty in total global emissions of 1.9 ± 0.6 Gt of CO2-equivalent emissions for 2020, and 15–40% uncertainties across most petrochemicals analyzed. The largest uncertainties stem from the inability to assign specific production processes to facilities owing to data limitations. Uncertain data on feedstock production and off-site energy generation contribute substantially, while on-site fuel combustion and chemical reactions have smaller roles. Allocation method choices for co-products are generally insignificant. Prioritizing facility-level process specification in data collection for just 20% of facilities could reduce global uncertainty by 80%. This underscores the necessity of quantifying uncertainty in petrochemical greenhouse gas emissions globally and outlines priorities for improved reporting. The dataset generated offers independent emissions factor estimates based on facility-specific information for 81 chemicals, supporting future analyses.

Review of Sources of Uncertainty and Techniques Used in Uncertainty Quantification and Sensitivity Analysis to Estimate Greenhouse Gas Emissions from Ruminants

Uncertainty quantification and sensitivity analysis are essential for improving the modeling and estimation of greenhouse gas emissions in livestock farming to evaluate and reduce the impact of uncertainty in input parameters to model output. The present study is a comprehensive review of the sources of uncertainty and techniques used in uncertainty analysis, quantification, and sensitivity analysis. The search process involved rigorous selection criteria and articles retrieved from the Science Direct, Google Scholar, and Scopus databases and exported to RAYYAN for further screening. This review found that identifying the sources of uncertainty, implementing quantifying uncertainty, and analyzing sensitivity are of utmost importance in accurately estimating greenhouse gas emissions. This study proposes the development of an EcoPrecision framework for enhanced precision livestock farming, and estimation of emissions, to address the uncertainties in greenhouse gas emissions and climate change mitigation.

China city-level greenhouse gas emissions inventory in 2015 and uncertainty analysis

Accounting and understanding greenhouse gas (GHG) emissions at the city level are of great importance because cities are the focus of future mitigation and adaption activities to address climate change, especially in developing nations like China. However, existing studies have not yet provided a complete and updated emissions inventory of city-level GHGs for China in terms of city and GHG-type coverage. In this paper, we rigorously assemble a dataset that contains GHG emissions including carbon dioxide, methane, nitrous oxide, and fluorinated GHGs for 305 Chinese cities, based on a high-resolution emissions database (China High-Resolution Emission Database, CHRED 3.0) and first-hand on-site data collection and verification by 19 groups of 137 researchers using a bottom-up method, and analyze the uncertainty of the emissions. Results show that total GHG emissions are high in cities in eastern China and low in cities in west, while per capita GHG emissions are high in northern cities and low in southern cities. The emissions of four types of GHGs (CO2, CH4, N2O and fluoridated GHGs) share different spatial distributions, due to variant industrial structure, energy structure, industry point sources and agricultural surface sources, etc. The uncertainty of GHG emissions is highly related to the source data of cities. Finally, we propose policy recommendations, including improving data quality and strengthening competitions of city-level emission reduction, to improve city-level emissions data integrity and low-carbon development strategies.

Uncertainty Analysis of a GHG Emission Model Output Using the Block Bootstrap and Monte Carlo Simulation

Uncertainty analysis of greenhouse gas (GHG) emissions is becoming increasingly necessary in order to obtain a more accurate estimation of their quantities. The Monte Carlo simulation (MCS) and non-parametric block bootstrap (BB) methods were tested to estimate the uncertainty of GHG emissions from the consumption of feedstuffs and energy by dairy cows. In addition, the contribution to variance (CTV) approach was used to identify significant input variables for the uncertainty analysis. The results demonstrated that the application of the non-parametric BB method to the uncertainty analysis, provides a narrower confidence interval (CI) width, with a smaller percentage uncertainty (U) value of the GHG emission model compared to the MCS method. The CTV approach can reduce the number of input variables needed to collect the expanded number of data points. Future studies can expand on these results by treating the emission factors (EFs) as random variables.

Reprint of: Uncertainty analysis for measuring greenhouse gas emissions in the building construction phase: a case study in China

Uncertainty analysis is useful in determining whether the results of life cycle assessment are sufficiently reliable and valid when making optimal decisions. However, only a few studies have measured carbon emissions by considering the inherent uncertainty during building construction phase that may result in the misinterpretation of critical parameters. To address such weakness, a multi-method-based uncertainty analysis framework was developed in view of the basic characteristics of the construction practice. This framework integrated the deterministic and probabilistic approaches to facilitate the uncertainty assessment in quantifying carbon emissions and to provide insights into the sensitive construction activities from the uncertainty perspective. The developed framework was examined through a mix-use project in Guangzhou China. Results showed that the uncertainties in the measurement method and geographic representativeness are the major uncertainty sources for the building construction phase. The total greenhouse gas emission for the target building was 8791.5 tonnes of carbon dioxide equivalent with a 9.8% coefficient of variation, which was in line with the result calculated by the deterministic method and with the result extrapolated based on the data collected from China. The results of the scenario analysis showed that the proportion of 1% in contribution analysis and the coefficient of variation of 18% in uncertainty analysis can be regarded as the baseline for determining the critical input parameters. This study lends a useful tool for monitoring the uncertainty of LCA studies in the construction practice. In addition, this framework can facilitate to avoid the misinterpretation of the final results during the decision-making process. Although this study focuses on Chinese construction industry, it also provides good references for measuring uncertainty of greenhouse gas emissions of construction industries around the world.

Climate change scenarios for Angola: an analysis of precipitation and temperature projections using four RCMs

ABSTRACTClimate change is expected to have a major impact on the world's environment, economy and society, especially in African countries such as Angola due to the dependence of many vital sectors on climate variability (e.g. agriculture) and their low adaptive capacity. With the advances in the availability of climate model data sets provided by programs such as the CORDEX, Angola can benefit from studies on future climate change using high‐spatial‐resolution data, which have previously been absent for this region. The purpose of this study is to analyse the projected changes in temperature and precipitation over Angola during the 21st century based on four regional climate models (RCMs). The spatial and temporal changes were analysed using a high‐ and medium‐low radiative forcing scenario to consider the uncertainty in greenhouse gas emissions. Changes in the average annual precipitation, average annual temperature and a drought index are studied for short‐, mid‐ and long‐term projections. Compared to the reference period, relevant changes are projected in temperature and precipitation indices for the different models under both emission scenarios. These changes include an increase in both the maximum and minimum temperature of up to 4.9 °C by the end of the century and an intensification of droughts. The precipitation projections are highly variable – increasing and decreasing – across the region and dependent on the RCMs. Despite these differences, the precipitation generally decreases over time (approximately −2% by 2100), with the southern region experiencing a stronger decrease in precipitation. Further investigation is needed to minimize projections uncertain, as the annual mean precipitation was overestimated by three of the RCMs (especially in the northern coastal units). Overall, these projected changes in climate can have important implications for the future of Angola, because they are expected to magnify existing problems, thus creating new risks for human and natural systems.

Life Cycle Economic and Environmental Implications of Pristine High Density Polyethylene and Alternative Materials in Drainage Pipe Applications

A life cycle assessment (LCA) and cost analysis were conducted to compare the environmental and economic performance of nanocomposite polymers that use pristine and recycled high density polyethylene (HDPE) polymer with pristine, and pristine/recycled HDPE polymeric materials in drainage pipe. We evaluate three performance metrics; (a) non-renewable energy consumption (NRE); (b) greenhouse gas (GHG) emissions; and (c) production costs of the three pipe material alternatives. Original life cycle inventory data for the production of nanoclay from the mineral Montmorillonite were collected for this case study in the United States. Life cycle inventory models were developed for the cradle-to-gate production of drainage pipe used in highway construction that consider the sensitivity of model parameter inputs on the life cycle impact and cost results for the three material options. The GHG emissions for the nanoclay composite pipe are 54 % lower than those for pristine HDPE pipe, and 16 % lower than those for pristine/recycle HDPE pipe. With a slight difference in GHG emissions between the pristine/recycled and nanoclay composite, the production of nanoclay does not introduce a significant environmental burden to the pipe material. On average, the pristine HDPE pipe is 13 and 17 % higher in cost than the pristine/recycled HDPE and nanoclay composite pipes, respectively. Results of the LCA and cost analysis support using recycled HDPE as a substitute for pristine HDPE due to its low energy requirements and production costs. The uncertainty in GHG emissions of manufacturing pristine HDPE causes the largest variation of GHG emissions in nanoclay composite pipe (+3/−2 %). The production cost of the nanocomposite pipe is most influenced by the energy cost of PCR-HDPE (+25/−11 %). Our study suggests that a nanocomposite design that replaces part of the pristine HDPE with recycled HDPE and nanoclay reduces certain environmental risks and material cost of corrugated pipe.

Uncertainty analysis for measuring greenhouse gas emissions in the building construction phase: a case study in China

Uncertainty analysis is useful in determining whether the results of life cycle assessment are sufficiently reliable and valid when making optimal decisions. However, only a few studies have measured carbon emissions by considering the inherent uncertainty during building construction phase that may result in the misinterpretation of critical parameters. To address such weakness, a multi-method-based uncertainty analysis framework was developed in view of the basic characteristics of the construction practice. This framework integrated the deterministic and probabilistic approaches to facilitate the uncertainty assessment in quantifying carbon emissions and to provide insights into the sensitive construction activities from the uncertainty perspective. The developed framework was examined through a mix-use project in Guangzhou China. Results showed that the uncertainties in the measurement method and geographic representativeness are the major uncertainty sources for the building construction phase. The total greenhouse gas emission for the target building was 8791.5 tonnes of carbon dioxide equivalent with a 9.8% coefficient of variation, which was in line with the result calculated by the deterministic method and with the result extrapolated based on the data collected from China. The results of the scenario analysis showed that the proportion of 1% in contribution analysis and the coefficient of variation of 18% in uncertainty analysis can be regarded as the baseline for determining the critical input parameters. This study lends a useful tool for monitoring the uncertainty of LCA studies in the construction practice. In addition, this framework can facilitate to avoid the misinterpretation of the final results during the decision-making process. Although this study focuses on Chinese construction industry, it also provides good references for measuring uncertainty of greenhouse gas emissions of construction industries around the world.

Comprehensive Numerical Modeling of Greenhouse Gas Emissions from Water Resource Recovery Facilities.

A numerical model was developed to comprehensively predict greenhouse gas (GHG) emissions from water resource recovery facilities. An existing activated sludge model was extended to include a nitrifier-denitrification process and carbon dioxide (CO₂) as a state variable. The bioreactor model was coupled to a process-based digester model and an empirical model of indirect CO₂emissions. Direct emissions were approximately 90% of total GHG emissions for a plantwide simulation using the Modified Ludzack-Ettinger process. Biogenic CO₂, nitrous oxide (N₂O), and methane (CH₄) represented 10, 43, and 34% of total emissions. Simulating a dissolved oxygen controlled closed-loop system reduced both sensitivity and uncertainty of GHG emissions. Nitrous oxide emissions were much more sensitive under different design and operating conditions compared to CH₄and CO₂, indicating a significant mitigation potential. An uncertainty analysis found that the uncertainty in GHGs emissions estimates could be significant. Nitrous oxide emissions dominated in both magnitude and uncertainty.

Analysis of change in relative uncertainty in GHG emissions from stationary sources for the EU 15

Total uncertainty in greenhouse gas (GHG) emissions changes over time due to “learning” and structural changes in GHG emissions. Understanding the uncertainty in GHG emissions over time is very important to better communicate uncertainty and to improve the setting of emission targets in the future. This is a diagnostic study divided into two parts. The first part analyses the historical change in the total uncertainty of CO2 emissions from stationary sources that the member states estimate annually in their national inventory reports. The second part presents examples of changes in total uncertainty due to structural changes in GHG emissions considering the GAINS (Greenhouse Gas and Air Pollution Interactions and Synergies) emissions scenarios that are consistent with the EU’s “20-20-20” targets. The estimates of total uncertainty for the year 2020 are made under assumptions that relative uncertainties of GHG emissions by sector do not change in time, and with possible future uncertainty reductions for non-CO2 emissions, which are characterized by high relative uncertainty. This diagnostic exercise shows that a driving factor of change in total uncertainty is increased knowledge of inventory processes in the past and prospective future. However, for individual countries and longer periods, structural changes in emissions could significantly influence the total uncertainty in relative terms.

African Lessons on Climate Change Risks for Agriculture

Climate change impact assessments on agriculture are subject to large uncertainties, as demonstrated in the present review of recent studies for Africa. There are multiple reasons for differences in projections, including uncertainties in greenhouse gas emissions and patterns of climate change; assumptions on future management, aggregation, and spatial extent; and methodological differences. Still, all projections agree that climate change poses a significant risk to African agriculture. Most projections also see the possibility of increasing agricultural production under climate change, especially if suitable adaptation measures are assumed. Climate change is not the only projected pressure on African agriculture, which struggles to meet demand today and may need to feed an additional one billion individuals by 2050. Development strategies are urgently needed, but they will need to consider future climate change and its inherent uncertainties. Science needs to show how existing synergies between climate change adaptation and development can be exploited.

Integrated assessment of uncertainties in greenhouse gas emissions and their mitigation: Introduction and overview

This paper provides the background and the context for the analyses presented in the seven papers of this Special Issue on Integrated Assessment of Uncertainties in Greenhouse Gas Emissions and their Mitigation. First, the main topic and content of the Special Issue is given, followed by an overall overview. Second, detailed overviews and summaries of the seven papers are given. The specific analytical and methodological features and findings of each paper are highlighted and the linkages between the various papers presented in the Special Issue are provided.

Modelling uncertainty in greenhouse gas emissions from UK agriculture at the farm level

Abstract We outline a framework for considering uncertainty in estimates of emissions of greenhouse gases (GHGs) where the objective is to determine least cost (profit foregone) adaptations to reduce emissions at the farm level. Three sources of uncertainty were identified as being associated with GHG emissions: lack of understanding of biological systems, poor validation of results and weather-induced variability. Output from existing models and other information on emissions were included in a farm-level optimisation model, Farm-adapt , set up to represent a dairy and beef farm in north west England. Monte Carlo simulation was used to examine the effect of uncertainty on total GHG emissions and the most cost-effective adaptations for reducing these emissions to 60% of the baseline level. We assumed a triangular distribution for all parameters and sampled 1000 times from these distributions. Farm-adapt results showed that cost-effective adaptations were to: (i) eliminate intensive beef production, (ii) reduce stored manures and increase frequency of manure spreading, (iii) substitute concentrate feed for grass and conserved grass in milk production and (iv) apply less mineral nitrogen to grassland. Monte Carlo simulation showed that there was a large degree of uncertainty in the level of absolute emissions associated with the optimal adaptations and the mean of the simulation output (9265 kg ha −1 year −1 CO 2 equivalent) was greater than the Farm-adapt results using default parameters (7787 kg ha −1 year −1 ). However, there was a high degree of certainty in the adaptations required to reduce farm-level emissions, indicating that the cost-effective adaptations were robust to uncertainty in the GHG emission data.

Limited sensitivity analysis of regional climate change probabilities for the 21st century

Quantifying uncertainty in regional climate change projections is important for a range of reasons. We examine the sensitivity of regional climate change probabilities to various uncertainties. We use a simple probabilistic energy balance model that samples uncertainty in greenhouse gas emissions, the climate sensitivity, the carbon cycle, ocean mixing, and aerosol forcing. We then propagate global mean temperature probabilities to General Circulation Models (GCMs) through the pattern‐scaling technique. In order to combine the resulting probabilities we devised regional skill scores for each GCM, season (DJF, JJA), and climate variable (surface temperature, and precipitation) in 22 world regions, based on model performance and model convergence. A range of sensitivity experiments are carried out with different skill score schemes, climate sensitivities, and emissions scenarios. It was shown that whether skill scores as applied in this paper were used or not, makes little difference to regional climate change probabilities. However, both these approaches provide more information than simply using the multi‐model ensemble average. For temperature change probabilities, emissions scenarios uncertainty tends to dominate the 95th percentile whereas climate sensitivity uncertainty plays a more important role at the 5th percentile. The sensitivity of precipitation change probabilities to the tested uncertainties are region specific, but some conclusions can be drawn. At the 95th percentile, the uncertainty that tends to dominate is emissions scenarios, closely followed by GCM weighting scheme and the climate sensitivity. At the 5th percentile, GCM weighting scheme uncertainty tends to dominate for JJA, but for DJF all uncertainties have similar proportionate influence.

Biofuel use assessments in Africa: Implications for greenhouse gas emissions and mitigation strategies

The energy balances of most African countries suggest that biofuels (woodfuel, crop and wood residues, and dung) constitute the largest share of total energy consumption (up to 97% in some sub-Saharan Africa countries). There is, however, an increasing scarcity of woodfuel (fuelwood and charcoal), the major biofuel, and a feared increase in greenhouse gas (GHG) emissions associated with biofuel combustion. The extent of GHG emissions is estimated from biofuel consumption levels that are in turn based on methodologies that might be inaccurate. A questionnaire, supplemented by informal interviews, are used to collect data, yielding information regarding end-uses, technologies used, scale of consumption, determinants of fuel consumption, and interfuel substitution (among other parameters). The survey revealed that cooking is the major end-use, with other common uses, such as space and water heating. Improved stoves that provide better combustion efficiency and, thus, reduce woodfuel consumption have not been widely disseminated and are associated with higher methane emissions than open fires. More than 90% of the households in Africa use open fires. Consumption is presented as per capita for households and as products and quantity of fuel in the small scale industries, commercial, and public sectors. Among the determinants for biofuel consumption are affordability, availability of the fuel, and interfuel substitutions. Flaws in estimating biofuel consumption yield large uncertainties in GHG emissions, with implications for the development of policies on energy planning and environmental protection. However, the application of scenarios can guide policy formulation.