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Carbon accreditation of forest development projects is essential for sequestering atmospheric CO2 under the provisions of the Kyoto Protocol. The carbon sequestration potential of surface coal-mined lands is not well known. The purpose of this work was to determine how to measure carbon sequestration and estimate the additional amount that could be sequestered using different reforestation methods compared to the common practice of establishing grasslands. I developed a thermal oxidation technique for differentiating sequestered soil carbon from inorganic and fossilized carbon found at high levels in mine soils along with a geospatial and statistical protocol for carbon monitoring and accounting. I used existing tree, litter, and soil carbon data for 14 mined and 8 adjacent, non-mined forests in the Midwestern and Eastern coal regions to determine, and model sequestered carbon across the spectrum of site index and stand age in pine, mixed, and hardwood forest stands. Finally, I developed the framework of a decision support system consisting of the first iteration of a dynamic model to predict carbon sequestration for a 60-year period for three forest types (white pine, hybrid poplar, and native hardwoods) at three levels of management intensity: low (weed control), medium (weed control and tillage) and high (weed control, tillage, and fertilization). On average, the highest amount of ecosystem carbon on mined land was sequestered by pine stands (148 Mg ha-1), followed by hardwood (130 Mg ha-1) and mixed stands (118 Mg ha-1). Non-mined hardwood stands contained 210 Mg C ha-1, which was about 62% higher than the average of all mined stands. After 60 years, the net carbon in ecosystem components, wood products, and landfills ranged from 20 to 235 Mg ha-1 among all scenarios. The highest net amount of carbon was estimated under mixed hardwood vegetation established by the highest intensity treatment. Under this scenario, a surface-mined land of average site quality would sequester net carbon stock at 235 Mg C ha-1, at a rate of 3.9 Mg C ha-1 yr-1, which was 100% greater than a grassland scenario. Reforestation is a logical choice for mined land reclamation if carbon sequestration is a management objective. / Ph. D.

Under current technical levels and industrial systems, geological carbon sequestration (GCS) is a viable solution to maintain and further reduce carbon dioxide (CO2) concentration and ensure energy security simultaneously. The pre-injection formation characterization and post-injection CO2 monitoring, verification, and accounting (MVA) are two critical and challenging tasks to guarantee the sequestration effect. The tasks can be accomplished using core analyses and well-logging technologies, which complement each other to produce the most accurate and sufficient subsurface information for pore-scale and reservoir-scale studies. In recent years, the unprecedented data sources, increasing computational capability, and the developments of machine learning (ML) and deep learning (DL) algorithms provide novel perspectives for expanding the knowledge from data, which can capture highly complex nonlinear relationships between multivariate inputs and outputs. This work applied ML and DL methods to GCS-related studies at pore and reservoir scales, including digital rock physics (DRP) and the well-logging data interpretation and analysis. DRP provides cost-saving and practical core analysis methods, combining high-resolution imaging techniques, such as the three-dimensional (3D) X-ray computed tomography (CT) scanning, with advanced numerical simulations. Image segmentation is a crucial step of the DRP framework, affecting the accuracy of the following analyses and simulations. We proposed a DL-based workflow for boundary and small target segmentation in digital rock images, which aims to overcome the main challenge in X-ray CT image segmentation, partial volume blurring (PVB). The training data and the model architecture are critical factors affecting the performance of supervised learning models. We employed the entropy-based-masking indicator kriging (IK-EBM) to generate high-quality training data. The performance of IK-EBM on segmentation affected by PVB was compared with some commonly used image segmentation methods on the synthetic data with known ground truth. We then trained and tested the UNet++ model with nested architecture and redesigned skip connections. The evaluation metrics include the pixel-wise (i.e. F1 score, boundary-scaled accuracy, and pixel-by-pixel comparison) and physics-based (porosity, permeability, and CO2 blob curvature distributions) accuracies. We also visualized the feature maps and tested the model generalizations. Contact angle (CA) distribution quantifies the rock surface wettability, which regulates the multiphase behaviors in the porous media. We developed a DL-based CA measurement workflow by integrating an unsupervised learning pipeline for image segmentation and an open-source CA measurement tool. The image segmentation pipeline includes the model training of a CNN-based unsupervised DL model, which is constrained by feature similarity and spatial continuity. In addition, the over-segmentation strategy was adopted for model training, and the post-processing was implemented to cluster the model output to the user-desired target. The performance of the proposed pipeline was evaluated using synthetic data with known ground truth regarding the pixel-wise and physics-based evaluation metrics. The resulting CA measurements with the segmentation results as input data were validated using manual CA measurements. The GCS projects in the Illinois Basin are the first large-scale injection into saline aquifers and employed the latest pulsed neutron tool, the pulsed neutron eXtreme (PNX), to monitor the injected CO2 saturation. The well-logging data provide valuable references for the formation evaluation and CO2 monitoring in GCS in saline aquifers at the reservoir scale. In addition, data-driven models based on supervised ML and DL algorithms provide a novel perspective for well-logging data analysis and interpretation. We applied two commonly used ML and DL algorithms, support vector machine regression (SVR) and artificial neural network (ANN), to the well-logging dataset from GCS projects in the Illinois Basin. The dataset includes the conventional well-logging data for mineralogy and porosity interpretation and PNX data for CO2 saturation estimation. The model performance was evaluated using the root mean square error (RMSE) and R2 score between model-predicted and true values. The results showed that all the ML and DL models achieved excellent accuracies and high efficiency. In addition, we ranked the feature importance of PNX data in the CO2 saturation estimation models using the permutation importance algorithm, and the formation sigma, pressure, and temperature are the three most significant factors in CO2 saturation estimation models. The major challenge for the CO2 storage field projects is the large-scale real-time data processing, including the pore-scale core and reservoir-scale well-logging data. Compared with the traditional data processing methods, ML and DL methods achieved accuracy and efficiency simultaneously. This work developed ML and DL-based workflows and models for X-ray CT image segmentation and well-logging data interpretations based on the available datasets. The performance of data-driven surrogate models has been validated regarding comprehensive evaluation metrics. The findings fill the knowledge gap regarding formation evaluation and fluid behavior simulation across multiple scales, ensuring sequestration security and effect. In addition, the developed ML and DL workflows and models provide efficient and reliable tools for massive GCS-related data processing, which can be widely used in future GCS projects. / Doctor of Philosophy / Geological carbon sequestration (GCS) is the solution to ease the tension between the increasing carbon dioxide (CO2) concentrations in the atmosphere and the high dependence of human society on fossil energy. The sequestration requires the injection formation to have adequate storage capability, injectivity, and impermeable caprock overlain. Also, the injected CO2 plumes should be monitored in real-time to prevent any migration of CO2 to the surface. Therefore, pre-injection formation characterization and post-injection CO2 saturation monitoring are two critical and challenging tasks to guarantee the sequestration effect and security, which can be accomplished using the combination of pore-scale core analyses and reservoir-scale well-logging technologies. This work applied machine learning (ML) and deep learning (DL) methods to GCS-related studies across multiple spatial scales. We developed supervised and unsupervised DL-based workflows to segment the X-ray computed-tomography (CT) image of digital rocks for the pore-scale studies. Image segmentation is a crucial step in the digital rock physics (DRP) framework, and the following analyses and simulations are conducted on the segmented images. We also developed ML and DL models for well-logging data interpretation to analyze the mineralogy and estimate CO2 saturation. Compared with the traditional well-logging analysis methods, which are usually time-consuming and prior knowledge-dependent, the ML and DL methods achieved comparable accuracy and much shorter processing time. The performance of developed workflows and models was validated regarding comprehensive evaluation metrics, achieving excellent accuracies and high efficiency simultaneously. We are at the early stage of CO2 sequestration, and relevant knowledge and tools are inadequate. In addition, the main challenge of CO2 sequestration field projects is the large-scale and real-time data processing for fast decision-making. The findings of this dissertation fill the knowledge gap in GCS-related formation evaluation and fluid behavior simulations across multiple spatial scales. The developed ML and DL workflows provide efficient and reliable tools for massive data processing, which can be widely used in future GCS projects.

Predictions of how forest productivity and carbon sequestration will respond to climate change are essential for making forest management decisions and adapting to future climate. However, current predictions can include considerable uncertainty that is not well quantified. To address the need for better quantification of uncertainty, we calculated and compared ecosystem model parameter, ecosystem model process, climate model, and climate scenario uncertainty for predictions of Southeastern U.S. pine forest productivity. We applied a data assimilation using Metropolis-Hastings Markov Chain Monte Carlo to fuse diverse datasets with the Physiological Principles Predicting Growth model. The spatially and temporally diverse data sets allowed for novel constraints on ecosystem model parameters and allowed for the quantification of uncertainty associated with parameterization and model structure (process). Overall, we found that the uncertainty is higher for parameter and process model uncertainty than the climate model uncertainty. We determined that climate change will result in a likely increase in terrestrial carbon storage and that higher emission scenarios increase the uncertainty in our predictions. In addition, we determined regional variations in biomass accumulation due to a response to the change in frost days, temperature, and vapor pressure deficit. Since the uncertainty associated with ecosystem model parameter and process uncertainty was larger than the uncertainty associated with climate predictions, our results indicate that better constraining parameters in ecosystem models and improving the mathematical structure of ecosystem models can improve future predictions of forest productivity and carbon sequestration. / Master of Science

Location is a critical component of business decisions. A firm's location decision may be influenced not only by market forces, such as the location of input suppliers, output processors and competitors, but also by government policies if such policies impact their expected profits and are applied non-uniformly across space. Likewise, a firm may adjust its business strategy, including opening and closing establishments and laying off employees as responses to changes in environmental regulations. In certain polluting industries, location decisions may include choosing potential storage sites for geologic carbon sequestration or finding landfills for industrial solid waste. There is extensive literature discussing the effects of environmental regulations or agglomeration economies on firm location decisions but few studies analyze the interactive effect of environmental regulations and agglomeration economies across regions in the United States. The potential consequences of changes in environmental regulations may include loss of polluting establishments, jobs, and income. Geological carbon sequestration offers long term storage opportunities to mitigate greenhouse gases (GHGs). Incorporating environmental risk into economic assessments of geological sequestration choices is crucial for finding optimal strategies in using alternative carbon storage sites with limited capacity. This dissertation consists of three essays that address the above issues. The first essay examines the interactive effects of air quality regulation and agglomeration economies on polluting firms' location decisions in the United States. Newly available annual (1989-2006) county-level manufacturing plant location data for the United States on seven pollution intensive manufacturing industries are applied in the analysis. Conditional Poisson and negative binomial models are estimated, an efficient GMM estimator is also employed to control for endogenous regulatory and agglomeration variables. Results indicate that births of pollution intensive manufacturers are deterred by stricter environmental regulation; and are attracted by local agglomeration economies. County attainment/nonattainment designations can impose heterogeneous impacts over space and across industries. The magnitude of the regulatory effect depends on the level of local agglomeration. Urbanization economies offset the negative impacts of environmental regulation, whereas localization economies can reinforce or offset the negative impacts of environmental regulation, depending on the industry. The second essay analyzes the effect of changes in regulatory environmental standards on the total stocks of establishments and local jobs and income Results indicate the effects vary across counties in the United States. When the standards were raised to 80 percent of the current level, from 2007 to 2009, the affected counties would lose a total of 326 establishments, 14,711 jobs with $705 million U.S. dollars of income each year. At the national economy level, the impacts of tightening environmental regulations are relatively small. The third essay constructs a dynamic optimization framework that deals with optimal utilization of alternative nonrenewable resource sites (geological formations) with possible negative externalities. We apply the model to an optimal usage problem of alternative long term CO₂ geologic storage sites for carbon. The storage sites are different in terms of capacity and potential leakage after CO₂ injection; the problem is determining the minimum cost for storing a fixed amount of CO₂ (sequestered) within a certain time period. Analytical solutions show the decision rule depends on the discount rate, storage capacities, marginal CO₂ storage costs, and environmental damage costs associated with CO₂ leakage from alternative sinks. The framework provides critical information about the optimal timing of switching from one resource sequestration site to another. / Graduation date: 2013 / Access restricted to the OSU Community at author's request from Feb. 1, 2013 - Feb. 1, 2015

Thesis (MPhil (Sustainable Development, Planning and Management))--University of Stellenbosch, 2010. / ENGLISH ABSTRACT: Sustainable agricultural management practices have a profound impact on soil carbon sequestration. The amount of carbon that can be stored in a given soil is influenced by climate, soil type, and the quality and quantity of organic inputs. Together, the interactive effect of these factors determines the Soil Organic Content (SOC). Sustainable agricultural management practices influencing Soil Organic Matter (SOM) include application of organic amendments, conservation tillage, and use of cover crops, crop rotations, crop residue management, and nutrient management. Increasing SOC enhances soil quality, reduces soil erosion, and increases agricultural productivity with considerable on-farm and off-farm benefits. To assess how management practices affect SOC, two case studies were conducted in Yavatmal district of Maharashtra in India and Lynedoch near Stellenbosch. The first case study examined the differences in SOC content on four farms each managed with 13 different sustainable agricultural techniques and one farm managed under conventional management practices. The second case study investigated the SOC differences between an organic and a conventional vegetable farm. The results of both studies show that farms that are managed under sustainable agricultural practices generally contain higher SOC content than farms that are managed under conventional agricultural practices. / AFRIKAANSE OPSOMMING: Om te bepaal hoe bestuurspraktyke Grondlikke Organise Koolstoff raak, is twee gevallestudies in die distrikte Yavatmal in Maharashtra, Indië, en Lynedoch buite Stellenbosch uitgevoer. Die eerste gevallestudie het die verskille in Grondlikke Organise Koolstoff -inhoud bekyk op vier plase waar 13 verskillende Volhoubare landboubestuurspraktyke het ‟n diepgaande impak op grondkoolstof-beslaglegging. Die hoeveelheid koolstof wat binne gegewe grond gestoor kan word, word deur klimaat, grondsoort en die gehalte en hoeveelheid organiese toevoer beïnvloed. Saam bepaal die interaktiewe effek van vermelde faktore die Grondlikke Organise Koolstoff -inhoud. Volhoubare landboubestuurspraktyke wat Grondlikke Organise Materiaal beïnvloed, sluit in die toediening van organiese verbeterings, bewaringsgrondbewerking, die gebruik van dekkingsoeste, oesrotasies, die hantering van oesresidu en voedingstofbestuur. Vermeerdering van Grondlikke Organise Koolstoff verhoog grondgehalte, verminder gronderosie en vermeerder landbouproduktiwiteit met aansienlike voordele op en verwyderd van die plaas. volhoubare landboutegnieke in die bestuurproses toegepas word, en een plaas wat volgens konvensionele bestuurspraktyke bedryf word. Met die tweede gevallestudie is ondersoek gedoen na die Grondlikke Organise Koolstoff -verskille tussen ‟n organiese en ‟n konvensionele groenteplaas. Die uitslae van albei studies dui daarop dat plase wat volgens volhoubare landboupraktyke bestuur word oor die algemeen hoër Grondlikke Organise Koolstoff-inhoud aantoon in vergelyking met plase wat volgens konvensionele landboupraktyke bedryf word.

Vast amounts of carbon are stored forest soils, a product of decaying organic matter. Increased CO2 in the atmosphere is predicted to lead to increasing global temperatures, and more extreme moisture regimes. Such increases in mean temperature could accelerate the rate of organic matter decay in soils and lead to additional release of CO2 into the atmosphere, thus exacerbating climate change. However, due to its impact on plant metabolism, high atmospheric CO2 concentrations may also lead to greater condensed tannins (CT) and reduced nitrogen (N) content in leaf litter. This reduction in litter quality has the potential to slow decay of organic matter in soil and therefore offset the accelerated decay resulting from a warmer climate. My research aimed to quantify the effects of climate and litter chemistry, specifically CT and N, on litter decay, N mineralization and associated microbes in the field. Strings of litterbags were laid on the forest floor along climate transects of mature Douglas-fir stands of coastal British Columbia rain-shadow forests. In-situ climate was monitored alongside carbon and nitrogen loss over 3.58 years of decay along three transects located at different latitudes, each transect spanning the coastal Western Hemlock and Douglas-fir biogeoclimatic zones. Microbial communities in the decaying litter and in forest soils were also analyzed using polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE). Microbial biogeography at field sites was partially influenced by climate, soil characteristics and spatial distance, but did not improve best fit decay models using climate and litter chemistry variables. Litter with greater initial CT and smaller N concentration slowed down early decay (0 - 0.58 yr) and net N mineralization. Warmer temperatures accelerated later decay (0.58 - 3.58 yr) and net N mineralization. Water-soluble CT were rapidly lost during decay, while other forms of CT were likely responsible for slower decay. The composition of fungal communities on decaying litter was affected by initial concentrations of CT and N. On a yearly basis, the slower decay of litter with high CT and reduced N content can offset accelerated rates of decay associated with warmer temperatures. Concurrent shifts in microbial communities and net N mineralization suggest potential benefits to trees. / Graduate / 2017-12-19

Master of Science / Department of Agricultural Economics / Timothy J. Dalton / Given the changing climate paradigm, food and poverty are likely to become more severe in Africa. Farmers can adapt to climate change, especially through conservation agriculture. This study relies on a minimum data approach developed by Antle and Valvidia (2006) to estimate the spatial distribution of opportunity cost for farmers in switching to conservation practices in Wa, Ghana. It assesses the economic feasibility of several scenarios that rely on production techniques currently studied by the CRSP SANREM project. We also explore the possibility that these practices can provide income from carbon sequestration payments implemented by the Kyoto protocol’s Clean Development Mechanisms. The methodology uses data from both a recent survey and information from secondary sources to assess simulated management practices. Results indicate that all the simulated management practices would theoretically benefit farmers. In fact, adoption rates for the four scenarios range from 52% to 65%, even without any carbon payment. Adding a proportional payment to the amount of carbon sequestered with these practices does not seem enough to influence farmers switch to switch to alternative scenarios. The analysis shows that these results hold even when additional fixed costs to adopt these practices are included. This case study demonstrates the usefulness of the minimum data approach in estimating the economic potential of conservation practices in Ghana. These production techniques may represent environmentally-friendly alternatives that are more profitable for farmers than current practices. The next step in assessing implementation of such practices would require studying farmers’ willingness to adopt these production systems, given their ex-ante economic returns.

The predominant land use in Denmark is agriculture, which has had negative effects on the aquatic environment, bothmarine and freshwater, due to excess nutrient runoff and resulting eutrophication. The current condition does not fullfillthe European Water Framework Directive’s goal of ‘Good ecological condition’ in all aquatic environments. InDenmark, forests only account for a small proportion of the land use, and despite an increase over the past twocenturies, the currently small forested area has had negative consequences for biodiversity since a majority of thespecies in Denmark are dependent on forests for habitat. The current efforts do not meet Denmark’s commitment tofulfill the United Nations Convention on Biological diversity. Similar to other countries, Denmark is obligated to reduceits carbon dioxide emissions according to the Paris agreement, with reduction goals of 40 % in 2030 and 80-95% in2050. The aim of the present thesis, is to assess whether reforestation on agricultural land can ensure that Denmarkreaches the international obligations for water quality and biodiversity at the same time as reducing climate impact byincreasing carbon sequestration, without significant land use conflict between agriculture and forest.This aim is pursued through an analysis of spatial data using a Geographical Information System, where threescenarios are created to assess differences in policy priorities.Based on the result of the spatial analysis, carbon sequestration estimates are calculated to assess the extent towhich forests could contribute to reducing the Danish climate impact, by increasing carbon sequestration. Theparameters used in the spatial analysis were found through a literature review, and the data for the spatial analysis wereaccessed in official and university databases.The main findings of the spatial analysis suggest that the areas with the highest potential agricultural value andthe areas with the highest potential for forest ecosystem services are not overlapping to a significant degree. Thisimplies that the areas that would have the highest levels of trade-offs between these goals when transitioning to forest,can continue the current land use without being needed for reforestation. The areas where agricultural value is low, andwhere reforestation would provide high levels of forest ecosystem services, are best suited for land use change. Theseareas were found to cover a substantial part of the study area, varying depending on three different scenarios, and areestimated to have the potential to contribute greatly to Denmark’s international commitments for water quality andbiodiversity. The carbon sequestration estimates show that if an area of approximately 7 % of Zealand was reforested,the sequestered amounts of carbon dioxide would correspond to a large portion of the emissions reductions necessary tofulfill Denmark’s obligations in the Paris Agreement.