<p>This research explores the impact of road infrastructure on deforestation, the role of forest governance and a carbon tax/credit mechanism in mitigating the effect on land use change and subsequent GHG emissions, with application to the states of Pará and Mato Grosso in Brazil. Few studies have addressed how policies to protect forested land affect the rate of deforestation associated with road and infrastructure improvement. This research makes three main contributions to the literature of roads and deforestation: 1) the concept of cost of access to the “closest” market in terms of time (expressed in person hours per ten ton load) is introduced to reflect variations in the road network infrastructure; 2) development of empirical evidence of the role of forest governance in diminishing the rate of deforestation linked to roads, using data from Brazil; and 3) and assessment of the efficacy of a carbon tax/credit scheme for mitigating the impact of infrastructure investment on land use and resultant changes in GHG emissions. Access cost ranged between 0.01 and 3084 person hours per load, however 80 percent of the pixels measured less than 784 person hours across the three years analyzed (2003, 2013, and 2018). This measure facilitated a contrast in spatial accessibility due to road infrastructure across pixels within the same year and across years on a same pixel. The use of a fractional logit model allowed the incorporation of proportions of different land uses within a same pixel at the same resolution of other <a></a>variables not available at the same fine scale. Strong forest governance reduced up to 25% the elasticities on forest lands with respect to access cost; in other words, the impact of roads on deforestation is reduced by one fourth when forest governance is strengthened. These larger impacts occur at the frontier where most of the efforts need to be addressed. Finally, provided a shock in road infrastructure, a carbon tax/credit level of $82/tCO2e permitted to abate an additional amount of GHG emissions estimated in 244 million tons of CO2e released due to changes in carbon stocks and flow emissions from agricultural activities induced from changes in road infrastructure. More importantly, this research provided insights of a proportion of GHG emissions that could be abated at different levels of a carbon tax/credit.</p>

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The urgent issue with a changing climate has shed a light on the agricultural system and related climate mitigatory opportunities. One natural climate solution that has reached a lot of attention during the later years is carbon sequestrations in soils. The pedosphere, the outermost layer of the earth, constitute a great carbon pool and does therefore possibly provide big opportunities to adjust carbon levels in the atmosphere. Several measures can be utilized to enhance carbon levels in soils but could possibly imply additional negative impacts on other sustainability aspects. One of these are biodiversity, which are a trait strongly connected to semi-natural grasslands. This study therefore presents an overview of the knowledge regarding carbon sequestration on semi-natural grasslands, framed in a Swedish context. Conflicts and synergies between the climate aspect and other values of semi-natural grasslands are here investigated. Firstly, a lack of reliable Swedish data was identified and problems with extrapolating data from international studies regarding soil carbon sequestration were pointed out, especially in the context of semi-natural grasslands and biodiversity. The report shows that Swedish semi-natural grasslands have a low level of carbon sequestration and moreover a low opportunity to act as a climate mitigator. Biodiversity is often a more prioritized factor for these lands and often interpreted as a hinder for climate mitigatory measures. There are, however, possible opportunities to improve the climatic impact these lands have, depending on several aspects, but do often require a broad system perspective. Synergies between climate and biodiversity were difficult to identify except the questioned statement that grazers possibly could enhance soil carbon sequestration and at the same time provide biodiversity benefits. Conflicts were based on difficulties in implementation of sequestration enhancing measures since they often, locally, implied impoverished biodiversity. / Klimatförändringar är ett aktuellt problem som även satt fokus på jordbrukets roll och dess möjligheter att minska dess koldioxidutsläpp och dämpa den globala uppvärmningen. En av de möjliga naturliga klimatlösningarna är att lagra kol i jorden. Pedosfären, jordskorpans yttersta skikt, är en stor kol-pool och utgör därför potentiellt en god möjlighet till att minska koldioxidhalten i atmosfären. Det finns flertalet möjligheter att öka kolinlagringen i jorden men dessa kan dock innebära negativa konsekvenser av andra hållbarhetsaspekter. En av dessa är biologisk mångfald, som är något som naturbetesmarker är starkt kopplade till. Denna studie presenterar en översikt över kolinlagring i naturbetesmarker i en svensk kontext. Konflikter och synergier mellan kolinlagring och andra värden av naturbetesmarker tas även upp. Först kan det nämnas att det finns osäkerheter i data från svenska naturbetesmarker och att data av kolinlagring från internationella studier ofta är svåra att extrapolera till svenska marker. Det fanns även få studier som behandlade både klimatperspektivet och biologisk mångfald på naturbetesmarker. Rapporten visar även att svenska naturbetesmarker har en låg nivå av kolinlagring och därmed en låg möjlighet att agera som en mildrande faktor gällande ett förändrat klimat, där en anledning är den begränsande totala ytan som finns definierad som naturbetesmark. Däremot finns det möjligheter att förändra klimatavtrycket dessa marker genererar. I en sådan ansats bör man inkludera en rad olika faktorer och använda ett brett systemperspektiv för vidare analys. Synergier är svåra att identifiera, bortsett från en ifrågasatt åsikt om att betande djur potentiellt båda kan bidra till biologisk mångfald och öka kolinlagringen på den betande marken. Konflikter utgörs ofta av svårigheten att många potentiellt kolinlagrande åtgärder hade direkt negativa konsekvenser för den biologiska mångfalden lokalt.

This study estimated monthly and annual Net Primary Productivity (NPP), an important indicator of carbon sequestration, in the Conterminous US from 1997 to 2007 using Carnegie-Ames-Stanford Approach. Vegetation condition, temperature, precipitation, photosynthetically active radiation and soil water holding capacity were used as model’s inputs. NPP values were lower than mean annual values during the year 2000 to 2003 which was probably due to extreme drought conditions during these years. Higher NPP per square meter was generally found in Savannah and Subtropical eco-divisions whereas Tropical/Subtropical deserts had the lowest NPP. Southeastern states had the highest NPP per square meter thus, made the highest contribution to the terrestrial carbon sequestration in US. Since the vegetation is one of the main factors in NPP and thus carbon sequestration, the results of this study could help in various environmental policy decisions on forest and cropland management at the state, EPA and federal levels.

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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.