Feasibility of Carbon Dioxide Storage Resource Use within Climate Change Mitigation Scenarios for the United States

To progress decarbonization in the United States, numerous techno-economic models that project CO2 storage deployment at annual injection rates of 0.3–1.7 Gt year–1 by 2050 have been built. However, these models do not consider many geological, technical, or socio-economic factors that could impede the growth of geological storage resource use, and there is uncertainty about the feasibility of the resulting projections. Here, we evaluate storage scenarios across four major modeling efforts. We apply a growth modeling framework using logistic curves to analyze the feasibility of growth trajectories under constraints imposed by the associated storage resource availability. We show that storage resources are abundant, and resources of the Gulf Coast alone would be sufficient to meet national demand were it not for transport limitations. On the contrary, deployment trajectories require sustained average annual (exponential) growth at rates of >10% nationally for two of the three reports and between 3% and 20% regionally across four storage hubs projected in both reports with regional resolution. These rates are high relative to historical rates of growth in analogous large scale energy infrastructure in the United States. Projections for California appear to be particularly infeasible. Future modeling efforts should be constrained to more realistic deployment trajectories, which could be done with simple constraints from the type of modeling framework presented here

Feasibility of Carbon Dioxide Storage Resource Use within Climate Change Mitigation Scenarios for the United States

To progress decarbonization in the United States, numerous techno-economic models that project CO2 storage deployment at annual injection rates of 0.3–1.7 Gt year–1 by 2050 have been built. However, these models do not consider many geological, technical, or socio-economic factors that could impede the growth of geological storage resource use, and there is uncertainty about the feasibility of the resulting projections. Here, we evaluate storage scenarios across four major modeling efforts. We apply a growth modeling framework using logistic curves to analyze the feasibility of growth trajectories under constraints imposed by the associated storage resource availability. We show that storage resources are abundant, and resources of the Gulf Coast alone would be sufficient to meet national demand were it not for transport limitations. On the contrary, deployment trajectories require sustained average annual (exponential) growth at rates of >10% nationally for two of the three reports and between 3% and 20% regionally across four storage hubs projected in both reports with regional resolution. These rates are high relative to historical rates of growth in analogous large scale energy infrastructure in the United States. Projections for California appear to be particularly infeasible. Future modeling efforts should be constrained to more realistic deployment trajectories, which could be done with simple constraints from the type of modeling framework presented here

Mean AGD in different AOIs for different factors and CON results.

<p>(a) Mean AGD in different AOIs under different IFOV conditions. (b) Mean AGD in different AOIs for different genders. (c) Mean AGD in different AOIs for different CON results.</p

DataSheet_1_Deep learning architectures for diagnosing the severity of apple frog-eye leaf spot disease in complex backgrounds.pdf

IntroductionIn precision agriculture, accurately diagnosing apple frog-eye leaf spot disease is critical for effective disease management. Traditional methods, predominantly relying on labor-intensive and subjective visual evaluations, are often inefficient and unreliable.MethodsTo tackle these challenges in complex orchard environments, we develop a specialized deep learning architecture. This architecture consists of a two-stage multi-network model. The first stage features an enhanced Pyramid Scene Parsing Network (L-DPNet) with deformable convolutions for improved apple leaf segmentation. The second stage utilizes an improved U-Net (D-UNet), optimized with bilinear upsampling and batch normalization, for precise disease spot segmentation.ResultsOur model sets new benchmarks in performance, achieving a mean Intersection over Union (mIoU) of 91.27% for segmentation of both apple leaves and disease spots, and a mean Pixel Accuracy (mPA) of 94.32%. It also excels in classifying disease severity across five levels, achieving an overall precision of 94.81%.DiscussionThis approach represents a significant advancement in automated disease quantification, enhancing disease management in precision agriculture through data-driven decision-making.</p

Descriptive statistical results and ANOVA analysis results for NG in different AOIs.

<p>Descriptive statistical results and ANOVA analysis results for NG in different AOIs.</p

Descriptive statistical results and ANOVA analysis results for GD in different AOIs.

<p>Descriptive statistical results and ANOVA analysis results for GD in different AOIs.</p

Descriptive statistical results and logistic regression results for COR.

<p>Descriptive statistical results and logistic regression results for COR.</p

Classification of AOIs.

<p>a) Right AOI. (b) Left AOI. (c) Forward roadway AOI. (d) Collision vehicle AOI.</p

Mean BTC for different factors and CON results.

<p>(a) Mean BTC under three different IFOV conditions. (b) Mean BTC for different genders. (c) Mean BTC for different CON results.</p

Descriptive statistics and ANOVA results for BTC.

<p>Descriptive statistics and ANOVA results for BTC.</p