A Hierarchical Framework for CO2 Storage Capacity in Deep Saline Aquifer Formations

Carbon dioxide (CO2) storage in deep saline aquifers is a vital option for CO2 mitigation at a large scale. Determining storage capacity is one of the crucial steps toward large-scale deployment of CO2 storage. Results of capacity assessments tend toward a consensus that sufficient resources are available in saline aquifers in many parts of the world. However, current CO2 capacity assessments involve significant inconsistencies and uncertainties caused by various technical assumptions, storage mechanisms considered, algorithms, and data types and resolutions. Furthermore, other constraint factors (such as techno-economic features, site suitability, risk, regulation, social-economic situation, and policies) significantly affect the storage capacity assessment results. Consequently, a consensus capacity classification system and assessment method should be capable of classifying the capacity type or even more related uncertainties. We present a hierarchical framework of CO2 capacity to define the capacity types based on the various factors, algorithms, and datasets. Finally, a review of onshore CO2 aquifer storage capacity assessments in China is presented as examples to illustrate the feasibility of the proposed hierarchical framework.

Spatial and temporal models in plant ecology: logic and causality

In the paper, I argue that in order to make credible ecological predictions for terrestrial ecosystems in a changing environment, we need empirical plant ecological models that are fitted to spatial and temporal ecological data. Here, it is advocated to use structural equation models in a hierarchical framework with latent variables. Furthermore, it is an advantage that the proposed hierarchical models are analogous to well-known theoretical epistemological models of how knowledge is obtained.

Analyzing the Role of Implicit Bias From a DisCrit Perspective

Using a disability studies/critical race theory (Discrit) lens, the authors reviewed and analyzed specific literature within the pyramid model (PM) framework—a three-tier hierarchical framework for promoting social-emotional competence and reducing challenging behavior among young children—to understand the model's framing of implicit bias and the specific strategies noted in the literature that help teachers to recognize and counteract implicit bias and subsequently reduce disciplinary inequities among Black preschool children. Findings revealed that although the PM literature discussed, defined, and emphasized the importance of cultural responsivity, it did not engage critically with the construct of implicit bias (i.e., racism and ableism), specifically as it relates to the experiences of children most vulnerable to disciplinary sanction. This chapter ends with suggestions to help readers rethink the PM framework as a way to shift practice toward more equitable experiences for Black children in their earliest years of schooling.

Improved parameter estimation and uncertainty propagation in Bayesian Radiocarbon-dated Event Count [REC] models

Data about the past contain chronological uncertainty that needs to be accounted for in statistical models. Recently a method called Radiocarbon-dated Event Count (REC) modelling has been explored as a way to improve the handling of chronological uncertainty in the context of statistical regression. REC modelling has so far employed a Bayesian hierarchical framework for parameter estimation to account for chronological uncertainty in count series of radiocarbon-dates. This approach, however, suffers from a couple of limitations. It is computationally inefficient, which limits the amount of chronological uncertainty that can be accounted for, and the hierarchical framework can produce biased, but highly precise parameter estimates. Here we report the results of an investigation in which we compared hierarchical REC models to an alternative with simulated data and a new R package called "chronup". Our results indicate that the hierarchical framework can produce correct high-precision estimates given enough data, but it is susceptible to sampling bias and has an inflated Type I error rate. In contrast, the alternative better handles small samples and fully propagates uncertainty into parameter estimates. In light of these results, we think the alternative method is more generally suitable for Palaeo Science applications.

A hierarchical definitional framework for a heterogeneous context: housing typologies in Tirana, Albania

PurposeThe proposed definitional framework can be used to define housing typologies of cities in developing countries. It aims to define housing typologies in the capital city of Albania, Tirana, using the proposed hierarchical framework within the dynamics of four political periods: Ottoman, establishment, socialist and postsocialist.Design/methodology/approachThis study proposes a new definitional approach for the housing typologies through a hierarchical framework that defines the typologies based on their political period and legality statuses departing from the case of Tirana, which is featured by a heterogeneous context. Such context is characterized by uncontrollable urban development, making typology definition problematic. Furthermore, beyond the form, spatial and functional features, it presents their exterior distinctiveness as an innovative element. The methodology used in the study includes archival research, image documentation, spatial, functional and exterior distinctiveness analysis of housing typologies and exploration of housing form features in different political periods.FindingsThe study identifies urban formal housing typologies in Tirana detached houses, apartments, mass housing, social housing, gated communities, informal detached houses and housing with in/formal additions.Originality/valueThis definitional approach can be used to define housing typologies for cities featured by heterogeneous urban context.