Strategic and regional assessments (SAs/RAs) can inform and influence subsequent impact assessments (IAs) in various ways. In Canada, IA systems that include SA/RA provisions often require their downstream consideration, but leave it to IA professionals to determine how, and to what extent, they should be used in that context. While previous research has investigated perspectives about the goals and utility of SAs/RAs in project IAs, there has been no investigation of the basis for, and origins of, these views. Drawing on experiential learning theory, this study undertook statistical analyses of data from recent surveys of IA regulators and practitioners in Canada to investigate whether different forms of IA experience and knowledge help explain these perspectives. Its findings indicate that amount (years) of IA experience was primarily associated with the view that SAs/RAs should improve project IA efficiency through government scoping decisions. Respondents' self-rated knowledge of SAs/RAs was often associated with perspectives regarding their broader role in improving the quality, effectiveness and efficiency of subsequent project IA scoping and conduct. These findings provide initial evidence that experiential learning can represent one mechanism through which IA professionals develop perspectives on the downstream role of these assessments in practice, while also highlighting that broader SA/RA knowledge may be especially influential in that regard.
The continued reliance on fossil fuels for energy production has led to increasing emissions of greenhouse gases (GHGs), particularly carbon dioxide (CO2), contributing significantly to global climate change. KOH-treated activated carbon has emerged as a promising adsorbent for gas separation and CO2 capture owing to its high surface area, well-developed microporosity, enhanced surface functionality, favorable adsorption kinetics, and cost-effectiveness. However, conventional Fick’s Law-based diffusion models generally assume constant diffusivity and may not adequately represent the dynamic transport behavior occurring during adsorption processes.To address this limitation, an extended Fick’s law (EFL) framework is developed in which diffusivity is treated as a function of adsorption rate, time, and spatial position. The resulting partial differential equation is solved using the Crank-Nicolson numerical method to obtain accurate predictions of gas adsorption behavior. In parallel, several machine learning (ML) approaches, including Gradient Boosting Regression (GBR), Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Deep Wavelet Neural Networks (DWNN), are employed as complementary predictive tools and benchmark models.The novelty of the proposed framework lies in combining a physics-based diffusion model with advanced data-driven techniques while incorporating dynamic diffusivity effects into the mass-transfer formulation. The developed model is validated against experimental adsorption data for CO2 and CH4 on KOH-treated activated carbon under different temperature and pressure conditions. The results demonstrate excellent agreement between model predictions and experimental observations, confirming the capability of the EFL model to accurately capture adsorption dynamics. The ML models also provide reliable predictions and reveal important nonlinear relationships among operating variables, enabling a comprehensive assessment of adsorption performance.A systematic sensitivity analysis is conducted to investigate the effects of temperature, pressure, and adsorbent type on adsorption behavior. The proposed framework provides valuable insights for adsorbent design, process optimization, and gas separation technologies. These findings contribute to the development of more efficient carbon capture systems and offer broader applications in energy, environmental, and chemical engineering processes requiring selective gas adsorption and separation.
Abstract Pipelines subjected to ground movements are typically assessed using beam-on-spring analysis. Current design guidelines recommend spring parameters developed based on studies on rigid pipe segments. However, the applicability of the parameters for simulating the responses of pipes with different flexural rigidity was not extensively evaluated. The current study focuses on developing a database for steel pipes with different flexural rigidity to assess the current design guide recommendations. Full-scale laboratory tests were conducted on 1.9-m-long steel pipes of three different diameters (26.7, 60.3, 114.3 mm) buried in dense sand at an embedment depth of 0.6 m. The force–displacement responses, pipe strains, midspan deflections, and soil surface images were obtained during the tests. The findings suggest that the lateral load–displacement response can depend on the pipe diameter, H / D ratio, and relative flexural rigidity of the pipe, whereas some previous studies indicated that the load–displacement response depends solely on the H / D ratio. The maximum longitudinal strains occurred at the pipe center for pipes with higher flexural stiffness, whereas the maximum strains occurred near the ends for flexible pipes. For the flexible pipes, soil resistance during tests is first mobilized near the pipe ends and then gradually extends toward the midspan, indicating different load-transfer mechanisms. Therefore, researchers should be cautious when normalizing peak lateral force and dimensionless yield displacement to estimate spring parameters from flexible pipe tests.
In this corrigendum, the proofs of Theorem 4.1 and Lemma 4.4 in [1] are amended.
Deep-ocean biodiversity plays a fundamental role in the global carbon cycle and Earth’s climate system. Despite this critical function, international biodiversity and climate change governance frameworks have rarely considered deep-sea life to any considerable extent historically. Recent developments create opportunities to close this gap, including through the Agreement under the United Nations Convention on the Law of the Sea on the Conservation and Sustainable Use of Marine Biological Diversity of Areas beyond National Jurisdiction (BBNJ Agreement), the Paris Agreement under the UN Framework Convention on Climate Change (UNFCCC), and the Kunming-Montreal Global Biodiversity Framework under the Convention on Biological Diversity (CBD). We review the history and developments of these conventions to date regarding how they address the interface between climate, biodiversity, and the deep ocean, and use this historical overview to identify specific opportunities and mechanisms that can be used to sustain and protect life in the deep ocean more effectively and coherently going forward. Practical suggestions include incorporating climate and carbon considerations in the creation of marine protected areas and other area-based management tools under the BBNJ Agreement, establishing cross-cutting mechanisms that can align efforts under the UNFCCC, CBD, and the BBNJ Agreement, and, under the Paris agreement, incorporating deep-ocean actions in nationally determined contributions, reconsidering what counts as mitigation or adaptation to encourage consideration of carbon conservation actions, and ensuring that climate change mitigation efforts do not harm deep-ocean biodiversity.
