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Published online: 26 November 2024 This study investigates the relationships between social and political trust and views on energy affordability, which are crucial for promoting sustainable energy practices. The findings indicate that social and especially political trust are negatively correlated with perceptions of energy affordability. The study also finds that the probability of being highly concerned about energy affordability is declining in the level of trust, especially in countries, as those in Southern European, that prioritize energy affordability. These findings confirm the critical role of trust in effective energy policies, fostering public support for transitions to sustainable energy systems. The study recommends that policymakers enhance transparency, accountability, and public engagement to build trust, thereby improving perceptions of energy affordability and supporting sustainable energy transitions.

Abstract Active sensors such as Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and Cloud Profiling Radar (CPR) aboard CloudSat are known to have great advantages in examining cloud vertical structures. However, these sensors were designed for a relatively short lifespan (∼3 yr), and trends using a combination of CALIPSO and CloudSat (CALCS) have yet to be explored. When clouds detected by CALCS are filtered for known sensitivity differences, the trends from the merged CALCS agree well with Moderate Resolution Imaging Spectroradiometer (MODIS) cloud trends from 2008 to 2017. Both CALCS and MODIS capture common features, although there are differences in high-level cloud altitudes. These trends are decomposed into El Niño–Southern Oscillation (ENSO) and non-ENSO components using a regression model with the multivariate ENSO index (MEI). The non-ENSO component is related to the cloud amount increase over the Arctic and the upward shift of high clouds (i.e., rising high clouds) over 60°S–60°N. The rising high clouds are further verified using MODIS measurements for the extended period from 2005 to 2022. Even though altitudes of high clouds rose, their cloud temperatures remained similar, supporting the fixed anvil temperature (FAT) hypothesis. Significance Statement Zonal cloud trends obtained from two active sensors, CALIPSO and CloudSat (CALCS), are compared with those from the MODIS passive imager. Despite the differences in high-level cloud altitudes, CALCS and MODIS zonal cloud trends capture common features. When the cloud trends are decomposed into ENSO and non-ENSO components, the non-ENSO component is related to the Arctic cloud increase and rising high clouds. Although altitudes of high clouds rose, their cloud temperatures remained similar.

Hydrogen energy is regarded as a promising solution for decarbonizing hard-to-abate sectors, while its role in the energy transition remains debatable. One of the key reasons is that uncertainty in technological progress has significant impacts on investment decision-making. To assess these effects, this study employs the MESSAGEix framework to develop a hydrogen energy system optimization model in China's context and integrates it with a stochastic scenario-tree generation method to assess the effects of uncertain technological progress on decarbonizing China's hydrogen energy system. The modeled system covers a full range of hydrogen production and consumption associated with different technical options for decarbonization, i.e., renewable energy-based water electrolysis (green hydrogen) and fossil-derived hydrogen coupled with carbon capture and storage. The model simulates a wide range of stochastic crucial cost metrics under the carbon-neutral constraint and compares it to a baseline without an emission constraint. Results show that disruptive technological breakthroughs in renewable electricity generation are essential to decarbonizing the hydrogen production system. The proposed hybrid modeling approach proves that computing is effective and could be applied to many other stochastic programming problems in long-term energy system planning.

The climate crisis, driven by greenhouse gas (GHG) emissions and environmental degradation, demands a transition to renewable energy for sustainable development. This paper analyzes the asymmetric effects of hydroelectric and biomass energy consumption on the ecological footprint (EFP) for 24 OECD countries from 1970 to 2022. By using a combination of advanced econometric approaches, including Method of Moments Quantile Regression (MMQR), Generalized Linear Models (GLM), and Robust Least Squares (RLS), with machine learning techniques such as Multivariate Adaptive Regression Splines (MARS) and Neural Networks (NN), this study will be able to identify complex nonlinearities that are not captured by traditional models. The results reveal that hydroelectric energy significantly reduces the EFP, particularly in high-pollution contexts, while biomass energy consumption worsens environmental degradation. These findings emphasize the urgent need for targeted policies to maximize the benefits of renewable energy sources and mitigate their risks. The study contributes to the literature by offering a comprehensive framework to analyze the environmental impacts of renewable energy, emphasizing the importance of methodological diversity and advanced modeling techniques as ways to achieve sustainability goals.

AbstractPromoting the shift toward clean and sustainable energy sources has been a key policy priority for the United Kingdom, as it is recognized as a critical pathway towards achieving carbon neutrality targets. Based on this, the study explored the impact of technological innovations, financial development, and income inequality on renewable energy consumption using data from 1980Q1 to 2021Q4. Taking into account the nonlinear and non‐normal distribution of the variables, the study employed wavelet‐based quantile techniques. Specifically, wavelet quantile ADF and PP tests were utilized to examine the stationarity properties of the data across different time horizons and quantiles. Additionally, wavelet quantile‐on‐quantile regression was used which focuses on various time horizons and quantiles. As a robustness check, wavelet quantile regression and average wavelet quantile‐on‐quantile regression were applied. The results revealed that across all time‐scales (short, medium and long‐term) and quantiles (0.1–0.90), economic growth, income inequality, and financial development positively influence renewable energy consumption. In addition, technological innovations were found to have mixed effect on renewable energy consumption. The study proposed policies based on these findings.

The interplay between excitatory pyramidal neurons and GABAergic interneurons is the basic building block of neocortical microcircuits and plays a critical role in carrying out higher cognitive functions. Cortical circuits are deeply and permanently disrupted by exposure to alcohol during brain development, the main non-genetic cause of intellectual disability. Here, I review experimental studies of fetal alcohol spectrum disorders, dealing with permanent cellular and molecular alterations of neocortical neurons and their connections.

The policymakers in emerging economies have accelerated their efforts to move toward sustainable development due to the associated challenges of environmental degradation and disparities in economic growth. The present study is an effort in that direction; it looks at trade-adjusted carbon emissions (TACE) to help policymakers develop a relevant policy response to mitigate climate change in the BRICST (comprising Brazil, Russia, India, China, South Africa, and Turkey) regions. The study investigates the impact of financial inclusion, green finance, and sustainable economic development on carbon emissions adjusted for trade. The study utilizes the panel dataset of the BRICST regions from 2000 to 2023 by employing the econometric methods of the Method of Moments Quantile Regression (MMQR) approach. It recommends green finance, financial inclusion, and sustainable economic development to curb TACE. The research findings indicate a noteworthy correlation between green finance, financial inclusion, sustainable economic development and TACE. The study recommends policy measures for the BRICST regions that align with the Sustainable Development Goals, focusing on green finance, financial inclusion, and sustainability. By promoting renewable energy, enhancing financial access, and encouraging international cooperation, these strategies aim to reduce TACE and foster sustainable development.