The German energy transition is widely recognized as a pioneering national-scale experiment in achieving deep decarbonization, yet scholarly understanding remains fragmented across disciplines, technologies and policy domains. Despite extensive literature on renewable expansion and sectoral reforms, a comprehensive review of how the German energy system is structurally evolving, particularly in the post-nuclear and coal phase-out era, has been lacking. This gap is critical given the need to assess whether the transition is achieving systemic coherence across infrastructure, governance, and market design in pursuit of climate neutrality. Here, we provide a systematic review and bibliometric analysis of German energy transition research (2010–2024), structured around five thematic pillars: policy and strategy, sustainable energy system (SES) foundations, renewable and storage technologies, smart grids and digitalization, and sector coupling. We combine quantitative insights from Web of Science datasets (n = 1,755) with qualitative synthesis to map the intellectual landscape, highlight emerging trends, and assess the role of German institutions in shaping the discourse. Our results show increasing integration across sectors, particularly electricity, heating, and transport, driven by Power-to-X technologies and flexible demand management. Furthermore, we trace German energy transition from a top-down, fossil-dominated grid to a decentralized, renewable-rich energy system enabled by smart technologies, citizen participation, and evolving legal frameworks. These findings suggest that German energy transition is maturing into a complex, adaptive socio-technical system. Although significant challenges remain, such as market volatility, grid congestion and the circularity of energy technologies, the interplay of data-driven system design, flexible markets and social innovation is creating a replicable model for other countries. By documenting these integrative dynamics, our review offers a consolidated reference for researchers, policymakers, and practitioners aiming to understand or emulate the energy transition. Graphical Abstract

Geological carbon sequestration (GCS) is a key option for climate change mitigation, and subsurface microorganisms can alter CO₂ behavior through biomethanation, bioliquefaction, and biomineralization. This review summarizes the major microbial processes involved in GCS and evaluates their effects on carbon stabilization, resource reutilization, and storage risk. We compare microbial distribution and metabolic functions across representative geological reservoirs and synthesize laboratory, numerical, and field approaches into a multi-scale framework for studying microbially mediated GCS. We also discuss engineering regulation strategies, site selection, monitoring, and risk control, together with current technical challenges and future research priorities. Overall, this review provides an integrated perspective on microbial mechanisms and practical guidance for safer and more effective GCS deployment.

Technological maturity serves as a critical indicator for evaluating the current state of technology development and forecasting future trends. It has been widely applied in high‐tech fields, defense, and strategic emerging industries. This paper develops a multidimensional technology maturity assessment framework based on a comprehensive evaluation of existing maturity assessment methods and relevant application studies. The proposed model correlates computed values with the nine levels of technology readiness levels (TRLs) and the four stages of Theory of Inventive Problem Solving (TRIZ), providing a quantitative supplement to these classifications. Utilizing this evaluation model, the maturity levels of five underground compressed air and hydrogen storage power generation (UCHPG) technologies were assessed. The results, as of 2023, reveal that compressed air energy storage (CAES) has attained the highest TRL of 9. Conversely, hydrogen energy storage and fuel cell (HES‐FC) systems achieved a TRL of 7, hydrogen‐heated CAES (HH‐CAES) and hydrogen energy storage and gas turbine (HES‐GT) systems both reached a TRL of 6, while advanced adiabatic CAES (AA‐CAES) obtained a lower TRL of 5. To validate the proposed assessment model, a comparative analysis was conducted with recent literature on the TRL classifications of these five technologies. The findings indicate that the multicriteria technology maturity evaluation model proposed in this paper is reliable, addressing the limitations of single‐criterion assessment methods and providing a more comprehensive evaluation of technology maturity.

Accelerating the research and demonstration of safe, economical, and efficient hydrogen storage technologies is essential for the development of the hydrogen energy industry. This study examines the development and evolution patterns of hydrogen storage technologies through bibliometric analysis and the latent Dirichlet allocation (LDA) topic model, utilizing different dimensions of literature and patent data. The main conclusions are as follows: (1) Research in hydrogen storage exhibits a three‐phase pattern. China accounts for over one‐third of the publications, yet the average number of citations per paper is relatively low. Research in the United States shows fluctuations, while Japanese studies, though initiated early, display a gradual growth rate. Conversely, India has experienced rapid development in recent years. In Asian countries such as China, Japan, and South Korea, universities are the primary institutions driving influential research publications. In contrast, government‐affiliated research agencies and scientific organizations play a more dominant role in the United States, Germany, and other European countries. (2) There is a growing interest in emerging materials technologies such as metal hydrides, hydrogen storage alloys, metal–organic frameworks (MOFs), and hydrogen storage metal oxides. Carbon capture and storage (CCS) enhance the application potential of hydrogen storage technologies. Interest in fundamental equipment and system integration research is declining, although interest in carbon‐based materials, air filtration, and catalytic reactors shows fluctuating growth. (3) Research on hydrogen storage materials has shifted from basic studies to performance optimization and diversification, with system integration increasingly incorporating intelligent technologies. Catalytic technology is moving from precious metals to efficient nonprecious metal catalysts, with two‐dimensional materials like graphene showing potential applications. Advancements in green recycling technologies have improved resource utilization efficiency, promoting environmentally friendly hydrogen energy applications.

By analyzing the mechanism by which industrial structure adjustment influences the reduction in pollution and carbon emissions (RPCE) in the Yellow River Basin, in this study, we calculated data for the RPCE in 57 prefecture-level cities from 2011 to 2020. Based on the Regression on Population, Affluence, and Technology (STIRPAT) model, we empirically examined factors affecting the RPCE in the Yellow River Basin. Additionally, different scenarios were established in order to simulate and predict the future trend of the RPCE in the Yellow River Basin. In the study, we found the following: (1) The RPCE in the Yellow River Basin shows a positive trend, with lower levels in upstream Gansu and Ningxia and particularly severe conditions in Zhongwei, Shizuishan, and Wuhai, making these key areas for RPCE. (2) Moreover, the RPCE effect of the advanced industrial structure in the Yellow River Basin is superior to that of the rationalized industrial structure, economic growth and population increases are conducive to RPCE, foreign investors in the Yellow River Basin tend to invest more in high-energy-consuming industries, and there is a “pollution haven” effect. (3) In terms of regional heterogeneity, the impact of industrial structure adjustment on the RPCE in the lower and middle reaches is greater than that in the upstream regions. (4) The acceleration of the transformation and upgrading of industry, stabilization of the population growth rate, and promotion of high-quality economic development are the optimal development paths for RPCE in the Yellow River Basin.

We examined university students’ stated responses to simulated generative-AI exercise advice. In a randomized 2 × 2 within-participant task, 812 students from four universities in Henan Province evaluated eight fixed, researcher-reviewed scenarios, yielding 6496 decisions. Advice correctness was crossed with the presence of a corresponding reason. Participant-clustered logistic generalized estimating equations modeled direct adoption; a supplementary multinomial model retained following, modifying, rejecting, and verifying as distinct choices. Correct advice was adopted in 85.0% of trials and incorrect advice in 18.8%. Within these packages, reasons increased correct-advice adoption from 79.2% to 90.7% and reduced incorrect-advice adoption from 21.7% to 15.9% (interaction OR = 3.936, 95% CI [3.018, 5.132]). The adjusted adoption gap widened by 17.28 percentage points (95% CI [13.88, 20.67]). Under incorrect advice, the reason-present texts increased rejection, while the change in verification was uncertain. Trust was more strongly associated with adoption under incorrect advice, including after excluding a familiarity item. Associations with the eight-item score were exploratory because of low reliability and prior exposure to matching knowledge questions. The findings distinguish overall adoption from correctness-sensitive choices within standardized exercise-advice scenarios; their relevance to live coaching requires further testing.