Found 565 results
Open Access
Perspective
31 March 2026Ecological Civilization (Eco-Civ) in the Perspective of Geographical Processes of Revitalization of Remote Rural Areas
The global urbanization process is currently taking diverse territorial forms, leading to increased consumption of rural space through the creation of eco-cities. Within this context of transformation and the shifting nature of urban spaces, concepts and ideological frameworks are emerging to address environmental degradation caused by population concentration. Ecological Civilization (eco-civ) originated in China as a broad framework for managing new territorial processes through the construction of new eco-cities or the development of a comprehensive rural revitalization program that strengthens the urban-rural relationship. The major questions arising from this new process of rural revitalization in Chinese territories—and from the very concept of ecological civilization—can be summarized as follows: a simplification of the countryside, a loss of rural identity, the emergence of a post-agrarian society, the urbanization of rural areas, and an exacerbation of urban dependence on rural areas. Consequently, alternative approaches are proposed, based on multiple place-based approaches and actions that develop and adapt the fundamental principles of environmental and spatial renewal to each specific territory.
Open Access
Article
31 March 2026Spatiotemporal Evolution of Extreme Rainstorm Events in China and Corresponding Population Exposure
In order to reveal the spatio-temporal evolution of extreme rainstorm events in China and the changing characteristics of population exposure in different periods, this study systematically explored the spatio-temporal evolution characteristics of four indicators of extreme rainstorm frequency, duration, peak and cumulative amount, as well as the difference of population exposure to extreme rainstorm events in 2000 and 2020, based on the relevant data of extreme rainstorm and population distribution grid data from 2000 to 2020, using spatial analysis and trend analysis methods. The results show that in space, the frequency, peak value, and cumulative amount of extreme rainstorms are increasing from northwest to southeast, the southeast coast is a high value area, and there is almost no extreme rainstorm in the northwest arid area; The high-value areas of duration are concentrated in the Qinghai Tibet Plateau and Northeast China. In terms of time, from 2000 to 2020, the frequency of extreme rainstorm in Northeast China increased, the southern part of the Qinghai Tibet Plateau and other regions decreased, the peak value of rainstorm in North China Plain and the eastern coast increased, Taiwan Province showed a significant downward trend, and the change rate of rainstorm accumulation was stronger in the south and weaker in the north. In terms of spatial concentration, the high value concentration area of extreme rainstorms generally shifts to South China, while the low value concentration area is stably distributed in the northwest and part of the north. In terms of population exposure, the distribution characteristics of 2000 and 2020 are low in the northwest and high in the southeast, and the exposure of capital cities in southeast coastal provinces to extreme rainstorm frequency and peak in 2020 is significantly higher than that in 2000. Population migration and the evolution of extreme rainstorm events are the main driving factors. This study clarifies the temporal and spatial evolution law of extreme rainstorm events in China and the characteristics of population exposure change, which provides a scientific basis for regional extreme rainstorm disaster risk assessment, disaster prevention and mitigation planning, and optimization of population and urban development layout, and has important practical significance for improving the ability to respond to extreme climate events and ensuring regional population security and sustainable development.
Open Access
Review
31 March 2026Metallic Iron, (Rain)Water, and the City: A Handout for Researchers and Policymakers
This study aims to promote residential rainwater harvesting everywhere rain falls. It recalls the history of urban rainwater (stormwater) management while insisting on the origin of the perception that rainwater is not a relevant source of potable water. It also argues that where rainwater is polluted, it can be easily treated using frugal technologies such as filtration on metallic iron-based filters. The study notes that stormwater is precipitation that is not harvested. Thus, harvesting rainwater prevents (quantitative) stormwater generation, and transforms stormwater from a threat (e.g., erosion, floods) to a resource (e.g., drinking water, food security) for human and environmental needs. The effective management of stormwater (i) enhances the quality of human life, (ii) sustains local biodiversity, and (iii) protects the whole environment. Thus, the failure to harvest rainwater should be considered irresponsible, if not unethical. This argument alone makes each conscientious citizen a changemaker. A number of local changemakers will organize to determine the best way to integrate overflow from individual residences to enhance the community’s liveability. This study provides a valuable consolidation of information that will facilitate the mainstreaming of rainwater harvesting as the pillar of holistic integrated water resource management.
Open Access
Article
30 March 2026Adjusted Net Savings and Sustainable Development in Africa: A Panel Evidence Approach
This study investigates the key drivers of sustainable development in African economies using Adjusted Net Savings (ANS) as an indicator of long-term sustainability. Employing second-generation panel data methods—namely the Augmented Mean Group (AMG) estimator, System GMM, and the Dumitrescu–Hurlin panel causality test—the analysis accounts for cross-sectional dependence, heterogeneity, and potential endogeneity across countries. The results indicate that economic growth significantly enhances sustainable development in the long run: a one-unit increase in GDP per capita is associated with approximately a 31-point increase in ANS. In contrast, renewable energy consumption exerts a negative short-run effect on sustainability (−0.38), reflecting transition-related costs and efficiency constraints in developing economies. Carbon intensity adversely affects sustainability, while the impact of trade openness remains heterogeneous across countries. Country-specific estimates further reveal substantial cross-country differences driven by variations in economic structure, energy systems, and institutional capacity. Overall, the findings suggest that achieving sustainable development in Africa requires aligning economic growth with environmental efficiency through well-sequenced renewable energy investments, green trade policies, and strengthened institutional frameworks.
JEL Codes: Q01; O44; Q56; C23; F18
Open Access
Article
30 March 2026Evaluation of Power Grid Investment Effectiveness in New Power Systems Considering Decision Psychology and Sustainable Development: An Empirical Study Based on Chinese Urban Power Grid Simulation
The evaluation of investment effectiveness in power grids oriented towards new-type power systems is a critical issue for advancing grid transformation and enhancing the scientific basis of investment decision-making. To address the current challenges—such as single-dimensional evaluation, strong subjectivity in index weighting, and insufficient consideration of risks and decision-makers’ psychological factors—this paper aims to construct a hybrid evaluation framework that comprehensively reflects both objective data and subjective decision-making preferences. First, a comprehensive evaluation index system is established, encompassing four dimensions: low-carbon performance, safety, economic efficiency, and intelligence. Second, an innovative integration of the Back Propagation Neural Network (BPNN), the CRITIC method, and the Entropy Weight Method (EWM) is conducted. The combination weights are determined through game theory to scientifically quantify the importance of each index. Based on this, the Improved Cumulative Prospect Theory (ICPT) is introduced to characterize decision-makers’ psychological behavior under uncertainty. Furthermore, by combining Grey Relational Analysis (GRA) and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), an ICPT-GRA-TOPSIS comprehensive evaluation model is constructed. An empirical study of 13 typical urban power grids in China reveals that the proposed model can effectively identify the strengths and weaknesses of investment effectiveness across different regions, categorizing them into development tiers such as “multi-objective collaborative leading type”, “key breakthrough but unbalanced type”, and “system-lagging type”. More importantly, the sensitivity analysis of decision-making psychology demonstrates that the evaluation of investment strategies is highly dependent on decision-makers’ risk attitudes and value orientations. This provides critical quantitative decision-making references for formulating differentiated, precise investment strategies for power grids, offering significant theoretical and practical value for guiding power grid enterprises in optimizing resource allocation and supporting the construction of new-type power systems.
Open Access
Article
30 March 2026Mechanical Properties of Developed Corncob-Urea Particles Hybrid Reinforced Polyester-Based Composites
This study investigates the development of hybrid-reinforced polyester composites using corncob and urea particles as reinforcement for sustainable applications. Composites were fabricated by the stir casting method with varying weight fractions of corncob and urea. The mechanical and physical properties of the developed composites were evaluated, while fracture surface morphology was examined using scanning electron microscopy (SEM). The burning rates of the samples were investigated to evaluate their flame-retardant potential. The results demonstrate that incorporating corncob and urea effectively enhances stiffness-related mechanical properties, including tensile and flexural moduli and hardness. Composite containing 12 wt% urea and 3 wt% corncob exhibited the highest flexural moduli and hardness with an improvement of 122% and 45%, respectively. Composite with 3 wt% corncob and 18 wt% urea has the highest flexural strength with an increase of 44%, composite with 9 wt% corncob and 18 wt% urea has the highest tensile modulus with an improvement of 22%. In addition, it was found that the presence of corncob and urea reduced burning rates, with the sample containing 15 wt% corncob and 18 wt% urea exhibiting the lowest burning rate, indicating better flame-retardant potential. Thus, the findings indicate that corncob–urea hybrid reinforcement offers a promising, sustainable approach to enhancing the mechanical stiffness and reducing the burning rate of polyester composites. These materials have potential for use in applications requiring improved durability and low burning rate potentials while reducing reliance on conventional synthetic additives.
Open Access
Article
30 March 2026CFD Investigation of Torque Generation in an Archimedes Screw Hydrokinetic Turbine
The Archimedes Screw hydrokinetic turbine (AST) is a promising technology for renewable energy generation in shallow, low-velocity, and bidirectional flows, but the mechanisms governing its torque production remain poorly understood. This study uses computational fluid dynamics (CFD) to investigate the performance and torque-generation mechanism of a three-flight AST inclined at 30° and operating in two configurations previously examined experimentally. Transient simulations were performed in ANSYS Fluent using a sliding mesh and flow-induced rotation approach within an unsteady Reynolds-averaged Navier–Stokes framework with the SST k–ω turbulence model. The results show that pressure forces dominate torque generation, while viscous contributions are comparatively small. Importantly, this behaviour is observed at a relatively low Reynolds number of approximately 4.5 × 104, indicating that Reynolds-number dependence becomes weak at Reynolds numbers substantially lower than those expected in practical deployments. For the first configuration, with the upstream edge of the turbine at the free surface, the CFD model predicted a maximum power coefficient of 0.85 at a tip speed ratio of 1.50, compared with an experimental value of 0.40 at 0.53. For the second configuration, with the downstream edge of the turbine at the free surface, the corresponding maximum power coefficient was 0.82 at a tip speed ratio of 1.51, compared with 0.34 at 0.54, as experimentally observed. The simulations also captured strong cyclic torque variations; the maximum variation in torque was over three times the mean value for both configurations. Comparison of the cavitation and pressure coefficients indicates little likelihood of cavitation at the experimental flow velocity but suggests possible cavitation onset at higher velocities.
Open Access
Article
27 March 2026Intra- and Inter-Watershed Variability in Benthic Macroinvertebrate Community Diversity, Taxa Richness, and Biotic Integrity: Citizen Scientist Sampling Within a Minnesota USA Region Dominated by Agriculture
Volunteer citizen scientists collected benthic macroinvertebrate samples from 35 streams throughout multiple watersheds in southeastern Minnesota, USA, during the period 1999–2013 to assess community diversity, taxa richness, and biotic integrity as indicators of water quality and general habitat conditions. In total, 452 invertebrate samples containing >46,000 organisms were collected, processed, and analyzed. Only 45% of the citizen scientists completed their 5-year sampling commitment. However, their samples generally demonstrated significant differences in total taxa richness, Ephemeroptera-Plecoptera-Trichoptera (EPT) taxa richness, Simpson and Shannon diversities, and a regional benthic index of biotic integrity (BIBI) within and/or among the watersheds examined. Streams in the two larger watersheds averaged significantly higher taxa richness and BIBI scores than those in smaller watersheds. Overall, streams in this region exhibited mostly poor or very poor biotic integrity based on their macroinvertebrate communities, indicating continued impacts from environmental stressors within these agricultural watersheds.
Open Access
Article
27 March 2026Collaborative Optimization of Berth Allocation and Marine Energy Utilization for Low-Carbon Ports
Ports, as key nodes for marine renewable energy consumption and integration with marine industries, are facing the dual pressures of low-carbon transformation and efficient energy utilization. To solve fossil fuel reliance and high carbon emissions from disconnected port berth scheduling and energy optimization, this study proposes a two-stage framework combining the improved Cuckoo Search Algorithm (ICSA) and Stackelberg game. In the first stage, a vessel-centric optimization framework is proposed, which integrates the time-of-use electricity pricing mechanism to coordinate ship operating decisions and port low-carbon objectives. The ICSA is employed to solve the low-carbon berth allocation problem, while synchronously generating the time-series load data of key port handling equipment. In the second stage, a demand response load matrix is established by fully exploiting the battery swapping characteristics of electric trucks and the cold load shifting capability of refrigerated containers. A tripartite Stackelberg game is then conducted among the port energy operator, distributed energy supplier, and port equipment aggregator to optimize energy pricing and multi-energy supply dynamically. Case studies show doubled shore power using vessels, 14% higher berth utilization, and 29.86% lower energy costs. Carbon emissions were significantly reduced, while the proportions of offshore natural gas and renewable energy saw notable increases. This study provides a new approach for the integration of marine energy into port operations, supporting the sustainable development of marine energy industries and the low-carbon transformation of coastal ports.
Open Access
Review
27 March 2026Artificial Intelligence in Photovoltaic Power Systems: A Bibliometric and Thematic Analysis of Knowledge Structures, Research Evolution, and Emerging Directions Toward Sustainable Energy Systems
Artificial intelligence (AI) has rapidly become a core enabling technology in photovoltaic (PV) power systems, supporting improvements in forecasting accuracy, operational control, fault diagnosis, and system-level energy management. Despite the rapid growth of this field, a comprehensive understanding of its intellectual structure, thematic evolution, and emerging methodological directions remains fragmented. To address this gap, this study develops an integrated bibliometric-thematic analysis framework to systematically map the knowledge structure, research trajectories, and methodological frontiers of AI applications in PV power systems. The analysis is based on 4752 peer-reviewed journal articles indexed in Scopus (2006–2025). It combines performance analysis, co-citation analysis, keyword co-occurrence analysis, and bibliographic coupling to answer five structured research questions. The results demonstrate that PV power forecasting constitutes the central intellectual backbone of AI-based PV research, with the highest citation concentration and the strongest thematic connectivity across clusters. Thematic evolution analysis reveals a clear methodological transition from conventional machine learning models toward hybrid deep learning architectures, uncertainty-aware prediction frameworks, and physics-based AI integration. Furthermore, emerging research frontiers are characterized by generative learning models, multi-source data fusion strategies, and resilience-oriented fault diagnostics, while critical gaps persist in benchmarking standardization, uncertainty quantification, system-level integration, and large-scale industrial deployment. Unlike prior reviews that focus on isolated technical applications, this study provides the first integrated performance analysis and science-mapping synthesis that connects intellectual foundations, thematic evolution, and frontier innovations across the entire AI-based PV ecosystem. The findings offer a structured research roadmap and actionable guidance for researchers, PV plant operators, and policymakers aiming to design intelligent, scalable, and resilient PV energy systems that support the global low-carbon transition.
