• Energy Research

The direction and environment of photovoltaics (PVs) may influence their energy output. The practical PV performance under various conditions should be estimated, particularly during initial design stages when PV model types are unknown. Previous studies have focused on a limited number of PV projects, which required the details of many PV models; furthermore, the models can be case sensitive. According to the 18 projects conducted in 7 locations (latitude 29.5–51.25N) around the world, we developed polynomials for the crystalline silicon PV energy output for different accessible input variables. A regression tree effectively evaluated the correlations of the outcomes with the input variables; those of high importance were identified. The coefficient of determination, indicating the percentage of datasets being predictable by the input, was higher than 0.65 for 14 of the 18 projects when the polynomial was developed using the accessible variables such as global horizontal solar radiation. However, individual equations should be derived for horizontal cases, indicating that a universal polynomial for crystalline silicon PVs with a tilt angle in the range 0°–66° can be difficult to develop. The proposed model will contribute to evaluating the performance of PVs with low and medium tilt angles for places of similar climates.

Bio-swales have gained significant attention as an effective means of stormwater management in urban areas, reducing the burden on conventional rainwater management systems. Despite this increasing interest, a comprehensive assessment of the status of bio-swale research is lacking. In response, this article employs two powerful information-visualizing software tools, the “Bibliometrix” R package and “CiteSpace”, to conduct a quantitative investigation of 323 English language sources published in the Web of Science prior to 2022. The objective is to provide a comprehensive examination of bio-swale research from multiple perspectives, shedding light on current advancements and future research trends. The findings reveal (1) a persistent annual increase in bio-swale-related publications and (2) the predominant focus on regulating services, such as hydrology, water quality, and biodiversity, with hot topics within these areas, including the influencing factors, climate, modeling, soil contaminants, and biodiversity at both macro and micro levels. Furthermore, our study concludes that future research necessitates interdisciplinary and interterritorial collaboration, a broader focus that encompasses the social, economic, ecological, and engineering aspects of bio-swales, and the adoption of diverse research methodologies. Given the currently limited research on biodiversity in bio-swales, this area holds the potential to become a future research hotspot. By harnessing the insights and findings of our study, researchers can gain a more profound understanding of the current state of bio-swale research and devise effective strategies to further propel this critical area of study.

Urban flooding disasters have become increasingly frequent in rural-urban fringes due to rapid urbanization, posing a serious threat to the aquatic environment, life security, and social economy. To address this issue, this study proposes a flood disaster risk assessment framework that integrates a Weighted Naive Bayesian (WNB) classifier and a Complex Network Model (CNM). The WNB is employed to predict risk distribution according to the risk factors and flooding events data, while the CNM is used to analyze the composition and correlation of the risk attributes according to its network topology. The rural-urban fringe in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is used as a case study. The results indicate that approximately half of the rural-urban fringe is at medium flooding risk, while 25.7% of the investigated areas are at high flooding risk. Through driving-factor analysis, the rural-urban fringe of GBA is divided into 12 clusters driven by multiple factors and 3 clusters driven by a single factor. Two types of cluster influenced by multiple factors were identified: one caused by artificial factors such as road density, fractional vegetation cover, and impervious surface percentage, and the other driven by topographic factors, such as elevation, slope, and distance to waterways. Single factor clusters were mainly based on slope and road density. The proposed flood disaster risk assessment framework integrating WNB and CNM provides a valuable tool to identify high-risk areas and driving factors, facilitating better decision-making and planning for disaster prevention and mitigation in rural-urban fringes.

Climate change represents a paramount challenge for humanity in the 21st century. Green infrastructure (GI), due to its myriad environmental and societal benefits, has emerged as an essential natural life support system and a pivotal strategy to combat climate change-induced risks. Consequently, GI has garnered considerable global interest. As of now, comprehensive and systematic environmental impact assessments of GI are underway worldwide. Nonetheless, there remains a conspicuous scarcity of life-cycle approaches to delineate the evolutionary trajectory of this domain. Employing three bibliometric software tools—the R language “Bibliometrix” package (version 4.0.1), CiteSpace (version 6.2.R2 Basic), and “VOSviewer” (version 1.6.18)—this study scrutinizes the progression of the GI paradigm until 2022. An exhaustive review of 1124 documents published on the Web of Science between 1995 and 2022 facilitates an overarching evaluation of GI, encompassing environmental, economic, and social facets from a life-cycle standpoint. The analysis results reveal that (1) the majority of current studies accentuate the economic and environmental efficacy of GI throughout its life cycle, with the social performance receiving comparatively less focus, potentially due to the difficulties in formulating a social life-cycle-assessment database; (2) contemporary research predominantly concentrates on the life-cycle carbon footprint of GI, warranting further exploration into its water and carbon footprints; and (3) multi-objective optimization emerges as a promising avenue for future GI investigations. This review thus furnishes a comprehensive understanding of the performance of GI from a life-cycle perspective.

An investigation into the effectiveness of bioretention cells (BCs) under potential climatic changes was conducted using representative concentration pathways. A case study of Guangzhou showed changes in peak runoff in climate change scenarios, with obvious growth in RCP8.5 and slight growth in RCP2.6. The performance of BCs on multiple parameters, including reduction of runoff volume, peak runoff, and first flush, were examined in different design storms using a hydrology model (SWMM). The effectiveness of BCs varied non-linearly with scale. Their performance fell by varying amounts in the various scenarios. BCs could provide sufficient effects in response to short-return-period and short-duration storms, but the performance of BCs decreased with heavy storms, especially considering climate change. Hence, BCs cannot replace grey infrastructure but should be integrated with them. The method developed in this study could be useful in the planning and design of low impact development in view of future climate changes.

Concurrent meteorological extremes (CMEs) represent a class of pernicious climatic events characterized by the coexistence of two extreme weather phenomena. Specifically, the juxtaposition of Urban Extreme Rainfall (UER) and Urban Extreme Heat (UEH) can precipitate disproportionately deleterious impacts on both ecological systems and human well-being. In this investigation, we embarked on a meticulous risk appraisal of CMEs within China’s Greater Bay Area (GBA), harnessing the predictive capabilities of three shared socioeconomic pathways (SSPs) namely, SSP1-2.6, SSP3-7.0, and SSP5-8.5, in conjunction with the EC-Earth3-Veg-LR model from the CMIP6 suite. The findings evidence a pronounced augmentation in CME occurrences, most notably under the SSP1-2.6 trajectory. Intriguingly, the SSP5-8.5 pathway, typified by elevated levels of greenhouse gas effluents, prognosticated the most intense CMEs, albeit with a temperate surge upon occurrence. Additionally, an ascendant trend in the ratio of CMEs to the aggregate of UER and UEH portends an escalating susceptibility to these combined events in ensuing decades. A sensitivity analysis accentuated the pivotal interplay between UER and UEH as a catalyst for the proliferation of CMEs, modulated by alterations in their respective marginal distributions. Such revelations accentuate the imperative of assimilating intricate interdependencies among climatic anomalies into evaluative paradigms for devising efficacious climate change countermeasures. The risk assessment paradigm proffered herein furnishes a formidable instrument for gauging the calamitous potential of CMEs in a dynamically shifting climate, thereby refining the precision of prospective risk estimations.

Climate change has led to the increased intensity and frequency of extreme meteorological events, threatening the drainage capacity in urban catchments and densely built-up cities. To alleviate urban flooding disasters, strategies coupled with green and grey infrastructure have been proposed to support urban stormwater management. However, most strategies rely largely on diachronic rainfall data and ignore long-term climate change impacts. This study described a novel framework to assess and to identify the optimal solution in response to uncertainties following climate change. The assessment framework consists of three components: (1) assess and process climate data to generate long-term time series of meteorological parameters under different climate conditions; (2) optimise the design of Grey-Green infrastructure systems to establish the optimal design solutions; and (3) perform a multi-criteria assessment of economic and hydrological performance to support decision-making. A case study in Guangzhou, China was carried out to demonstrate the usability and application processes of the framework. The results of the case study illustrated that the optimised Grey-Green infrastructure could save life cycle costs and reduce total outflow (56-66%), peak flow (22-85%), and TSS (more than 60%) compared to the fully centralised grey infrastructure system, indicating its high superior in economic competitiveness and hydrological performance under climate uncertainties. In terms of spatial configuration, the contribution of green infrastructure appeared not as critical as the adoption of decentralisation of the drainage networks. Furthermore, under extreme drought scenarios, the decentralised infrastructure system exhibited an exceptionally high degree of removal performance for non-point source pollutants.

Promoting park vitality is fundamental for advancing both residents’ well-being and sustainable urban development. Current research often sidesteps the temporal fluctuations and combined effects of environmental factors on park vitality. Drawing on real-time user density data from Tencent, this investigation analyzed park vitality across 64 urban parks in Fuzhou, China, divided into five specific temporal periods on weekdays and weekends. Through the application of geographic detector models, this study examined the impact and interplay of both intrinsic and extrinsic environmental characteristics on park vitality over these different times. Our primary findings include: (1) environmental attributes affecting park vitality vary temporally, with aspects like commercial density, leisure facility density, and park size consistently influencing vitality; (2) on weekdays, external attributes linked to convenience are predominant, while on weekends, internal attributes connected to recreation take precedence; and (3) there is a synergetic interaction between environmental determinants, often leading to either additive or more intricate effects on park vitality. Based on these insights, we propose recommendations for spatial planning and time-based policies to enhance the alignment between urban settings and park quality. This research provides actionable strategies for enhancing park vitality, both within China and internationally.

Urban stormwater management is a critical challenge facing cities globally, with natural-based solutions (NBS) emerging as a promising approach for mitigating the impacts of urban stormwater runoff. This bibliometric review examined the research trends and hot topics related to NBS for urban stormwater management. The study utilized a combination of qualitative and quantitative methods to analyze 176 articles from the Web of Science database, covering the period from 2016 to 2022. Results showed that NBS is a widely researched topic with a growing trend in publications in recent years, led by the United States, China, and several European countries. The majority of NBS articles were research papers (82%) with a focus on environmental performance rather than social and economic dimensions. Quantitative methods were more frequently used in research articles, particularly statistical analysis/modeling. Interviews and discussions were the most common qualitative method used. The review identified the most relevant countries, affiliations, authors, and journals in the field. Furthermore, hot topics in NBS research were highlighted, including ecosystem services, climate change, and sustainability. The study also emphasized that future research perspective should focus on interdisciplinary and collaborative research, scaling up and mainstreaming NBS, and exploring new ways of integrating different disciplines and stakeholders in the research process. The findings of this review provided insights into the current state of NBS research and offer valuable information for researchers, policymakers, and practitioners in the field of urban stormwater management.

This paper presents Synergetic Development Assessment (SDA) as a methodology to evaluate the environmental, economic, and social performance of an urban river system landscape from the perspective of sustainability. SDA is based on synergetics and its “order parameters” theory, proposed as a science to study the self-organization of complex systems. A case study of river system landscapes in China was carried out by, first, simplifying the composite system into three subsystems: environmental, economic, and social; then, going on to construct a hierarchical structure to explore the order parameters as the evaluation index. The Analytic Hierarchy Process was used to get the weight of the evaluation index to complete the assessment index system. At the same time, a Sequential Synergy Degree Model was built to accomplish the SDA. We find that from 2005 to 2015, the order degree of the environmental subsystem developed slowly, with fluctuations, and that river pattern is the key factor. Meanwhile, the order degree of the economic subsystem fluctuated widely, which significantly depended on the changing value of water resources, and the order degree of social subsystem improved continuously, with social culture lagging far behind. As a whole, the synergy degree of the composite system developed orderly at a corresponding low level, which was in low synergy from 2005 to 2009 and then in general synergy up to 2015.