Disentangle the price dispersion of residential solar photovoltaic systems: Evidence from Germany

Although Germany has the largest capacity of installed residential photovoltaic (PV) systems in Europe, comprehensive evidence on transparent pricing information remains missing. This study disentangles why PV quote prices are subject to significant dispersion and analyzes which factors influence particularly low- and high-priced systems in Germany. We create a comprehensive cross-sectional dataset of 19 561 PV system quotes from 2011 to 2022 and use regression analyses to investigate the effects of system characteristics, installation scope, and location-related parameters on quoted prices. Our results reveal highly volatile annual price dispersion consistent over 11 years and large price differences despite similar system characteristics. Applying hedonic regression techniques, we reveal spatially fine-resolved price heterogeneity with up to 20 % difference in the German PV market. System characteristics such as battery usage, installation scope, and system capacity have the most significant effect sizes and are instead control variables. More insightful, the installer density shows price-lowering effects, whereas more PV installations per region, higher solar radiation, and higher labor wages cause price-increasing effects. Quantile regression results reveal that installer density promotes the price reduction of high-priced systems more. Scaffolding, AC installation, and elevation are significant price-increasing factors but with small effect sizes. Finally, DC optimizers affect the levels of high-priced systems more than low-priced ones

A Spatiotemporal Study and Location-Specific Trip Pattern Categorization of Shared E-Scooter Usage

This study analyzes the temporally resolved location and trip data of shared e-scooters over nine months in Berlin from one of Europe’s most widespread operators. We apply time, distance, and energy consumption filters on approximately 1.25 million trips for outlier detection and trip categorization. Using temporally and spatially resolved trip pattern analyses, we investigate how the built environment and land use affect e-scooter trips. Further, we apply a density-based clustering algorithm to examine point of interest-specific patterns in trip generation. Our results suggest that e-scooter usage has point of interest related characteristics. Temporal peaks in e-scooter usage differ by point of interest category and indicate work-related trips at public transport stations. We prove these characteristic patterns with the statistical metric of cosine similarity. Considering average cluster velocities, we observe limited time-saving potential of e-scooter trips in congested areas near the city center

Open access decision support for sustainable buildings and neighborhoods: The nano energy system simulator NESSI

The urgency of climate change mitigation, rising energy prices and geopolitical crises make a quick and efficient energy transition in the building sector imperative. Building owners, housing associations, and local governments need support in the complex task to build sustainable energy systems. Motivated by the calls for more solution-oriented, practice-focused research regarding climate change and guided by design science research principles, we address this need and design, develop, and evaluate the web-based decision support system NESSI. NESSI is an open-access energy system simulator with an intuitive user flow to facilitate multi-energy planning for buildings and neighborhoods. It calculates the technical, environmental, and economic effects of 14 energy-producing, consuming, and storing components of the electric and thermal infrastructure, considers time-dependent effects, and accounts for geographic as well as sectoral circumstances. Its applicability is demonstrated with the case of a single-family home in Hannover, Germany, and evaluated through twelve expert interviews

Routine frailty assessment predicts postoperative complications in elderly patients across surgical disciplines – a retrospective observational study

BACKGROUND: Frailty is a frequent and underdiagnosed functional syndrome involving reduced physiological reserves and an increased vulnerability against stressors, with severe individual and socioeconomic consequences. A routine frailty assessment was implemented at our preoperative anaesthesia clinic to identify patients at risk. OBJECTIVE: This study examines the relationship between frailty status and the incidence of in-hospital postoperative complications in elderly surgical patients across several surgical disciplines. DESIGN: Retrospective observational analysis. SETTING: Single center, major tertiary care university hospital. Data collection took place between June 2016 and March 2017. PATIENTS: Patients 65 years old or older were evaluated for frailty using Fried's 5-point frailty assessment prior to elective non-cardiac surgery. Patients were classified into non-frail (0 criteria, reference group), pre-frail (1-2 positive criteria) and frail (3-5 positive criteria) groups. MAIN OUTCOME MEASURES: The incidence of postoperative complications was assessed until discharge from the hospital, using the roster from the National VA Surgical Quality Improvement Program. Propensity score matching and logistic regression analysis were performed. RESULTS: From 1186 elderly patients, 46.9% were classified as pre-frail (n = 556), and 11.4% as frail (n = 135). The rate of complications were significantly higher in the pre-frail (34.7%) and frail groups (47.4%), as compared to the non-frail group (27.5%). Similarly, length of stay (non-frail: 5.0 [3.0;7.0], pre-frail: 7.0 [3.0;9.0], frail 8.0 [4.5;12.0]; p < 0.001) and discharges to care facilities (non-frail:1.6%, pre-frail: 7.4%, frail: 17.8%); p < 0.001) were significantly associated with frailty status. After propensity score matching and logistic regression analysis, the risk for developing postoperative complications was approximately two-fold for pre-frail (OR 1.78; 95% CI 1.04-3.05) and frail (OR 2.08; 95% CI 1.21-3.60) patients. CONCLUSIONS: The preoperative frailty assessment of elderly patients identified pre-frail and frail subgroups to have the highest rate of postoperative complications, regardless of age, surgical discipline, and surgical risk. Significantly increased length of hospitalisation and discharges to care facilities were also observed. Implementation of routine frailty assessments appear to be an effective tool in identifying patients with increased risk. Now future studies are needed to investigate whether patients benefit from optimization of patient counselling, process planning, and risk reduction protocols based on the application of risk stratification

A Spatiotemporal Study and Location-Specific Trip Pattern Categorization of Shared E-Scooter Usage

This study analyzes the temporally resolved location and trip data of shared e-scooters over nine months in Berlin from one of Europe’s most widespread operators. We apply time, distance, and energy consumption filters on approximately 1.25 million trips for outlier detection and trip categorization. Using temporally and spatially resolved trip pattern analyses, we investigate how the built environment and land use affect e-scooter trips. Further, we apply a density-based clustering algorithm to examine point of interest-specific patterns in trip generation. Our results suggest that e-scooter usage has point of interest related characteristics. Temporal peaks in e-scooter usage differ by point of interest category and indicate work-related trips at public transport stations. We prove these characteristic patterns with the statistical metric of cosine similarity. Considering average cluster velocities, we observe limited time-saving potential of e-scooter trips in congested areas near the city center

Brazilian Flora 2020: Leveraging the power of a collaborative scientific network

International audienceThe shortage of reliable primary taxonomic data limits the description of biological taxa and the understanding of biodiversity patterns and processes, complicating biogeographical, ecological, and evolutionary studies. This deficit creates a significant taxonomic impediment to biodiversity research and conservation planning. The taxonomic impediment and the biodiversity crisis are widely recognized, highlighting the urgent need for reliable taxonomic data. Over the past decade, numerous countries worldwide have devoted considerable effort to Target 1 of the Global Strategy for Plant Conservation (GSPC), which called for the preparation of a working list of all known plant species by 2010 and an online world Flora by 2020. Brazil is a megadiverse country, home to more of the world's known plant species than any other country. Despite that, Flora Brasiliensis, concluded in 1906, was the last comprehensive treatment of the Brazilian flora. The lack of accurate estimates of the number of species of algae, fungi, and plants occurring in Brazil contributes to the prevailing taxonomic impediment and delays progress towards the GSPC targets. Over the past 12 years, a legion of taxonomists motivated to meet Target 1 of the GSPC, worked together to gather and integrate knowledge on the algal, plant, and fungal diversity of Brazil. Overall, a team of about 980 taxonomists joined efforts in a highly collaborative project that used cybertaxonomy to prepare an updated Flora of Brazil, showing the power of scientific collaboration to reach ambitious goals. This paper presents an overview of the Brazilian Flora 2020 and provides taxonomic and spatial updates on the algae, fungi, and plants found in one of the world's most biodiverse countries. We further identify collection gaps and summarize future goals that extend beyond 2020. Our results show that Brazil is home to 46,975 native species of algae, fungi, and plants, of which 19,669 are endemic to the country. The data compiled to date suggests that the Atlantic Rainforest might be the most diverse Brazilian domain for all plant groups except gymnosperms, which are most diverse in the Amazon. However, scientific knowledge of Brazilian diversity is still unequally distributed, with the Atlantic Rainforest and the Cerrado being the most intensively sampled and studied biomes in the country. In times of “scientific reductionism”, with botanical and mycological sciences suffering pervasive depreciation in recent decades, the first online Flora of Brazil 2020 significantly enhanced the quality and quantity of taxonomic data available for algae, fungi, and plants from Brazil. This project also made all the information freely available online, providing a firm foundation for future research and for the management, conservation, and sustainable use of the Brazilian funga and flora