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Maximising the environmental benefits of gardens through optimal planting choices and understanding occupants’ engagement
Get PDFFlooding risk in urban areas has increased due to the expansion of impervious
surfaces, removal of garden vegetation, and predicted rise in heavier rainfall
events due to climate change. Domestic gardens cover up to 30% of UK urban
areas, so plants they contain could have a significant environmental impact, with
the potential to retain rainfall, reduce runoff and mitigate localised flooding.
The hypothesis was tested that plants with certain traits, including higher
evapotranspiration rates and hairy leaves, or mixtures of plants with diverse
traits, would provide greater rainfall retention compared to certain
monocultures and non-vegetated surfaces. To test this, popular perennial garden
plants representing these traits were grown in monocultures or mixed planting
and exposed to simulated rainfall and short-term flooding. Species with higher
transpiration rates and/or hairy-leaved canopies (such as Oenothera lindheimeri
‘Whirling Butterflies’), or planting combinations including them, provided
greater rainfall retention compared to planting without these traits (for example,
Oenothera gardens reduced runoff by 6-20%). Plant function, and therefore
ecosystem service provision, of higher transpiring ‘drought-tolerant’ Oenothera
and Verbena bonariensis was unaffected by flooding, and increased the flooding
tolerance of companion plants when grown in mixed pairs, reducing substrate
moisture by t 79% compared to monocultures of lower transpiring plants.
Gardens are privately designed spaces, therefore people’s preferences and
willingness to change is also crucial to maximise the environmental benefits of
planting. An experimental survey found that a combination of environmental
information and trait-based planting recommendations based upon the
preceding chapters, made participants more willing to change preference in
favour of plants linked to greater environmental benefits. Climate change
concern also increased respondents’ positivity towards pro-environmental
planting by 141%. Simple planting recommendations and concern for the climate
were both highlighted as key avenues to explore for influencing plant choices
and improving the potential environmental benefits of gardens
Cybersecurity resilience and innovation ecosystems for sustainable business excellence: Examining the dramatic changes in the macroeconomic business environment
Get PDFData and information systems are valuable, rare, and often inimitable resources for any organization willing to innovate its products, processes, and business models, with the ultimate goal of gaining a competitive edge in a digital world. Data and information systems are also valuable and rare resources when organizations interact with each other within their ecosystems as data flows are deployed conjointly by organizations to achieve innovation and performance outcomes for their ecosystem. As such data and information systems within organizations, interorganizational relationships and ecosystems need to be protected. For this reason, organizations are required to strengthen their cybersecurity systems. This seems a necessary precondition to assist organizations and ecosystems to innovate their products, processes, and business models especially during times of dramatic changes (such as wars or pandemics) that can pose threats to organizational and ecosystem data protection. Accordingly, cybersecurity resilience allows to address those threats triggered by dramatic changes. In this light, this study aims to investigate how components of cybersecurity resilience can influence organizations’ innovation capabilities and ultimately sustainable business excellence as well as the moderating influences of macroeconomic policies and regulations. By building on a cross-sectional research design we found that cybersecurity resilience positively influences innovation capabilities that in their turn positively influence sustainable business excellence. We also find that macroeconomic policies and regulations moderate the relationship between government efficacy and sustainable business excellence
Comprehending and resolving the challenges of the Nigerian insolvency law in practice: the performance improvement approach
Get PDFMeaningful legal change requires the transformation of the law in practice, a dimension that has received limited attention in existing insolvency scholarship. A critical aspect of insolvency law in practice is the regulation of insolvency practitioners, who are often blamed for the failure to deliver on the objectives of the law. The paper argues that charge may be misplaced and regulatory responses such as training and barriers to entry too narrow as responses. It asserts that the challenges of practice should be identified through systematic and systemic investigations informed by relevant data. For that reason, it proposes the use of performance improvement frameworks as investigative tools. It demonstrates their utility through an application to the Nigerian context. By offering a structured, inclusive, and data-driven approach, the paper provides a valuable tool that can be used in any jurisdiction to advance research and policy in this emerging aspect of insolvency scholarship
Exploring the role of product attributes in 9-ending pricing strategies: a study on online retailing
No full textThis study investigates the use of 9-ending pricing strategies in e-commerce by analyzing over 50,000 shoe prices. Using web scraping and a logit model from a German online retailer, the research assesses how product attributes influence the adoption of 9-ending prices. Key findings reveal that 9-ending prices are predominantly used for female and newly introduced products, as well as for items with lower and standard prices. The study also explores the effects of exclusivity and sustainability on pricing strategies, showing that their impact varies with different 9-ending price categories. Overall, this research demonstrates the complex nature of 9-ending pricing strategies, with the 9-zero removal model supporting all hypotheses, whereas the 99c and 95c models show differential effects. This extends our understanding of pricing tactics in online retail and highlights the significance of product attributes for marketing and sales strategies
Sea ice pattern effect on Earth’s energy budget is characterized by hemispheric asymmetry
Get PDFEarth’s energy budget is sensitive to the spatial distribution of sea surface temperature and sea ice concentration (SIC) change, but the global radiative effect of changes in SIC spatial distribution has not been quantified. We show that SIC-induced radiation anomalies at the top of the atmosphere are sensitive to the location of SIC reduction in each season, which qualitatively explains how and why the effect of sea ice loss on Earth’s energy budget is determined by its spatial pattern. Idealized experiments indicate that SIC-induced surface warming is greater in the Arctic regions, resulting in a more negative Planck feedback. Global low-level cloud cover responses to Arctic and Antarctic SIC reduction are also distinct, leading to more negative SIC-cloud feedback in Arctic regions. SIC-induced albedo feedback is sensitive to latitude due to inhomogeneous solar radiation at the surface. As a result, the simulated radiative effect of SIC anomalies during 1980–2019 is dominated by variations in the spatial pattern of SIC
A general model for the seasonal to decadal dynamics of leaf area
Get PDFLeaf phenology, represented at the ecosystem scale by the seasonal dynamics of leaf area index (LAI), is a key control on the exchanges of CO2, energy, and water between the land and atmosphere. Robust simulation of leaf phenology is thus important for both dynamic global vegetation models (DGVMs) and land-surface representations in climate and Earth System models. There is no general agreement on how leaf phenology should be modeled. However, a recent theoretical advance posits a universal relationship between the time course of “steady-state” gross primary production (GPP) and LAI—that is, the mutually consistent LAI and GPP that would pertain if weather conditions were held constant. This theory embodies the concept that leaves should be displayed when their presence is most beneficial to plants, combined with the reciprocal relationship of LAI and GPP via (a) the Beer's law dependence of GPP on LAI, and (b) the requirement for GPP to support the allocation of carbon to leaves. Here we develop a global prognostic LAI model, combining this theoretical approach with a parameter-sparse terrestrial GPP model (the P model) that achieves a good fit to GPP derived from flux towers in all biomes and a scheme based on the P model that predicts seasonal maximum LAI as the lesser of an energy-limited rate (maximizing GPP) and a water-limited rate (maximizing the use of available precipitation). The exponential moving average method is used to represent the time lag between leaf allocation and modeled steady-state LAI. The model captures satellite-derived LAI dynamics across biomes at both site and global levels. Since this model outperforms the 15 DGVMs used in the TRENDY project, it could provide a basis for improved representation of leaf-area dynamics in vegetation and climate models
Investigation of factors that affect post-fire recovery of photosynthetic activity at global scale
Get PDFThe time taken for ecosystems to recover after wildfire affects the rate of carbon sequestration, and this in turn impacts land–atmosphere exchanges and hydrological processes. Factors affecting post-fire recovery time have been investigated at site or regional scale, but there is comparatively little information about this at a global scale. In this study, we use solar-induced chlorophyll fluorescence (SIF) to estimate the recovery of photosynthetic activity after fire for more than 10,000 fires representing the range of ecosystems across the globe. We then examined the factors that influence post-fire recovery time, initially using the relaxed lasso technique to identify the most important factors and then using a linear regression model incorporating these factors. We show that vegetation characteristics, the characteristics of the fire, and post-fire climate all influence recovery time. Gross primary production (GPP) is the most important factor, with faster recovery in ecosystems with higher GPP. Fire properties which indicate substantial vegetation damage, such as fire intensity and duration, result in longer recovery times. Post-fire climate also affects recovery time: anomalous temperature and temperature seasonality, and higher than normal dry days increase recovery time while higher-than-average precipitation decreases recovery time. There is an additional impact of vegetation type (biome), which may reflect differences in plant adaptations to fire between biomes. We show that there is a clear relationship between the proportion of plants that resprout after fire in a biome and recovery time, with ecosystems characterised by higher abundance recovering faster
Management mathematics in sport – moneyball and soccer
No full textThe data revolution has not passed soccer by as an industry. However, it has not transformed the sport in the same way it has in, say, baseball. In this article we firstly describe the current landscape of analytics in soccer, using England as the focus of our attention. We document the extent to which academic contributions from management mathematics and across a range of disciplines have shaped the development of analytic methods in the sport to date, and consider the extent to which they will be essential as analytics continues to advance in the sport, most notably via the use of causal inference methods. We then propose an approach to analytics in soccer that builds both on the nature of soccer as an industry and the academic advances to date in the analysis of observational data. We note that such an approach can and should be consistent, with a system of ranking players and teams at its core, and must rely on causal methods of inference
Intact but protracted facial and prosodic emotion recognition among autistic adults.
Get PDFDespite extensive research efforts, it is unclear how autistic and non-autistic individuals compare in their ability to recognize emotions. Differences in demographic and task factors have been proposed as explanations for divergent findings, but limitations in samples and designs have obscured insight into this possibility. This study investigated the extent of emotion recognition differences among autistic adults and the influence of these factors upon them. We recruited a large sample of autistic and non-autistic adults (N = 1,239) spanning across adulthood (18-76 years). In three online experiments, we compared their performance in recognizing emotions from basic facial expressions, complex expressions conveyed by the eyes alone, and prosodic elements of speech. Autistic individuals performed as well as non-autistic ones in terms of recognition accuracy/sensitivity across measures and emotional categories but took longer to do so. We also detected comparable influences of age, estimated intelligence quotient, and gender (as well as task demands) on both groups. While autistic adults may differ in how they process emotional stimuli, they can do so effectively when given sufficient time. Accordingly, efforts to help autistic individuals improve their ability to recognize emotions may be more fruitful if they focus on efficiency over accuracy. Additionally, reaction time data may offer greater insight than accuracy into differences between autistic and non-autistic individuals on emotion recognition tasks. The similar effects of the demographic and task factors we analyzed on both groups suggest that explanations of the discrepancies in prior literature lie elsewhere