Search results

Accurate values for infiltration rate are important to reliably estimate heat losses from buildings. Infiltration rate is rarely measured directly, and instead is usually estimated using algorithms or data from fan pressurisation tests. However, there is growing evidence that the commonly used methods for estimating infiltration rate are inaccurate in UK dwellings. Furthermore, most prior research was conducted during the winter season or relies on single measurements in each dwelling. Infiltration rates also affect the likelihood and severity of summertime overheating. The purpose of this work is to measure infiltration rates in summer, to compare this to different infiltration estimation methods, and to quantify the differences.

Fifteen whole house tracer gas tests were undertaken in the same test house during spring and summer to measure the whole building infiltration rate. Eleven infiltration estimation methods were used to predict infiltration rate, and these were compared to the measured values. Most, but not all, infiltration estimation methods relied on data from fan pressurisation (blower door) tests. A further four tracer gas tests were also done with trickle vents open to allow for comment on indoor air quality, but not compared to infiltration estimation methods.

The eleven estimation methods predicted infiltration rates between 64 and 208% higher than measured. The ASHRAE Enhanced derived infiltration rate (0.41 ach) was closest to the measured value of 0.25 ach, but still significantly different. The infiltration rate predicted by the “divide-by-20” rule of thumb, which is commonly used in the UK, was second furthest from the measured value at 0.73 ach. Indoor air quality is likely to be unsatisfactory in summer when windows are closed, even if trickle vents are open.

The findings have implications for those using dynamic thermal modelling to predict summertime overheating who, in the absence of a directly measured value for infiltration rate (i.e. by tracer gas), currently commonly use infiltration estimation methods such as the “divide-by-20” rule. Therefore, infiltration may be overestimated resulting in overheating risk and indoor air quality being incorrectly predicted.

Direct measurement of air infiltration rate is rare, especially multiple tests in a single home. Past measurements have invariably focused on the winter heating season. This work is original in that the tracer gas technique used to measure infiltration rate many times in a single dwelling during the summer. This work is also original in that it quantifies both the infiltration rate and its variability, and compares these to values produced by eleven infiltration estimation methods.

Stakeholders' uncertainty about firms' value drives their urge to get information, as well as managerial disclosure choices. In this study, the authors examine whether and how an important source of uncertainty – the recent COVID-19 pandemic's effect on corporate social responsibility (CSR) disclosure – is beyond managerial and stakeholders' control.

The authors develop a novel construct for daily CSR disclosure by employing computer-aided text analysis (CATA) on the press releases issued by 125 New Zealand Stock Exchange (NZX) listed from 28 February 2020 to 31 December 2020. To capture COVID-19 intensity, the authors use the growth rate of the population-adjusted cumulative sum of confirmed cases in New Zealand on a specific day. To examine the association between the COVID-19 outbreak and companies' CSR disclosure, the authors employed ordinary least squares (OLS) regression by clustering standard error at the firm level.

The authors find a one standard deviation increase in the COVID-19 outbreak leads to a 28% increase in such disclosures. These results remained robust to a series of sensitivity tests and continue to hold after accounting for potential endogeneity concerns. In the channel analysis, the study demonstrates that the positive relationship between COVID-19 and CSR disclosure is more pronounced in the presence of a well-structured board (i.e. a large, more independent board and with a higher proportion of women on it). In further analysis, the authors find the documented relationship varies over the pandemic's life cycle and is moderated by government stringency response, peer CSR pressure and media coverage.

This paper is the first study that contributes to the scant literature examining the impact of the COVID-19 outbreak on CSR disclosure. Prior research either investigates the relationship of the CSR-stock return during the COVID-19 market crisis or examines the relationship between corporate characteristics including the quality of financial information and the reactions of stock returns during COVID-19. The authors extend such studies by providing empirical evidence that managers respond to COVID-19 by increasing CSR disclosure.

The study aims to shed light on the historical and contemporary trends of biodiversity accounting literature, while simultaneously offering insights into the future of research in this sector. The paper also aims to raise awareness among accounting researchers about their role in preserving biodiversity and informing improvements in policy and practice in this area.

The Bibliometrix R-package is used to carry out an algorithmic historiography. The reference publication year spectroscopy (RPYS) methodology is implemented. It is a unique approach to bibliometric analysis that allows researchers to identify and examine historical patterns in scientific literature.

The work provides a distinct and comprehensive discussion of the four distinct periods demarcating the progression of scientific discourse regarding biodiversity accounting. These periods are identified as Origins (1767–1864), Awareness (1865–1961), Consolidation (1962–1995) and Acceleration (1996–2021). The study offers an insightful analysis of the main thematic advancements, interpretative paradigm shifts and theoretical developments that occurred during these periods.

The paper offers a significant contribution to the existing academic debate on the prospects for accounting scholars to concentrate their research efforts on biodiversity and thereby promote advancements in policy and practice in this sector.

The article represents the first example of using an algorithmic historiography approach to examine the corpus of literature dealing with biodiversity accounting. The value of this study comes from the fusion of historical methodology and perspective. To the best of the authors’ knowledge, this is also the first scientific investigation applying RPYS in the accounting sector.

Corporate firms often follow their peer firms to articulate multiple financial decisions. Among the others, trade credit policy is a vital financial decision that can impart its dynamic role in achieving financial efficiency. Therefore, the current analysis aims to assess the role of herding behavior in determining the trade credit policies of corporate firms and its relevant effect on corporate financial performance.

For this purpose, the financial data of 13089 nonfinancial sector firms from 50 countries are employed and the dynamic generalized method of moments (GMM) model to estimate the regression is applied.

The empirical findings first reveal that corporate firms actively mimic their peer firms regarding trade credit policies. However, this mimicking behavior hampers the financial performance due to noncompatibility with peers’ trade credit policies. Peer firms often develop such trade credit policies that are not applicable to corporate firms.

Mainly, the findings of the study suggest two implications. First, it highlights the peer effect in terms of trade credit patterns. Second, it elaborates an adverse effect regarding financial performance due to herding of peers’ trade credit policies.

This study adds new thoughts regarding herding behavior in terms of trade credit policy and its possible consequences for corporate financial performance. No study explores such a relationship.

The study aims to investigate how the presence and absence of institutional equivalents (interaction of industry peers and local peers) affect the earnings management practices of firms.

The study uses discretionary accruals to operationalize earnings management. A sample of 18,744 Bombay Stock Exchange (BSE) listed firm years spanning over 12 financial years (March 2010–March 2021) has been considered and analyzed through panel data regression models.

The author’s results show that the earnings management practices of a firm's institutional equivalents and the firm's own earnings management are positively associated, implying that firms closely follow their institutional equivalents. This association is found to be more pronounced among focal firms when the difference between the earnings management levels of industry peers and local peers is greater. Further, the author find that large firms aggressively imitate their industry peers and local peers, whereas profitability does not influence their imitation behavior.

The author’s findings have implications for understanding peer imitation processes, particularly when firms face increasingly multifaceted institutional environments. It suggests auditors and analysts take into account the earnings management practices of local and industry peers while analyzing the client's financial statements and making forecasts, respectively.

The study is among the pioneering attempts to explore the domain of earnings management from the lens of institutional equivalence and provides compelling evidence that the interaction of industry peers and local peers impacts the earnings management practices of firms.

Global climate change characterized by an increase in temperature has become the focus of attention all over the world. China is a sensitive and significant area of global climate change. This paper specifically aims to examine the association between agricultural productivity and the climate change by using China’s provincial agricultural input–output data from 2000 to 2019 and the climatic data of the ground meteorological stations.

The authors used the three-stage spatial Durbin model (SDM) model and entropy method for analysis of collected data; further, the authors also empirically tested the climate change marginal effect on agricultural productivity by using ordinary least square and SDM approaches.

The results revealed that climate change has a significant negative effect on agricultural productivity, which showed significance in robustness tests, including index replacement, quantile regression and tail reduction. The results of this study also indicated that by subdividing the climatic factors, annual precipitation had no significant impact on the growth of agricultural productivity; further, other climatic variables, including wind speed and temperature, had a substantial adverse effect on agricultural productivity. The heterogeneity test showed that climatic changes ominously hinder agricultural productivity growth only in the western region of China, and in the eastern and central regions, climate change had no effect.

The findings of this study highlight the importance of various social connections of farm households in designing policies to improve their responses to climate change and expand land productivity in different regions. The study also provides a hypothetical approach to prioritize developing regions that need proper attention to improve crop productivity.

The paper explores the impact of climate change on agricultural productivity by using the climatic data of China. Empirical evidence previously missing in the body of knowledge will support governments and researchers to establish a mechanism to improve climate change mitigation tools in China.