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Recently, there has been a shift toward the embodied energy assessment of buildings. However, the impact of material service life on the life-cycle embodied energy has received little attention. We aimed to address this knowledge gap, particularly in the context of the UAE and investigated the embodied energy associated with the use of concrete and other materials commonly used in residential buildings in the hot desert climate of the UAE.

Using input–output based hybrid analysis, we quantified the life-cycle embodied energy of a villa in the UAE with over 50 years of building life using the average, minimum, and maximum material service life values. Mathematical calculations were performed using MS Excel, and a detailed bill of quantities with >170 building materials and components of the villa were used for investigation.

For the base case, the initial embodied energy was 57% (7390.5 GJ), whereas the recurrent embodied energy was 43% (5,690 GJ) of the life-cycle embodied energy based on average material service life values. The proportion of the recurrent embodied energy with minimum material service life values was increased to 68% of the life-cycle embodied energy, while it dropped to 15% with maximum material service life values.

The findings provide new data to guide building construction in the UAE and show that recurrent embodied energy contributes significantly to life-cycle energy demand. Further, the study of material service life variations provides deeper insights into future building material specifications and management considerations for building maintenance.

As net-zero pledges gain momentum globally, more and more accommodation businesses seek to quantify their carbon emissions. Building on Chan (2021), this study aims to explore what drives Australian accommodation providers to measure the carbon footprint of their businesses and what barriers hinder them from doing so.

Empirical data were collected by conducting ten semi-structured interviews with owners, senior executives, consultants, certification bodies and hotel management companies. The set of interviews represented different segments of the hotel industry and various accommodation types. Data were analysed with thematic analysis.

The major drivers for adopting carbon footprint analysis are as follows: the analysis being perceived as an important contribution to a company's corporate responsibility, the owner or manager's environmental concern, the assessment being a requirement for obtaining an eco-certification and the business benefits associated with implementing the initiative. The major barriers hindering adoption include the following: difficulties with data gathering, the lack of a standard methodology, a lengthy decision-making process and a lack of resources.

Based on the empirical findings and three theories on ecological responsiveness, this study develops a conceptual framework for implementing carbon footprint analysis in the accommodation context and recommends strategies to increase the adoption of carbon footprint analysis.

This study responds to Chan and Hsu's (2016) call for further research on carbon footprint in the hotel context and represents the first attempt to explore the drivers and barriers specifically associated with implementing carbon footprint analysis in the accommodation sector.

The purposes of this study are to contribute to the limited green growth (GG) literature in emerging markets, to analyze GG from a financial economy perspective and to determine the contribution of financial development and innovation to GG in Brazil, Russian Federation, India, China and South Africa and Türkiye (BRICS-T). BRICS-T countries significantly impact the world population, international politics, energy resources and economy. In addition, BRICS-T countries are one of the leading countries in the world with their sustainability efforts. Investigating the GG model in these countries may contribute to structuring emerging economies around the principles of GG and advancing global green transformation efforts.

The authors applied panel data analysis from 2001 to 2019. GG is economic growth free from environmental depletion in the model. National income, personnel expenditure and foreign direct investments are macroeconomic variables. These variables measure economic development and promote economic and social progress, which is essential for GG. Capital accumulation and innovation are essential tools in GG transformation. Therefore, financial development and patent applications represent the moderating variables. The authors estimate the fixed effect model with Parks-Kmenta robust.

Empirical results show that national income growth and foreign direct investments positively affect GG. Personnel expenditure negatively affects GG. On the contrary, financial development and patent growth have little moderating role.

This study contributes to the literature on creating a GG model in emerging countries. The study is original in its model and sample.

Increasing overhang of serviced apartments poses a serious concern to the national property market. This study aims to examine the impacts of macroeconomic determinants, namely, gross domestic product (GDP), consumer confidence index (CF), existing stocks (ES), incoming supply (IS) and completed project (CP) on serviced apartment price changes.

To achieve more accurate, quality price changes, a serviced apartment price index (SAPI) was constructed through a self-developed hedonic price index model. This study has collected 1,567 transaction data in Kuala Lumpur, covering 2009Q1–2018Q4 for price index construction and data were analysed using the vector autoregressive model, the vector error correction model and the fully modified ordinary least squares (OLS) (FMOLS).

Results of the regression model show that only GDP, ES and IS were significantly associated with SAPI, with an R² of 0.7, where both ES and IS have inverse relationships with SAPI. More precisely, it is predicted that the price of serviced apartments will be reduced by 0.56% and 0.21% for every 1% increase in ES and IS, respectively.

Therefore, government monitoring of serviced apartments’ future supply is crucial by enforcing land use-planning regulations via stricter development approval of serviced apartments to safeguard and achieve more stable property prices.

By adopting an innovative approach to estimating the response of price change to supply and demand in a situation where there is no price indicator for serviced apartments, the study addresses the knowledge gap, especially in terms of understanding what are the key determinants of, and to what extent they influence, the SAPI.

The purpose of this paper is to study the spillover effects of foreign direct investment (FDI) on skilled–unskilled wage inequality in the Indian manufacturing industries.

The authors show theoretically with a model of spillover that if foreign firms (receiving FDI) have a negative spillover effect on domestic firms (not receiving FDI), then the level of capital and skilled workers in the domestic firms falls down. Consequently, the authors conduct an empirical analysis by using system GMM estimation technique on the firm-level data of the Indian organised manufacturing sector.

The authors show that wage inequality worsens when there is negative spillover effects like competition spillover or skill spillover effect of FDI in India.

To the best of the authors’ knowledge, this is the first attempt to measure the various spillover effects of FDI on the wage inequality in the Indian manufacturing industries by using firm-level data.

This study analyses configurations of national culture as boundary conditions of countries’ national systems of innovation (NSI). Drawing from the NSI approach, we argue that culture’s role is that of a contingency factor shaping the relationship between investments in innovation and national innovation outputs.

We assessed the moderation effect of national culture through a systematic, two-stage approach using fuzzy-set Qualitative Comparative Analysis (fsQCA), which allows the analysis of changes induced by the moderator variables. Analyses were conducted with a diverse sample of 61 countries over a period spanning 12 years, from 2011 to 2022.

Findings reveal that investments in innovation, but not individual cultural dimensions, is a necessary condition for high innovation outputs. Furthermore, several configurations of cultural dimensions were identified as moderators of the relationship between investments in innovation and innovation outputs.

This study provides insights into cross-national innovation research by exposing the role of cultural configurations, rather than just individual cultural dimensions, as boundary conditions involved in the achievement of high levels of innovation.

This paper aims to examine the extent to which performance indicators (PIs) reported by New Zealand (NZ) higher education institutions (HEIs) correspond with accounting standards and guidance and the effects issuance of principles-based authoritative guidance and early adoption of Public Benefit Entity Financial Reporting Standard 48 (PBE FRS 48) have on the PIs disclosed.

Using a content analysis index derived from accounting standards and guidance, we conduct a longitudinal assessment of the 2016 and 2019 statements of service performance published by 22 NZ HEIs.

The PIs reported extend beyond the service performance elements proposed by standard-setters. Despite few indicators on intermediate and broader outcomes, the measures disclosed by HEIs are reflective of their role in the NZ economy and the national Tertiary Education Strategy. The results show that principles-based authoritative guidance and early adoption of PBE FRS 48 influence the focus and type of measures disclosed, while there is no evidence of improvements in the reporting of impacts, outcomes and information useful for performance evaluation.

This paper provides timely insights for standard-setters and regulators on the influence principles-based accounting standards and guidance have on non-financial reporting practices.

This study contributes to the scant literature on HEIs’ service performance reporting. It presents a model for conceptualising HEIs’ PIs that can be used as a basis for future research on non-financial reporting. It also reflects on the tension between accountability and “accountingisation”, suggesting that, although the PIs reported support formal accountability, they do not communicate whether HEIs’ activities and outputs meet their social purpose.

The purpose of this study is to provide the enhancement of power quality of a high power-rated voltage source inverter driven induction motor with a three-phase, three-level neutral point clamped converter placed at the front end, while a passive power filter is connected in shunt with it. The improvement in power quality can be achieved by reducing the total harmonic distortion in source current. The controllers were designed for the linearization of the high-power induction motor drive. A control method is presented for the regulation of the common DC-link voltage.

The induction motor is modeled using its dynamic equations, and a decoupling controller is designed to linearize the nonlinear dynamics of the drive through feedback. The common DC-link voltage of the proposed front-end connected converter is monitored and controlled through a control method which feeds the pulse width modulated inverter that drives the induction motor. A passive power filter is designed to meet the reactive power requirement of the system in addition to improve the power quality.

Simulations were carried out for the proposed topology of the drive mechanism, and the outcomes were analyzed by a comparative analysis of the drive system both in the presence of the passive filter as well as in the absence of the filter. The total harmonic distortion is found to be reduced enough to meet the standards with the designed filter, and the reactive power is also compensated considerably. The input power factor at the supply side is maintained almost to unity, and the DC-link voltage of the proposed circuit topology is maintained at the desired level. The overall performance of the drive system was found to be useful and economical.

A new topology of a front-end connected three-level neutral point clamped converter to a high power-rated induction motor drive is proposed. The drive is fed by a pulse width modulated inverter with a common DC-link with the front end connected converter. A passive filter is designed with respect to the reactive power requirement of the system and connected in shunt to the converter at the supply side. Control schemes are designed and used for the drive system and also for the regulation of the common DC-link voltage of the proposed front end connected converter.

This study focuses on the banking systems evaluation in Portugal, Italy, Ireland, Greece and Spain (known as the PIIGS) during the financial and post-financial crisis period from 2009 to 2018.

A conditional robust nonparametric frontier analysis (order-m estimators) is used to measure banking efficiency combined with variables highlighting the effects of Non-Performing Loans. Next, a truncated regression is used to examine if institutional, macroeconomic, and financial variables affect bank performance differently. Unlike earlier studies, we use the Corruption Perception Index (CPI) as an institutional variable that affects banking sector efficiency.

This research shows that the PIIGS crisis affects each bank/country differently due to their various efficiency levels. Most of the study variables — CPI, government debt to GDP ratio, inflation, bank size — significantly affect banking efficiency measures.

The contribution of this article to the relevant banking literature is two-fold. First, it analyses the efficiency of the PIIGS banking system from 2009 to 2018, focusing on NPLs. Second, this is the first empirical study to use probabilistic frontier analysis (order-m estimators) to evaluate PIIGS banking systems.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.