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This paper provides novel evidence on the role of gender in the performance of university students, which is particularly relevant to the debate on the performance of female students in science, technology, engineering and mathematics (STEM) subjects.

Our approach relies on the metafrontier approach proposed by Huang et al. (2014), which measures students' efficiency within a given faculty and the impact of the faculty’s technology on students’ efficiency. We use a sample of 53,159 first-year students in 8 faculties from a large university in southern Italy from 2002–2003 to 2010–2011.

Students’ efficiency is relatively low, reflecting an essential role of unobserved heterogeneity. The different technologies of somewhat similar faculties have minimal impact on efficiency. There is a performance gap against women in five faculties, which on average is strongest for the faculties in the pure and applied science area. This gap increases with the proportion of female students and decreases with female lecturers.

The metafrontier has the benefit of providing relevant policy information on the drivers of student success by relying on data that universities routinely generate and preserve.

The stochastic metafrontier approach allows us to separate the group-specific frontiers from the metafrontier, yielding a decomposition of the efficiency scores of various faculties into technical efficiency scores and technological gaps.

The purpose of this chapter is to cover banking efficiency using the concept of the Meta frontier function and to study group and subgroup differences in the production technology. This study estimates the technical efficiency (TE) and technology gap ratios (TGRs) for banks in Islamic countries. Using the assumption of the convex hull of the Meta frontier production set using the virtual Meta frontier within the nonparametric approach as presented by Battese and Rao (2002), Battese et al. (2004), and O'Donnell et al. (2007, 2008) and after relaxing this assumption, the study investigates if there is a significant difference between these two methods. To overcome the deterministic criterion addressed to nonparametric approach, the bootstrapping technique has been applied. The first part of this chapter covers the analytical framework necessary for the definition of a Meta frontier function and its estimation using nonparametric data envelopment analysis (DEA) in the case where we impose the assumption of the convex production set and follows in the case of relaxation of this assumption. Then we estimated the TE and the TGR in concave and nonconcave Meta frontier cases by applying the Bootstrap-DEA approach. The empirical part will be reserved for highlighting these methods on data bank to study the technical and technological performance level and prove if there is a difference between the two methods. Three groups of banks namely commercial, investment, and Islamic banks in 17 Islamic countries over a period of 16 years between 1996 and 2011 are used.

Using the most recent dataset from 2013–2014 to 2017–2018, the study examines the efficiency of 75 coal-fired power plants in the Indian thermal power sector. The authors obtained robust estimates of efficiency scores by employing Seiford and Zhu’s (2002) DEA-based classification invariance technique to account for CO₂ emissions as an undesirable output. Meta-frontier analysis and the Tobit regression are used to compute technology heterogeneity across power plants belonging to public and private groups and investigate the factors driving carbon-adjusted efficiency, respectively. The results reveal that, on average, the efficiency of power plants during the study period is 78.26%, showing significant room for reduction in CO₂ emissions alongside augmentation in electricity generation. Private plants are more efficient than public ones, and relative performance inefficiency is the primary source of inefficiency in the thermal power sector. Regression analysis indicates that domestic-equipped plants perform with lesser levels of efficiency, and plants with more units are more inefficient than plants with fewer units. Carbon productivity significantly improves efficiency since fewer fossil fuels with high carbon will generate more electricity.

The study seeks to shed light on the estimates of the carbon shadow price in the literature relying on frontier techniques. The shadow price of undesirable outputs, such as greenhouse gas emissions, assists policymakers in determining the most cost-effective methods for reducing emissions.

The study relies on the PSALSAR and PRISMA approaches for a systematic literature review. The Web of Science and Scopus databases were used for the references.

Both parametric and nonparametric methods have been employed in the literature to estimate the shadow prices of undesirable outputs. Also, results were discussed according to the methodological and application aspects, and broad conclusions on obtained results were provided, bridging climate change mitigation policies and the shadow price of undesirable outputs.

The present study applies an integrated method, PSALSAR, to conduct a systematic review of 53 studies published between 2014 and 2023 in which efficiency models were applied to estimate the shadow price of undesirable outputs, especially CO₂. After presenting the most applicable parametric and nonparametric estimation models, a systematic summary of included articles was provided, highlighting the key features of publications.

Small-scale farmers are highly heterogeneous with regard to their types of farming, levels of technology adoption, degree of commercialization and many other factors. Such heterogeneous types, respectively groups of small-scale farming systems require different forms of government interventions. This paper applies a machine learning approach to analyze the typologies of small-scale farmers in South Africa based on a wide range of objective variables regarding their personal, farm and context characteristics, which support an effective, target-group-specific design and communication of policies.

A cluster analysis is performed based on a comprehensive quantitative and qualitative survey among 212 small-scale farmers, which was conducted in 2019 in the Limpopo Province of South Africa. An unsupervised machine learning approach, namely Partitioning Around Medoids (PAM), is applied to the survey data. Subsequently, the farmers' risk perceptions between the different clusters are analyzed and compared.

According to the results of the cluster analysis, the small-scale farmers of the investigated sample can be grouped into four types: subsistence-oriented farmers, semi-subsistence livestock-oriented farmers, semi-subsistence crop-oriented farmers and market-oriented farmers. The subsequently analyzed risk perceptions and attitudes differ considerably between these types.

This is the first typologisation of small-scale farmers based on a comprehensive collection of quantitative and qualitative variables, which can all be considered in the analysis through the application of an unsupervised machine learning approach, namely PAM. Such typologisation is a pre-requisite for the design of more target-group-specific and suitable policy interventions.

Women make a major contribution to the agricultural sector, especially in developing countries. Despite this, women still face many obstacles in carrying out their agricultural activities. These obstacles have a negative impact on their productivity and create a gender gap. This paper analyses the difference in agricultural productivity between male-headed and female-headed households in Burkina Faso.

Using data from the Permanent Agricultural Survey (EPA), we applied the Blinder-Oaxaca decomposition method to determine the size of the gender gap and identify the variables explaining this gap. In this study, we used the value of production per farm worker as a measure of productivity.

The results indicate a gender gap of 43.8 percentage points in favor of male-headed households. Around 131% of this difference is explained by differences in observable household characteristics. The factor that most explains this difference in productivity is the difference in the total area of land available to households.

This finding calls for women’s access to land to be considered in the design and implementation of agricultural development policies.

One of the main contributions of this article in relation to previous studies lies in the unit of analysis. Rather than focusing on individual producers, as in previous studies, we have instead considered the household as the unit of analysis, since in developing countries such as Burkina Faso, production decisions are taken at household level. It contributes to inform economic policy decisions by providing decision-makers with the factors on which they can act to bring about an increase in agricultural productivity by reducing the gap between male-headed households and female-headed households.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-11-2023-0923

Today’s one of the most important difficulties is tackling climate change and its effects on the environment. The Paris Agreement states that nations must balance the amount of greenhouse gases they emit and absorb until 2050 to contribute to the mitigation of greenhouse gases and to support sustainable development. According to the agreement, each country must determine, plan and regularly report on its contributions. Thus, it is important for the countries to predict and analyze their net zero performances in 2050. Therefore, the aim of this study is to evaluate European Continent Countries' net zero performances at the targeted year.

The European Continent Countries that ratified the Paris Agreement are specified as decision making units (DMUs). Input and output indicators are specified as primary energy consumption, freshwater withdrawals, gross domestic product (GDP), carbon-dioxide (CO₂) and nitrous-oxide (N₂O) emissions. Data from 1980 to 2019 are obtained and forecasted using autoregressive integrated moving average (ARIMA) until 2050. Then, the countries are clustered based on the forecasts of primary energy consumption and freshwater withdrawals using k-means algorithm. As desirable and undesirable outputs arise simultaneously, the performances are computed using Pure Environmental Index (PEI) and Mixed Environmental Index (MEI) data envelopment analysis (DEA) models.

It is expected that by 2050, CO₂ emissions of seven countries remain constant, N₂O emissions of seven countries remain stable and five countries’ both CO₂ and N₂O emissions remain constant. While it can be seen as success that many countries are expected to at least stabilize one emission, the likelihood of achieving net zero targets diminishes unless countries undertake significant reductions in emissions. According to the results, in Cluster 1, Turkey ranks last, while France, Germany, Italy and Spain are efficient countries. In Cluster 2, the United Kingdom ranks at last, while Greece, Luxembourg, Malta and Sweden are efficient countries.

In the literature, generally, CO₂ emission is considered as greenhouse gas. Moreover, none of the studies measured the net-zero performance of the countries in 2050 employing analytical techniques. This study objects to investigate how well European Continent Countries can comply with the necessities of the Agreement. Besides CO₂ emission, N₂O emission is also considered and the data of European Continent Countries in 2050 are estimated using ARIMA. Then, countries are clustered using k-means algorithm. DEA models are employed to measure the performances of the countries. Finally, forecasts and models validations are performed and comprehensive analysis of the results is conducted.

This paper explores the impact of multiple team membership (MTM) on the productivity of team members in engineering consulting firms. MTM refers to employees participating concurrently in multiple teams, a concept closely linked to projectification. Despite the fact that this concept can enhance collaboration, it also introduces coordination challenges that may negatively affect productivity.

Through an inductive approach involving 12 semi-structured interviews with engineering consulting professionals specializing in water and energy infrastructure projects, this paper examines the factors affecting team member productivity in an MTM setting. Following the interviews, a Delphi technique was employed, engaging 16 experts to rank the factors and sub-factors identified from the interview data. This two-stage approach ensured a comprehensive and validated assessment of productivity factors.

This study develops 8 factors process model grounded in structuration theory to explain the socio-technical mechanisms by which multiple team membership shapes productivity outcomes in engineering consulting firms specialized in water and energy infrastructure projects. Key findings surface micro-foundations, tensions in technology provisions, planning processes, and career development that inform theoretical advances and practical improvements.

This research contributes empirically insights into managing MTM in expert service contexts. Applying Giddens' structuration theory, this study reveals how agency and structures shape productivity across organizational, team, and individual levels. In practice, this study provides recommendations for improving productivity within projectified environments, mainly for team members working in an MTM environment in engineering consulting firms specializing in water and energy infrastructure projects.

This paper aims to present an example of the measurement of airport efficiency, a topic of great interest in civil aviation today. The methodology used is data envelopment analysis (DEA) and the Malmquist Index. The calculation of airport efficiency with up-to-date data for each period is of great importance in the context of sustainability. The study selected ten airports with high air traffic in Turkey as the sample set. The objective of this study is to evaluate the current state of airport efficiency, identify the sources of inefficiency and make appropriate policy recommendations based on the findings obtained.

The study is based on DEA and Malmquist Index Analysis. The number of personnel and terminal size data of ten selected decision-making units (DMU) are used as inputs, while passenger, cargo and aircraft traffic data are used as outputs. A five-year period, spanning from 2018 to 2022, is considered as the data set in the study.

Upon analysis of the data from the ten airports included in the study, it was found that the current input-output combination yielded efficient results, with the exception of certain characteristics, such as the impact of seasonal conditions or tourism. Concurrent with the growth in aircraft, passenger and freight traffic, the number of personnel employed at these airports has also increased. It was concluded that technological efficiency is of paramount importance for each airport, and that investments in technology should be increased.

A separate assessment was conducted for each of the ten airports included in the study sample. Each airport was evaluated in terms of its strengths and weaknesses, and areas of low efficiency were identified. Consequently, more general conclusions were reached than airport-specific evaluations.

In order to ensure the long-term sustainability of the sector, it is essential that the efficiency measurements of airports are calculated using up-to-date data on a regular basis. The results obtained from these calculations provide guidance for the strategic plans to be implemented in the long term, as well as for the solution proposals for operational problems. In this context, this study not only provides information to policymakers and airport managers about the current situation, as it includes recent data, but also contributes to the literature in this sense, as it includes policy recommendations.

The interconnected challenges of climate change and social inclusivity have placed unprecedented pressure on businesses to adopt responsible practices. While previous research has explored the individual impacts of environmental, social, and governance (ESG) performance and diversity initiatives, there remains a dearth of comprehensive investigations into how these factors collectively influence carbon emission scores. Drawing on the legitimacy theory, we explore whether ESG and diversity scores predict global companies' carbon emission scores. As concerns about the environmental impact of businesses grow, understanding the relationships between ESG performance, diversity management, and carbon emissions becomes imperative for sustainable corporate practices.

The primary dataset for this study includes 1,268 worldwide firm-year data for 2021. The sample is subjected to missing data examination as a component of the filtration process. Data preprocessing is performed before machine learning analysis, including verifying missing data. Our research resulted in the final sample, which includes 627 worldwide firm data from 2021. Data regarding all publicly traded companies was obtained from Refinitiv Eikon.

Our findings showed that corporate carbon emission performance in global corporations is influenced by ESG performance and total diversity score.

Firms involve in ESG as well as diversity practices to be able to achieve sustainable success. Yet, the forecasting of carbon emissions based on ESG scores and diversity scores remains inadequately established due to conflicting findings and enigmas prevalent in the literature.