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This chapter provides an empirical assessment of the effects of infrastructure provision on structural change and aggregate productivity using industrylevel data for a set of developed and developing countries over 1995–2010. A distinctive feature of the empirical strategy followed is that it allows the measurement of the resource reallocation directly attributable to infrastructure provision. To achieve this, a two-level top-down decomposition of aggregate productivity that combines and extends several strands of the literature is proposed. The empirical application reveals significant production losses attributable to misallocation of inputs across firms, especially among African countries. Also, the results show that infrastructure provision has stimulated aggregate total factor productivity growth through both within and between industry productivity gains.

The purpose of this paper is to examine the effects of several structural shocks in oil prices on the Vietnamese economy and answer three key research questions: Is there a relationship between oil price shocks and macroeconomic indicators in Vietnam? How do different types of oil price impulses affect Vietnamese inflation and economic performance? To what extent do structural shocks in oil prices explain variations in Vietnam’s macroeconomic indicators?

Lower triangular Cholesky decomposition is performed on a short-term impact matrix in a two-block structural vector autoregressive model. The data set is defined monthly, from January 2000 to December 2021. The contributions of structural shocks in oil prices to the domestic variances are analysed using variance decomposition methods. In this study, both forecast error variance decomposition and historical decomposition are used.

The consequences of oil price fluctuations on Vietnamese output and inflation depend on different sources of oil price shocks. In comparison, oil supply shocks have an insignificant effect on both domestic industrial output and consumer price index inflation; however, positive shocks in aggregate and precautionary oil demands increase these domestic indicators substantially and sustainably. An analysis of variance decompositions reveals that supply-side oil shocks have very limited explanatory power for variations in domestic variables. Nevertheless, the contributions of unanticipated demand-side booms to domestic variations in the past and projected forecasts are considerable.

The findings from this research uncover potential risks for Vietnam’s economic prospects if the consequences of oil price shocks are not managed effectively.

Given the lack of economic sensitivity to supply-side oil shocks and the strong response to shifts in oil demands, greater pressure on the domestic economy is likely when Vietnam increases its dependence on oil imports.

This paper aims to promote the higher quality development of high-tech enterprises in China. While science and technology have greatly promoted human civilization, resources have been excessively consumed and the environment has been sharply polluted. Therefore, it is particularly important for current enterprises to make use of scientific and technological innovation to maximize the benefits of mankind, minimize the loss of nature, and promote the sustainable development of our country.

By using DEA-Banker-Charnes-Cooper (BCC) model and DEA-Malmquist model, this paper comprehensively examines the innovation efficiency of high-tech enterprises from both static and dynamic perspectives, and conducts a provincial comparative study with the panel data of ten representative provinces from 2011 to 2020.

The research findings are as follows: the rapid number increase of high-tech enterprises in most provinces (cities) is accompanied by an ineffective input–output efficiency; the quality of high-tech enterprises needs to comprehensively examine both input–output efficiency and total factor productivity; and there is not a positive correlation between element investment and innovation performance.

Because the DEA model used in this paper assumes that the improvement direction of invalid units is to ensure that the input ratio of various production factors remains unchanged but sometimes the proportion of scientific and technological activities personnel and the total research and development investment is not constant. In the future, the nonradial DEA model can be considered for further research. Due to historical data statistics, more provinces, cities and longer panel data are difficult to obtain. The samples studied in this paper mainly refer to the provinces and cities that ranked first in the number of national high-tech enterprises in 2020. Limited by the number of samples, DEA analysis failed to select more input and output indicators. In the future, with the accumulation of statistical data, the existing efficiency analysis will be further optimized.

Aiming at the misunderstanding of emphasizing quantity and neglecting quality in the cultivation of high-tech enterprises, this paper comprehensively uses DEA-BCC model and DEA Malmquist index decomposition method to make a comprehensive comparative study on the development of high-tech enterprises in ten representative provinces (cities) from two aspects of static efficiency evaluation and dynamic efficiency evaluation.

This study aims to propose a refined dynamic network slacks-based measure (DNSBM) to evaluate the efficiency of China's regional green innovation system which consists of basic research, applied research and commercialization stages and explore the influencing factors of the stage efficiency.

A two-step procedure is employed. The first step proposes an improved DNSBM model with flexible settings of stages' input or output efficiency and uses second order cone programming (SOCP) to solve the non-linear problem. In the second step, least absolute shrinkage and selection operator (LASSO) and Tobit models are used to explore the influencing factors of the stage efficiency. Global Dynamic Malmquist Productivity Index (GDMPI) and Dagum Gini coefficient decomposition method are introduced for further discussion of the productivity change and regional differences.

On average, Chinese provincial green innovation efficiency should be improved by 24.11% to become efficient. The commercialization stage outperforms the stages of basic research and applied research. Comparisons between the proposed model and input-oriented, output-oriented and non-oriented DNSBM models show that the proposed model is more advanced because it allows some stages to have output-oriented model characteristics while the other stages have input-oriented model characteristics. The examination of the influencing factors reveals that the three stages of the green innovation system have quite diverse influencing factors. Further discussion reveals that Chinese green innovation productivity has increased by 39.85%, which is driven mainly by technology progress, and the increasing tendency of regional differences between northern and southern China should be paid attention to.

This study proposes an improved dynamic three-stage slacks-based measure (SBM) model that allows calculating output efficiency in some stages and input efficiency in the other stages with the application of SOCP approach. In order to capture productivity change, this study develops a GDMPI based on the DNSBM model. In practice, the efficiency of regional green innovation in China and the factors that influence each stage are examined.

This study aims to analyze and compare the relationship between international trade in global value chains (GVC) and greenhouse gas (GHG) emissions for Brazil and China from 2000 to 2016.

The input-output method apply to multiregional tables from Eora-26 to decompose the GHG emissions of the Brazilian and Chinese productive structure.

The data reveals that Chinese production and consumption emissions are associated with power generation and energy-intensive industries, a significant concern among national and international policymakers. For Brazil, the largest territorial emissions captured by the metrics come from services and traditional industry, which reveals room for improving energy efficiency. The analysis sought to emphasize how the productive structure and dynamics of international trade have repercussions on the environmental dimension, to promote arguments that guide the execution of a more sustainable, productive and commercial development strategy and offer inputs to advance discussions on the attribution of climate responsibility.

The metrics did not capture emissions related to land use and deforestation, which are representative of Brazilian emissions.

Comparative analysis of emissions embodied in traditional sectoral trade flows and GVC, on backward and forward sides, for developing countries with the main economic regions of the world.

Ensuring the early detection of structural issues in aircraft is crucial for preserving human lives. One effective approach involves identifying cracks in composite structures. This paper employs experimental modal analysis and a multi-variable Gaussian process regression method to detect and locate cracks in glass fiber composite beams.

The present study proposes Gaussian process regression model trained by the first three natural frequencies determined experimentally using a roving impact hammer method with crystal four-channel analyzer, uniaxial accelerometer and experimental modal analysis software. The first three natural frequencies of the cracked composite beams obtained from experimental modal analysis are used to train a multi-variable Gaussian process regression model for crack localization. Radial basis function is used as a kernel function, and hyperparameters are optimized using the negative log marginal likelihood function. Bayesian conditional probability likelihood function is used to estimate the mean and variance for crack localization in composite structures.

The efficiency of Gaussian process regression is improved in the present work with the normalization of input data. The fitted Gaussian process regression model validates with experimental modal analysis for crack localization in composite structures. The discrepancy between predicted and measured values is 1.8%, indicating strong agreement between the experimental modal analysis and Gaussian process regression methods. Compared to other recent methods in the literature, this approach significantly improves efficiency and reduces error from 18.4% to 1.8%. Gaussian process regression is an efficient machine learning algorithm for crack localization in composite structures.

The experimental modal analysis results are first utilized for crack localization in cracked composite structures. Additionally, the input data are normalized and employed in a machine learning algorithm, such as the multi-variable Gaussian process regression method, to efficiently determine the crack location in these structures.

This study investigates the effect of intellectual capital (IC) and its components on the technical efficiency of Indian commercial banks after controlling the influence of bank-specific and macroeconomic variables.

The study selects a sample of 37 listed Indian commercial banks from 2005 to 2019 and uses the two-step data envelopment analysis (DEA) approach. Banks' technical efficiency scores are first estimated, while the relationship between IC and technical efficiency is examined in the second stage using the panel data Tobit model.

This study's findings suggest a fluctuating trend in the technical efficiency of Indian banks. Notably, from 2015 onwards, a declining technical efficiency trend is observed for all banks. However, private-sector banks outperform public-sector banks in terms of technical efficiency. This study's regression analysis indicates a positive relationship between IC and banks' technical efficiency scores. Further, by decomposing IC into its components like human capital, structural capital and capital employed, the study's findings show that human capital and structural capital enhance banks' technical efficiency. Notably, capital employed reduces technical efficiency. Moreover, bank size, diversification, capitalization, net interest margin and the country's growth rate significantly drive Indian banks' efficiency. In contrast, their operating cost ratio and the country's inflation negatively influence the same.

This study makes a novel endeavor to examine the IC and bank's technical efficiency nexus in the Indian context, encompassing a period of landmark banking reforms.

Due to its high leverage nature, a bank suffers vitally from the credit risk it inherently bears. As a result, managing credit is the ultimate responsibility of a bank. In this chapter, we examine how efficiently banks manage their credit risk via a powerful tool used widely in the decision/management science area called data envelopment analysis (DEA). Among various existing versions, our DEA is a two-stage, dynamic model that captures how each bank performs relative to its peer banks in terms of value creation and credit risk control. Using data from the largest 22 banks in the United States over the period of 1996 till 2013, we have identified leading banks such as First Bank systems and Bank of New York Mellon before and after mergers and acquisitions, respectively. With the goal of preventing financial crises such as the one that occurred in 2008, a conceptual model of credit risk reduction and management (CRR&M) is proposed in the final section of this study. Discussions on strategy formulations at both the individual bank level and the national level are provided. With the help of our two-stage DEA-based decision support systems and CRR&M-driven strategies, policy/decision-makers in a banking sector can identify improvement opportunities regarding value creation and risk mitigation. The effective tool and procedures presented in this work will help banks worldwide manage the unknown and become more resilient to potential credit crises in the 21st century.

The discourse on global value chains (GVC) is undergoing a transformation in terms of its conceptualisation, theorisation and pragmatic applications. Today, the production systems have become more complex as global economic order continues to witness marked geo-economic manoeuvring. Thus, the direction of discourse on GVC ought to move from mere theoretical propositions toward becoming more evidence based. There have been recent studies that have used the governance and upgrading propositions by Gary Gereffi and others to seek quantitative evidence. This study aims to decipher the quantitative discourse on GVC and to set the emerging and future research agenda.

Through a systematic literature review, the authors first analyse the quantitative studies on GVC carried out during the last two decades. The authors then outline a future research agenda and examine a few relevant modelling techniques that could potentially be used to solicit newer evidence in GVC research.

The authors categorise the quantitative discourse on GVC into three crucial themes, namely, GVC framework, GVC participation and position, environmental aspects and regionalisation in GVC. The most commonly used quantitative techniques are gravity model, panel data estimation, structural decomposition analysis and computable general equilibrium modelling.

This paper contributes to the GVC discourse in two ways. Firstly, the authors argue that the theoretical frameworks within the GVC discourse should be complemented by evidence-based quantitative studies. Secondly, the authors suggest potential modelling techniques that can be used on the emerging and future research agenda.

Total factor productivity (TFP) change is an important driver of long-run economic growth in the construction sector. However, examining TFP alone is insufficient to identify the cause of TFP changes. Therefore, this paper employs the infrequently used Geometric Young Index (GYI) and stochastic frontier analysis (SFA) to measure and decompose the TFP Index (TFPI) at the firm-level from 2009 to 2018 based on Malaysian construction firms' data.

To improve the TFPI estimation, normally unobserved environmental variables were included in the GYI-TFPI model. These are the physical operation of the firm (inland versus marine operation) and regional locality (West Malaysia versus East Malaysia). Consequently, the complete components of TFPI (i.e. technological, environmental, managerial, and statistical noise) can be accurately decomposed.

The results reveal that TFP change is affected by technological stagnation and improvements in technical efficiency but a decline in scale-mix efficiency. Moreover, the effect of environmental efficiency on TFP is most profound. In this case, being a marine construction firm and operating in East Malaysia can reduce TFPI by up to 38%. The result, therefore, indicates the need for progressive policies to improve long-term productivity.

Monitoring and evaluating productivity change allows an informed decision to be made by managers/policy makers to improve firms' competitiveness. Incentives and policies to improve innovation, competition, training, removing unnecessary taxes and regulation on outputs (inputs) could enhance the technological, technical and scale-mix of resources. Furthermore, improving public infrastructure, particularly in East Malaysia could improve regionality locality in relation to the environmental index.

This study contributes to knowledge by demonstrating how TFP components can be completely modelled using an aggregator index with good axiomatic properties and SFA. In addition, this paper is the first to apply and include the GYI and environmental variables in modelling construction productivity, which is of crucial importance in formulating appropriate policies.