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Available. Open Access. Open Access
Article
Publication date: 21 February 2020

Faiza Ahsan, Abbas Ali Chandio and Wang Fang

This paper aims to examine the effects of CO2 emissions, energy consumption, cultivated area and the labour force on the production of cereal crops in Pakistan from the period…

6711

Abstract

Purpose

This paper aims to examine the effects of CO2 emissions, energy consumption, cultivated area and the labour force on the production of cereal crops in Pakistan from the period 1971-2014.

Design/methodology/approach

The study used the Johansen cointegration test, the autoregressive distributed lag (ARDL) approach and Granger causality test to estimate the long-run cointegration and direction of the relationship between the dependent and independent variables.

Findings

The outcomes of the Johansen cointegration test confirmed the existence of a long-term cointegrating relationship between the production of cereal crops, CO2 emissions, energy consumption, cultivated area and the labour force. The results of the long-run coefficients of CO2 emissions, energy consumption, cultivated area and labour force have a positive impact on cereal crops production. The long-run relationships reveal that a 1 per cent increase in CO2 emissions, energy consumption, cultivated area and labour force will increase cereal crops production by 0.20, 0.11, 0.56 and 0.74 per cent, respectively. Moreover, the findings show that there is a bidirectional causality running from CO2 emissions and cultivated area to cereal crops production. Moreover, there is a unidirectional causality running from energy consumption to cereal crops production.

Originality/value

The present study also fills the literature gap for applying the ARDL procedure to examine this relevant issue for Pakistan.

Details

International Journal of Climate Change Strategies and Management, vol. 12 no. 2
Type: Research Article
ISSN: 1756-8692

Keywords

Available. Open Access. Open Access
Article
Publication date: 11 March 2022

Haiyan Jiang, Jing Jia and Yuanyuan Hu

This study aims to investigate whether firms purchase directors' and officers' liability (D&O) insurance when the country-level economic policy uncertainty (EPU) is high.

1898

Abstract

Purpose

This study aims to investigate whether firms purchase directors' and officers' liability (D&O) insurance when the country-level economic policy uncertainty (EPU) is high.

Design/methodology/approach

This study uses D&O insurance data from Chinese listed firms between 2003 and 2019 to conduct regression analyses to examine the association between D&O insurance and EPU.

Findings

The results show that government EPU, despite being an exogenous factor, increases the likelihood of firms' purchasing D&O insurance, and this effect is more pronounced when firms are exposed to great share price crash risk and high litigation risk, suggesting that firms intend to purchase D&O insurance possibly due to the accentuated stock price crash risk and litigation risk associated with EPU. In addition, the results indicate that the effect of EPU on the D&O insurance purchase decision is moderated by the provincial capital market development and internal control quality.

Practical implications

The study highlights the role of uncertain economic policies in shareholder approval of D&O insurance purchases.

Originality/value

The study enriches the literature on the determinants of D&O insurance purchases by documenting novel evidence that country-level EPU is a key institutional factor shaping firms' decisions to purchase D&O insurance.

Details

China Accounting and Finance Review, vol. 24 no. 1
Type: Research Article
ISSN: 1029-807X

Keywords

Available. Open Access. Open Access
Article
Publication date: 16 April 2019

Zhishuo Liu, Yao Dongxin, Zhao Kuan and Wang Chun Fang

There is a certain error in the satellite positioning of the vehicle. The error will cause the drift point of the positioning point, which makes the vehicle trajectory shift to…

1015

Abstract

Purpose

There is a certain error in the satellite positioning of the vehicle. The error will cause the drift point of the positioning point, which makes the vehicle trajectory shift to the real road. This paper aims to solve this problem.

Design/methodology/approach

The key technology to solve the problem is map matching (MM). The low sampling frequency of the vehicle is far from the distance between adjacent points, which weakens the correlation between the points, making MM more difficult. In this paper, an MM algorithm based on priority rules is designed for vehicle trajectory characteristics at low sampling frequencies.

Findings

The experimental results show that the MM based on priority rule algorithm can effectively match the trajectory data of low sampling frequency with the actual road, and the matching accuracy is better than other similar algorithms, the processing speed reaches 73 per second.

Research limitations/implications

In the algorithm verification of this paper, although the algorithm design and experimental verification are considered considering the diversity of GPS data sampling frequency, the experimental data used are still a single source.

Originality/value

Based on the GPS trajectory data of the Ministry of Transport, the experimental results show that the accuracy of the priority-based weight-based algorithm is higher. The accuracy of this algorithm is over 98.1 per cent, which is better than other similar algorithms.

Details

International Journal of Crowd Science, vol. 3 no. 1
Type: Research Article
ISSN: 2398-7294

Keywords

Available. Open Access. Open Access
Article
Publication date: 6 December 2023

Md. Mahadi Hasan and A.T.M. Adnan

Growing food insecurity is a leading cause of fatalities, particularly in developing nations like Sub-Saharan Africa and Southeast Asia. However, the rising energy consumption and…

1076

Abstract

Purpose

Growing food insecurity is a leading cause of fatalities, particularly in developing nations like Sub-Saharan Africa and Southeast Asia. However, the rising energy consumption and carbon dioxide (CO2) emissions are mostly associated with food production. Balancing the trade-offs between energy intensity and food security remains a top priority for environmentalists. Despite the critical role of the environment in food security, there is a scarcity of substantial studies that explore the statistical connections among food security, CO2 emissions, energy intensity, foreign direct investment (FDI) and per capita income. Therefore, this study aims to provide more precise and consistent estimates of per capita CO2 emissions by considering the interplay of food security and energy intensity within the context of emerging economies.

Design/methodology/approach

To examine the long-term relationships between CO2 emissions, food security, energy efficiency, FDI and economic development in emerging economies, this study employs correlated panel-corrected standard error, regression with Newey–West standard error and regression with Driscoll–Kraay standard error models (XTSCC). The analysis utilizes data spanning from 1980 to 2018 and encompasses 32 emerging economies.

Findings

The study reveals that increasing food security in a developing economy has a substantial positive impact on both CO2 emissions and energy intensity. Each model, on average, demonstrates that a 1 percent improvement in food security results in a 32% increase in CO2 levels. Moreover, the data align with the Environmental Kuznets Curve (EKC) theory, as it indicates a positive correlation between gross domestic product (GDP) in developing nations and CO2 emissions. Finally, all experiments consistently demonstrate a robust correlation between the Food Security Index (FSI), energy intensity level (EIL) and exchange rate (EXR) in developing markets and CO2 emissions. This suggests that these factors significantly contribute to environmental performance in these countries.

Originality/value

This study introduces novelty by employing diverse techniques to uncover the mixed findings regarding the relationship between CO2 emissions and economic expansion. Additionally, it integrates energy intensity and food security into a new model. Moreover, the study contributes to the literature by advocating for a sustainable development goal (SDG)-oriented policy framework that considers all variables influencing economic growth.

Details

Journal of Business and Socio-economic Development, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2635-1374

Keywords

Available. Open Access. Open Access
Article
Publication date: 28 February 2023

Ahmad Hariri, Pedro Domingues and Paulo Sampaio

This paper aims to classify journal papers in the context of hybrid quality function deployment QFD and multi-criteria decision-making (MCDM) methods published during 2004–2021.

2983

Abstract

Purpose

This paper aims to classify journal papers in the context of hybrid quality function deployment QFD and multi-criteria decision-making (MCDM) methods published during 2004–2021.

Design/methodology/approach

A conceptual classification scheme is presented to analyze the hybrid QFD-MCDM methods. Then some recommendations are given to introduce directions for future research.

Findings

The results show that among all related areas, the manufacturing application has the most frequency of published papers regarding hybrid QFD-MCDM methods. Moreover, using uncertainty to establish a hybrid QFD-MCDM the relevant papers have been considered during the time interval 2004–2021.

Originality/value

There are various shortcomings in conventional QFD which limit its efficiency and potential applications. Since 2004, when MCDM methods were frequently adopted in the quality management context, increasing attention has been drawn from both practical and academic perspectives. Recently, the integration of MCDM techniques into the QFD model has played an important role in designing new products and services, supplier selection, green manufacturing systems and sustainability topics. Hence, this survey reviewed hybrid QFD-MCDM methods during 2004–2021.

Details

International Journal of Quality & Reliability Management, vol. 40 no. 10
Type: Research Article
ISSN: 0265-671X

Keywords

Available. Content available
Article
Publication date: 14 June 2022

Yanping Fang, Lynn Paine and Rongjin Huang

This special issue reveals how lesson study in China continues to serve as a powerful platform to support change in teaching. The papers included in this issue explore how…

1244

Abstract

Purpose

This special issue reveals how lesson study in China continues to serve as a powerful platform to support change in teaching. The papers included in this issue explore how university faculty members and researchers support teachers to cross boundaries resulting from the introduction of key competencies-based (hexin suyang 核心素养) curriculum reform (KCR).

Design/methodology/approach

The theme of continuity and change is examined against the backdrop of Chinese lesson study's (CLS's) consistent supporting role in enabling curriculum reform. These analyses make use of concepts involved in understanding boundary crossing, such as using boundary objects and their roles, to help make sense of the new theories, tools, and resources as well as relationships engendered in responding to the reform's demand. While recognizing the continuity at play in Chinese LS, the authors use the lens of learning at the boundary of research-practice partnerships (RPPs) (Farrell et al., 2022) to contemplate the future of CLS.

Findings

The papers touch on three major themes: (1) the role of university-school partnerships in meeting the new demands of key competencies reform; (2) resourceful tools, strategies and structures to support boundary crossing for teachers; and (3) roles and relationships for mutual learning in university-school partnerships. Together these three themes, considered across the papers in this issue, point to the need to redefine CLS to engender versatility and hybridity and to enlist mutual learning relationships in future university-school partnerships. Such redefinition positions lesson study to both continue and change.

Research limitations/implications

The papers in this issue are expected to promote mutualist learning in future CLS research-practice partnerships. To do so, research needs to move from focusing on change of a single case teacher to clarifying what experts and teachers each learn from the LS and from each other. Attention also needs to focus on the collaborative discourse and ways such discourse is able to promote mutual learning, emotional support in facing change as well as critical and constructive problem solving.

Practical implications

Practically, to better support boundary crossing, this special issue encourages academics and teachers to identify and work around boundary objects and their enabling features to enhance knowledge and identity of both university and teacher participants for more effective research-practice partnerships.

Originality/value

This special issue offers a pioneering set of studies that contributes to an in-depth understanding of how CLS is supporting the current competencies-based reform in China. It also provides concrete future directions for research and practice to enhance university-school partnerships' response to reform.

Available. Open Access. Open Access
Article
Publication date: 14 August 2017

Xiu Susie Fang, Quan Z. Sheng, Xianzhi Wang, Anne H.H. Ngu and Yihong Zhang

This paper aims to propose a system for generating actionable knowledge from Big Data and use this system to construct a comprehensive knowledge base (KB), called GrandBase.

2147

Abstract

Purpose

This paper aims to propose a system for generating actionable knowledge from Big Data and use this system to construct a comprehensive knowledge base (KB), called GrandBase.

Design/methodology/approach

In particular, this study extracts new predicates from four types of data sources, namely, Web texts, Document Object Model (DOM) trees, existing KBs and query stream to augment the ontology of the existing KB (i.e. Freebase). In addition, a graph-based approach to conduct better truth discovery for multi-valued predicates is also proposed.

Findings

Empirical studies demonstrate the effectiveness of the approaches presented in this study and the potential of GrandBase. The future research directions regarding GrandBase construction and extension has also been discussed.

Originality/value

To revolutionize our modern society by using the wisdom of Big Data, considerable KBs have been constructed to feed the massive knowledge-driven applications with Resource Description Framework triples. The important challenges for KB construction include extracting information from large-scale, possibly conflicting and different-structured data sources (i.e. the knowledge extraction problem) and reconciling the conflicts that reside in the sources (i.e. the truth discovery problem). Tremendous research efforts have been contributed on both problems. However, the existing KBs are far from being comprehensive and accurate: first, existing knowledge extraction systems retrieve data from limited types of Web sources; second, existing truth discovery approaches commonly assume each predicate has only one true value. In this paper, the focus is on the problem of generating actionable knowledge from Big Data. A system is proposed, which consists of two phases, namely, knowledge extraction and truth discovery, to construct a broader KB, called GrandBase.

Details

PSU Research Review, vol. 1 no. 2
Type: Research Article
ISSN: 2399-1747

Keywords

Available. Open Access. Open Access
Article
Publication date: 7 December 2021

Le Tao, Yun Su and Xiuqi Fang

The intended nationally determined contributions (INDCs) is a major outcome of the Paris Agreement on international cooperation to reduce emissions, and is likely to be the future…

1119

Abstract

Purpose

The intended nationally determined contributions (INDCs) is a major outcome of the Paris Agreement on international cooperation to reduce emissions, and is likely to be the future scenario for carbon emissions. This paper aims to obtain the fine spatial pattern of carbon emissions in 2030, identify hot spots and analyze changes of carbon emissions with a spatial grid method.

Design/methodology/approach

Based on the integrated quantified INDCs of each economy in 2030, the authors predict the population density pattern in 2030 by using the statistics of current population density, natural growth rates and differences in population growth resulting from urbanization within countries. Then the authors regard population density as a comprehensive socioeconomic indicator for the top-bottom allocation of the INDC data to a 0.1° × 0.1° grid. Then, the grid spatial pattern of carbon emissions in 2030 is compared with that in 2016.

Findings

Under the unconditional and conditional scenarios, the global carbon emission grid values in 2030 will be within [0, 59,200.911] ktCO2 and [0, 51,800.942] ktCO2, respectively; eastern China, northern India, Western Europe and North America will continue to be the major emitters; grid carbon emissions will increase in most parts of the world compared to 2016, especially in densely populated areas.

Originality/value

While many studies have explored the overall global carbon emissions or warming under the INDC scenario, attention to spatial details is also required to help us make better emissions attributions and policy decisions from the perspective of the grid unit rather than the administrative unit.

Details

International Journal of Climate Change Strategies and Management, vol. 14 no. 1
Type: Research Article
ISSN: 1756-8692

Keywords

Available. Open Access. Open Access
Article
Publication date: 12 July 2022

Zheng Xu, Yihai Fang, Nan Zheng and Hai L. Vu

With the aid of naturalistic simulations, this paper aims to investigate human behavior during manual and autonomous driving modes in complex scenarios.

1210

Abstract

Purpose

With the aid of naturalistic simulations, this paper aims to investigate human behavior during manual and autonomous driving modes in complex scenarios.

Design/methodology/approach

The simulation environment is established by integrating virtual reality interface with a micro-simulation model. In the simulation, the vehicle autonomy is developed by a framework that integrates artificial neural networks and genetic algorithms. Human-subject experiments are carried, and participants are asked to virtually sit in the developed autonomous vehicle (AV) that allows for both human driving and autopilot functions within a mixed traffic environment.

Findings

Not surprisingly, the inconsistency is identified between two driving modes, in which the AV’s driving maneuver causes the cognitive bias and makes participants feel unsafe. Even though only a shallow portion of the cases that the AV ended up with an accident during the testing stage, participants still frequently intervened during the AV operation. On a similar note, even though the statistical results reflect that the AV drives under perceived high-risk conditions, rarely an actual crash can happen. This suggests that the classic safety surrogate measurement, e.g. time-to-collision, may require adjustment for the mixed traffic flow.

Research limitations/implications

Understanding the behavior of AVs and the behavioral difference between AVs and human drivers are important, where the developed platform is only the first effort to identify the critical scenarios where the AVs might fail to react.

Practical implications

This paper attempts to fill the existing research gap in preparing close-to-reality tools for AV experience and further understanding human behavior during high-level autonomous driving.

Social implications

This work aims to systematically analyze the inconsistency in driving patterns between manual and autopilot modes in various driving scenarios (i.e. multiple scenes and various traffic conditions) to facilitate user acceptance of AV technology.

Originality/value

A close-to-reality tool for AV experience and AV-related behavioral study. A systematic analysis in relation to the inconsistency in driving patterns between manual and autonomous driving. A foundation for identifying the critical scenarios where the AVs might fail to react.

Details

Journal of Intelligent and Connected Vehicles, vol. 5 no. 3
Type: Research Article
ISSN: 2399-9802

Keywords

Available. Open Access. Open Access
Article
Publication date: 3 October 2024

Xiaoyue Chen, Bin Li, Tarlok Singh and Andrew C. Worthington

Motivated by the significant role of uncertainty in affecting investment decisions and China's economic leadership in Asia, this paper investigates the predictive role of exposure…

352

Abstract

Purpose

Motivated by the significant role of uncertainty in affecting investment decisions and China's economic leadership in Asia, this paper investigates the predictive role of exposure to Chinese economic policy uncertainty at the individual stock level in large Asian markets.

Design/methodology/approach

We estimate the monthly uncertainty exposure (beta) for each stock and then employ the portfolio-level sorting analysis to investigate the relationship between the China’s uncertainty exposure and the future returns of major Asian markets over multiple trading horizons. The raw returns of the high-minus-low portfolios are then adjusted using conventional asset pricing models to investigate whether the relationship is explained by common risk factors. Finally, we check the robustness of the portfolio-level results through firm-level Fama and MacBeth (1973) regressions.

Findings

Applying portfolio-level sorting analysis, we reveal that exposure to Chinese uncertainty is negatively related to the future returns of large stocks over multiple trading horizons in Japan, Hong Kong and India. We discover this is unexplained by common risk factors, including market, size, value, profitability, investment and momentum, and is robust to the specification of stock-level Fama and MacBeth (1973) regressions.

Research limitations/implications

Our analysis demonstrates the spillover effects of Chinese economic policy uncertainty across the region, provides evidence of China's emerging economic leadership, and offers trading strategies for managing uncertainty risks.

Originality/value

The findings of the study significantly improve our understanding of stock return predictability in Asian markets. Unlike previous studies, our results challenge the leading role of the US by providing a new intra-regional return predictor, namely, China’s uncertainty exposure. These results also evidence the continuing integration of the Asian economy and financial markets. However, contrary findings for some Asian markets point toward certain market-specific features. Compared with market-level research, our analysis provides deeper insights into the performance of individual stocks and is of particular importance to investors and other market participants.

Details

China Accounting and Finance Review, vol. 26 no. 5
Type: Research Article
ISSN: 1029-807X

Keywords

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