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Article
Publication date: 7 August 2017

Jinjin Wang, Zhengxin Wang and Qin Li

In recent years, continuous expansion of the scale of the new energy export industry in China caused a boycott of American and European countries. Export injury early

Abstract

Purpose

In recent years, continuous expansion of the scale of the new energy export industry in China caused a boycott of American and European countries. Export injury early warning research is an urgent task to develop the new energy industry in China. The purpose of this paper is to build an indicator system of exports injury early warning of the new energy industry in China and corresponding quantitative early warning models.

Design/methodology/approach

In consideration of the actual condition of the new energy industry in China, this paper establishes an indicator system according to four aspects: export price, export quantity, impact on domestic industry and impact on macro economy. Based on the actual data of new energy industry and its five sub-industries (solar, wind, nuclear power, smart grid and biomass) in China from 2003 to 2013, GM (1,1) model is used to predict early warning index values for 2014-2018. Then, the principal component analysis (PCA) is used to obtain the comprehensive early warning index values for 2003-2018. The 3-sigma principle is used to divide the early warning intervals according to the comprehensive early warning index values for 2003-2018 and their standard deviation. Finally, this paper determines alarm degrees for 2003-2018.

Findings

Overall export condition of the new energy industry in China is a process from cold to normal in 2003-2013, and the forecast result shows that it will be normal from 2014 to 2018. The export condition of the solar energy industry experienced a warming process, tended to be normal, and the forecast result shows that it will also be normal in 2014-2018. The biomass and other new energy industries and nuclear power industry show a similar development process. Export condition of the wind energy industry is relatively unstable, and it will be partially hot in 2014-2018, according to the forecast result. As for the smart grid industry, the overall export condition of it is normal, but it is also unstable, in few years it will be partially hot or partially cold. The forecast result shows that in 2014-2018, it will maintain the normal state. In general, there is a rapid progress in the export competitiveness of the new energy industry in China in the recent decade.

Practical implications

Export injury early warning research of the new energy industry can help new energy companies to take appropriate measures to reduce trade losses in advance. It can also help the relevant government departments to adjust industrial policies and optimize the new energy industry structure.

Originality/value

This paper constructs an index system that can measure the alarm degrees of the new energy industry. By combining the GM (1,1) model and the PCA method, the problem of warning condition detection under small sample data sets is solved.

Details

Grey Systems: Theory and Application, vol. 7 no. 2
Type: Research Article
ISSN: 2043-9377

Keywords

Content available
Article
Publication date: 28 January 2020

Harkunti Pertiwi Rahayu, Louise K. Comfort, Richard Haigh, Dilanthi Amaratunga and Devina Khoirunnisa

This study aims to identify the gaps in current policy and propose a viable framework for policy improvement regarding people-centered tsunami early warning chain in…

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1908

Abstract

Purpose

This study aims to identify the gaps in current policy and propose a viable framework for policy improvement regarding people-centered tsunami early warning chain in Padang City. The objectives are: to describe the gaps and flaws in the current policy regarding local tsunami early warning chain, to identify potential actors to be involved in the tsunami early warning chain and to assess the roles and capacity of actors, and their potential for involvement in early warning.

Design/methodology/approach

This study is an exploratory study using social network analysis (SNA) on regulations and other legal documents, and primary data sources from a focus group discussion and semi-structured interviews.

Findings

The study found that the existed regulation lacks extension nodes to relay warnings to the populations at risk, often referred to as “the last mile.” Moreover, receiving warning information from both formal and informal sources is important to mobilize people evacuation more effectively during an emergency. The study found that mosque communities and disaster preparedness leaders are the potential actors who should be involved in the local early warning chain.

Practical implications

The research findings were presented as a recommendation to Padang City Government and have been legalized as the new tsunami early warning chain procedure in the Padang City Mayor Regulation 19/2018.

Originality/value

This research investigated local tsunami early warning dissemination in Padang City using SNA. The study demonstrates a close collaboration between researchers, practitioners and the community.

Details

International Journal of Disaster Resilience in the Built Environment, vol. 11 no. 2
Type: Research Article
ISSN: 1759-5908

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Article
Publication date: 4 October 2011

Hongjin Xiang, Feng Zongxian and Liu Xuyuan

Based on the American antidumping cases against China, the purpose of this paper is to construct an early warning model for Chinese exports.

Abstract

Purpose

Based on the American antidumping cases against China, the purpose of this paper is to construct an early warning model for Chinese exports.

Design/methodology/approach

In order to overcome the drawbacks of the existing early warning models for antidumping, first, the authors screen out six most relevant indices that play a key role in US textile corporations' decision of antidumping petition against China from 2002 to 2006, then design a early warning system for antidumping petition based on panel data logit model.

Findings

The regression result indicates that unemployment ratio and import‐penetration ratio significantly influence the antidumping filing decisions; when the other invariables keep the same, with the market share of China textile goods increasing by 1 per cent point, the odds ratio of antidumping petitions against China textiles increases by about 3.7 per cent.

Originality/value

As far as the authors are aware there is no definite research yet about early warning system of antidumping events, and this paper aims to specifically address this issue.

Details

Journal of Chinese Economic and Foreign Trade Studies, vol. 4 no. 3
Type: Research Article
ISSN: 1754-4408

Keywords

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Article
Publication date: 1 April 2001

Clarence N.W. Tan and Herlina Dihardjo

Outlines previous research on company failure prediction and discusses some of the methodological issues involved. Extends an earlier study (Tan 1997) using artificial…

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1136

Abstract

Outlines previous research on company failure prediction and discusses some of the methodological issues involved. Extends an earlier study (Tan 1997) using artificial neural networks (ANN) to predict financial distress in Australian credit unions by extending the forecast period of the models, presents the results and compares them with probit model results. Finds the ANN models generally at least as good as the probit, although both types improved their accuracy rates (for Type I and Type II errors) when early warning signals were included. Believes ANN “is a promising technique” although more research is required, and suggests some avenues for this.

Details

Managerial Finance, vol. 27 no. 4
Type: Research Article
ISSN: 0307-4358

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Article
Publication date: 26 July 2021

Zhiqiang Geng, Lingling Liang, Yongming Han, Guangcan Tao and Chong Chu

Food safety risk brought by environmental pollution seriously threatens human health and affects national economic and social development. In particular, heavy metal…

Abstract

Purpose

Food safety risk brought by environmental pollution seriously threatens human health and affects national economic and social development. In particular, heavy metal pollution and nutrient deficiency have caused regional diseases. Thus, the purpose of this paper is to present a risk early warning method of food safety considering environmental and nutritional factors.

Design/methodology/approach

A novel risk early warning modelling method based on the long short-term memory (LSTM) neural network integrating sum product based analytic hierarchy process (AHP-SP) is proposed. The data fuzzification method is adopted to overcome the uncertainty of food safety detection data and the processed data are viewed as the input of the LSTM. The AHP-SP method is used to fuse the risk of detection data and the obtained risk values are viewed as the expected output of the LSTM. Finally, the proposed method is applied on one group of sterilized milk data from a food detection agency in China.

Findings

The experimental results show that compared with the back propagation and the radial basis function neural networks, the proposed method has higher accuracy in predicting the development trend of food safety risk. Moreover, the causal factors of the risk can be figured out through the predicted results.

Originality/value

The proposed modelling method can achieve accurate prediction and early warning of food safety risk, and provide decision-making basis for the relevant departments to formulate targeted risk prevention and control measures, thereby avoiding food safety incidents caused by environmental pollution or nutritional deficiency.

Details

British Food Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0007-070X

Keywords

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Article
Publication date: 6 September 2013

Sara Haji‐Kazemi and Bjørn Andersen

The purpose of this paper is to present an overview of the concept of early warning signs in projects and explain how a performance measurement system can be utilized as a…

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1576

Abstract

Purpose

The purpose of this paper is to present an overview of the concept of early warning signs in projects and explain how a performance measurement system can be utilized as a source of data for an early warning approach signaling that a project is about to experience problems at some stage in the future.

Design/methodology/approach

Combination of action research and semi‐structured interviews and document analysis supplemented by a post‐mortem analysis after project close‐out.

Findings

Detection of early warning signals in projects can be better enabled through the application of a performance measurement system with properly defined key performance indicators. Utilization of this tool can positively affect the overall success of the project.

Research limitations/implications

The case study involved only one project from the oil and gas industry.

Practical implications

The empirical case study was developed to illustrate the usefulness of exploiting a performance measurement system in a project. A procedure was demonstrated for developing and implementing an early warning system based on performance measurement, and specific performance indicators have been described for other projects to copy.

Originality/value

This paper highlights the gap in the literature concerning the link between early warning and project management and the link between early warning and performance measurement. It offers a new idea on how performance measurement can be used as an effective early warning system and is intended to be primarily of use to project management practitioners and practically‐oriented academics who are interested in developing fresh insights into new approaches for better management of projects.

Details

International Journal of Managing Projects in Business, vol. 6 no. 4
Type: Research Article
ISSN: 1753-8378

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Article
Publication date: 6 February 2009

Yuduo Lu, Dan Li and Wenshi Wang

The purpose of this paper is to research the impact of foreign direct investment (FDI) on China's economic growth, so as to measure reasonable scales of FDI and the safe…

Abstract

Purpose

The purpose of this paper is to research the impact of foreign direct investment (FDI) on China's economic growth, so as to measure reasonable scales of FDI and the safe coefficient of China's FDI utilization, make timely predictions, and suggest specific foreign capital management and controlling strategies for the policy makers to adopt under various conditions.

Design/methodology/approach

This paper builds early warning systems (EWSs)for China's FDI utilization, applies grey correlation model GM (1,1) to predict early warning indexes, and uses both of the grey correlation and analytic hierarchy process (AHP) to evaluate the weights of the indexes.

Findings

The paper finds that FDI can promote China's economic growth, make great contribution to the technology spillover and improve China's employment environment as well as the quality of employment. But its contribution is less than the domestic capital in the aspects of China's industrial structure, area structure and trade structure adjustment, and more seriously, FDI exacerbates the imbalance of the area distribution in China. Moreover, foreign capital focuses on the occupation and monopoly of the domestic market, which will reduce import and export trade and harm the development of China's economy.

Research limitations/implications

Owing to data constraints, this paper is not detailed and comprehensive enough, and needs further exploration in the empirical work.

Practical implications

Given the strong evidence of the EWS for FDI utilization, this paper finds a precise way to evaluate the influence of FDI on China's economic growth, by which the government can implement different capital management and controlling strategies to smooth the openness process of FDI in China.

Originality/value

This paper applies EWS into the FDI utilization to evaluate the safe coefficient and achieve the warning indexes, which are evaluated by the combination of the grey correlation and AHP.

Details

Journal of Chinese Economic and Foreign Trade Studies, vol. 2 no. 1
Type: Research Article
ISSN: 1754-4408

Keywords

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Article
Publication date: 2 August 2013

Harlan Platt and Marjorie Platt

Early warning models are a widely employed development in modern finance. A good early warning model predicts with a high degree of accuracy the likelihood that a healthy…

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1119

Abstract

Purpose

Early warning models are a widely employed development in modern finance. A good early warning model predicts with a high degree of accuracy the likelihood that a healthy company will either go bankrupt or become financially distressed. Now that B2B companies supply products worldwide, the risk of disruptions to business continuity due to supplier failure is international. This paper aims to focus on early warning models.

Design/methodology/approach

This paper extends the research comparing indicators of financial health to the subject of how industrial globalization affects early warning models. In specific, it considers models developed across two continents: North America and East Asia. The targets of the research are global auto suppliers, companies that deliver parts and equipment to original equipment auto manufacturers.

Findings

The findings are particularly important because of the collapse and resurrection of US original equipment manufacturers (OEMs). The modeling effort tested the ability of a single global model of financial distress to capture the determinants of auto supplier health on the two continents. Individual models for each continent proved to be superior to a single model.

Originality/value

This paper is the first to compare bankruptcy models for auto suppliers between China and the USA.

Details

Journal of Asia Business Studies, vol. 7 no. 3
Type: Research Article
ISSN: 1558-7894

Keywords

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Article
Publication date: 3 May 2013

Xiaofei Li, Cesar L. Escalante, James E. Epperson and Lewell F. Gunter

The late 2000s Great Recession led to a surge of bank failures in the USA with nearly 300 banks failing from 2009 to 2010. Recalling the farm crises of the 1980s where the…

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1438

Abstract

Purpose

The late 2000s Great Recession led to a surge of bank failures in the USA with nearly 300 banks failing from 2009 to 2010. Recalling the farm crises of the 1980s where the farm sector was pinpointed as one of the major precursors of such crises, this study is an attempt to validate if the agricultural sector can once again be considered as a major instigator of the current financial crises.

Design/methodology/approach

An early warning model is developed based on factors that may cause bank failures, with special attention given to the role of the agricultural lending portfolios of commercial banks. The model will have several time period versions that will determine the length of time prior to the actual bank bankruptcy declarations that early warning signals could be detected.

Findings

The empirical results indicate that credit exposure to the farm sector does not necessarily enhance a bank's tendency to fail or its probability of success or survival. This lends support to the reality that agricultural loan delinquency rates are consistently below the banks' overall loan delinquency rates, thus confirming that agricultural lenders are in relatively stronger financial health. This study instead finds that costly funding arrangements, increasing interest rate risk, and declining asset quality can be possible early warning signals that can be detected as far back as two or three years before eventual bank failure.

Originality/value

This study differentiates itself from previous studies by its special focus on the role of the agricultural finance industry in the ensuing economic crises. This study's early warning model also presents an extended version of previous empirical models as it accounts for measures of capital adequacy, asset quality, management risk, profitability, liquidity risk, loan portfolio composition and risk, funding arrangement, structural and macroeconomic variables.

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Article
Publication date: 20 November 2019

Daniel Hagemann and Monika Wohlmann

The global financial and economic crisis resulting from the US housing crisis has shown that house prices can have far-reaching consequences for the real economy. For…

Abstract

Purpose

The global financial and economic crisis resulting from the US housing crisis has shown that house prices can have far-reaching consequences for the real economy. For macroprudential supervision, it is, therefore, necessary to identify house price bubbles at an early stage to counteract speculative price developments and to ensure financial market stability. This paper aims to develop an early warning system to signal speculative price bubbles.

Design/methodology/approach

The results of explosivity tests are used to identify periods of excessive price increases in 18 industrialized countries. The early warning system is then based on a logit and an ordered logit regression, in which monetary, macroeconomic, regulatory, demographic and private factors are used as explanatory variables.

Findings

The empirical results show that monetary developments have the highest explanatory power for the existence of house price bubbles. Further, the study reveals currently emerging house price bubbles in Norway, Sweden and Switzerland.

Practical implications

The results implicate a new global housing boom, particularly in those countries that did not experience a major price correction during the global financial crisis.

Originality/value

The ordered logit model is an advanced approach that offers the advantage of being able to differentiate between different phases of a house price bubble, thereby allowing a multi-level assessment of the risk of speculative excesses in the housing market.

Details

Journal of European Real Estate Research , vol. 12 no. 3
Type: Research Article
ISSN: 1753-9269

Keywords

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