Ecological environmental early-warning model for strategic emerging industries in China based on logistic regression

Sun, Li; Miao, Cheng-lin; Yang, Li

doi:10.1016/j.ecolind.2017.09.036

Cited by 22 publications

(9 citation statements)

References 15 publications

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“…Sun et al selected 60 listed companies as samples and used logistic regression early warning model to analyze the impact of industrial development of new industries on ecological environment. e model successfully warned ecological problems and provided a theoretical basis for the sustainable development of strategic emerging industries [8]. Diner et al put forward a comprehensive model for comprehensive evaluation of emerging industries through the Danp model and the Moora model.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Analysis of the Correlation between Emerging Industry Development and University Students’ Entrepreneurship Based on Big Data

Yufeng

Huang

2022

Discrete Dynamics in Nature and Society

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With the strong support of local governments for strategic emerging industries such as high-end equipment manufacturing, new materials, and new energy, strategic emerging industries are playing an increasingly important role in the economy and society. With the increasing enthusiasm of college graduates for independent entrepreneurship, college students’ entrepreneurship is constantly integrated with the development of strategic emerging industries. Based on this background, aiming at the practical problems of the development of strategic emerging industries, this study innovatively puts forward the method of using big data technology and GM model to realize the dynamic model analysis of the development of strategic emerging industries and college students’ entrepreneurial behavior. This article analyzes the correlation between dynamic big data such as industrial scale, industrial market, and industrial direction of local strategic emerging industries and university entrepreneurship, so as to provide theoretical support for the development strategy of strategic emerging industries. Through the neural network algorithm, this article evaluates the entrepreneurship of college students, so as to provide a digital basis for the layout of strategic emerging industries to attract talents and entrepreneurship. Experiments show that the big data integration system established by GM correlation analysis and ant colony Elman regression artificial neural network has high accuracy and can well identify the priority relevance of the industrial direction of strategic emerging industries to college students’ entrepreneurship. It provides theoretical support for regional policy makers to better formulate college students’ entrepreneurship strategy and the development plan of emerging industries.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Analysis of the Correlation between Emerging Industry Development and University Students’ Entrepreneurship Based on Big Data

Yufeng

Huang

2022

Discrete Dynamics in Nature and Society

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“…All changes in environmental structure or state that endanger the development of humans and other living things are referred to as environmental problems. Environmental pollution and ecological damage are also included [1]. e environment plays a critical role in the growth of the social economy and culture.…”

Section: Introductionmentioning

confidence: 99%

Evaluation Model of Rural Ecological Environment Governance Quality Using Decision Tree Algorithm

Yan

Feng

2022

Mobile Information Systems

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In rural economic development and environmental protection, assessing the quality of rural ecological environment management is critical. This paper uses a decision tree algorithm to assess the quality of rural ecological environment governance based on an in-depth review of related literature. The data are recursively processed from top to bottom in this paper, and a set of disordered and irregular examples is summed up into a set of classification rules represented by a tree structure, and an evaluation model is obtained, in which all pollution parameters are taken as environmental quality factors. It effectively overcomes the traditional environmental quality prediction model’s inflexibility and inaccurate boundary value. The simulation results show that the quality evaluation method in this paper has a classification accuracy of 96.82 percent and a recall rate of 96.63 percent, which is higher than the comparison algorithm. The experimental results support the effectiveness and applicability of the decision tree-based method for assessing the quality of rural environmental governance. This model can not only assist researchers in correctly analyzing and mastering the migration and change rules of pollutants in the air but also has promising social and economic implications.

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“…Early warning, that is, the provision of timely and effective information through identified institutions, allows individuals exposed to a hazard to take action to avoid or reduce their risk and prepare an effective response [1]. Similarly, timely environmental warnings can predict the degradation and deterioration of environmental quality caused by industrial development activities [2]. They have a pioneering, predictive, and advanced function, as well as a vigilance effect on the evolutionary trend, direction, speed, and consequences [3].…”

Section: Introductionmentioning

confidence: 99%

Environmental Warning System Based on the DPSIR Model: A Practical and Concise Method for Environmental Assessment

Wang

Sun²,

2018

Sustainability

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Though we are in urgent need of environmental warnings to slow environmental deterioration, currently, there is no internationally concise method for environmental warnings. In addition, the existing approaches do not combine the three aspects of ecology, resources, and environment. At the same time, the three elements of the environment (air, water, and soil) are separated in most environmental warning systems. Thus, the method this paper gives is an innovative attempt and aims to make environmental assessment more practical. This paper establishes the index system of an environmental early warning based on the Driving–Pressure–State–Influence–Response (DPSIR) model. The Analytic Hierarchy Process (AHP) method was used to determine the weights. Next, single and integrated index methods further assess the environmental warning state, in which the weighted summation method is used to summarize the data and results. The case of Tianjin is used to confirm the applicability of this method. In conclusion, the method in this paper is more well-behaved and, therefore, more suitable to assist cities in their environmental assessment.

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Ecological environmental early-warning model for strategic emerging industries in China based on logistic regression

Cited by 22 publications

References 15 publications

[Retracted] Analysis of the Correlation between Emerging Industry Development and University Students’ Entrepreneurship Based on Big Data

[Retracted] Analysis of the Correlation between Emerging Industry Development and University Students’ Entrepreneurship Based on Big Data

Evaluation Model of Rural Ecological Environment Governance Quality Using Decision Tree Algorithm

Environmental Warning System Based on the DPSIR Model: A Practical and Concise Method for Environmental Assessment

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